In order to solve the problems of difficulty in finding target points, sparse rewards, and slow convergence when using deep reinforcement learning algorithms for path planning of agricultural robots, a path-planning method based on multi-target point navigation integrated improved deep Q-network algorithm (MPN-DQN) was proposed. The laser simultaneous localization and mapping (SLAM) was used to scan the global environment to construct a prior map and divide the walking row and crop row areas, and the map boundary was expanded and fitted to form a forward bow-shaped operation corridor. The middle target point was used to segment the global environment, and the complex environment was divided into a multi-stage short-range navigation environment to simplify the target point search process. The deep Q-network algorithm was improved from three aspects: action space, exploration strategy and reward function to improve the reward sparsity problem, accelerate the convergence speed of the algorithm, and improve the navigation success rate. Experimental results showed that the total number of collisions of agricultural robots equipped with the MPN-DQN algorithm was 1, the average navigation time was 104.27 s, the average navigation distance was 16.58 m, and the average navigation success rate was 95%.

The connection of new energy power generation equipment to the power generation side leads to the emergence of “weak inertia” characteristics on the power generation side, which affects the safe and stable operation of the system. The synchronous phase measurement unit (PMU) was used to measure the electromechanical oscillation response, and based on the electromechanical oscillation parameter under small perturbation, an inertia assessment method for the power generation side was proposed. Based on the characteristics of the inertia response process, the unbalanced power allocation equation related to the inertia of each generator was derived. Based on the relationship between the small-signal state equation and the characteristic root of the multi-machine system, the formula for calculating the inertia of the generation side of a multi-machine system was derived. The inertia calculation of the generation side of a single-machine system was introduced, and the measurement methods of inertia ratio and the intrinsic oscillation frequency in the inertia calculation formula were described. The correctness of the proposed method was verified by simulation examples of a single-machine system, a dual-machine interconnection system, a WSCC 3-machine 9-node system, and a 10-machine 39-node system. Results show that the generation side inertia evaluation values obtained with the proposed method in several systems are close to the actual values and have good adaptability. The method can be used for power system generation side inertia evaluation.

A multi-layer degradation module was proposed aiming at the problem that most remote sensing image super-resolution models rarely consider the impact of noise, blur, JPEG compression, and other factors on image reconstruction, as well as the limitations of Transformer modules in capturing high-frequency information. A CNN-Transformer hybrid network was designed, where CNN captures high-frequency details and Transformer extracts global information. These two components were combined by an attention-based aggregation module, enhancing local high-frequency detail reconstruction while maintaining global structural coherence. The model was tested on six random scenes from the AID dataset and compared with the MM-realSR model in PSNR and SSIM. Results show an average PSNR improvement of 1.61 dB and a SSIM increase of 0.023 over MM-realSR.

A tomato leaf disease detection model based on the improved CenterNet algorithm was proposed in order to address the false detection and missed detection phenomena in traditional tomato leaf disease detection. A feature fusion module that integrated the attention mechanism was constructed in order to enhance the model's cross-scale feature fusion capability. The multi-branch convolutional module RFB was added to the backbone network in order to expand the receptive field and enhance the ability to extract target features. The pyramid convolution PyConv was introduced into the backbone network to enhance the extraction of multi-scale features by calculating receptive fields of different scales and reduce information loss. Pruning optimization strategies were designed in order to reduce the impact of introducing modules on the number of model parameters and computational load. The test results showed that the accuracy rate, recall rate, mAP₅₀ and mAP_50:95 of the improved model reached 96.3%, 80.2%, 91.4% and 78.7% respectively. The proposed model can effectively improve the accuracy of tomato leaf disease detection, and the model has good generalization.

