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.

An interactive visualization generation method for time series data based on transfer learning was proposed in order to address the inconsistency in data distribution across time-series data and facilitate the application of pattern analysis to other data. Transfer component analysis was applied to transfer features extracted from each time series data. The user’s analysis on one of the time series data served as labels. The classifier was trained on the source domain and applied to multiple target domains in order to achieve pattern recommendations. Two case studies and expert interviews with real-world weather data and bearing signal data were conducted to verify the effectiveness and practicality of the method by improving the efficiency of temporal data exploration and reducing the impact of inconsistent data distribution.

A light-weight, real-time approach named RTGN (real-time grasp net) was proposed to improve the accuracy and speed of robotic grasp detection for novel objects of diverse shapes, types and sizes. Firstly, a multi-scale dilated convolution module was designed to construct a light-weight feature extraction backbone. Secondly, a mixed attention module was designed to help the network focus more on meaningful features. Finally, the pyramid pool module was deployed to fuse the multi-level features extracted by the network, thereby improving the capability of grasp perception to the object. On the Cornell grasping dataset, RTGN generated grasps at a speed of 142 frame per second and attained accuracy rates of 98.26% and 97.65% on image-wise and object-wise splits, respectively. In real-world robotic grasping experiments, RTGN obtained a success rate of 96.0% in 400 grasping attempts across 20 novel objects. Experimental results demonstrate that RTGN outperforms existing methods in both detection accuracy and detection speed. Furthermore, RTGN shows strong adaptability to variations in the position and pose of grasped objects, effectively generalizing to novel objects of diverse shapes, types and sizes.

The dense small target detection algorithm LSA_YOLO based on YOLOv5s for UAVs with complex backgrounds and multiples of small targets with dense distribution was proposed for UAV images. A multi-scale feature extraction module LM-fem was constructed to enhance the feature extraction capability of the network. A new hybrid domain attention module S-ECA relying on multi-scale contextual information has been put forward and a algorithm focus on target information was established aiming to suppress the interference of complex backgrounds. The adaptive weight dynamic fusion structure AFF was designed to assign reasonable fusion weights to both shallow and deep features. The capability of algorithm in detecting dense small targets in complex backgrounds was improved given the application of S-ECA and AFF in the structure of PANet. The loss function Focal-EIOU was utilized instead of the loss function CIOU to accelerate model detection efficiency. Experimental results on the public dataset VisDrone2021 public dataset show that the average detection accuracy for all target classes improves from 51.5% for YOLOv5s to 57.6% for LSA_YOLO when the set input resolution is set to 1 504 × 1 504.

An enhanced genetic algorithm was proposed to address the challenge of area coverage path planning for a tilt-rotor unmanned aerial vehicle (TRUAV) amidst multiple obstacles. A preliminary coverage path plan for the designated task area was devised, utilizing the minimum spanning and back-and-forth path generation algorithms. The area coverage dilemma was transformed into a traveling salesman problem to optimize the sequence of the coverage path. A fishtail-shaped obstacle avoidance strategy was proposed to circumvent obstacles within the region. The nearest neighbor algorithm was introduced to generate a superior initial population than a genetic algorithm. A three-point crossover operator and a dynamic interval mutation operator were adopted in the genetic processes to improve the proposed algorithm's global search capacity and prevent the algorithm from falling into local optima. The efficacy of the proposed algorithm was rigorously tested through simulations in polygonal areas with multiple obstacles. Results showed that, compared to the sequential path coverage algorithm and the genetic algorithm, the proposed algorithm reduced the length of the coverage path by 7.80%, significantly enhancing the coverage efficiency of TRUAV in the given task areas.

Inspired by the idea that feature information between different pixel-level visual tasks can guide and optimize each other, a traffic scene perception algorithm based on multi-task learning theory was proposed for joint semantic segmentation and depth estimation. A bidirectional cross-task attention mechanism was proposed to achieve explicit modeling of global correlation between tasks, guiding the network to fully explore and utilize complementary pattern information between tasks. A multi-task Transformer was constructed to enhance the spatial global representation of specific task features, implicitly model the cross-task global context relationship, and promote the fusion of complementary pattern information between tasks. An encoder-decoder fusion upsampling module was designed to effectively fuse the spatial details contained in the encoder to generate fine-grained high-resolution specific task features. The experimental results on the Cityscapes dataset showed that the mean IoU of semantic segmentation of the proposed algorithm reached 79.2%, the root mean square error of depth estimation was 4.485, and the mean relative error of distance estimation for five typical traffic participants was 6.1%. Compared with the mainstream algorithms, the proposed algorithm can achieve better comprehensive performance with lower computational complexity.

