[Objective] With the increasing proportion of new energy, traditional grid-following (GFL) control based on phase-locked loop (PLL) synchronization gradually exhibits inherent stability limitations in weak grid conditions. Meanwhile, grid-forming (GFM) control with self-synchronizing source characteristics has emerged as a hot solution. However, existing research predominantly focuses on the voltage regulation or synchronization stability of GFM control, with less attention to its frequency modulation capability and characteristics. [Methods] This paper systematically reviewed four mainstream GFM control methods, including droop control, virtual synchronous generator (VSG) control, matching control, and virtual oscillator control (VOC), explained their frequency modulation principles, and analyzed their advantages and disadvantages from the aspects of the control loop and application scenarios. On this basis, a grid-connected simulation model for new energy systems was built to conduct a simulation-based analysis of the frequency modulation response characteristics of different kinds of frequency modulation control across diverse scenarios. Finally, this study summarized challenges of GFM control in strategy optimization, parameter tuning, and multi-unit coordination, with the future development prospects pointed out. [Results] Droop control regulates the active power of generating units by responding to system frequency deviations, featuring advantages of the simple structure and strong grid strength adaptability. However, its lack of inertia support results in relatively weaker frequency modulation performance. On the basis of droop control, VSG control simulates the inertia response characteristics of conventional synchronous machines and can better suppress the change performance of system frequency. However, it faces challenges in parameter tuning, fault ride-through, and multi-unit coordination. Matching control utilizes the dynamic characteristics of DC capacitors to simulate the inertia properties of traditional synchronous machines and thus restrain change performance of system frequency, but it fails to provide sustained support in the frequency quasi-steady state. VOC generates frequency responses similar to droop control via oscillator dynamic equations that directly govern amplitude and frequency. However, it is difficult for its high output harmonics to satisfy grid connection requirements. [Conclusion] Virtual synchronous machine control has become the most promising research direction in GFM control due to its technical advantages of balancing frequency modulation performance and strong grid strength adaptability in participating in system frequency modulation. However, technical challenges including synchronization stability, fault ride-through, and coordinated control need to be tackled. In the future, in-depth research should be conducted on control strategies and parameter optimization, and multi-unit collaborated control to facilitate the large-scale application of GFM control.

[Objective] With the widespread integration of distributed energy, the complex topology and exponentially increased monitoring data of distribution networks pose new challenges to fault diagnosis. Traditional fault diagnosis methods mainly rely on monitoring data and human experience. With the rapid development of cloud computing and communication technology, artificial intelligence methods are widely applied in the field of fault diagnosis. However, existing artificial intelligence methods have a high dependence on training data, requiring a large number of basic data as support. Therefore, an intelligent fault diagnosis method for distribution networks was proposed by leveraging digital twin technology to improve the efficiency and accuracy of fault diagnosis. [Methods] A digital twin of the distribution network was constructed using digital twin technology, and virtual diagnosis results were used to guide actual system operation. Additionally, wavelet packet decomposition was utilized to obtain the energy of each frequency band of the signal to construct feature vectors, which were input into the improved convolutional autoencoder (CAE) model for learning to identify the fault type. The digital twin system included a physical layer, a data layer, a model layer, and a service layer, achieving virtual-real mapping, with the virtual twin reflecting the state of the physical entity in real time. In the simulation experiment, the three-port ring network structure of a 10 kV distribution network in an area was used as the basis, and a complete experimental dataset was constructed, including 7 520 pieces of normal and fault sample data. [Results] The performance analysis results of the proposed model show that after 100 iterations of training, the diagnostic accuracy of the improved CAE model is close to 0.98. Moreover, the intelligent diagnosis results of the digital twin system demonstrate that the fault types diagnosed by the proposed method are basically consistent with the actual fault types, and for five common fault types, it maintains an ideal diagnostic accuracy. The average accuracy reaches 0.95, and the diagnosis time is only 5.39 s. A comparison of diagnoses using different methods indicates that the proposed method has a higher diagnostic accuracy. [Conclusion] The application of digital twin technology to the intelligent fault diagnosis of distribution networks, by adopting the approach of virtual-real integration, further improves the accuracy and real-time performance of fault diagnosis, thus providing a new technical means for the intelligent fault diagnosis of distribution networks. This contributes to enhancing the reliability and safety of distribution networks and holds important theoretical and practical value for the development of smart grids. Furthermore, future research will focus on how to cope with the changes in the structure of distribution networks to improve the applicability of the proposed fault diagnosis method.

[Objective]In modern manufacturing, as the demand for machining accuracy and efficiency continues to rise, in-situ measurement technology, serving as a real-time and precise measurement method, is gradually becoming an indispensable part of modern machining processes. Industrial robots are widely applied in in-situ measurement and processing of complex parts due to their large work space, high motion flexibility, and ease of programming. However, limited by complicate measurement space constraints and the nonlinear transmission and expression of measurement motion in the joint space of the robots, they exhibit posturenon-smoothness at their ends and even suffer collisions during measurement along a predefined path. To tackle this issue, a posture optimization method based on the redundant motion parameter of the robot measurement system was proposed. [Methods]Firstly, the forward and inverse kinematics model of the robot was established, and the motion transmission relationship between the measurement motion and the joint space of the robot was calculated with this model. Considering that there existed a redundant motion parameter in the robot measurement system that rotates around the axis of the measuring tool, a set of redundant measurement postures satisfying the joint angle limitconstraint and posture singularity constraint at each path point were constructed. Subsequently, to avoid collision between the robot and obstacles, an oriented bounding box was employed to model themeasurement of large-size complex curved components, and then the Gilbert-Johnson-Keerthi (GJK) method was implemented for rapid collision detection between the robot and the obstacles to screen a set of robot in-situ measurement postures that meeting collision-free constraints. Finally, the Dijkstra's shortest path technique was applied to determine the robot measurement posture sequence with the minimum joint angle variation, enhancing the stability of robots during in-site measurement in restricted space. [Results]The proposed method was verified by experiments. The joint angle data of the robot during the measurement process were collected. The results indicate that the method significantly enhances the joint motion smoothness of the robot, reduces the cumulative changes in joint angles, and effectively lowers the amplitude and standard deviation of joint angle fluctuations. The robot is enabled to perform smooth and collision-free measurement of parts in restricted space. [Conclusion]The proposed optimization method for in-situ measurement postures of robots based on the redundant motion parameter can effectively avoid global interference between the robot and obstacles and significantly enhance the smoothness of robot postures during the measurement process.

[Objective] Accurate electric load forecasting is the key to the smooth operation and effective management of power systems, which can enable power companies to effectively dispatch power generation equipment, thereby improving their operational efficiency and economic benefits. However, electric load data are affected by a variety of external factors and have significant time dependence, which makes their accurate prediction difficult. Therefore, an electric load forecasting model combining multi-factor modeling and time series analysis was proposed, which taking into account the analysis of the complex influences of multiple factors and the time dependence characteristics of electric load, so as to realize accurate electric load forecasting. [Methods] To break through the respective limitations of multi-factor analysis methods and time series forecasting modeling methods, an improved electric load forecasting model combining long short-term memory (LSTM) network and Bayesian optimization algorithm was proposed with the help of deep learning and a multi-factor analysis method. Firstly, a comprehensive multi-factor feature pool was constructed, including the historical time series features of electric load and a variety of external factors to fully capture the complex relationships between electric load data and multiple influencing factors. Secondly, the LSTM network was used as the core model, and its unique gating mechanism and memory unit were used to capture the time dependence of electric load data and the complex association between multiple factors. The Bayesian optimization algorithm was introduced to tune the hyperparameters of the LSTM model, and the Gaussian process was used as the surrogate model to make full use of the prior information to improve the training efficiency and prediction performance of the model. [Results] Five real transformer datasets were used to train and test the model. The effectiveness of the model was verified by several evaluation indicators. The proposed electric load forecasting method based on multi-factor feature engineering modeling has significantly better prediction performance than the model using only a single factor for forecasting on five different transformer datasets, which further highlights the effectiveness of the multi-factor feature pool. The maximum coefficient of determination of the LSTM model is 0.920 7, and the minimum mean square error and the minimum mean absolute error are 0.042 and 0.024, respectively. The results demonstrate the superior performance of the proposed method in complex electric load forecasting tasks. [Conclusion] The electric load forecasting model combining multi-factor modeling and time series analysis fully considers the complexity of external factors and the time dependence characteristics of electric load and innovatively introduces a comprehensive feature pool to participate in LSTM model training and testing. The LSTM network combined with multi-factor feature pool modeling has high prediction accuracy and robustness, which provides a new technical idea for electric load forecasting, has important reference value for the planning and dispatch of smart grid, and lays a foundation for further development of accurate load forecasting technology.

[Objective] To address the problems of traditional load forecasting methods, such as low information utilization efficiency, large errors, and difficulties in adapting to the diversity and randomness of actual power load changes, a power system load forecasting method based on land spatial information perception was proposed. This method could improve the accuracy and reliability of load forecasting, provide key data support for power system planning and construction, and meet the needs of economic and social development. [Methods] This algorithm adopted the strategy of classification by area with the use of urban land spatial information and power grid load data. For developed areas (with loads known), historical load data were used for curve fitting to carry out load forecasting. As for newly developed areas (with loads unknown), the average load density of the same land type in developed areas was used for equivalentprocessing to form the basic information of load forecasting, and then load forecasting was carried out. At the same time, this algorithm subdivided the historical load data and equivalent load data. The algorithm integratedthe exponential model, the growth curve model, and the elastic coefficient model and mainly used the combination of dynamic weights to form the best fitting scheme. Using the above methods, a power system load forecasting technology based on land spatial information perception was formed. The historical data from 2014 to 2020 were used as the benchmark for parameter fitting, and the historical load data of industrial power, residential power, commercial power, public facilities power, and other power types were sorted out. The load data of 2021 were taken as the forecasting object. The differences of the exponential model, the growth curve model, and the elastic coefficient model in total forecast and forecast based on classification by area were compared and analyzed through experiments. The results show that the forecasting accuracy based on classification by area is about 33% higher than that of total forecasting. On this basis, the best fitting effects based on dynamic weights and mean weights were compared and analyzed. The calculation results show that the best fitting forecasting error is only 1.12% when dynamic weights are used, which is 12% smaller than the forecasting error in the case of using mean weights. In conclusion, the scheme proposed can significantly improve the accuracy and reliability of load forecasting. [Results] The results of this paper show that the accuracy of the load forecasting model can be improved by using the method of classification by area to classify spatial information according to the land and load types. The algorithm has better dynamic adaptability by dynamically adjusting parameter weights and integrating single forecasting models, able to achieve the best fitting results and enhance forecasting accuracy. [Conclusion] The highlights of this paper are as follows. Firstly, the data processing method of classification by area is adopted to improve the utilization rate of spatial information and load information of the urban power grid. Secondly, the traditional load forecasting models are integrated by using dynamic weights, which breaks the limitations of single models. This algorithm further improves the accuracy and reliability of urban power grid load forecasting through the above two approaches.