Aiming at the problem of collaborative multi-task assignment of heterogeneous UAVs under multiple constraints, a three-objective optimization model was constructed, which considered the UAV flight distance cost, time cost, combat effectiveness and multiple constraints. A multi-objective particle swarm optimization algorithm based on hybrid strategies was proposed to solve the model. An average action efficiency index of ammunition was proposed to evaluate the task strike efficiency, and considering the possibility of deadlock during task execution, a calculation of waiting time was proposed in the process of modeling. In order to solve the problem that traditional particle swarm optimization falls into local optimality, and ensure that feasible solutions satisfying constraints are searched, a constraint-based particle dynamic optimal initialization strategy, a dominance relationship-based advantageous individual selection strategy, and a task-based small module particle update and correction strategy were proposed, respectively. The overall performance of the algorithm in terms of convergence accuracy and diversity was effectively improved by these strategies. The validity of the model and the algorithm was verified through multi-scenario simulation experiments and ablation experiments. Results show that the solution sets obtained by the proposed algorithm are more convergent, diverse and evenly distributed than the comparative algorithms, and the collaborative multi-task assignment of heterogeneous UAVs is efficiently realized by the proposed algorithm.

Existing anomaly detection methods of time series data focus on extracting the temporal variation features, while the spatial dependency features between multiple variables are ignored. To address this problem, a detection method based on a spatiotemporal graph attention network was proposed. The original multivariate time series data were transformed into a time-series graph with spatiotemporal dependencies, and a spatiotemporal graph attention network was designed to separately extract the temporal variation features and spatial dependency features. The periodic patterns of fused spatiotemporal features were learned by a multilayer perceptron, and an anomaly detection was performed based on the anomaly scores between prediction values and observation values. Experimental results on public datasets showed that the proposed method significantly outperformed state-of-the-art baseline methods in terms of anomaly detection accuracy and robustness.

A cloud removal network for remote sensing imagery that integrated synthetic aperture radar (SAR) and optical data was proposed to address the issues of unstable performance and uneven color tones in existing deep learning-based cloud removal methods. The true texture information from SAR images and the spatial-spectral feature information from optical images were used to construct feature reconstruction tasks both globally and locally, and these tasks guided the network to rebuild missing information in cloud-covered areas. The dual-activation gated convolutional blocks and the channel attention blocks were utilized to build a spatial-spectral feature inference and reconstruction block which significantly enhanced the network’s ability to extract features from useful information in non-cloud areas. The SEN12MS-CR-TS dataset was divided into four subsets based on different cloud morphologies and cloud contents for training and testing. The experimental results showed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the proposed method were 1.038 4 dB and 0.091 5, respectively, which were higher than those of the best cloud removal methods. Thus the remote sensing image thick cloud removal network, which integrates SAR and optical data, can effectively remove clouds from images and reconstruct the details beneath the clouds.

An improved slime mold algorithm with artificial bee colony (ISMABC) was proposed aiming at the problem of cooperative path planning for multiple unmanned aerial vehicles (UAVs). A path planning cost model was established, and the path planning problem in a three-dimensional environment was transformed into an optimization problem by introducing a fitness function and constraint conditions, which was solved by the proposed algorithm to obtain the optimal path. The slime mold algorithm was improved by employing the good point set strategy and a nonlinear convergence factor. Then the population diversity was increased, and the convergence speed of the algorithm was accelerated. An elite opposition-based learning strategy was applied to the global best individual in order to enhance population quality. A global best position guidance was introduced based on the exploration capability of the artificial bee colony in order to improve the exploitation capability of the slime mold algorithm. Comparative analysis of optimization on 14 test functions and some functions from the CEC2017 test suite showed that the optimization ability and convergence speed of the ISMABC algorithm were significantly enhanced. The algorithm was applied to solve the problem of cooperative path planning for multiple UAVs in order to verify the feasibility of the ISMABC algorithm. Comparative analysis shows that the ISMABC algorithm can be used to plan paths with minimal cost that satisfies the constraints for each UAV.