The capacity planning method for multi-type microgrid shared hydrogen energy storage system considering the shared trading mechanism was proposed by considering the characteristics of hydrogen storage in multi-energy supply and storage in order to promote the consumption of new energy in regional distribution grids and solve the difficulty of traditional forms of energy storage in meeting the long-term storage demand brought about by the intermittent generation of new energy. The basic framework of multi-microgrid energy trading with hydrogen storage configured by microgrid cluster operators was designed by considering multiple regulation needs of heterogeneous microgrids. Then the complex interaction of interests between hydrogen storage and multiple microgrids was considered during shared operation as well as the demand response of electricity and heat within microgrids. A pricing mechanism based on the master-slave game for shared hydrogen storage transactions was proposed to ensure the sustainable development of the sharing model. The capacity planning for shared hydrogen energy storage was analyzed in order to reduce the investment costs for microgrid cluster operators and ensure the shared benefits. Results show that the proposed capacity planning method can shorten the payback period of hydrogen energy storage for microgrid cluster operators by 2.36 years.

A model based on improved YOLOv5m was proposed for wolfberry pest detection in a complex environment. The next generation vision transformer (Next-ViT) was used as the backbone network to improve the feature extraction ability of the model, and the key target features were given more attention by the model. An adaptive fusion context enhancement module was added to the neck to enhance the model’s ability to understand and process contextual information, and the precision of the model for the small object (aphids) detection was improved. The C3 module in the neck network was replaced by using the C3_Faster module to reduce the model footprint and further improve the model precision. Experimental results showed that the proposed model achieved a precision of 97.0% and a recall of 92.1%. The mean average precision (mAP50) was 94.7%, which was 1.9 percentage points higher than that of the YOLOv5m, and the average precision of aphid detection was improved by 9.4 percentage points. The mAP50 of different models were compared and the proposed was 1.6, 1.6, 2.8, 3.5, and 1.0 percentage points higher than the mainstream models YOLOv7, YOLOX, DETR, EfficientDet-D1, and Cascade R-CNN, respectively. The proposed model improves the detection performance while maintaining a reasonable model footprint.

A new design scheme of crab-like hexapod origami robot was proposed by combining the origami structure with the multi-legged robot design and coupling Miura origami and six-fold origami aiming at the problems that the existing origami robots have a single structure and insufficient flexibility in movement. The motion configuration of the origami robot was expanded, and the motion flexibility of the origami robot was improved. Each leg of the robot has two degrees of freedom under the symmetry hypothesis. The vertices of the robot legs were treated as joints, and the crease lines were regarded as links. A planar link equivalent model of the robot legs was established with the folding angle as the motion variable. The theoretical range of motion for the robot’s foot was determined through simulation calculations. Then tapered panel technique was utilized to thicken the folding surfaces and prevent physical interference between adjacent folding surfaces. A three-dimensional model of the origami crab-like hexapod robot was constructed. The relationship between the folding angle and foot motion was analyzed based on the equivalent model of planar links, and the foot motion trajectory and gait of the robot were designed. The experimental prototype of origami bionic hexapod robot was designed and manufactured by using 3D printing technology, and the lateral movement of the robot was realized based on STM32 microcontroller control. Results show that the origami bio-inspired robot can realize the conversion from plane configuration to a crab-like configuration. The robot can move smoothly left and right under the coordinated movement of six legs.

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 vehicle motion planning algorithm based on deep reinforcement learning was proposed to satisfy the efficiency and comfort requirements of intelligent connected vehicles at unsignalized intersections. Temporal convolutional network (TCN) and Transformer algorithms were combined to construct the intention prediction model for surrounding vehicles. The multi-layer convolution and self-attention mechanisms were used to improve the capability of capturing vehicle motion feature. The twin delayed deep deterministic policy gradient (TD3) reinforcement learning algorithm was employed to build the vehicle motion planning model. Taking the driving intention of surrounding vehicle, driving style, interaction risk, and the comfort of ego vehicle into consideration comprehensively, the state space and reward functions were designed to enhance understanding the dynamic environment. Delaying the policy updates and smoothing the target policies were conducted to improve the stability of the proposed algorithm, and the desired acceleration was output in real-time. Experimental results demonstrated that the proposed motion planning algorithm can perceive the real-time potential interaction risk based on the driving intention of surrounding vehicles. The generated motion planning strategy met the requirements of the efficiency, safety and comfort. It showed excellent adaptability to different styles of surrounding vehicles and dense interaction scenarios, and the success rates exceeded 92.1% in various scenarios.