[Objective]In the field of traditional permanent magnet motor fault monitoring, while contact signals are widely used, they usually only reflect one operational state of motors, leading to insufficient information and difficulty in comprehensively identifying the operational state of permanent magnet synchronous motors. To enrich the amount of information, additional sensors are needed, which not only increases the complexity of the system but is also difficult to be practically applied. Therefore, improving the accuracy and convenience of permanent magnet motor state monitoring has become an important research objective. With the development of intelligent monitoring technology, the application of non-contact signals has received increasing attention. The audio signals generated by the operation of permanent magnet motors contain rich state information, providing a new direction for fault diagnosis. Compared with contact signals, audio signals can reflect in real time such characteristics as motor vibration and noise caused by faults, which have significant research value. However, these signals are easily interfered by environmental noise, which results in poor signal quality and unclear feature information and is thereby not conducive to the state monitoring of permanent magnet synchronous motors. Therefore, a deep learning model based on voiceprint recognition was proposed for permanent magnet synchronous motors, aiming to efficiently monitor and diagnose operational states of motors through deep learning technology. [Methods]Firstly, the wavelet denoising algorithm was used to reduce noise interference, improve signal quality, and thus enhance the signal-to-noise ratio, ensuring that the model can more clearly extract Mel cepstral features and laying the foundation for fault identification and classification. However, direct use of convolutional neural networks (CNNs) to extract Mel cepstral features may weaken the correlation between features, affecting the accuracy of fault identification. To address this, a spatial attention mechanism was introduced, which enhanced the spatial position correlation of features through weighting, leading the model to focus on the most critical parts and thus improving the effectiveness of feature extraction. To boost the recognition accuracy of the model, normalization of Mel cepstral features was performed, and the AAM-softmax loss function was employed. This function strengthened inter-class constraints, improving the distinguishing capability of the model between different categories, thereby enhancing the recognition accuracy and generalization ability, and optimizing the training process, so that the model was enabled to better adapt to different operating conditions. [Results]Simulation test results indicate that the proposed model performs excellently on the training set, accurately identifying the different operational states of the motor, and demonstrates strong generalization ability on the test set. The experimental results confirm that the deep learning-based voiceprint recognition method can effectively monitor the various operational states of permanent magnet motors with high accuracy and practicality. [Conclusion]In summary, the proposed deep learning model based on voiceprint recognition for permanent magnet synchronous motors can effectively eliminate noise and extract key features. By introducing the spatial attention mechanism and the AAM-softmax loss function, the model significantly enhances the recognition accuracy and generalization ability. With broad prospects for development, this model can be widely applied in state monitoring and fault diagnosis of permanent magnet motors and promote the development of intelligent maintenance technology of motors.

[Objective]Transmission lines are an important link in the transmission and use of electrical energy, and their safety and stability play a crucial role in the normal operation of the power system. Therefore, daily inspections of transmission lines are of great importance. Major accidents usually develop from small defects and hidden dangers. Daily inspections usually use manual, unmanned aerial vehicle, visualization channels, and other means. Regardless of the method, a large number of visualization, infrared, or ultraviolet photos need to be processed. However, due to the particularity of transmission lines, the installation conditions involve multiple environments, and the inspection image background is usually complex. Although the manual review method has high accuracy, it relies heavily on experience and has extremely low efficiency. Therefore, how to quickly and accurately identify inspection images of overhead transmission lines is the key to identifying defects in overhead transmission lines. The traditional image recognition method for transmission line inspection is prone to low defect recognition accuracy under complex background interference. [Methods]Therefore, to enhance the recognition accuracy of detection images of overhead transmission lines under complex backgrounds, a defect detection method that balances recognition efficiency and accuracy was proposed. The proposed method was based on compressed image technology combined with the YOLOv5 model. Firstly, an asymmetric feature aggregation compression algorithm based on sparse convolution was designed. The original image was encoded to reduce the space required by image storage data for storage and transmission. After being transmitted to the decryptor through the information channel, the compressed image was decoded and restored to improve the learning efficiency of local set features. At the same time, by the integration of the channel-spatial attention module (CSAM), the attention channel weight matrix and spatial weight matrix were obtained from the feature map, and the importance of the feature map region was determined through the weight matrix. In this way, the processing efficiency of the YOLOv5 model was improved. [Results]The compressed and restored image was input into the improved YOLOv5 model. The channel attention module (CAM) and the spatial attention module (SAM) were used to process the attention data on the channel and space of the image, respectively. The features of the target area were enhanced through global average pooling and maximum pooling, and the SAM was introduced to enhance the attention of channel attention to feature position information, so as to detect defective devices. The effectiveness of the proposed method was verified experimentally. [Conclusion]The inspection image data set of an overhead line was used as the basis for training and testing the proposed detection method. The results show that the sizes of the detection images are significantly reduced after compression using the proposed technique, and the sizes of the restored images are reduced by about 3 MB, compared to those of the original images, without distortion. The improved YOLOv5 model has high detection precision, with detection accuracy reaching 0.91 and detection time being as short as 0.87 s. The algorithm ensures detection accuracy while reducing image size and improving detection speed.

[Objective] In mobile edge computing (MEC) systems in dynamic environments, traditional task offloading strategies generally have problems such as inflexible scheduling, weak adaptability to environmental changes, and limited delay control capabilities, making it difficult to meet the processing requirements of delay-sensitive tasks. To this end, this paper proposed a MEC offloading optimization method that integrated unmanned aerial vehicle (UAV)-assisted mechanisms to improve the system′s service quality and task response efficiency. [Methods] Considering the dynamic user distribution and frequent link state fluctuations in UAV-MEC scenarios, this paper jointly modeled task offloading, user scheduling, and UAV trajectory control as a Markov decision process (MDP), and used the deep Q-network (DQN) framework to learn approximate optimal strategies. In state modeling, factors such as UAV energy consumption constraints, user task attributes, and timeliness requirements were fully considered, with action space discretization implemented to adapt to the DQN architecture. The reward function introduced delay loss and timeout penalty mechanisms to guide the agent in adaptively learning effective offloading strategies. [Results] The simulation results show that the proposed method is superior to the benchmark strategies such as full local computing and full edge offloading in terms of cumulative rewards, average task processing delay, and the number of task timeout penalties, showing good strategy convergence and environmental adaptability, especially when the communication link fluctuates or computing resources are limited. [Conclusions] The proposed DQN-based UAV-assisted edge computing joint optimization strategy can significantly improve the system′s processing efficiency and scheduling performance for time-sensitive tasks in a dynamic and complex environment, providing a feasible method path and theoretical support for the design and optimization of high-mobility mobile edge computing systems.

[Objective] Rice husk ash (RHA), as bulk agricultural waste generated during rice processing, has been increasing in annual production with the growth of global rice consumption. The use of RHA as a supplementary cementitious material for concrete can significantly reduce carbon emissions during concrete production by replacing part of cement and also effectively improve the durability and mechanical properties of concrete, which is in line with the demand for sustainable development of the construction industry. However, the durability of RHA concrete is influenced by a number of factors such as particle size distribution, concrete mix design, and curing conditions. Existing studies are fragmented and lack a systematic performance evaluation framework and optimization design strategy. Therefore, this study aims to review the durability of RHA concrete, analyze the mechanism and influencing factors of its deterioration resistance, and provide theoretical support for the development of application standards and breakthroughs in promotion bottlenecks, so as to promote its wide application in green building materials. [Methods] The focus of this study was the durability of RHA concrete, and the research progress on its shrinkage, carbonation resistance, chloride penetration resistance, acid resistance, freeze-thaw cycle resistance, and fire resistance was systematically investigated. The research included the analysis of the physicochemical properties of RHA (e.g., silica content, particle size, and volcanic ash activity) and investigation of the effects of different RHA contents and treatments (e.g., chemical activation, heat treatment, and wet curing) on the properties of concrete. In addition, the synergistic effects of RHA with other mineral admixtures (e.g., coal fly ash, slag, and metakaolin) and its optimization effect on durability were explored. [Results] After the various aspects of performances in the field of durability were sorted out and summarized, the corresponding research content and key technologies were summarized. It is found that RHA concrete has significant improvements in shrinkage inhibition, carbonation resistance, chloride penetration resistance, acid corrosion resistance, freeze-thaw cycle durability, and fire resistance. In addition, the systematic study concludes that RHA can achieve multi-dimensional enhancement of concrete durability performance, which aligns with the development needs of sustainable building materials. However, the engineering application of RHA concrete still faces multiple challenges: the quality difference of RHA due to the difference in raw material sources requires the establishment of strict control standards for activity index and particle size distribution; the sensitivity of alkali-silica reaction requires the precise control of RHA dosage and alkali content; the long wet curing period increases construction and time costs significantly. [Conclusion] Future research should focus on standardization of RHA quality, synergies with other mineral admixtures, innovative treatment methods, and monitoring of long-term performance to promote the widespread use of RHA concrete in construction and reduce the environmental impact of conventional cement.

[Objective]Transmission lines are an important component of the power system, and most line faults are caused by lightning strikes. Lightning interference identification is an important basis for ensuring the correctness of traveling wave fault analysis. To quickly identify lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines and ensure the stability of the power system, an automatic identification method for lightning strike faults was proposed. [Methods]The dictionary learning algorithm was used to denoise the transmission line signal, and the minimum error objective function was established for signal amplitude fluctuation. The dictionary matrix was optimized by dictionary update through hot start and Newton iteration to obtain the denoised lightning strike fault signals of transmission lines. This effectively reduced noise interference and improved identification accuracy. The wavelet time entropy method was used to extract key features from the denoised lightning strike fault signals of transmission lines. The wavelet coefficients formed by wavelet transform were used to reconstruct the coefficients in a specific layer. A sliding time window was defined to calculate entropy and information content, and features were extracted from the transient signal of lightning current in transmission lines to provide data support for lightning strike fault identification. Different characteristic signals of lightning strikes were collected, and features were trained using ensemble learning algorithms. Multiple weak classifiers were generated and fused into a strong classifier through weights, which was used to classify each transient signal sample of lightning current. The generalization ability of the classifier was improved, and it was enabled to cope with different types of lightning strike fault signals. The classifier was optimized using the sparrow algorithm, and the optimal parameters of the classifier were obtained by randomly initializing the sparrow population, calculating fitness values, screening sparrows, updating sparrow discoverers and joiners, and performing mutation operations. The optimal parameters were input into the optimized classifier to achieve automatic identification of lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines. The sparrow algorithm, as a heuristic optimization algorithm, has the characteristics of adaptability and a strong global search ability. It can quickly find the optimal parameters of the classifier in a complex search space, improving optimization efficiency and identification speed. [Results]The experimental results show that the proposed method has a signal-to-noise ratio (SNR) of over 40 dB, a mean square error (MSE) of identification to be less than 1.5, an identification efficiency of over 90％, and identification time of about 2.5 s after denoising. It can accurately and efficiently identify lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines. [Conclusion]This method provides a new technical means for automatic identification of lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines. It significantly enhances identification accuracy and efficiency, providing strong support for the safe and stable operation of the power system. At the same time, this method can also be extended to identification of other fault types of transmission lines, which has a wide application value.

