A comprehensive evaluation and categorization of text-to-image generation tasks were conducted. Text-to-image generation tasks were classified into three major categories based on the principles of image generation: text-to-image generation based on the generative adversarial network architecture, text-to-image generation based on the autoregressive model architecture, and text-to-image generation based on the diffusion model architecture. Improvements in different aspects were categorized into six subcategories for text-to-image generation methods based on the generative adversarial network architecture: adoption of multi-level hierarchical architectures, application of attention mechanisms, utilization of siamese networks, incorporation of cycle-consistency methods, deep fusion of text features, and enhancement of unconditional models. The general evaluation indicators and datasets of existing text-to-image methods were summarized and discussed through the analysis of different methods.

A multi-agent reinforcement learning algorithm based on priority experience replay and decomposed reward function was proposed in multi-agent pursuit and evasion games. Firstly, multi-agent twin delayed deep deterministic policygradient algorithm (MATD3) algorithm based on multi-agent deep deterministic policy gradient algorithm (MADDPG) and twin delayed deep deterministic policy gradient algorithm (TD3) was proposed. Secondly, the priority experience replay was proposed to determine the priority of experience and sample the experience with high reward, aiming at the problem that the reward function is almost sparse in the multi-agent pursuit and evasion problem. In addition, a decomposed reward function was designed to divide multi-agent rewards into individual rewards and joint rewards to maximize the global and local rewards. Finally, a simulation experiment was designed based on DEPER-MATD3. Comparison with other algorithms showed that DEPER-MATD3 algorithm solved the over-estimation problem, and the time consumption was improved compared with MATD3 algorithm. In the decomposed reward function environment, the global mean rewards of the pursuers were improved, and the pursuers had a greater probability of chasing the evader.

Aiming at the problems of data sparsity, cold start, low interpretability of recommendation, and insufficient personalization in recommender system, the integration of knowledge graph into recommender system was analyzed. From the demand of recommender system, the concept of knowledge graph, and the integration approach of recommender system and knowledge graph, the problems of current recommender system and the solutions of recommender system after integrating knowledge graph were summarized. It was reviewed that, in recent years, the attention mechanism, neural network and reinforcement learning methods were combined, by which the principles of node trade-off, node integration, and paths exploring were used to make full use of the complex structural information in knowledge graph, so as to improve the satisfaction degree with the recommender system. The challenges and possible future development direction of the recommender system integrating the knowledge graph were put forward in terms of knowledge graph completeness, dynamics, availability of higher-order relationships, and the performance of the recommendation.

A feature fusion emotion recognition method based on modality attention multi-path convolutional neural network was proposed, extracting the connection between the signals of each channel from the electroencephalogram (EEG) and functional near infrared spectroscopy (fNIRS) data induced by emotional video to improve the accuracy of emotion recognition. The EEG and fNIRS data were constructed as graph structure data, and the feature of each mode signal was extracted by multi-path graph convolution. The information of connection between different modal channels was fused by modality attention graph convolution. The modality attention mechanism can give different weights to different modal nodes, thus the graph convolution layer can more fully extract the connection relationship between different modal nodes. Experimental tests were carried out on four types of emotional data collected from 30 subjects. Compared with the results of EEG only and fNIRS only, the recognition accuracy of the proposed graph convolution fusion method was higher, which increased by 8.06% and 22.90% respectively. Compared with the current commonly used EEG and fNIRS fusion method, the average recognition accuracy of the proposed graph convolution fusion method was improved by 2.76%~7.36%. The recognition rate of graph convolution fusion method increased by 1.68% after adding modality attention.

Based on existing research on droplet impact on moving and rotating surfaces, the phenomenon of droplet impact on moving and rotating surfaces needs to be briefly summarized. Moving surfaces can be divided into three forms: translating solid surfaces, rotating solid surfaces, and moving liquid films. The comprehensive study and summary on the impact of liquid droplets on the moving surface from three directions: experimental system, model establishment and numerical simulation. The research on droplet impact movement and rotating surfaces has a certain foundation, while the research on high impact velocity, small droplets, rotating surfaces and other situations is relatively blank. The theoretical and experimental results of rotating surface wave propulsion also lack numerical simulation supplementation. Based on the above situation, the research prospects of droplet impact on moving and rotating surfaces are proposed.

