With the growing demand of industrial automation， 3D point cloud anomaly detection has played an increasingly important role in product quality control. However， the existing methods often rely on a single feature， leading to information loss and accuracy reduction. To address these issues， an unsupervised point cloud anomaly detection method based on multi-representation fusion was proposed， called MRF （Multi-Representation Fusion）. MRF used multi-angle rotation and various coloring schemes to render point clouds into multi-modal images， and employed pre-trained 2D convolutional neural networks to extract rich semantic features. Simultaneously， pre-trained Point Transformer was adopted to extract 3D structural features. After the above， by fusing 2D image semantic features and 3D structural features， MRF was able to capture point cloud information more comprehensively. In the anomaly detection stage， abnormal point clouds were identified effectively by using a method based on positive sample memory banks and nearest neighbor search. Experimental results on MVTec 3D AD dataset show that MRF achieves a point cloud-level AUROC （Area Under the Receiver Operating Characteristic curve） of 0.972 and a point-level AUPRO （Area Under the Per-Region Overlap） of 0.948， significantly outperforming existing methods. It can be seen that the effectiveness and robustness of MRF makes it a highly promising solution for industrial applications.

When multiple feature modalities are fused， there is a superposition of noise， and the cascaded structure used to reduce the differences between modalities does not fully utilize the feature information between modalities. To address these issues， a cross-modal Dual-stream Alternating Interactive Network （DAINet） method was proposed. Firstly， a Dual-stream Alternating Enhancement （DAE） module was constructed to fuse modal features in interactive dual-branch way. And by learning mapping relationships between modalities and employing bidirectional feedback adjustments of InFrared-VISible-InFrared （IR-VIS-IR） and VISible-InfRared-VISible （VIS-IR-VIS）， the cross suppression of inter-modal noise was realized. Secondly， a Cross-Modal Feature Interaction （CMFI） module was constructed， and the residual structure was introduced to integrate low-level and high-level features within and between infrared-visible modalities， thereby minimizing differences and maximizing inter-modal feature utilization. Finally， on a self-constructed infrared-visible multi-modal typhoon dataset and a publicly available RGB-NIR multi-modal dataset， the effectiveness of DAE module and CMFI module was verified. Experimental results demonstrate that compared to the simple cascading fusion method on the self-constructed typhoon dataset， the proposed DAINet-based feature fusion method improves the overall classification accuracy by 6.61 and 3.93 percentage points for the infrared and visible modalities， respectively， with G-mean values increased by 6.24 and 2.48 percentage points， respectively. These results highlight the generalizability of the proposed method for class-imbalanced classification tasks. On the RGB-NIR dataset， the proposed method achieves the overall classification accuracy improvements of 13.47 and 13.90 percentage points， respectively， for the two test modalities. At the same time， experimental results of comparing with IFCNN （general Image Fusion framework based on Convolutional Neural Network） and DenseFuse methods demonstrate that the proposed method improves the overall classification accuracy by 9.82， 6.02， and 17.38， 1.68 percentage points for the two test modalities on the self-constructed typhoon dataset.

The trend of green logistics pushes the use of electric vehicles into cold chain logistics. Concerning the problem that maintaining a low temperature environment requires a lot of energy in electric vehicle cold chain distribution， as well as the phenomena that the limited driving range and long charging time of electric vehicles make high operation cost， the Refrigerated Electric Vehicle Routing Problem （REVRP） in electric vehicle distribution was thought deeply. Considering the characteristics of electric vehicle energy consumption and the charging requirements of social recharging stations， a linear programming model was developed with the objective of minizing total distribution cost， and the objective function was composed of fixed cost and variable cost， in the variable cost， transportation cost and cooling cost were included. The capacity constraints and power constraints were considered in the model， and a Hybrid Ant Colony Optimization （HACO） algorithm was designed to solve this model. Especially， more attention was paid to designing transfer rules suitable for social recharging stations and four local optimization operators. Based on improving the Solomon benchmark examples， the small-scale and large-scale example sets were formed， and the performance of ACO algorithm and the optimization operators were through experiments. The experiment results show that ACO algorithm and CPLEX （WebSphere ILOG CPLEX） solver can find the exact solution in the small-scale example set， and ACO algorithm can save the operation time by 99.6% . In the large-scale example set， compared with ACO algorithm， HACO algorithm combing the four optimization operators has the average optimization efficiency increased by 4.45%. The proposed algorithm can obtain a feasible solution for REVRP in a limited time.