[Objective]The use of carbon steel in high-temperature working conditions is strictly limited due to its poor oxidation resistance. Ni-Al intermetallic compounds have many practical industrial applications due to their high melting points and good high-temperature oxidation resistance. In this study, a Ni-Al reaction-modified coating was prepared on the surface of carbon steel to explore the further application of coatings containing Ni-Al intermetallic compounds with high-temperature oxidation resistance and improve the high-temperature service life of carbon steel. [Methods]A Ni-WC coating was prepared on the carbon steel substrate by high-velocity oxygen-fuel (HVOF) spraying, and then an Al coating was prepared on it by arc spraying. The Ni-Al within the coating reacted under diffusion treatment for different time at 800 ℃ to afford intermetallic compounds and thereby enhance high-temperature oxidation resistance. The high-temperature oxidation resistance of the coating was tested by recording their oxidation weight gain curves. [Results]The results show that the Al/Ni-WC composite coating generates Al-rich Ni-Al intermetallic compounds by in-situ reaction at the Al/Ni interface during high-temperature oxidation. The Al/Ni-WC coating forms Al-rich NiAl₃ during diffusion treatment at 800 ℃, and the Al atoms on the surface react with the atmospheric oxygen atoms to form Al₂O₃. With the extension of the reaction time, the NiAl₃ phase formed at the Al/Ni interface is transformed into the Ni₂Al₃ phase with better high-temperature oxidation resistance. The two Ni-Al intermetallic compounds have high melting points and high-temperature oxidation resistance, and Al₂O₃ formed on the surface of Al/Ni-WC composite coatings slows down the diffusion of O atoms. The thickness of the diffusion layer of the Al/Ni-WC composite coating increases almost linearly with the prolongation of the diffusion treatment time. The thickness of the NiAl₃ layer of the Al/Ni-WC coating after the diffusion treatment at 800 ℃ increases at a fast rate, and a thicker Ni₂Al₃ layer is generated by in-situ reaction after 10 h of heating. After 20 h of diffusion treatment at 800 ℃, a Ni₂Al₃ layer slightly thicker than the NiAl₃ layer is generated by in-situ reaction. Due to the higher melting point and stability of the Ni₂Al₃ layer, the Al/Ni-WC coating after diffusion treatment at 800 ℃ has better high-temperature oxidation resistance. After cyclic oxidation, there are ceramic phases Al₂O₃ and WC as well as NiAl phases in Al/Ni-WC coating subjected to 50 h of diffusion treatment at 900 ℃, which also show better high-temperature oxidation resistance under the combined effect of ceramic phases and NiAl phases. In this paper, the thickness of the diffusion layer of the Al/Ni-WC coating after diffusion treatment for different time was also measured, and the diffusion relationship between Ni and Al was obtained. The kinetic index of diffusion reaction is 0.790 45. The protective effect of the composite coatings on the carbon steel substrate is improved significantly. [Conclusion]The high-temperature oxidation resistance of the carbon steel substrate is significantly improved under the combined effect of the Ni-Al intermetallic compounds and Al₂O₃ film formed during the oxidation process as well as the original WC in the coating. As indicated by the oxidation weight gain experiment, after the introduction of ceramic phases in the Ni layer, the high-temperature oxidation resistance of the metal-ceramic composite coating is significantly better than that of the carbon steel substrate, namely that the carbon steel substrate can be better protected by the coating.

[Objective]The prediction of substation project cost in power grid construction projects has always been an important issue influencing project cost management. However, the currently commonly used substation cost prediction methods have problems such as insufficient prediction accuracy and low computational efficiency, which restricts the application of prediction models in actual projects. To improve the accuracy and computational efficiency of prediction, a substation project cost prediction method was proposed by combining the improved particle swarm optimization (IPSO) algorithm and least squares support vector regression (LSSVR) algorithm. [Methods]First, considering the differences in equipment, technology, and operation and maintenance between conventional substations and intelligent substations, the characteristics of these two types of substations were analyzed, and targeted preprocessing was performed on the relevant data to remove the noisy data, fill in the missing values, and convert valid information into feature vectors to be used as inputs of the LSSVR model. Next, to avoid the problem that the traditional particle swarm optimization (PSO) algorithm was prone to fall into the locally optimal solution, a hybrid adjustment strategy was introduced to optimize the inertia weights and learning factors of the PSO algorithm, which made the optimization process more stable and had a strong global search capability. With the help of this strategy, IPSO algorithm could achieve a better balance between global and local search. Finally, the IPSO algorithm was used to optimize the parameters of the LSSVR model, and a substation project cost prediction model was built. [Results]It is found from comparison with other prediction models that the proposed IPSO-LSSVR algorithm has significant advantages in prediction accuracy. Specifically, the prediction error of the model is significantly lower than those of other methods, and the deviation can be controlled within 5％. The IPSO algorithm can effectively avoid falling into local optima, which ensures that the LSSVR model can provide accurate prediction results in various situations. [Conclusion]The substation project cost prediction method based on IPSO optimized LSSVR overcomes the shortcomings of traditional prediction methods in terms of prediction accuracy and computational efficiency. In practical application, this method can provide a more accurate prediction basis for the cost management of power grid construction projects and thereby help the rational formulation of project budgets and the effective allocation of resources.

[Objective] Traditional cost prediction methods for power grid substation engineering often rely on single influencing factors or linear assumption models, which fail to comprehensively capture the complex non-linear relationships among multiple factors, resulting in low prediction accuracy. Furthermore, existing methods face challenges such as dimensionality explosion or information loss when handling high-dimensional categorical variables, and especially, overfitting is prone to occur in small-sample datasets. Therefore, this study aims to develop a robust cost prediction model for substation engineering that effectively integrates multi-source influencing factors, adapts to non-linear relationships, and performs well in small-sample scenarios, thereby providing more accurate technical support for investment decisions in power grid enterprises. [Methods] To address these issues, a substation engineering cost prediction model (ME-XGB) based on the fusion of mean encoding (ME) and the extreme gradient boosting (XGBoost) framework was proposed. First, 13 key influencing factors were extracted from dimensions such as equipment and materials, construction techniques, construction scale, geographical environment, and design standards, covering both categorical and continuous variables. For categorical variables exhibiting non-linear relationships with cost, ME was applied for feature engineering. This method converted categorical variables into continuous features by calculating the mean of the target variable (cost per unit capacity) within each category and combining with a smoothing factor to retain category information while avoiding dimensionality explosion. Second, the XGBoost algorithm was utilized to construct the prediction model. The generalization ability of the model was enhanced by integrating multiple decision trees to iteratively correct residuals and incorporating regularization terms and hyperparameter tuning. Experiments were conducted using 200 substation engineering samples from a power grid company, which were randomly divided into a training set (80％) and a test set (20％). The performance of ME-XGB was compared with MK-TESM based on a Mann-Kendall (MK) trend test method and a three exponential smoothing method (TESM), backpropagation (BP) neural network, and the original XGBoost model by using mean absolute error (M_AE) and goodness of fit (R²) as evaluation metrics. [Results] Experimental results demonstrate that the ME-XGB model significantly outperforms comparative models in prediction accuracy on the test set. Specifically, the median and mean M_AE values of ME-XGB are 5 and 6.875, where are lower than those of MK-TESM, BP neural network, and the original XGBoost. Additionally, the R² value of ME-XGB reaches 0.857 9, significantly higher than those of the other models, indicating stronger explanatory power for data variations. Boxplot analysis further reveals that ME-XGB has the narrowest distribution range of prediction errors, confirming its greater stability. Hyperparameter tuning results show that settings of hyperparameters such as tree depth and learning rate effectively balance model complexity and overfitting risks. [Conclusion] The proposed ME-XGB model addresses the challenges of non-linear representation and dimensionality control for categorical variables through ME, while leveraging the ensemble learning capability of XGBoost to significantly enhance prediction performance in small-sample scenarios. ME-XGB outperforms traditional models in terms of M_AE, R², and error stability, providing a more reliable cost prediction tool for power grid enterprises. Future research can further explore the modeling of dynamic influencing factors and extend the application of the model to cross-regional projects through transfer learning.

[Objective] With the gradual improvement of requirements for motor energy efficiency grade, outer-rotor low-speed permanent magnet motors are widely used in the industrial field, due to their advantages of high torque density, high efficiency, and energy saving. To meet the working conditions of heavy-load start-up and long-term low-speed heavy-load operation of industrial sector, the design of outer-rotor low-speed permanent magnet motors is developing in the direction of improving motor torque density. Accordingly, the issue of high heat generation caused by the high torque density of motors is becoming a focus of research. [Methods] To address the problem of high temperature rise in outer-rotor low-speed permanent magnet motors under heavy-load operation conditions, this paper established the physical model of outer-rotor low-speed permanent magnet motors and calculated the distribution of motor losses. First, based on the basic theory of computational fluid dynamics, according to the heat source distribution and structural characteristics of outer-rotor low-speed permanent magnet motors, the study designed and installed axial and circumferential Z-shaped water-cooled structures in stator bracket near the inner surface of the stator core. The simulation model with water inlet and outlet at the motor bottom was also established. The flow field and temperature field of two water-cooled structures were simulated and analyzed using Fluent software. The circumferential Z-shape structure was determined as a more suitable water-cooled design structure. Second, by the calculation method coupling fluid flow and heat transfer, the temperature field of the motor equipped with a circumferential Z-shaped 9-channel water-cooled structure was analyzed using Fluent software. Whether the water-cooled structure meeting the heat dissipation requirements of the outer-rotor low-speed permanent magnet motors was verified with the maximum temperature of the permanent magnet and insulation. Finally, based on the theoretical analysis, this paper determined the factors influencing heat dissipation in water-cooled structures, including water channel number, cooling water flow rate, and radial width of water channel section. The influences of different factors on motor temperature rise were studied using Fluent software. [Results] The results indicate that the flow rate distribution of the circumferential Z-shaped water-cooled structure is more uniform with a smaller inlet and outlet pressure difference, which is more suitable for outer-rotor low-speed permanent magnet motors. As the number of water channels, cooling water flow rate, and radial width of water channel section increase, the heat dissipation is enhanced. However, after each factor reaches a certain value, the motor temperature tends to stabilize. According to the analysis results, the final design includes 7 water channels with a radial width of 17 mm and a cooling water flow rate of 0.5 m/s. [Conclusion] The research results can provide a theoretical basis for the application of water-cooled systems of outer-rotor low-speed permanent magnet motors in high-load working environments.

[Objective]In the power system, overhead transmission lines are a critical link in the transmission of electrical energy, and their safe and stable operation is crucial. However, with the continuous changes in the natural environment and rapid growth of vegetation, trees in transmission line corridors have become one of the main hidden dangers affecting line safety. The high proximity between trees and transmission lines may not only cause faults such as short circuits and tripping but also lead to fires in severe cases, posing a serious threat to the safety of the power grid and people's lives and property. Therefore, to improve the accuracy of identifying tree obstacles, this paper designed a method to identify tree obstacles for overhead transmission lines based on unmanned aerial vehicle (UAV) inspection images. [Methods]To improve the quality of UAV inspection images, histogram equalization was used to enhance the contrast of the images, making the detailed information in the images clearer. The use of transformation functions further enhances the edge features of the images, laying the foundation for subsequent feature extraction. The FROST filter was used to remove image noise, ensuring accuracy of subsequent processing while preserving edge details. The images were smoothed using binarization methods, and the color features of tree obstacles and the texture features of conductor sag of the transmission line were extracted from the inspection images. In response to the missing edge information in images due to factors such as shooting angle and lighting, an interpolation algorithm was used to supplement the missing image edge values, ensuring the integrity of feature extraction. On this basis, the Euclidean distance between adjacent data was calculated to obtain the annotation results of feature fusion. Consequently, hidden dangers in overhead transmission line corridors were identified. [Results]The experimental results show that the proposed method performs well in the task of identifying hidden dangers brought by tree obstacles for the overhead transmission lines. It not only accurately identifies 5 areas of hidden dangers from tree obstacles but also has a small error between the identification results and the actual number of hidden dangers due to tree obstacles, demonstrating excellent identification ability. In the accuracy analysis of the location coordinates of hidden dangers, the proposed method identifies coordinates of (1.43 m, 8.3 m) and (1.49 m, 9.8 m) in areas b and d, respectively, which are closest to the actual data. This proves the high accuracy of the proposed method in identifying the actual distance of tree obstacle areas. In addition, compared to the other methods, the proposed method has more accurate identification results for various levels of hidden dangers brought by tree obstacles, and the values are closer to the actual situation in the experimental area, which verifies its superiority and reliability in practical applications. [Conclusion]The proposed method can effectively identify the hidden danger areas of transmission lines and accurately judge the number and characteristics of hidden dangers, having high practicability. From the above results, it can be seen that the proposed method combines UAV inspection images with advanced image processing technology to achieve automated and intelligent identification of tree obstacles in overhead transmission line corridors. In addition, by integrating color features and texture features, the accuracy and robustness of identification have been improved. This research achievement is of great significance for improving the safety and stability of the power system and has made positive contributions to promoting the construction and development of smart grids.