A hierarchical architecture coordinated control strategy considering driving styles for active four-wheel steering (AFWS) and direct yaw moment control (DYC) systems was proposed to improve the handling stability of distributed drive electric vehicles and accommodate the driving styles of different drivers. This strategy employed a three-layer control architecture, including the upper controller, the middle controller, and the lower controller. A reference model for handling stability considering driving styles was established in the upper controller. The stability factors of vehicles with different driving styles were determined through driver-in-the-loop experiments, and the vehicle states were categorized into stable, transitional, and unstable regions based on the phase plane theory. A hybrid game control model for AFWS and DYC based on Stackelberg leader-follower game and Pareto cooperative game was established in the middle controller to improve the vehicle’s handling stability under complex driving conditions. The lower controller was used to optimize the wheel drive torque distribution with the goal of minimizing the tire load rate. The driver-in-the-loop test platform was built based on the Simulink simulation software and the Logitech G29 driving simulator, and open-loop and in-loop tests with drivers were conducted. The results indicated that the proposed control strategy can adapt to the driving styles of different drivers and meet their personalized needs, thereby improving the vehicle’s handling stability.

A curved palm visuotactile sensor was proposed to address the challenges faced by existing robotic visuotactile sensors, as most of the sensors employ planar sensing structures and have the limitations of incomplete contact information acquisition and insufficient depth perception in complex 3D environments and delicate manipulation tasks. The innovative curved structural design that extended the effective sensing area into the 3D space significantly enhanced the tactile perception depth and contact information integrity. A point cloud reconstruction framework using the binocular stereo vision-based refractive ray tracing model was established to generate high-precision 3D tactile point cloud data. An efficient point cloud saliency detection algorithm that enabled intelligent recognition and real-time extraction of key contact regions was proposed to improve the efficiency and accuracy of data processing. Experimental results demonstrated that the structural design and the point cloud reconstruction framework of the proposed sensor exhibited strong robustness, achieving a 3D tactile point cloud perception accuracy of 0.20 mm, a point cloud reconstruction frame rate of 30 frames per second, and a maximum pressing depth of 10 mm. The sensor can precisely extract the salient contact point clouds in real time, providing robust technical support for robotic fine manipulation in complex environments.

To achieve fault type judgment, fault line localization, and fault distance judgment in the grid, a novel hybrid network model integrating fast Fourier transform (FFT), convolutional neural networks (CNN), and graph convolutional networks (GCN) was proposed for grid fault diagnosis. Voltage and current signals were decomposed in time and frequency domains by FFT to extract fundamental waveform amplitude and phase. CNN extracted the temporal features of the decomposed data, and layer normalization was introduced to enhance the model stability. The spatial topology of the grid was processed with GCN to extract and integrate spatial features. The model’s effectiveness was verified through modeling and simulation of the IEEE 39-bus power grid system. Experimental results show that the proposed model possesses strong generalization capabilities, and the fault diagnosis accuracy under various tasks, sampling intervals, and noise conditions outperforms existing models.

A day-ahead market economic dispatch model that incorporated the flexible ramping products (FRPs) provided by energy storage systems was proposed to fully harness the flexible regulatory capabilities of various resources to ensure real-time flexibility, thereby addressing the problem that conventional thermal power units cannot meet the power system’s flexibility demands against the backdrop of the construction of a new type of power system. The demand composition and the opportunity cost of FRPs were introduced. A decision tree model for FRPs participation in flexible ramping deployment in the day-ahead and real-time markets was obtained by conducting the probabilistic analysis of the decision-making schemes for FRPs in both the day-ahead and real-time markets, and the cost and revenue analyses were performed under the scenarios where FRPs are either abundant or scarce. Based on this, a day-ahead market economic dispatch model that considered energy storage providing FRPs was established. After the economic dispatch in the day-ahead market, the cost and FRPs revenue settlement was achieved based on the probability of accepting FRPs in the day-ahead market and the expected deployment probability in the real-time market. A case study analysis using an improved IEEE 30-bus system was conducted to validate the superiority of the model with energy storage participation, and the impact of the acceptance probability in the day-ahead market and the expected deployment probability in the real-time market on the system was discussed.

