Rolling bearing fault diagnosis is an important technology for health monitoring and pre-maintenance of mechanical equipment, which is of great significance for improving equipment operation reliability and reducing maintenance costs. This article reviews the research progress of fault diagnosis methods for rolling bearings, with a focus on analyzing the applications, advantages, and disadvantages of traditional data-driven methods, deep learning methods, graph embedding methods, and Transformer methods in this field. In addition, further analysis was conducted on the main issues of current research, including complex network structures, insufficient information attention, difficulties in graph data processing, and challenges in long-term dependency modeling. In response to these challenges, future research should focus on designing more lightweight and efficient models, improving computational efficiency, robustness of the models, and strengthening attention and deep mining of fault features.

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

The facial expression reflects a person’s emotion, cognition, and even physiological or mental state to a large extent. It has important application value in medical treatment, business, criminal investigation, education, and human-computer interaction. Automatic facial expression recognition technology has become an important research topic in computer vision. To solve the problems of insufficient feature extraction, loss of local key information, and low accuracy in facial expression recognition, this article proposes a facial expression recognition network based on attention double branch enhanced fusion. Two parallel branches are used to capture global enhancement features and local attention semantics respectively, and the fusion and complementarity of global and local information is realized through decision-level fusion. The experimental results show that the features extracted by the network are made more complete by fusing and enhancing the global and local features. The proposed method achieves 89.41% and 88.84% expression recognition accuracy on the natural scene face expression datasets RAF-DB and FERPlus, respectively, which is an excellent performance compared with many current methods and demonstrates the effectiveness and superiority of the proposed network model.

A new improved ant colony genetic algorithm(IACGA) is proposed to solve the problems of the basic ant colony optimization algorithm(ACO), such as the slow convergence rate and the local optimal solution. Based on the ACO model, local pheromone increment is updated differently according to the path quality constructed by ants, the current optimal path pheromone residual factor is adjusted adaptively, pheromone updating rules are improved, genetic operator is embedded and genetic algorithm(GA) is dynamically fused. It makes full use of the positive feedback mechanism of ACO and the searching ability of GA to realize the dynamic fusion of ACO and GA. The experimental results show that compared with the ACO and the GA, the improved algorithm can find the global optimal solution quickly, and the solution quality is relatively good, which improves the efficiency of the ant colony algorithm in solving the traveling salesman problem(TSP).

Unsupervised anomaly detection based on reconstruction is receiving focused research due to its low annotation requirement with gradually improving accuracy. However, the reconstruction quality and detection effect of existing methods still need to be improved. In this paper, we propose an unsupervised anomaly detection method based on multi-hierarchical feature reconstruction, specifically, we set the reconstruction object as the intermediate features of the network instead of the general image. First, we adopt Transformer as the backbone network and use an encoder pre-trained on a large-scale dataset to extract meaningful features. Second, we construct a decoder corresponding to the encoder, and the training goal is to make the intermediate features of the decoder approximate the intermediate features with those of the encoder. During training, to prevent overgeneralization, we propose a perturbation module to simulate anomalies to interfere with the decoder’s input. Experiments on the Hyper-Kvasir and BraTS 2021 dataset show that the proposed method can effectively improve the anomaly detection metrics, achieving the best performance with excellent stability.

To address the issues of low contrast, unclear local details, and the difficulty of existing deep learning models in capturing long-range dependencies in time-frequency images generated by current time-frequency construction methods, this paper proposes a bearing fault diagnosis framework (MATE-ViT) based on multi-channel contrast-limited adaptive time-frequency enhancement (CLAHE) and vision transformer (ViT). First, an improved CLAHE algorithm is used to independently enhance the multi-channel time-frequency images, effectively improving the local contrast and detail expression of the images, thereby enhancing the recognizability of fault features. Next, a perception-driven image segmentation and rearrangement strategy is applied, dividing the enhanced image into local feature patches and rearranging them to maximize the complementarity between local features and global information, thus providing optimized input feature representations for subsequent global modeling by the transformer. Finally, the ViT model is applied to extract global features from the enhanced images, achieving accurate classification of bearing fault types. Experimental results show that the proposed MATE-ViT framework outperforms mainstream deep learning models in terms of diagnostic accuracy and robustness under various noise levels and operating conditions, offering a more effective and reliable approach for bearing fault diagnosis.