[Objective]Power engineering drawings are essential for production planning, construction, and check and acceptance stages in engineering projects. However, traditional manual recognition methods suffer from low efficiency, high error rates, and high costs, which makes them unsuitable for modern complex engineering projects. In recent years, significant progress has been made on computer vision technology in the field of automatic recognition. Nevertheless, existing algorithms still face challenges such as low recognition efficiency and poor accuracy in identifying slanted and deformed characters in power engineering drawings. [Methods]To address these shortcomings, a character recognition algorithm based on the visual geometry group (VGG) network and Hu invariant moment was proposed, which aimed to improve the recognition efficiency and accuracy for power engineering drawings by combining scale-adaptive deep convolutional features with Hu invariant moment features. Firstly, deep convolutional features were extracted using the VGG network, and the output layer was adaptively selected to achieve scale-adaptive feature extraction for templates and images. This approach avoided the multiple times of feature extraction in traditional sliding window techniques, only extracting features once for each template and image and thereby significantly improving processing efficiency. Secondly, to address the challenges of slanted and deformed characters, Hu invariant moment features were integrated as supplementary features. Their translation and rotation invariance were leveraged to enhance robustness against complex character shapes. [Results]The performance superiority of the proposed algorithm was validated in terms of efficiency and accuracy by comparing it with existing algorithms. The results demonstrate that the proposed algorithm offers significant advantages. Its execution time is approximately one-fourth that of the traditional convolutional neural networks (CNNs)-based character recognition algorithm, namely that the proposed algorithm has greatly improved processing speed. By integrating Hu invariant moment features, the algorithm exhibits strong robustness in recognizing slanted and deformed characters. Adopting an adaptive output layer selection strategy further enhances the accuracy and robustness of feature extraction, surpassing the fixed-layer feature extraction methods. [Conclusion]The proposed algorithm shows greater adaptability in complex scenarios, holding promising application potential. The highlights of this study are as follows: the proposed scale-adaptive deep convolutional feature extraction method achieves single-step feature extraction in character recognition of power engineering drawings and thereby significantly improves efficiency; the integration of Hu invariant moment features enhances the ability to recognize complex character shapes, particularly robustness against slanted and deformed characters. This study not only provides an efficient character recognition algorithm but also offers new perspectives for the automated processing of power engineering drawings with computer vision. Future research may focus on further optimizing the robustness of character features to improve system performance.

[Objective]In response to the problems of large volume and small stroke of current micro-positioning platforms, a new displacement amplification mechanism of a lever-compound bridge type was proposed, and a three-degree-of-freedom piezoelectric micro-positioning platform that could achieve large-stroke motion was designed. [Methods]First, the structure of the piezoelectric micro-positioning platform was proposed, and its working principle was analyzed. The piezoelectric actuator generated initial displacement, which was first amplified by the compound bridge structure and then amplified again by the lever mechanism. The final amplified displacement was output to the moving platform. In addition, the lever and compound bridge mechanisms for displacement amplification were designed and optimized, and the stiffness model and displacement amplification ratio mathematical model of the lever and compound bridge mechanisms for displacement amplification were established with theories of statics, material mechanics, and elasticity. According to the theoretical model, the dimensions of the lever and compound bridge mechanisms as displacement amplifiers were optimized, and the overall amplification factor after optimization was 20.6. Then, the structure of the flexible hinge was optimized, and finite element simulation was conducted on four typical notch shapes. After the analysis of the relationship between the output displacement and input force for the four flexible hinges, the flexible hinge of the straight beam type was selected. Finite element simulation was performed on the overall piezoelectric micro-positioning platform to analyze the displacement output performance and rotational output performance around the x-axis and y-axis of the piezoelectric ceramic actuator after deformation amplification. Finally, output testing was conducted on the designed piezoelectric micro-positioning platform. Experimental verification was conducted on the z-axis direction displacement output performance of the displacement amplifier and its rotational output performance around the x-axis and y-axis. [Results]Finite element simulation shows that the maximum output displacement of the piezoelectric micro-positioning platform in the z-axis direction is 740 μm with a simulation magnification factor of 15. The maximum output rotation angles around the x-axis and y-axis are 0.83° and 0.86°, respectively. The output test of the piezoelectric micro-positioning platform shows that the boost curve does not coincide with the buck curve, and there is a hysteresis phenomenon. The maximum output displacement in the z-axis direction is 706 μm. When the voltage is 30 V, the hysteresis displacement is the largest, which is 65 μm. The maximum output angle around the x-axis is 0.8°, and that around the y-axis is 0.79°. When the voltage is 30 V, the maximum hysteresis angle around the x-axis is 0.062°. When the applied voltage is 45 V, the maximum hysteresis angle around the y-axis is 0.047°. Compared with the simulation results, the maximum errors in motion along the z-axis direction and in rotation around the x-and y-axes are 34 μm, 0.03°, and 0.07°, respectively, with corresponding maximum relative errors of 4.6％, 3.6％, and 8.1％. [Conclusion]The piezoelectric micro-positioning platform based on the lever and compound bridge mechanisms for displacement amplification effectively solves the problem of small output displacement of piezoelectric ceramics, achieving large-stroke motion.

[Objective]In the application process of deep neural networks, their huge computing requirements and storage overhead have become bottlenecks that restrict their widespread application on edge devices. Edge devices are limited by deficient computing resources and storage space, which makes it particularly difficult for them to efficiently run complex deep neural network models. Therefore, how to reduce the complexity and computational load of deep neural networks while maintaining model accuracy to meet the requirements of edge devices for lightweight edge resources has become an important research topic at present. To improve the performance of deep neural networks in edge devices, an optimization method for deep neural networks was proposed which combines the ant colony algorithm and dual-angle parallel pruning. [Methods]The structural characteristics of deep neural networks were analyzed, and a deep neural network model with multiple hidden layers was constructed. The ant colony algorithm was utilized to search for approximate optimal solutions in complex spaces by simulating the pheromone transmission mechanism in the process of ants foraging. Similar nodes in the hidden layers of the constructed model were clustered to identify highly similar neuron nodes and group them into the same category, which reduced the scale and complexity of the network. On this basis, dual-angle parallel pruning processing was further carried out on redundant nodes and free nodes after clustering. On the one hand, from the perspective of the sparsity of the weight matrix, nodes with small weights were pruned to reduce computational overhead. On the other hand, from the perspective of node contribution, the contribution of each node to the overall output result was evaluated, and nodes with small contribution were pruned. [Results]The experimental results show that compared to the original model without pruning, the deep neural network pruned using the proposed method has a higher data volume of 120 MB, an average network complexity of 88.32％, and scalability of 99％ while maintaining a high accuracy within the same computation time. This means that under limited resource conditions, deep neural networks can run more efficiently and better adapt to the needs of edge devices with the help of the proposed method. The experimental results not only validate the effectiveness of the proposed method but also provide new ideas for the deployment and application of deep neural networks on edge devices. [Conclusion]The proposed method applies ant colony algorithm to the pruning process of deep neural networks, achieving effective clustering of similar nodes in the hidden layers and providing accurate targets for subsequent pruning. At the same time, the dual-angle parallel pruning strategy further improves the efficiency and effectiveness of pruning, ensuring the balance between accuracy and scalability of the pruned model. The proposed method can not only promote the widespread application of deep neural networks on edge devices but also provide useful reference and guidance for complex network optimization problems in other fields.

A pipeline stress detection method based on dual field stress magnetic coupling is proposed to address the difficulty in quantitatively detecting stress at defects in long oil and gas pipelines. This method combines the changes in J-A model parameters under different stress states of pipelines to establish a magnetic stress detection model; The effects of different elastic stresses, plastic strains, and external magnetic fields on magnetization intensity and magnetic signal characteristics were determined separately; Finally, by introducing a proportional coefficient, a pipeline elastic and plastic stress detection model based on the dual magnetic field method was established, and the magnetic signal of the steel bar was measured as experimental verification under a stress of 10 kN-80 kN and an external magnetic field of 0 A/m-10 A/m. The experimental results indicate that the detection model proposed in this article has a high degree of fit with the experiment under high stress conditions.

[Objective] Power engineering projects are typically characterized by high costs and long durations, and their construction processes are influenced by various factors such as climate conditions and material costs. Traditional methods for cost and duration prediction are mainly based on experience, which can lead to underestimated or excessive cost estimates, resulting in project delays or resource waste. With the rapid development of machine learning techniques, data-driven methods have been introduced in cost and duration prediction. However, due to the small size of datasets in power engineering, traditional machine learning models often suffer from overfitting, which limits their predictive performance. Thus, a hybrid model combining support vector regression (SVR), classification and regression trees (CART), multivariate linear regression (MLR), and grey wolf optimization (GWO) was proposed to improve prediction accuracy and generalization ability on small datasets by enhancing the update strategy and parameter search method. [Methods] The main approach of this paper was to combine machine learning models with an improved GWO (iGWO) algorithm to develop an efficient framework for predicting the cost and duration of power engineering projects. SVR, CART, and MLR models were used as baseline machine learning methods. GWO was employed to search for optimal parameters to prevent overfitting, with two improvements introduced: using chaotic sequences to initialize the wolf pack positions to ensure population diversity, and optimizing the update strategy of the grey wolves′ positions and enhancing the search ability by sharing information within the surrounding pack. [Results] Experimental results show that the proposed hybrid model outperforms traditional methods in cost and duration prediction. Performance comparisons on the training and testing sets indicate that traditional machine learning models are prone to overfitting, which results in poor generalization. In contrast, the model combined with GWO improves this issue. The MLR+GWO hybrid model performs better than the other models on both the training and testing sets. Further experimental results reveal that the convergence speed of the hybrid model is significantly accelerated by the iGWO algorithm. It reaches optimal fitness within 6 to 8 iterations, while the traditional GWO algorithm requires 11 to 12 iterations to achieve similar results. Additionally, the improved algorithm effectively avoids the issue that the traditional GWO algorithm is prone to falling into local optima. [Conclusion] The hybrid model based on linear regression and iGWO demonstrates performance advantages in predicting costs and durations of power engineering projects. The iGWO algorithm enhances the global search capability and convergence speed through optimized initialization sequences and update strategies. The proposed hybrid model exhibits better generalization performance, compared to traditional machine learning models. Compared with traditional methods, this approach performs better in terms of prediction accuracy and training efficiency.

[Objective]With the urgent need for cost reduction and efficiency improvement, as well as the increasing demand for refined control in power engineering, the importance of multi-element, whole-process, and refined cost management in infrastructure projects is becoming increasingly evident in the context of smart infrastructure. Traditional project cost methods cannot meet the requirements of optimizing the full life cycle cost of smart infrastructure projects. Simple cost management mode must shift to intelligent cost management mode. [Methods]To further improve the refinement level of cost management under the massive data of power transmission and transformation projects and achieve the goal of covering all stages of the entire process, a cloud edge collaborative cost analysis method for power transmission and transformation projects based on principal component analysis (PCA) and improved analytic hierarchy process (AHP) was proposed after in-depth research on the existing project cost management mode, which could address the problem that traditional project cost analysis methods were difficult to accurately analyze complex and massive data in smart grids projects. This method effectively improved the refinement level of project cost management. The proposed method first designed a targeted cost system for power transmission and transformation projects based on cloud edge collaborative architecture. A three-layer cost factor evaluation index system was developed, including a target layer, a criterion layer, and a scheme layer, which was used to evaluate the cost impact indicators. In the edge computing center of the system, the PCA was used to reduce the dimension of massive project cost data and upload it to the cloud center. The particle swarm optimization (PSO) algorithm optimizes the weights of evaluation indicators in the AHP, which effectively eliminates the subjective bias of the original AHP. In the cloud center, the optimized AHP was used to achieve reliable calculation of project cost. [Results]With the cost data of the selected power transmission and transformation projects, experimental analysis was conducted on the proposed method. The PSO algorithm converged after 45 iterations to complete the parameter optimization of AHP, which is better in optimization speed and accuracy. By comparing with other cost methods, the proposed method has an project cost calculation value closest to the actual value with an error of less than 7％ and a minimum error of only 4.16％, which is significantly better than the other compared methods. [Conclusion]Under the cloud edge collaborative architecture, the proposed method effectively uses the edge computing and PCA to complete data dimensionality reduction. The optimized AHP-PSO algorithm achieves smaller analysis errors and higher evaluation reliability under more reasonable evaluation index weights, which effectively meets the requirements of refined cost management for smart infrastructure projects throughout the entire process and all elements.