A new 3D underwater AUV path planning method (IADQN) was proposed due to the low quality of the generated path and poor dynamic obstacle avoidance ability of AUV path planning methods in complex marine environments. In order to resolve the problem of insufficient obstacle recognition and avoidance ability of AUVs in unknown underwater environments, an adaptive potential field method was proposed to improve the efficiency of action selection of AUVs. In order to address the problem of low sample selection efficiency in the traditional deep Q network (DQN) experience replay strategy, a priority experience replay strategy was adopted to select samples with higher contributions to training from the experience pool to improve the efficiency of training. AUV dynamically adjusts the reward function according to the current state to accelerate the convergence speed of IADQN during training. Simulation results show that, compared with the DQN scheme, IADQN plans a time-saving and collision-free path efficiently in a real ocean environment; the AUV running time is reduced by 6.41 s, and the maximum angle with the ocean current is reduced by 10.39°.

To improve the cooling performance of power modules in new energy vehicles, a fluid–thermal–solid coupling numerical method was used to analyze the thermal management of an insulated gate bipolar transistor (IGBT) power module. A three-stage design optimization method, including contribution quantification, surrogate modeling, and overall optimization, was proposed. A numerical model of the IGBT power module was established in ANSYS Fluent, and the resulting relative error between the simulation and the experimental data was 3.7%. The effects of substrate ceramic material, coolant flow rate, and Pin-Fin geometry on thermal performance were analyzed, showing that convective thermal resistance and ceramic layer resistance are the main factors affecting chip thermal resistance. Based on surrogate modeling and multi-objective optimization, the design of a 750 V/820 A H-Boost IGBT power module was optimized. The optimized design reduced chip thermal resistance by 21.1%, pressure drop by 39.3%, and module mass by 6.1%.

Aiming at the problems of the existing methods, such as the difficulty of multi-scale feature extraction and the inaccuracy of target edge segmentation in remote sensing images, a new semantic segmentation algorithm was proposed. CNN and Efficient Transformer were utilized to construct a dual encoder to decouple context and spatial information. A feature fusion module was proposed to enhance the information interaction between the encoders, effectively fusing the global context and local detail information. A hierarchical Transformer structure was constructed to extract feature information at different scales, allowing the encoder to focus effectively on objects at different scales. An edge thinning loss function was proposed to mitigate the problem of inaccurate target edge segmentation. Experimental results showed that mean intersection over union (MIoU) of 72.45% and 82.29% was achieved by the proposed algorithm on the ISPRS Vaihingen and ISPRS Potsdam datasets, respectively. On the SOTA, SIOR, and FAST subsets of the SAMRS dataset, the MIoU of the proposed algorithm was 88.81%, 97.29%, and 86.65%, respectively, overall accuracy and mean intersection over union metrics were better than those of the comparison models. The proposed algorithm has good segmentation performance on various types of targets with different scales.

A traffic scene perception algorithm (SDFormer++) based on the principle of cross-task bidirectional feature interaction for autonomous driving in urban street scenarios was proposed by leveraging the explicit and implicit correlations between the semantic segmentation tasks and the depth estimation tasks to improve the overall performance of traffic scene perception algorithms. An interaction-gated linear unit was added into the cross-task feature extraction stage to form high-quality task-specific feature representations. A multi-task feature interaction module that used the bidirectional attention mechanism was constructed to enhance the initial task-specific features by utilizing the feature information of shared cross-domain tasks. A multi-scale feature fusion module was designed to integrate information at different levels to obtain fine high-resolution features. Experimental results on the Cityscapes dataset showed that the algorithm achieved a mean intersection over union (mIoU) of 82.4% for pixel segmentation, a root mean square error (RMSE) of 4.453 for depth estimation, an absolute relative error (ARE) of 0.130 for depth estimation, and an average distance estimation error of 6.0% for five typical traffic participants, all of which outperformed the existing mainstream multi-task algorithms such as InvPT++ and SDFormer.