Physical information was divided into rule information and numerical information, in order to explore the role of physical information in training neural network when solving differential equations with physics-informed neural network (PINN). The logic of PINN for solving differential equations was explained, as well as the data-driven approach of physical information and neural network interpretability. Synthetic loss function of neural network was designed based on the two types of information, and the training balance degree was established from the aspects of training sampling and training intensity. The experiment of solving the Burgers-Fisher equation by PINN showed that PINN can obtain good solution accuracy and stability. In the training of neural networks for solving the equation, numerical information of the Burgers-Fisher equation can better promote neural network to approximate the equation solution than rule information. The training effect of neural network was improved with the increase of training sampling, training epoch, and the balance between the two types of information. In addition, the solving accuracy of the equation was improved with the increasing of the scale of neural network, but the training time of each epoch was also increased. In a fixed training time, it is not true that the larger scale of the neural network, the better the effect.

The effects of leg structure design, foot-end design and sensor design on touchdown detection were comprehensively discussed by analyzing the existing legged robot touchdown detection methods. The touchdown method for direct detection of external sensors, the touchdown detection method based on kinematics and dynamics, and the touchdown detection method based on learning were summarized. Touchdown detection methods were summarized in three special scenarios: slippery ground, soft ground, and non-foot-end contact. The application scenarios of touchdown detection technology were analyzed, including the three application scenarios of motion control requirements, navigation applications, and terrain and geological sensing. The development trends were pointed out, which related to the four major touchdown detection methods of hardware improvement and integration, multi-mode touchdown detection, multi-sensor fusion touchdown detection, and intelligent touchdown detection. The specific relationships between various touchdown detection algorithms were summarized, which provided guidance for the development of follow-up technology for touchdown detection and specific applications of touchdown detection.

A thorough analysis and cross-comparison of recent relevant works was provided, outlining a closed-loop process for EEG data analysis based on deep learning. EEG data were introduced, and the application of deep learning in three key stages: preprocessing, feature extraction, and model generalization was unfolded. The research ideas and solutions provided by deep learning algorithms in the respective stages were delineated, including the challenges and issues encountered at each stage. The main contributions and limitations of different algorithms were comprehensively summarized. The challenges faced and future directions of deep learning technology in handling EEG data at each stage were discussed.

A path planning algorithm based on the fusion of the improved A* algorithm and the random obstacle avoidance dynamic window method (ROA-DWA) was proposed in order to address the issues of excessive traversal nodes, redundant points, non-smooth paths, lack of global guidance, susceptibility to local optima, and low safety in traditional A* algorithm and dynamic window approach (DWA) for robot path planning. The search efficiency was improved by adjusting the weights of heuristic functions, Floyd’s algorithm, redundant point deletion strategy, static and dynamic obstacle classification, and speed adaptive factor. The length of the path and the number of inflection points were reduced, and the influence of known obstacles on the path was minimized to improve the efficiency of dynamic obstacle avoidance, which enabled the robot to smoothly arrive at the target point and improved the safety of the robot, and better adapted to complex dynamic and static environments. The experimental results show that the algorithm has better global optimality and local obstacle avoidance ability, and shows better advantages in large maps.

An intelligent method of asphalt pavement crack recognition based on semantic segmentation was proposed, solving the shortcomings of traditional manual inspection of asphalt pavement, such as low efficiency and lack of objectivity. Considering the effects of data set size, algorithm type, network type and depth, and loss function type, the optimal crack intelligent identification scheme and corresponding model were proposed for both large and small scale data sets through the comparative study of 22 semantic segmentation models. Based on the asphalt pavement of sixth ring road in Beijing, the crack segmentation dataset R-Crack was established. The proposed intelligent identification scheme was verified and the crack parameters were automatically quantified. Results showed that the highest detection accuracy reached 83.45%. The average errors of crack length and width were 2.84% and 2.39% respectively by comparing the calculation results of crack parameters obtained through manual and automatic detection methods, The proposed intelligent recognition scheme provided a basis for the intelligent detection practice of asphalt pavement cracks in the expressway and other scenes.