[Objective] With the increasing demands for flight safety of unmanned aerial vehicles (UAVs), the dynamic characteristics of landing gear systems have become a critical research focus in UAV design. This study focuses on the landing contact mechanical behavior of rubber footpads in six-link landing gears and investigates the problems of nonlinear mechanical characteristics in modeling. By constructing a precise dynamic contact model, this research aims to elucidate the mechanical response mechanisms of rubber buffers under impact loads and provide theoretical support for optimizing the structural design of cushioning systems at landing gear foot ends. [Methods] A nonlinear contact mechanics model for rubber materials was developed based on the theoretical framework of the continuous contact force method. Innovatively integrating Hertzian contact theory with the Mooney-Rivlin strain energy function, the model accurately characterized the hyperelastic characteristic of rubber materials and the dynamic coupling effects at contact interfaces through non-ideal elastic collision relationships. On the ABAQUS platform, a finite element model adopting the Mooney-Rivlin hyperelastic constitutive model was established, and the landing collision process was numerically simulated using an implicit dynamic solver. A drop impact test bench equipped with force sensors was constructed to obtain experimental data for model validation. This integrated methodology, combining theoretical modeling, numerical simulation, and experimental validation, effectively overcomes the limitations of traditional empirical formulas. [Results] Systematic analysis reveals the influence of multiple physical parameters on contact mechanical characteristics. When the drop height increases within the range of 50 mm to 200 mm, the peak contact force exhibits proportional growth, with an increment of 1.78 kN. Within the load mass range of 5 kg to 20 kg, the peak contact force demonstrates an approximately linear relationship with load mass, showing an increase of 1.02 kN. Notably, increasing footpad thickness has an insignificant effect on reduction in impact force, while optimizing the footpad shape can effectively mitigate impact-induced vibrations. Comparative studies on structural shapes demonstrate that conical footpads, compared to traditional cylindrical designs, exhibit more even force distribution and effectively mitigate impact-induced vibrations. Experimental validation confirms the effectiveness of the model, with the peak contact force error being merely 6％ and a phase shift of key parameters controlled within 3 ms under the condition of 100 mm drop height. [Conclusion] The contact force demonstrates approximately directly proportional relationships with both drop height and footpad thickness, though the effect of thickness is relatively weak. Footpad shape optimization significantly reduces impact-induced vibrations, with conical footpads exhibiting superior cushioning performance. This study achieves theoretical breakthroughs in two aspects. A dynamic contact prediction method for rubber buffers was proposed by combining the continuous contact force method with the hyperelastic constitutive model, resolving the technical bottleneck of traditional approaches in addressing nonlinear coupling effects. A quantitative evaluation framework for cushioning performance was established by investigating the influence of multiple physical parameters on contact force at landing gear foot ends, providing a reliable theoretical basis for foot-end parameter optimization.

[Objective] With the increasing demand for application of unmanned aerial vehicles (UAVs) in complex scenarios such as power grid inspection and emergency rescue, the limitations of single UAV in task execution have become increasingly prominent. Multi-UAV formation can effectively improve inspection efficiency and expand operation coverage, but significant challenges remain in formation maintenance, collaborative trajectory optimization, and environmental adaptability to complex environments during practical application. An optimal control method that integrated virtual spring forces with the hp-adaptive pseudospectral method was proposed to address the difficulties of formation maintenance and path optimization during large planar maneuvers of UAV swarms, thus enhancing the stability, flexibility, and disturbance resistance of collaborative flight of UAV formations and providing technical support for high-demand scenarios for UAVs such as power grid inspection. [Methods] First, a multi-UAV system dynamics model was built, and a virtual spring mechanism was incorporated into the formation control system to realize flexible constraints and elastic self-adjustment between UAVs. By combining the virtual spring method with the traditional leader-follower method, a formation strategy that could balance rigid support and adaptive adjustment ability of formations was designed. On this basis, the hp-adaptive pseudospectral method was then applied to solve the optimal control problem of UAV formations. By discretizing state and control variables at Legendre-Gauss nodes and constructing global interpolation polynomials, the trajectory optimization problem was transformed into a nonlinear programming (NLP) problem, with constraints such as dynamics, energy consumption, and velocity combined to conduct a high-precision numerical solution. In simulation experiments, a typical four-UAV diamond formation was set up, and the algorithm′s adaptability to different terrains, wind disturbances, and mission requirements was comprehensively explored. [Results] Simulation results show that the proposed virtual spring-based hp-adaptive pseudospectral method can realize smooth formation turning and velocity control. During a 90° large maneuver, UAVs can not only satisfy multiple constraints such as path deflection and speed change, but also maintain a stable formation. Compared with traditional leader-follower and artificial potential field methods, the new method demonstrates significant advantages in position error, formation maintenance, and wind resistance. Under 10m/s strong wind, the formation stability of the proposed method exceeds 70％, showing significant advantages over its competing algorithms. 3D terrain simulations and real flight tests further validate the algorithm′s adaptability and robustness, and the method still maintains lower formation deformation rates and trajectory tracking error under multiple terrains such as hills, mountains, and canyons, with the features of reasonable energy consumption control and strong engineering practicability. [Conclusion] By innovatively integrating the virtual spring elastic constraint with the hp-adaptive pseudospectral method, an optimal control technique for UAV formation trajectory planning in complex environments was proposed. The rigidity constraint limitations of traditional formation methods are overcome, flexible maintenance and adaptive adjustment of formations are realized, and the accuracy and efficiency of collaborative trajectory optimization are significantly improved by the method. The research results provide an efficient and reliable technical path for collaborative flight of UAV swarms in demanding tasks such as power grid inspection and emergency rescue. Future studies may further increase the method′s application potential in multi-formation collaboration and complex obstacle environments, promoting the intelligent and practical development of UAV formations.

[Objective] With the rapid development of artificial intelligence, object detection technology based on visible light images has become increasingly advanced and has been widely applied in fields such as autonomous driving, security monitoring, and intelligent transportation. However, in low-light scenes (such as nighttime or dimly lit environments), the performance of object detection algorithms based on visible light images decreases significantly. This is primarily due to severe information loss in visible light images under low-light conditions, making it difficult to extract target features. To solve this problem, multi-modal object detection technology combining visible light and infrared images was proposed, which could effectively enhance object detection performance in low-light scenes. However, the multi-modal method is costly and requires precise registration of images from different modalities, which increases system complexity and processing burden. In response, this study proposed an object detection network with infrared sensing (InSCnet), aimed at using a visible light camera to predict infrared thermal radiation characteristics, thus improving the network′s object detection capability in low-light scenes without increasing modality. [Methods] The InSCnet network used visible light images as input and generated infrared images through an infrared prediction branch (IPB), which predicted thermal radiation characteristics to enhance the network′s perception of low-light scenes. A complementary fusion filter (COFF) module was designed to effectively integrate multi-scale visual and thermal radiation features. By complementing these two features, the COFF module enhanced their mutual complementarity and avoided the network′s over-reliance on a single modality. In addition, a hybrid feature pyramid (HyFP) module was employed to further improve the fusion and extraction of multi-scale global and local features through feature pyramids and attention mechanisms, ensuring that the network maintained high detection accuracy under varying low-light conditions. [Results] Experimental results show that InSCnet performs excellently on the LLVIP pedestrian detection dataset, with S_mAP50 reaching 0.830 and S_mAP50-95 reaching 0.426. Moreover, experiments conducted on the DroneVehicle dataset show a S_mAP50 of 0.702, confirming its ability to handle multi-class low-light detection. [Conclusion] InSCnet improves object detection performance in low-light scenes by introducing infrared thermal radiation characteristics and a feature fusion mechanism. The network can effectively detect objects that are difficult to identify in visible light images under low-light conditions, providing an effective solution for object detection in such environments. Future research will further explore ways to optimize the network structure.

[Objective]In the context of rapidly advancing network technology, due to the insufficient efficiency and accuracy, traditional network detection techniques struggle to meet the complex demands of network management. Particularly in power communication networks, the statistics and management of network traffic, structure, and load become intricate, which makes it difficult for network management technicians to quickly propose effective remedial measures when cyber security events occur. This affects the quality of Internet services and the stability of social order. Therefore, a network cooperative detection system based on a multi-Agent model was proposed to enhance the efficiency and accuracy of network detection. [Methods]The efficiency and accuracy of network detection were significantly improved by integrating active and passive detection functions into a network topology algorithm and incorporating various agents and dynamic decision-making mechanisms. The active detection technology used the Traceroute algorithm to discover active devices and open ports in the network, while the passive detection technology collected detailed information from network traffic in line with protocols such as simple network management protocol (SNMP). A more comprehensive view of network assets was obtained by the combination of the two. In the specific research, a module deployment and technical architecture that integrated active and passive network detection technologies was designed, and an organizational structure of the distributed detection system was established. [Results]Simulation experiments and analysis show that under the same testing environment and process, compared to single passive and active network detection systems, the network cooperative detection system has stronger communication performance and shorter detection time while consuming less time. [Conclusion]In summary, the network cooperative detection system demonstrates superior communication performance and detection efficiency in simulation experiments, capable of detecting more hosts in a short time with greater data traffic and a broader coverage range, which further validates the feasibility and effectiveness of the proposed system. During actual tests, in complex network environments that include multiple operating systems, the network cooperative detection system based on the multi-Agent model detects the most hosts and is able to clearly identify the composition of host operating systems. This system not only improves the efficiency and accuracy of network detection but also has significant importance for applications requiring real-time responses, which enhances the response speed and processing capabilities of network management. It holds important theoretical and practical value for network security and optimization. There is still room for optimization and improvement in the theoretical mechanisms and detection time of network cooperative detection systems that can meet a wide range of engineering requirements, which remains a core issue in the field of network detection research.

[Objective] With the continuous expansion of power grid scale and the increasing complexity of the operating environment, surface corrosion of transmission equipment has become a critical factor threatening the safe operation of power grids. Traditional manual inspection methods are not only inefficient but also struggle to accurately identify subtle corrosion features on equipment surfaces, especially in complex natural environments where the boundaries of corrosion areas are often blurred, posing significant challenges for precise recognition. To address this, a fine-grained recognition method for corrosion areas on the surface of transmission equipment based on image semantic segmentation was proposed, aiming to achieve precise detection and recognition of corrosion areas through deep learning technology. [Methods] The core of this method was the construction of a semantic segmentation network integrated with an attention mechanism. This network, by introducing both channel attention and spatial attention mechanisms, could effectively capture the subtle features and precise boundaries of corrosion areas. Specifically, the channel attention mechanism enhanced the response to channels with prominent corrosion features by analyzing the relationships among various channels in the feature map. Meanwhile, the spatial attention mechanism strengthened the spatial feature representation of corrosion areas by focusing on the spatial location information in the feature map. After the initial segmentation, the K-means++clustering algorithm was employed to perform clustering analysis on the RGB values of the pixels in the segmented images. By optimizing the selection of initial clustering centers, this algorithm effectively avoided the issue of local optimum that could arise with the traditional K-means algorithm, thereby more accurately dividing corroded and uncorroded areas. To further improve recognition accuracy, the structural similarity index was introduced to evaluate each clustered area, and fine-grained recognition of corrosion areas was achieved at the pixel level by calculating the structural similarity between areas. [Results] Experimental results demonstrate that the proposed method exhibits remarkable performance on a dataset of transmission equipment images in complex natural environments, achieving a significantly improved corrosion area recognition accuracy and an obvious improvement in boundary localization accuracy compared to traditional methods. [Conclusion] In summary, the semantic segmentation network integrated with an attention mechanism, combined with the K-means++clustering algorithm and SSIM evaluation, pioneers an efficient and precise new approach for fine-grained recognition of corrosion areas on the surface of transmission equipment. By incorporating the attention mechanism, the proposed method effectively addresses the challenges posed by complex corrosion features and blurred boundaries, significantly enhancing recognition accuracy. Meanwhile, the combination of the clustering algorithm and SSIM evaluation enables pixel-level detailed differentiation, further improving the fineness and practicality of recognition and providing solid technical support for the safe monitoring and maintenance of power grids. Not only does the proposed method ensure the safe and stable operation of power grids, but it also offers valuable insights and inspiration for the application of image recognition and segmentation technologies in other fields.