Aiming at the problem of poor recommendation effect caused by the data sparsity of the recommendation system, a hybrid recommendation algorithm based on rating filling and trust information was proposed namely RTWSO (Real-value user item restricted Boltzmann machine Trust Weighted Slope One). Firstly, the improved restricted Boltzmann machine model was used to fill the rating matrix, so as to alleviate the sparseness problem of the rating matrix. Secondly, the trust and trusted relationships were extracted from the trust relationship, and the matrix decomposition based implicit trust relationship similarity was also used to solve the problem of trust relationship sparsity. The modification including trust information was performed to the original algorithm, improving the recommendation accuracy. Finally, the Weighted Slope One (WSO) algorithm was used to integrate the matrix filling and trust similarity information as well as predict the rating data. The performance of the proposed hybrid recommendation algorithm was verified on Epinions and Ciao datasets. It can be seen that the proposed hybrid recommendation algorithm has the recommendation accuracy improved by more than 3% compared with the composition algorithm, and recommendation accuracy increased by more than 1.2% compared with the existing social recommendation algorithm SocialIT (Social recommendation algorithm based on Implict similarity in Trust). Experimental results show that the proposed hybrid recommendation method based on rating filling and trust information, improves the recommended accuracy to a certain extent.

To solve the storage and network transmission problem of the three-Dimensional (3D) mesh model, a new 3D model compression algorithm was proposed. Based on the slicing for 3D mesh, the proposed algorithm was composed of the following three steps: slice vertex calculation, slice boundary sampling and encoding for the image obtained by slicing. For a given 3D mesh model, the bounding box of the model was firstly calculated; then the model was sliced along the longest direction of the bounding box. In the procedure of slicing, the intersection point of the slice with the edge of the mesh was calculated, and as a result, all the intersection points in the same slice constituted a polygon. Then the boundary of the polygon was uniformly resampled so that each layer of the slice had the same number of vertices. After resampling of the polygon boundary, the coordinates of vertices in each slice were converted into the polar form. In this way, all ρ-coordinates and θ-coordinates of the vertices in each slice could constitute one image respectively, and the original 3D model could be represented by these two images. The new representation method has two obvious advantages: first, the dimension of the data is reduced, thus the amount of the data is effectively reduced; second, the data in these two images have great data correlation, and as a result, the entropy of the data is further reduced. Based on these two advantages, the proposed algorithm compressed these two images by difference coding technique and arithmetic coding technique, and then the compressed files were obtained. Compared with Incremental Parametric Refinement (IPR) method, the coding efficiency of the proposed algorithm was increased by 23% under the same quality of the decoded model. The experimental results show that the proposed algorithm can obtain good compression efficiency, and effectively reduce the data amount in the application of 3D model storage and transmission.

Traditional K-means algorithm is sensitive to the initial start center. To solve this problem, a method was proposed to optimize the initial center points through adopting the theory of good point set and Leader method. According to the different combination ways, the new algorithms were called KLG and KGL respectively. Better points could be obtained by the theory of good point set rather than random selection. The Leader method could reflect the distribution characteristics of the data object. The experimental results conducted on the UCI database show that the KLG and KGL algorithms significantly outperform the traditional and other initialization K-means algorithms.

Considering that contact duration has significant influence on whether packet can be transmitted successfully or not, the authors proposed a Probabilistic Routing Protocol using History of Encounters and Transitivity based on Contact Duration (PRoPHET-CD), which combined contact duration with encounter frequency to estimate delivery probability. This protocol could improve the delivery probability significantly and reduce the interruption of packet transmission. The simulation results show that the protocol of PRoPHET-CD can significantly enhance the message delivery probability and reduce the overhead ratio.

Wireless Sensor Network (WSN) was usually vulnerable to conspiracy attack from its adversaries when using bivariate polynomial key pre-distribution protocol. In order to solve this problem, a pre-distribution key management scheme for WSN was improved by reducing the amount of pairwise keys sharing between sensor nodes and changing the means of establishing pairwise keys between clusters. Analysis shows that the improved scheme not only maintains the advantages such as higher security of the original scheme, but also saves the limited memory storage of sensor nodes further, reduces the communication overhead of sensor nodes, extends the lifespan of the networks and can withstand conspiracy attack effectively.