Few existing studies cover the state-of-the-art multi-objective particle swarm optimization (MOPSO) algorithms. To fill the gap in this area, the research background of multi-objective optimization problems (MOPs) was introduced, and the fundamental theories of MOPSO were described. The MOPSO algorithms were divided into three categories according to their features: Pareto-dominated-based MOPSO, decomposition-based MOPSO, and indicator-based MOPSO, and a detailed description of their existing classical algorithms was also developed. Next, relevant evaluation indicators were described, and seven representative algorithms were selected for performance analysis. The experimental results demonstrated the strengths and weaknesses of each of the traditional MOPSO and three categories of improved MOPSO algorithms. Among them, the indicator-based MOPSO performed better in terms of convergence and diversity. Then, the applications of MOPSO algorithms in production scheduling, image processing, and power systems were briefly introduced. Finally, the limitations and future research directions of the MOPSO algorithm for solving complex optimization problems were discussed.

A lightweight Yolov5 garbage detection solution was proposed aiming at the issue of poor real-time performance in garbage detection classification on edge devices. The Stem module was introduced to enhance the model’s ability to extract features from input images. The C3 module of the backbone was improved to increase feature extraction capabilities. Depthwise separable convolution was used to replace the 3×3 downsampling convolutions in the network, achieving model lightweighting. The K-means++ algorithm was employed to recompute anchor box values for objects, enabling the model to better predict target box sizes during training. Experimental research and comparisons show that the improved model achieves a 0.8% increase in mAP_0.5 and a 3% increase in mAP_0.5:0.95, while reducing model parameters by 77.9% and improving inference speed by 21.9% compared with the Yolov5s model, significantly enhancing the detection performance of the model.

Aiming at the problem that temporal-spatial features affect the trajectory prediction accuracy of autonomous vehicle, a temporal-spatial multi-head attention (TSMHA) vehicle trajectory prediction model was proposed. For the feature information of spatial and temporal dimensions, the multi-head attention mechanism was used to extract the spatial interaction perception and temporal motion pattern of the vehicle. The processed temporal-spatial feature information was transmitted to the gate fusion model for feature fusion, in order to obtain complementary features and remove redundancy. Using the encoder-decoder structure based on long short-term memory (LSTM), future trajectories were recurrently generated considering the potential interaction between trajectories during encoding and decoding. In the training process, the L2 loss function was used to reduce the difference between the predicted trajectory and the ground-truth trajectory. Experimental results show that, compared with the comparison models, the accuracy of the proposed model was improved by 3.95% in the highway, 15.64% in the urban roads, and 31.40% in the roundabout scenario.

The flexible job shop scheduling problem with human-machine dual resource limitations was studied. A mixed integer programming model was developed to minimize the completion time ensuring the worker fatigue was below the limited level in the manufacturing process. An improved adaptive large neighborhood search algorithm was proposed to resolve highly complex sub-problems such as job sequencing, machine assignment, worker assignment and worker fatigue. Eight heuristic rules were used to build the initial solutions, and six types of destruction operators and six types of repair operators were introduced to achieve an efficient search of the solution space in the proposed algorithm. The effectiveness of the proposed algorithm was demonstrated by comparing the numerical examples of various scales. Compared with the Gurobi optimizer, genetic algorithm, Jaya algorithm and standard ALNS algorithm, the proposed algorithm has good optimization performance and can successfully address the issue of worker fatigue in the job shop scheduling.

A new steam Carnot battery based on high-temperature and low-temperature phase change materials was proposed in order to analyze the new route of multi-energy complementation of integrated energy system in industrial parks. A thermodynamic cycle calculation model considering the equipment performance and mass flow rate was established. The effects of design parameters and multi-stage compression structure on the system heat pump coefficient, round-trip efficiency, power storage loss and efficiency of the heating were analyzed. The phase change temperature of low-temperature phase change material and the phase change temperature of high-temperature phase change material are the main factors affecting the performance of steam Carnot battery. The high cycle performance region of steam Carnot battery was obtained. The parameters and structure of the steam Carnot battery were optimized. Results showed that the round-trip efficiency could reach 56.96%, the coefficient of performance of the heat pump could reach 2.55, and the efficiency of the heating could reach 68.74%.

A many-core parallel optimization scheme for large-point FFT was proposed according to the structural characteristics and programming specifications of the domestic Sunway 26010 processor, which was used in the Sunway Taihu Light supercomputer. The scheme was derived from the classic Cooley-Tukey FFT algorithm, and was accelerated in parallel by iteratively decomposing the one-dimensional large-point data into two-dimensional small-scale matrices. The "column-sharing, row-continuity" strategy was specially proposed in order to solve the problem of reading, writing, transposing and calculating of the "column FFT" of the matrix. The computing resources and transmission bandwidth of the many-core processor were fully utilized by reasonable data allocation, rearrangement and exchange combined with other optimization methods such as SIMD vectorization, twiddle factor optimization, double-buffering, register communication and stride transmission. The experimental results prove that the single core-group of 64 slave cores running parallel program can achieve a maximum speed-up of 65x and an average speed-up of more than 48x compared with the main core running the FFTW library.