[Objective] Agricultural parks, characterized by abundant renewable energy resources, play a pivotal role in advancing green and low-carbon transformation under the carbon peaking and carbon neutrality goals. However, current agricultural parks face challenges such as low energy utilization efficiency, imbalanced multi-energy distribution, and insufficient local renewable energy accommodation capacity, which hinder agricultural productivity and sustainable development. To address these issues, this study proposed a deep learning-based optimization method for constructing a more economical and low-carbon integrated energy system (IES) in agricultural parks. [Methods] First, a multi-objective optimal scheduling model for agricultural park IES was established, integrating economic objectives such as fuel costs of gas turbines, grid interaction costs, and equipment maintenance costs, while formulating mathematical constraints for multi-energy coupling systems. Second, an improved long short-term memory (LSTM) neural network was employed to predict photovoltaic/wind power outputs and load demands. The hyperparameters of the LSTM model, including hidden layer units and learning rates, were dynamically optimized using quantum particle swarm optimization (QPSO) to enhance prediction accuracy. Finally, to mitigate premature convergence in the traditional golden sine algorithm (GSA), an enhanced GSA algorithm was proposed by incorporating Lévy flight strategies to expand the search space and designing dynamic weight mechanisms to balance global exploration and local exploitation capabilities. [Results] Case studies demonstrate that the errors of improved QPSO-LSTM prediction model are controlled within 5％, outperforming traditional optimization algorithms in accuracy and robustness against local optima. For scheduling optimization, the enhanced GSA algorithm achieves a 69.7％ reduction in daily operational costs and a 27.9％ improvement in local renewable energy accommodation rates compared to unscheduled scenarios, significantly surpassing conventional GSA and other methods. These results validate the algorithm′s effectiveness in balancing economic efficiency and low-carbon requirements for multi-energy coordination. [Conclusion] The proposed deep learning-based optimization framework enables high-precision power prediction and cost-effective scheduling for agricultural park IES. It significantly reduces operational costs while enhancing renewable energy utilization, demonstrating superior performance in synergizing economic and low-carbon objectives. This study provides a reliable technical pathway for the efficient and sustainable operation of agricultural park IES.

[Objective]High-value agricultural products such as precious medicinal materials and organic fruits generally have requirements for high-quality control and preservation and usually require initial processing before entering the market circulation. Therefore, the location of the initial processing center plays an important coordinating role in balancing the dispersed rural procurement logistics on the production end and the urban distribution logistics with dense distribution points. Given the common characteristics of poor coordination between rural procurement logistics and urban distribution logistics and a high proportion of transportation costs for high-value agricultural products, how to reduce costs and increase efficiency while ensuring customer satisfaction is a key issue that urgently needs to be addressed in the location-path planning of high-value agricultural products. [Methods]A two-stage logistics location-path planning model was proposed with the goals of minimizing total cost and maximizing customer satisfaction. The first stage focused on the location of the drying center, considering construction costs, transportation convenience, service radiation range, etc., to construct a location model and optimize the initial processing center location that matches the Chinese herbal medicine production area and users' locations. In the second stage, logistics transportation paths were planned depending on the selected initial processing center location, with vehicle capacity, speed, and time windows taken as constraints. A multi-objective path planning model was constructed by integrating transportation, penalties, cargo damage costs, and customer satisfaction. To solve the above model, particle swarm algorithm, differential evolution concept, and population evolution factors were integrated into the bacterial foraging algorithm, and a hybrid multi-objective bacterial foraging optimization-niche multi-objective particle swarm optimization (MOBFO-NMOPSO) algorithm was proposed for multi-objective optimization. The designed algorithm improved solution accuracy by introducing niche multi-objective particle swarm optimization (NMOPSO). Differential evolution was introduced in replication operations to preserve population diversity. Population evolution factors were introduced into migration operations to improve the convergence speed of the algorithm. For the verification of the effectiveness of the model and algorithm, the proposed MOBFO-NMOPSO algorithm was compared with nondominated sorting genetic algorithm II (NSGA-II), multi-objective bacterial foraging optimization (MOBFO), NMOPSO, grey wolf optimizer with estimation of distribution algorithms (GWOEDA), genetic algorithm (GA), and other algorithms, which verified the advantages of the proposed algorithm in solving performance and speed. Then, with the actual data of S enterprise's Chinese herbal medicine supply chain as a research example, the two-stage location-path planning problem was comprehensively solved by considering the construction cost of the drying center, vehicle transportation cost, time penalty cost, and cargo damage cost. [Results]The simulation results show that the optimized transportation cost of the enterprise is reduced by 10.26％, and customer satisfaction is improved by 44.84％, which verifies the effectiveness of the model in solving high-value agricultural product logistics planning problems. Finally, considering the quantity of Chinese herbal medicine production areas, logistics costs, and customer satisfaction, actual logistics path solutions were designed for S enterprise's Chinese herbal medicine supply chain, taking into account different extreme and compromise solutions, for the enterprise to choose from. [Conclusion]The two-stage location-path planning model and the improved MOBFO-NMOPSO algorithm constructed in this study can effectively enhance the competitiveness of the supply chain by reducing the total cost of the supply chain, stabilize the supply-demand cooperation relationship by improving customer satisfaction, and effectively promote the coordinated and steady development of the supply chain of high-value agricultural products by constructing a two-stage logistics planning system to improve the operation efficiency of high-value agricultural products.

[Objective]Petrochemical products are prone to leakage during production, transportation, and use, causing serious harm to the marine environment, marine biological resources, and coastal city environment. Therefore, there is a need to optimize the oil-water separation technology, in order to recover marine oil spills and purify the marine environment, which has important application value for marine oil pollution control and other aspects. The adsorption method, as a kind of oil-water separation technology with a good development prospect, is suitable for the treatment of marine oil spills. Its ability to deal with pollution is related to the adsorption material and adsorption structure. At present, most of the research on the adsorption method focuses on the field of material optimization, while less on adsorption structure improvement. To optimize the oil-water separation technology of the adsorption method, a feasible optimization scheme for the adsorption method from the structural level was proposed by leveraging bionics, which provides ideas for reference in this field. [Methods]In this paper, the traditional adsorption structure consisting of a single oil adsorption material was optimized, and a novel adsorption structure was proposed based on bionic engineering. Firstly, the sparse and porous characteristics of the stems of aquatic plants were analyzed, and a similar porous material, copper foam, was chosen as the main material of the adsorption structure. Then, the theoretical reason for the higher oil-water separation capacity of the internal vascular structure of oil-bearing crops was analyzed. The structure of parallel rows of oil and water channels produced the Marangoni effect, which created a tensile force between oil and water. Based on this, a laminar structure was designed, and a novel oil-water separation structure formed by interlocking of oleophilic hydrophobic and hydrophilic oleophobic materials was proposed. The hydrophilic oleophobic material was prepared by immersing copper foam into a mixture of potassium persulfate and potassium hydroxide. The oleophilic hydrophobic material was prepared by immersing copper foam into a mixture of n-hexane and polydimethylsiloxane (PDMS). The adsorption capacity of the new combined stacked adsorption structure was experimentally compared with that of the conventional single oil adsorption structure. The two adsorption structures were placed in equal volumes of emulsified and unemulsified oil-water mixtures at volume fractions of 1％, 5％, and 10％, and the oil adsorption amounts of the two structures within the same time were determined. [Results]The results show that the adsorption rate and oil adsorption amount of the new structure are higher than those of the conventional single adsorption structure under most conditions. [Conclusion]The feasibility of the adsorption structure scheme with the staggered superposition of the oleophilic hydrophobic layer and the hydrophilic oleophobic layer is proved, and the addition of the hydrophilic layer can effectively improve the oil adsorption capacity of the adsorption structure. The research results provide ideas and technical guidance for the optimization of oil-water separation technology from the structural aspect.

[Objective] 6061 aluminum alloy is widely used because of its good comprehensive properties. However, the quality of its welded joints generally has certain limitations. This study aims to effectively improve the quality of 6061 aluminum alloy welded joints by using the method of laser shock peening, deeply explore the changes of mechanical properties and microstructure of welded joints before and after laser shock peening, and analyze the internal influence mechanisms, so as to provide a solid theoretical basis and practical guidance for the optimization of aluminum alloy welding process. [Methods] A 6061 aluminum alloy welded joint was selected as the research object, and its surface was treated by laser shock peening technology. In the process of treatment, the parameters of laser frequency, shock range, pulse width, and overlap rate of laser pulses were strictly controlled. The influence of laser energy on 6061 aluminum alloy welded joints was studied. The mechanical properties of welded joints before and after laser shock peening were analyzed, such as tensile strength and hardness. At the same time, the changes of microstructure characteristics such as grain size and shock layer thickness at the weld were observed and compared by means of microstructure analysis technologies, including optical microscopy, scanning electron microscopy, and electron backscatter diffraction (EBSD). [Results] First of all, in terms of the relationship between laser energy and tensile strength of welded joints, there is a clear positive correlation. Specifically, with the gradual increase in laser energy, the tensile strength of welded joints also increases steadily. Secondly, the detection of the hardness of the weld surface shows that the hardness is significantly improved after laser shock peening, and the increase is about 23％. Finally, from the microstructure point of view, the thickness of the laser shock layer changes significantly, greatly increasing from the initial 15.83μm to 30.77μm, which indicates that the laser shock has a deep impact on the surface of the material. At the same time, the grain size of the weld center also changes significantly, decreasing from the original 33.68 μm to 14.5 μm. The grains obviously become finer, namely that the microstructure is optimized. [Conclusion] Based on the above research results, it can be concluded that laser shock peening technology shows excellent effect in the treatment of 6061 aluminum alloy welded joints. The high energy generated on the surface of metal materials can effectively reduce the adverse effects of plastic deformation on the surface and interior of materials and promote grain refinement, which is the key factor to improve the mechanical properties of welded joints. Through laser shock peening, the tensile strength and hardness of welded joints are effectively improved, which not only helps to improve the reliability and durability of 6061 aluminum alloy welded structures in practical applications but also provides strong technical support for further expanding the application range of aluminum alloys in high-end manufacturing. In the future, the optimal process parameter combination of laser shock peening can be further studied in order to improve the quality of 6061 aluminum alloy welded joints more accurately and efficiently and promote the continuous development and innovation of aluminum alloy welding technology.