A multi-goal multi-agent path planning algorithm was proposed to realize the efficient assignment of tasks to each agent and plan the shortest possible paths for the agents without collision with other agents. The definition of conflict between agents in continuous time and the way of conflict resolution were defined, and the concepts of safety interval and labeling were introduced based on A* algorithm aiming at the problem of low success rate due to the use of discrete time in traditional path planning algorithms. Then the A* algorithm can plan optimal paths that satisfy continuous time constraints. A conflict hierarchical strategy was proposed to reduce the number of nodes extended in the algorithm solving process aiming at the large amount of computation caused by collision detection and conflict avoidance in the multi-agent path planning problem. The experimental results show that the proposed algorithm can solve a better solution and has better applicability, with lower total path cost and higher success rate in the scenario of densely distributed agents.

A new path planning algorithm was proposed to address the limitations of the conventional rapidly-exploring random trees (RRT) path planning algorithm, including excessive redundant sampling points, increased randomness, and lack of smooth paths. A model named the optimal path area prediction network (OPAPN) was developed to predict potential optimal path areas within the map using deep learning techniques. A global feature extraction module, a hybrid attention mechanism, and switchable atrous convolution techniques were incorporated in the model. The network’s understanding of the map’s overall layout and start/goal information was enhanced by the components to reduce unnecessary computational burdens. The number of sampling points was reduced significantly through heuristic sampling in the optimal path regions predicted by OPAPN, and the algorithm’s convergence speed was accelerated via a dual-tree expansion strategy. Both simulation experiments and real-world tests showed that the proposed algorithm performed well in convergence time, node count, and path length, confirming its practical application value.

A snow scene construction algorithm based on perception estimation was proposed to address the problems of difficulty in collecting data from existing snow scene sample libraries and limited sample sizes during the training of autonomous driving perception performance. The snow scene was divided into two models: snow-covered model and snow-line model, and a snow-covered plane construction algorithm based on spatial perception was proposed to analyze the subtle gradient changes in the image and estimate the preliminary snow area. The preliminary snow areas were refined using connected domain analysis, and the areas were fused with the original image to obtain snow-covered scene images. A snow scene construction algorithm based on a random snow-line model was proposed to generate different motion directions for snowflakes. Snow-covered model and snow-line model were integrated, and various basic snow-line forms were utilized to construct snow scenes. Experimental results from the comparison of multi-frame traffic video data show that, in the snowy scenes constructed using the snowy scene fusion method, as the amount of snow increases, both the interference information and the details of the image are significantly enhanced, subjectively approaching the actual snow scene. Moreover, objective evaluation metrics for snow image quality decrease with the increase of snow cover and snow-line density.

To address the energy consumption optimization problem in cooperative control of mixed vehicle platoon while ensuring passenger comfort, a collaborative control method combining real-time optimization with distributed model predictive control and an intelligent driver model was proposed. For connected autonomous vehicles in the platoon, passenger comfort constraints were established. Utilizing a precise fuel consumption model, a real-time optimized distributed model predictive control method was designed to reduce real-time energy consumption while ensuring the consistency and stability of the platoon. For human-driven vehicles in the platoon, an intelligent driver-following model that ensures passenger comfort and low energy consumption was adopted. The following stability condition was then derived. Simulation experiments were conducted in the scenarios of constant speed and variable speed leader vehicles to verify the tracking performance of the proposed control method under the constraints of passenger comfort. The average engine power from the initial state to the steady state was used as the energy consumption optimization index, and multiple sets of comparative simulation experiments were conducted. Simulation results show that, compared with the comparative algorithm, the proposed control method can effectively reduce the energy consumption of the mixed vehicle platoon.