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 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.

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.

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.

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.

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 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.

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.

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.

A multi-behavior aware service recommendation method based on hypergraph graph convolutional neural network (MBSRHGNN) was proposed to resolve the problem of insufficient high-order service feature extraction in existing service recommendation methods. A multi-hypergraph was constructed according to user-service interaction types and service mashups. A dual-channel hypergraph convolutional network was designed based on the spectral decomposition theory with functional and structural properties of multi-hypergraph. Chebyshev polynomial was used to approximate hypergraph convolution kernel to reduce computational complexity. Self-attention mechanism and multi-behavior recommendation methods were combined to measure the importance difference between multi-behavior interactions during the hypergraph convolution process. A hypergraph pooling method named HG-DiffPool was proposed to reduce the feature dimensionality. The probability distribution for recommending different services was learned by integrating service embedding vector and hypergraph signals. Real service data was obtained by the crawler and used to construct datasets with different sparsity for experiments. Experimental results showed that the MBSRHGNN method could adapt to recommendation scenario with highly sparse data, and was superior to the existing baseline methods in accuracy and relevance.

A novel detection algorithm (efficient multi-scale attention (EMA) and small object detection based on YOLOv5, ES-YOLOv5) was proposed by targeting small ship targets in SAR scenes aiming at the issues of inconspicuous imaging features and low detection accuracy caused by arbitrary orientation of small targets in synthetic aperture radar (SAR) imaging. A small target detection layer was added to adjust the receptive field size, making it more suitable for capturing small target scale features and facilitating multi-scale fusion. An EMA mechanism was introduced to focus on key target information and enhance feature representation capability. The circular smooth label (CSL) technique was utilized to adapt to the periodicity of angles, achieving high-precision angle classification. The experimental results demonstrate that the proposed method achieves an average detection accuracy of 90.9% at an intersection over union (IoU) threshold of 0.5 on the RSDD-SAR dataset. The algorithm outperforms the baseline algorithm YOLOv5 by 6% in improving the precision of detecting small SAR ship targets, significantly enhancing the model’s detection performance.

A road extraction model based on multi-scale feature fusion lightweight DeepLab V3+ (MFL-DeepLab V3+) was proposed aiming at the problems of high computational complexity and poor road extraction effect of the current semantic models used in the field of remote sensing image road extraction. The lightweight MobileNet V2 network was used to replace the original model’s Xception network as the backbone network in order to reduce the parameters of the model and the computational complexity of the model. Deep separable convolution was introduced into the Atlas spatial pyramid pooling (ASPP) module. A multi-scale feature fusion with attention (MFFA) was proposed in the decoding area in order to enhance the road extraction ability of the model and optimize the extraction effect on small road segments. Experiments based on the Massachusetts roads dataset showed that the parameter size of the MFL-DeepLab V3+ model was significantly reduced with a parameter compression of 88.67% compared to the original model. The road extraction image had clear edges, and its accuracy, recall, and F1-score were 88.45%, 86.41% and 87.42%, achieving better extraction performance compared to other models.

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 new estimation network was proposed for improving the insufficient occlusion handling ability of existing human pose estimation methods. An occluded parts enhanced convolutional network (OCNN) and an occluded features compensation graph convolutional network (OGCN) were included in the proposed network. A high-low order feature matching attention was designed to strengthen the occlusion area features, and high-adaptation weights were extracted by OCNN, achieving enhanced detection of the occluded parts with a small amount of occlusion data. OGCN strengthened the shared and private attribute compensation node features by eliminating the obstacle features. The adjacency matrix was importance-weighted to enhance the quality of the occlusion area features and to improve the detection accuracy. The proposed network achieved detection accuracy of 78.5%, 67.1%, and 77.8% in the datasets COCO2017, COCO-Wholebody, and CrowdPose, respectively, outperforming the comparative algorithms. The proposed network saved 75% of the training data usage in the self-built occlusion dataset.

The research on the influence of emotions on false memory helps explore the memory-processing mechanisms of the brain. The EEG signals of false memories under different emotion states were collected. The microstate analysis was used to obtain the template maps for each emotion group named from microstate 1 to microstate 5, the time segmentation of the four stages of memory recognition (early processing, familiar processing, episodic recall processing and post-extraction processing) for the emotion groups were divided according to the microstate fitting results, and the phase-locked brain functional networks were constructed in microstates with significant difference in time coverage. The results analyzed of EEG signals from both the temporal perspective and the spatial perspective show that the brain processing patterns of the emotion groups begin to appear different from the episode recall processing stage. The positive group remains in the active microstates 3 and 5 of the prefrontal region and has strong brain function, the negative group remains in microstate 1 and has poor brain function, and the neutral group remains in the active microstates 3 and 4 of the central region. The positive group spends more time and mental resources on plot association and reasoning, while the negative group stays depressed for a longer time, and the neutral group devotes more time and mental resources to information integration.

A small target detection method for unmanned aerial vehicle (UAV) based on adaptive up-sampling and spatial correlation enhancement was proposed, to resolve the problem of false detection and missed detection caused by the small size of UAV and the difficulty of feature extraction under complex backgrounds. Firstly, the important contextual information was obtained by multi-scale dilated convolution, and then the attention feature fusion module was used to suppress the information conflict of multi-scale feature fusion; Secondly, a new up-sampling method of sub-pixel convolution and bilinear interpolation adaptive fusion was adopted to balance the computation and to fuse more UAV feature information; Finally, spatial correlation enhancement strategies for local and global spatial features were performed on deep features to improve the sensitivity of foreground targets in complex backgrounds and enhance target expression to suppress background noise. Ablation experiments and comparative experiments were implemented on the self-made UAV dataset. The mAP0.5 and mAP0.5:0.95 of the proposed algorithm were increased by 2.4% and 2.7% respectively, compared with those of the original YOLOv5 algorithm. Furthermore, the detection speed was able to achieve 58.5 frames per second. The performance of the proposed algorithm was also verified on the VisDrone2019 dataset, and its mAP0.5 and mAP0.5:0.95 were respectively higher than those of the YOLOv5 algorithm by 4.6% and 1.3%.

A driver-automation shared control strategy based on non-cooperative game (NCG) theory was proposed in order to reduce the conflict operations between the driver and intelligent system during the co-driving. The lane-keeping shared control problem was mathematically described by the first-order differential equation based on the linear two degree-of-freedom vehicle model. The NCG theory was employed to resolve the weight allocation problem of the shared control system, where the decision makers would act on the same dynamic system. The driving control authority was designed. Then the smooth transition of driving control authority between the driver and intelligent system was achieved by utilizing the preview offset distance (POD) to update the confidence matrix. The desired front wheel angle of lane-keeping shared control was transformed into an online quadratic programming problem formulated as a quadratic cost function with linear inequality constraints based on the model predictive control (MPC) framework. The shared control strategy was validated on the driver-in-the-loop CarSim/Simulink platform. Results demonstrate that such strategy can well-guarantee lateral tracking accuracy and the priority of the driver’s control authority.

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 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.