[Objective]To produce high-quality generator guard rings with straight busbars, minimal machining allowance, and low residual stress, a new process was proposed to reinforce the guard rings through the linkage of a hydraulic press and an ultra-high-pressure pump. [Methods]The mold was pressed down on the guard ring by a hydraulic press. An ultra-high-pressure liquid was injected into the interior of the guard ring by using an ultra-high-pressure pump to cause plastic deformation. The deformation shape of the guard ring was controlled by precisely controlling the pressure of the hydraulic press and the ultra-high-pressure liquid injected by the ultra-high-pressure pump. When the guard ring deformed to a certain extent, due to the increase in deformation resistance, the mold needed to be replaced to continue expansion until the expected forming size was reached. Compared to the traditional hydraulic expansion process for generator guard rings, the new process used equipment with a lower cost and a higher efficiency. The material used in the manufacturing of the generator guard ring was 50Mn18Cr5. Using the analysis function of ANSYS Workbench, a finite element analysis was conducted on the changes of the outer diameter and stress of the guard ring during the expansion process. At the same time, according to the requirements of the automatic control system, the automatic control program and human-machine interface (HMI) configuration interface were designed, with a programmable logic controller (PLC)used to control the production process of the generator guard ring. Various parameters such as the diameter change, deformation speed, system pressure, and temperature of the guard ring during the expansion process were displayed on the HMI configuration interface. By detecting the diameter changes of the guard ring during the expansion process, the pressure of the hydraulic press and the ultra-high-pressure pump was precisely controlled to ensure the smooth progress of hydraulic expansion. At the same time, if both the hydraulic press and the ultra-high-pressure pump failed, the automatic control system would also issue an automatic alarm, which can protect the safety of workers and avoid major safety accidents. [Results]The finite element analysis shows that the maximum error between the change in the outer diameter of the guard ring during the hydraulic expansion process and the actual production result is only 2.312 mm, which proves the feasibility of the new process. The intuitive presentation of the working status and parameters of the equipment through the HMI configuration interface not only ensures the production quality and efficiency of the generator guard ring but also makes the operation more convenient. [Conclusion]The new process that utilizes the linkage between a hydraulic press and an ultra-high-pressure pump can effectively produce high-quality generator guard rings with straight busbars, minimal machining allowance, and low residual stress, providing an efficient and low-cost technical solution for guard ring manufacturing.

[Objective] With the rapid development of long-span suspension bridges, wind-induced vibration has gradually become a crucial factor affecting their safety and comfort. Small horizontal-axis wind turbines installed on bridges can not only effectively suppress vortex-induced vibration but also provide wind energy for powering ancillary facilities. However, the impact of small horizontal-axis wind turbines on bridges has not been comprehensively and systematically studied, especially their specific influence on bridge buffeting response. Therefore, this study aims to explore the influence of small horizontal-axis wind turbines on bridge buffeting response and assess the effects of different wind turbine layout schemes on bridge dynamic response, so as to provide a theoretical basis and practical guidance for control of wind-induced vibration of bridges by wind turbines. [Methods] This study took the typical flat box girder of the Great Belt Bridge in Denmark as the research object and employed such means as wind tunnel tests, finite element analysis, and harmonic superposition, combined with the actual wind environment and structural characteristics of the Great Belt Bridge, to simulate and analyze the influence of wind turbines on bridge buffeting response. Static three-component force coefficients of the bridge with wind turbines installed were measured in wind tunnel tests, and time-history response data of the bridge subjected to wind loads were generated depending on relevant data. Based on the quasi-steady assumption and Davenport buffeting force model, combined with the finite element model, the dynamic response of the bridge under different wind speeds was calculated and simulated. Six different wind turbine layout schemes were designed during the research process, considering variations in parameters such as the rotation axis height and layout spacing of wind turbines, to investigate the effects of different layout schemes on the lateral and vertical displacement and acceleration responses of the bridge. [Results] The results indicate that the installation of small horizontal-axis wind turbines increases the displacement and acceleration responses of the bridge to a certain extent. However, by selecting appropriate wind turbine layout schemes, it is possible to control vortex-induced vibration with a small effect on the structural safety and comfort of the bridge. The overall increase in lateral displacement of the bridge tends to decrease as the rotation axis height of the wind turbine blades decreases. For vertical response, the smallest increase in vertical displacement occurs when the wind turbine layout spacing is three times the beam height. Furthermore, by fitting the static wind loads caused by wind turbines on the bridge, this study proposed estimation formulas for drag and lift unit loads of bridges with wind turbines installed, which could effectively assess the impact of wind turbines on bridges under different layout schemes. [Conclusion] The impact of small horizontal-axis wind turbines on bridge dynamic response can be reduced through reasonable layout parameters (such as rotation axis height and layout spacing) without significantly affecting the structural safety and comfort of the bridge. The installation height and spacing of wind turbines have significant impacts on the dynamic response of the bridge, and reasonable layout schemes should be selected according to the specific conditions of the bridge to ensure its structural safety and comfort. This study proposed a mathematical model relating to the layout spacing and rotation axis height of wind turbines and wind load data of the bridge, providing theoretical support for optimizing control of wind-induced vibration of bridges by wind turbines in the future.

[Objective]In recent years, the development and progress of science and technology has brought many conveniences to people's production and lives, whereas potential hazards (such as vibration and noise) have also arisen. The generation of vibration aggravates the damage of parts, and the emergence of noise endangers people's health. To deal with these problems, damping alloys that can absorb vibration energy have gradually garnered people's attention. However, traditional damping alloys have a single damping mechanism and poor mechanical performance, failing to realize a variety of practical applications. Therefore, it is urgent to develop a new material that takes into account both. The emergence of multi-principal-element alloys makes the idea possible. The unique construction makes the alloys have good mechanical properties and damping properties. Therefore, it is of great significance to study the relationship between the microstructure and damping properties of multi-principal-element alloys. [Methods]To realize good damping properties and excellent mechanical properties of alloys, Fe₃CoNiCuCr_x (x=2.0, 2.5, 2.75, 3.0, 3.5) medium-entropy alloys were prepared by an arc melting furnace and a vacuum induction furnace. The phase structures of the alloys were studied by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The microstructure, phase distribution, and phase volume fraction of the alloys were characterized by a scanning electron microscope equipped with an electron backscatter diffraction (EBSD) probe. The tensile properties of the alloys were tested by a universal mechanical testing machine, and the damping properties of the alloys were tested by a dynamic mechanical analyzer. The effects of the variation of the Cr molar ratio on the phase composition and damping properties of the alloys were studied by increasing the molar ratio of the Cr element. [Results]The phase composition of the alloys changes from the face-centered cubic (FCC) phase to the FCC phase and the body-centered cubic (BCC) phase with the increase in the molar ratio of the Cr element. The formation of the two-phase structure makes the phases in the alloys grow competitively, thus reducing the average grain size of the alloys. The tensile strengths of the alloys increase from 329 MPa to 779 MPa. However, the plasticity of the alloys deteriorates, and the plastic strain is reduced from 35.45％ to 1.72％. When the Cr molar ratio reaches 3.0, the damping capacity index of the alloy reaches the highest value of 0.052. With an increase in the molar ratio of the Cr element, the volume fractions of the BCC phase in the alloys gradually increase, which improves not only the tensile strengths of the alloys but also their ferromagnetic damping properties. The match of the volume fractions and morphologies of the FCC and BCC phases improves the interface damping properties of the alloys, and the unique mesh structure formed by embedding the soft FCC into the hard BCC matrix improves the damping properties of the alloys to a certain extent. [Conclusion]Under the joint influence of ferromagnetic damping, interface damping, and morphologies of the alloy, the Fe₃CoNiCuCr_3.0 alloy has not only excellent tensile properties but also high damping properties.

[Objective] Martensitic stainless steel (0Cr13Ni5Mo) has good forging, casting, and corrosion resistance performance and is therefore widely used in hydropower, chemical industry, and high-pressure vessels. However, the special working environment can accelerate failure of the 0Cr13Ni5Mo material, especially for hydraulic flow passage components which are subjected to sand impact and corrosion due to the complex water body. Therefore, it is necessary to take certain protective measures to improve the surface properties of the 0Cr13Ni5Mo material and thereby delay its failure. [Methods] To enhance the service life of the flow passage components, iron-based alloy coatings with different Nb content (0％, 5％, 7％, and 9％) were prepared on the surface of the 0Cr13Ni5Mo substrate by laser cladding technology. X ray diffraction (XRD), scanning electron microscopy (SEM), a Vickers hardness tester, and an electrochemical workstation were used to investigate the effects of Nb addition on the microstructure, phase composition, microhardness, and electrochemical properties of the iron-based alloy coatings. [Results] The results show that the microstructure consists of grey matrix, massive and petal-like VC reinforced phases, and reticulated Cr carbides. With the increase in Nb content, the size of massive and granular Nb carbides in the matrix increases gradually, and the carbide morphology changes from massive and granular to petal-like and butterfly-like, and the reticulated Cr carbides decrease gradually. The phase composition analysis shows that the four coating samples prepared by laser cladding are composed of martensitic phase with BCC structure, austenitic phase with FCC structure, carbide reinforced phase VC, and Cr₂₃C₆ phase. With the increase in Nb content, the peaks of NbC phase appear in the XRD curves of S2, S3, and S4 samples, indicating that the carbide reinforced phase NbC is generated by the in-situ reaction of Nb during the laser cladding process, and the austenitic phase in the matrix increases gradually. The microhardness of all samples increases with the addition of Nb, with the S3 sample exhibiting the highest microhardness of 645 HV. In electrochemical tests, the self-corrosion potential of the samples gradually increases with the increase in Nb content, and the self-corrosion current gradually decreases. Samples S3 and S4 show typical anodic polarization characteristics, including the active dissolution zone, the passivation zone, and the over-passivation zone which is formed after the rupture of the passivation film. Sample S4 has a large amount of carbides, which causes micro-electro-coupling corrosion, leading to a decrease in corrosion resistance. Sample S3 has the best electrochemical performance with a self-corrosion potential of -179.3 mV, and the self-corrosion current density is only 10.3％ of that of sample S1 and reaches 9.258×10^-8 A/cm². Its improved corrosion resistance is due to the Cr reduction in the MC-type carbides, which results in the dissolution of more Cr into the matrix phase and thereby an increase in the Cr content in the matrix phase. [Conclusion] In this study, a Nb-containing iron-based alloy coating of 0Cr13Ni5Mo material for hydraulic flow passage components was designed and prepared to promote the development of laser cladding technology for surface-strengthened coatings of flow passage components to a certain extent.

[Objective] Interior permanent magnet (IPM) motors are widely adopted as submersible motors in oil well applications due to their structural stability, high efficiency, and superior power factor. However, the constrained wellbore diameter and elevated ambient temperatures in deep-well environments impose stringent requirements for enhanced torque density and anti-demagnetization capability of IPM motors. To address these challenges, a novel sine-shaped permanent magnet (PM), synthesized from flat and arched PM, was adopted in this study. The adopted PM optimizes rotor space utilization above conventional flat PM, thereby increasing permanent magnet volume and d-axis permanent magnet thickness, improving torque density and anti-demagnetization capability of IPM motors. [Methods] Maintaining constant dimensions of the flat PM, the finite element analysis (FEA) was employed to systematically evaluate the effects of arched PM sagitta on short-circuit current, anti-demagnetization capability, and no-load and on-load electromagnetic performance. Moreover, the equivalent ring method was implemented to quantify the maximum stress of the rotor core under various sagittas, ensuring the mechanical integrity of the optimized rotor structure. [Results] Although the short-circuit current of the IPM motor will increase as the sagitta increases, its permanent magnets exhibit stronger anti-demagnetization capability. While increasing the sagitta will increase the maximum stress of the rotor core, it is much lower than the yield stress of the core material, meeting practical needs sufficiently. Moreover, the effect of a smaller sagitta on the reluctance torque of the IPM motor can be ignored. When the sagitta is greater than 3 mm, the maximum reluctance torque decreases significantly as the sagitta increases, while the total output torque keeps increasing and torque ripple decreases. [Conclusion] Taking into account the overall influence of the sagitta on the motor′s performance, a suitable sine-shaped PM size was selected, and a prototype was manufactured and tested. The experimental results are in good agreement with the 2D FEA simulation results, verifying the accuracy of the simulation analysis, which provides a new approach for the rotor design of the IPM motors.