A multi-agent parking simulation framework was constructed in order to formulate autonomous vehicle (AV) parking demand management strategies. Two charging strategies for empty-load driving were proposed: a static charge based on driving distance and a dynamic charge based on road congestion levels. Rate calculation method was analyzed. Cost functions for parking lots, residential parking, and continuous empty cruising were established under these charging policies. A logit model was used to describe the choice behavior under different parking modes. The simulation of urban mobility (SUMO) was used to conduct a large-scale road network simulation experiment in Nanning’s main urban area. AV parking behavior and road network operation under both strategies were analyzed. The simulation results showed that the empty-load driving mileage of AVs decreased by 20.16% and 10.85% under the static and dynamic charging strategies, respectively. Total vehicle delay decreased by 39.80% and 43.52%, respectively. The dynamic charging strategy was adjustable in real-time based on road conditions, and operational efficiency of the road network was significantly enhanced.

A new multimodal sentiment analysis model (MTSA) was proposed on the basis of cross-modal Transformer, aiming at the difficult retention of the modal feature heterogeneity for single-modal feature extraction and feature redundancy for cross-modal feature fusion. Long short-term memory (LSTM) and multi-task learning framework were used to extract single-modal contextual semantic information, the noise was removed and the modal feature heterogeneity was preserved by adding up auxiliary modal task losses. Multi-tasking gating mechanism was used to adjust cross-modal feature fusion. Text, audio and visual modal features were fused in a stacked cross-modal Transformer structure to improve fusion depth and avoid feature redundancy. MTSA was evaluated in the MOSEI and SIMS data sets, results show that compared with other advanced models, MTSA has better overall performance, the accuracy of binary classification reached 83.51% and 84.18% respectively.

In response to the complex three-dimensional space environment and the high computational complexity of low altitude penetration path planning for multi-UAVs, the existing multi-objective bald eagle search algorithm has the shortcomings of easily approaching the center point and low accuracy. A 3D multi-UAVs low altitude penetration method based on the improved multi-objective bald eagle search algorithm (IMBES) was proposed. Models for the 3D environment, threat sources, UAV physical constraints, multi-UAVs cooperative constraints, and path smoothness were constructed to define a multi-objective cost function. A coupling chaotic mapping initialization was designed to enhance the quality of the initial population. An adaptive Gauss walk strategy based on the “scout eagle” was devised to balance development and search capabilities. Fast non-dominated sorting was introduced to further enhance algorithm efficiency. By leveraging the correspondence between the bald eagle position and UAV speed, turning angle, and climbing angle, the IMBES efficiently explored the UAV configuration space to identify the optimal Pareto front. Experimental results showed that the success rate of the IMBES was 70.5%. Compared with existing path planning methods, the proposed method demonstrates strong optimization capabilities and low energy consumption, making it suitable for collaborative low-altitude penetration by multiple UAVs.

In response to the need for ankle rehabilitation training, a lightweight, easy-to-wear flexible ankle exoskeleton robot was designed using modular drive units and Bowden cables through analysis of ankle joint mechanics. The robot can provide assistance for ankle plantarflexion/dorsiflexion and inversion/eversion movements. Position control and torque control are used for flexible exoskeleton during the dorsiflexion and plantarflexion stages, respectively. Position control is mainly based on traditional proportional integral derivative(PID), while torque control uses force as a feedback signal to establish an admittance model between the interaction force difference and the Bowden cable core displacement compensation. The admittance parameters are dynamically adjusted through the Sigmoid deformation function to meet the requirements of assistive torque output and human-machine interaction compliance. Experimental data showed that the position tracking error was stable within 0.46 cm, and the force output error was stable within ?1.5-1.5 N, meeting the needs of human rehabilitation training.