A method for recognizing machining features based on graph neural networks was proposed in order to address the difficulties in identifying intersecting features and accurately determining machining feature surfaces in existing deep learning-based approaches. Features of nodes and adjacent edges were extracted through a compression activation module, and a dual-layer attention network at the node and adjacent edge levels was constructed in order to segment the machining features corresponding to each node. The surface features and edge features of the part model were fully used combined with the topological structure of the part model. The recognition problem of non-face merged intersecting features was effectively addressed by employing attention mechanisms for deep learning on the feature information. The proposed method was experimentally compared with three other feature recognition methods on a dataset of parts with multiple machining features. The optimal results were obtained in terms of accuracy, average class accuracy and intersection-over-union metrics. The recognition accuracy exceeded 95%.

A parking charge strategy based on dispatching autonomous vehicles was proposed in order to improve the efficiency of the parking system that accommodates both human-driven vehicles and autonomous vehicles. This strategy provides autonomous vehicles dispatch service to the human-driven vehicle when there is no available parking space in the parking lot but there are autonomous vehicles. The parking system will dispatch a number of autonomous vehicles among multiple parking lots to create an available parking space for the human-driven vehicle in its target parking lot after charging a certain dispatch fee of the human-driven vehicle’s user. Since each parking lot’s dispatch fee can affect the human-driven vehicle users’ parking choices, and thus affect the operation efficiency of the parking system. An agent-based parking simulation model was constructed, and differentiated dispatch fee of every parking lot was set by the genetic algorithm. The simulation results show that the differentiated parking charge strategy based on dispatching the autonomous vehicles can significantly reduce the driving time, walking time, total travel time and mileage of the human-driven vehicle users, increase the revenue of the parking system, reduce the social cost and effectively alleviate the parking problem.

A lightweight and efficient aerial image detection algorithm called Functional ShuffleNet YOLO (FS-YOLO) was proposed based on YOLOv8s, in order to address the issues of low detection accuracy for small targets and a large number of model parameters in current unmanned aerial vehicle (UAV) aerial image detection. A lightweight feature extraction network was introduced by reducing channel dimensions and improving the network architecture. This facilitated the efficient reuse of redundant feature information, generating more feature maps with fewer parameters, enhancing the model’s ability to extract and express feature information while significantly reducing the model size. Additionally, a content-aware feature recombination module was introduced during the feature fusion stage to enhance the attention on salient semantic information of small targets, thereby improving the detection performance of the network for aerial images. Experimental validation was conducted using the VisDrone dataset, and the results indicated that the proposed algorithm achieved a detection accuracy of 47.0% mAP0.5 with only 5.48 million parameters. This represented a 50.7% reduction in parameter count compared to the YOLOv8s benchmark algorithm, along with a 6.1% improvement in accuracy. Experimental results of DIOR dataset showed that FS-YOLO had strong generalization and was more competitive than other state-of-the-art algorithms.

The compound operation scheduling optimization in four-way shuttle warehouse system was studied to improve the efficiency of storage system operations. A mathematical model was established with the goal of minimizing inbound and outbound operation times to optimize the scheduling problem of the system. This model was based on the combined operation of a four-way shuttle and an elevator, and the collaborative operation characteristics in both horizontal and vertical directions were considered. Furthermore, the model was analyzed under various operating modes by examining the connection between the start and end operation times of the four-way shuttle and the elevator, as well as the starting operation tiers. The method based on the task classification was proposed to initialize the population of the genetic algorithm. The crossover and the mutation of the population were completed to solve the model, and then the task allocation and sequence of the system were optimized. Some experiments were conducted to verify the effectiveness of the improved genetic algorithm. The influence of the number of four-way shuttles on the operation time and system cost was analyzed, and the operation efficiencies of single and double elevators in the system were compared. The effectiveness of the genetic algorithm based on the task classification was verified, and the results showed that the operation efficiency was improved by at least 10.3%, by using the proposed algorithm.