[Objective] Seepage failure seriously threatens engineering safety, and research on the process of multi-layer sand seepage failure is of practical significance for disaster prediction and optimization of prevention and control engineering, which is, however, still not deep enough currently. This study employed experimental methods to analyze the seepage failure process of multi-layer sand, aiming to obtain the influence of sand layer arrangements on the occurrence conditions, failure modes, and water-sand transport process of seepage failure, and further analyzed the formation mechanism of multi-layer sand seepage failure and the transport characteristics of water-sand two-phase flow. [Methods] The independently developed high-speed seepage test equipment for water-sand two-phase flow was used to conduct indoor experimental research on the process of multi-layer sand seepage failure, and an experimental method of seepage failure for water sand separation was proposed in the study. By conducting seepage failure experiments with different sand layer arrangements, the occurrence conditions and failure modes of multi-layer sand seepage failure were determined, and water-sand transport characteristics were revealed during the process of water-sand two-phase flow. [Results] The distribution of multi-layer sand has an impact on the mode and occurrence conditions of seepage failure. Sand layers with small particle sizes bear large local hydraulic gradients, and when they differ significantly from particle sizes in downstream areas, the critical hydraulic gradient is small. When the sand layers are arranged in the order of particle size from fine to coarse, the sample has filtration characteristics, and the critical hydraulic gradient is relatively large at this time. The distribution of multi-layer sand has an impact on the flux of water-sand transport caused by seepage failure. In the absence of downstream sand layer protection, fine sand layers may experience soil flow failure, which leads to the rapid formation of a large amount of sand inrush during seepage development. While there is a protective sand layer at the downstream area of the fine sand layer, the amount of sand inrush will be significantly reduced. The distribution of multi-layer sand has an impact on the water-sand transport flux. During the process of multi-layer sand piping, the sand structure gradually deteriorates, and the flux increases accordingly. The water-sand flux curve shows regular fluctuations due to the alternating compression and expansion of the sand layer. [Conclusion] The experiment results indicate that multi-layer sand can withstand a large hydraulic gradient under reasonable inverted filter settings. Fine sand layers bear a greater hydraulic gradient than other sand layers and are prone to soil flow failure in the absence of downstream sand-layer protection. When the downstream protective sand layer has a large grain size, the piping failure is more likely to occur. When soil flow occurs, a large amount of sand inrush occurs rapidly, and the water-sand two-phase flow process shows a regular fluctuation when piping occurs. The obtained critical conditions for seepage failure and data of water-sand transport process in this study can serve as reference for further experimental research and the correctness verification of numerical simulation.

[Objective] With the continuous expansion of the power grid scale and the increasing complexity of its structure, traditional power grid modeling and visualization methods have gradually revealed many issues. For example, modeling accuracy often fails to meet the refined display requirements of complex grid structures. Application scenarios are limited and unable to effectively address diverse business needs. Moreover, there is a lack of scientific and effective verification mechanisms to ensure the accuracy and reliability of modeling results. To address these challenges, this study proposed a 3D power grid modeling and verification method integrating GIS-GIM and DETR networks, to achieve high-precision 3D grid modeling and establish an effective verification system, providing a solid data foundation and reliable decision support for grid planning, operation and maintenance, and management. [Methods] The first step involved integrating the grid information model (GIM) into the geographic information system (GIS). By leveraging GIS′s powerful geospatial analysis and display capabilities, and combining GIM′s detailed descriptions of grid equipment and topological structures, a more comprehensive 3D grid modeling approach was achieved, visually presenting the grid′s overall layout and equipment distribution from a geospatial perspective. Second, the DETR network was improved by optimizing its structure, adjusting parameter settings, and employing more effective training strategies, enabling it to more accurately detect and classify 3D grid equipment. During training, a large volume of 3D grid equipment data was collected to build a rich and diverse dataset. The data were then annotated and preprocessed to improve the model′s generalization ability. Last, the improved DETR network was applied to the 3D grid modeling process to detect and classify equipment in the modeling results individually, ensuring the accuracy of equipment information and the overall accuracy of the modeling results. [Results] To validate the effectiveness of the proposed method, experimental analyses were conducted on 100 sets of equipment data from three newly built substations. The results show that, compared to traditional modeling methods, the proposed 3D grid modeling method that integrates GIS-GIM and DETR networks significantly improves modeling accuracy, enabling more precise restoration of the spatial positions, structural forms of grid equipment, and connection relationships between equipment. Regarding the verification of the modeling results, the verification network demonstrates a good performance with an accuracy rate of 93.14％, indicating that the method can effectively detect potential errors and deviations in the modeling process and ensure the reliability of modeling results. [Conclusion] The proposed 3D power grid modeling and verification method, integrating GIS-GIM and DETR networks, performs excellently in improving grid modeling accuracy and establishing an effective verification mechanism, meeting the high-precision requirements of actual grid modeling. The method contributes significantly to improving the scientific basis for grid planning and provides intuitive 3D visualization for daily operations, maintenance, fault diagnosis, and repair, supporting reliable decision-making in grid management. It holds important theoretical significance and broad application prospects.

[Objective] In the context of the rapid development of the aviation industry, the scale and level of air transportation have significantly improved, air transportation becomes an indispensable mode of transportation in economic activities. However, the issue of cargo loading path planning in air transportation limits the optimization of transportation efficiency and cost. To address the challenges of enhancing operational efficiency and optimizing costs in air transportation, this paper proposed an air transportation loading path optimization algorithm based on an adaptive genetic algorithm. [Methods] To elucidate the loading path optimization algorithm for air transportation, this study analyzed the actual needs of air transportation loading and the computational conditions of the path planning platform and explored the transportation cost factors influencing the optimization of air transportation loading paths. On this basis, an improved genetic algorithm with adaptive capabilities was employed, utilizing adaptive fitness functions, crossover probabilities, and mutation probabilities to circumvent the issues of poor stability and slow convergence speeds inherent in traditional algorithms. The essence of this algorithm was the dynamic adjustment of crossover probabilities and mutation probabilities to align with the evolutionary state of the population, thereby augmenting the algorithm′s global search capability and convergence speed. During the research, the encoding method of the adaptive genetic algorithm, the establishment of the fitness function, and the calculation method and control principle of crossover probabilities and mutation probabilities were detailed, along with the specific execution steps of the loading path optimization algorithm. The algorithm was implemented on the MATLAB platform and tested using actual distribution data from an air transportation airport. [Results] The simulation results demonstrate that compared to traditional genetic algorithm, intelligent water drop algorithm, and improved ant colony algorithm, the air loading path optimization algorithm based on the adaptive genetic algorithm exhibits significant advantages in both transportation efficiency and overall transportation cost. In other words, the air loading path optimization algorithm can effectively reduce the average transportation cost and enhance transportation efficiency. However, actual air transportation loading processes are influenced by complex environment factors, such as the size limitation of aircraft cargo hold and the complex road conditions during delivery. These problems have not been deeply considered in the algorithm, which indicates that there is still room for improvement in the algorithm. [Conclusion] In summary, the air transportation loading path planning algorithm based on adaptive genetic algorithm introduces an improved genetic algorithm with adaptive mechanism, which shows better global search ability and convergence speed in solving the air transportation loading path planning problem. This paper provides a new idea for air transportation loading path planning and is also of important theoretical and practical value for the field of air logistics. Future studies will aim to take into account more actual operating environment factors to further optimize algorithm performance.

[Objective] Power engineering projects have long construction cycles and are affected by various uncertainties, which may lead to significant cost increase. Therefore, accurately estimating contingency costs is crucial for project management. However, traditional experience-based methods have large errors and are difficult to adapt to complex engineering environments. Existing methods based on fuzzy expert systems and machine learning have improved performance but still face challenges such as parameter optimization difficulties and severe overfitting. To address these issues, a new contingency cost estimation method was proposed. It leverages the advantages of adaptive network-based fuzzy inference system (ANFIS) in handling uncertainty problems and introduces a principal component analysis (PCA) module to mitigate overfitting and improve prediction accuracy. [Methods] A contingency cost estimation method integrating risk analysis and ANFIS was proposed. First, the relationship between contingency costs and 13 key risk factors affecting power engineering costs was modeled. Then, ANFIS was introduced to utilize fuzzy logic for handling uncertainty problems. ANFIS processed input variables through fuzzification and leveraged neural networks for inference, avoiding the dependency on fuzzy rule sets in traditional fuzzy expert systems. To further enhance prediction accuracy, a PCA module was incorporated into ANFIS, which eliminated redundant information through dimensionality reduction and alleviated overfitting issues associated with small datasets. [Results] In the experiments, 210 power engineering contingency cost data samples were selected, with 80% randomly assigned to the training set and 20% to the test set. The performance of four methods was compared:a Mamdani fuzzy inference based method, a support vector machine (SVM) based method, an ANFIS based method, and an improved ANFIS based method. The experimental results indicate that while the ANFIS based method outperforms the existing two methods on the training set, it suffers from severe overfitting on the test set. After incorporating the PCA module, the improved ANFIS based method achieves significantly better test performance, demonstrating stronger generalization ability and faster convergence. [Conclusion] The proposed contingency cost estimation method based on the improved ANFIS combines the advantages of fuzzy inference and neural networks, enhancing prediction accuracy for power engineering contingency costs. The key innovations of this study are as follows. A contingency cost estimation method was proposed, effectively addressing the uncertainty problem by combining the advantages of fuzzy logic and neural networks. In addition, a PCA module is introduced into ANFIS, which reduces redundant information through dimensionality reduction, effectively avoids model overfitting, and improves generalization capability. The proposed method provides an intelligent solution for power engineering budget management and can be extended to other fields involving uncertainty cost estimation.

[Objective] With the continuous expansion of network scale and the evolving complexity of attack techniques, network traffic anomaly detection has become a critical link in ensuring network security and maintaining the stable operation of key information infrastructure. However, traditional machine learning methods generally face bottlenecks such as slow convergence and insufficient feature representation accuracy when handling complex network traffic feature extraction, which limits their effectiveness in practical anomaly detection scenarios. To address these challenges, an innovative spatiotemporal fusion deep learning model, C²-GRU, was proposed in this paper, which was based on a convolutional neural network (CNN)-enhanced learner with a gated recurrent unit (GRU). The proposed model aims to enhance the multi-dimensional detection performance for abnormal traffic. [Methods] A dual-fusion deep learning framework was designed, leveraging the strength of CNN in spatial feature extraction and the capability of GRU in temporal feature modeling. A C-GRU model was constructed to achieve preliminary spatiotemporal feature fusion. It was then cascaded with CNN to form the C²-GRU model, which extracted spatiotemporal features through dual parallel convolution operations. This approach effectively captured the multidimensional features of abnormal traffic in complex network environments. [Results] The experimental results demonstrate that the proposed model achieves optimal overall performance on the KDD99 dataset. Specifically, the fused model attains an accuracy of 99.89％ and an area under curve (AUC) of 0.990 2, significantly outperforming individual CNN and GRU models. Furthermore, compared to traditional anomaly detection models, the proposed model not only achieves high recognition performance but also exhibits a relatively short model runtime, which highlights its superior engineering applicability. [Conclusion] The proposed C²-GRU hybrid model, employing a dual-convolution fusion strategy, effectively enhances spatiotemporal feature learning, suitable for abnormal traffic detection in complex network environments. It has dual advantages in anomaly recognition accuracy and computational efficiency, capable of offering technical support for securing key information infrastructure and mitigating the economic losses caused by network attacks. The model is of significant practical reference value for ensuring network information security.