In order to realize gesture recognition and hand state recognition at the same time, a single inertial measurement unit-based gesture recognition and touch recognition prototype was built, considering the inertial measurement unit at high sample rate has the capability of collecting motion signals and vibration signals simultaneously. The differences within hand state data and gesture data in the time and frequency domains were visually analyzed. Hand state, slipping gesture and circling gesture data sets were established. Considering the difference within data features, differential feature extraction methods were proposed, and neural network structures for hand state classification and gesture classification were constructed. Neural network models were trained by the data sets to achieve 99% accuracy rate in the comprehensive hand state recognition task, and 98% accuracy rate in both the slipping gesture recognition task and the circling gesture recognition task. A prototype program framework for real-time data stream processing, state shifting, and unknown class judgment was proposed. And a real-time program based on the hand state recognition model entities and the gesture recognition model entities was built, and the overall computational latency of the actual operation and the single model computational latency were measured, in order to prove the capability of real-time computing. Experimental results of model evaluation and real-time computing verification showed that, accurate and real-time hand states and gesture recognition with high sample rate inertial measurement units was feasible.

Foundational models in natural language processing, computer vision and multimodal learning have achieved significant breakthroughs in recent years, showcasing the potential of general artificial intelligence. However, these models still fall short of human or animal intelligence in areas such as causal reasoning and understanding physical commonsense. This is because these models primarily rely on vast amounts of data and computational power, lacking direct interaction with and experiential learning from the real world. Many researchers are beginning to question whether merely scaling up model size is sufficient to address these fundamental issues. This has led the academic community to reevaluate the nature of intelligence, suggesting that intelligence arises not just from enhanced computational capabilities but from interactions with the environment. Embodied intelligence is gaining attention as it emphasizes that intelligent agents learn and adapt through direct interactions with the physical world, exhibiting characteristics closer to biological intelligence. A comprehensive survey of embodied artificial intelligence was provided in the context of foundational models. The underlying technical ideas, benchmarks, and applications of current embodied agents were discussed. A forward-looking analysis of future trends and challenges in embodied AI was offered.

In motor imagery tasks, the brain often involves simultaneous activation of multiple regions, and traditional convolutional neural networks struggle to accurately represent the coordinated neural activity across these regions. Graph convolutional network GCN is suitable for representing the collaborative tasks of different brain regions by considering the connections and relationships between nodes (brain regions) in graph data. Attention-fused filter bank dual-view GCN（AFB-DVGCN）was proposed. A dual-branch network was constructed using filter banks to extract temporal and spatial information from different frequency bands. Information complementarity was achieved by a convolutional spatial feature extraction method for dual-view graphs. In order to improve the classification accuracy, the effective channel attention mechanism was utilized to enhance features and capture the interaction information between different feature maps. Validation results in the publicly available datasets BCI Competition IV-2a and OpenBMI show that AFB-DVGCN has achieved good classification performance, and the classification accuracy is significantly higher than that of the comparison networks.

Robotic harvesters face challenges in identifying apples under complex natural conditions such as unstable lighting, high fruit diversity, and severe leaf occlusion, which impedes the capture of key features, reducing harvesting efficiency and accuracy. An enhanced apple detection algorithm based on the YOLOv7 model for complex scenarios was proposed. A limited contrast adaptive histogram equalization technique was employed to enhance the contrast of apple images, reducing the background interference and clarifying the target contours. A multi-scale hybrid adaptive attention mechanism was introduced. The features were decomposed and reconstructed, and the spatial and channel attention directives were synergistically integrated to optimize multi-layer feature modeling over various distances, thereby boosting the model’s capability to extract apple features and resist background noise. Full-dimensional dynamic convolution was implemented to refine the feature selection process through a meticulous attention mechanism. The number of detection heads was increased to address the challenges of detecting small targets. The Meta-ACON activation function was used to optimize the attention allocation during feature extraction process. Experimental results demonstrated that the improved YOLOv7 model, achieved average accuracy and recall rates of 85.7% and 87.0%, respectively. Compared to Faster R-CNN, SSD, YOLOv5, and the original YOLOv7, the average detection precision was improved by 15.2, 7.5, 4.5, and 2.5 percentage points, and the average recall was improved by 13.7, 6.5, 3.6, and 1.3 percentage points, respectively. The model exhibits exceptional performance, providing robust technical support for apple growth monitoring and mechanical harvesting research.

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.