In the reliability evaluation of many high-reliability and high-value products， product reliability often cannot be accurately evaluated due to the lack of objective test data. Aiming at this problem， a reliability evaluation method for high-reliability products based on improved evidence fusion was proposed in order to make full use of reliability information from different sources. Firstly， combining the characteristics of reliability engineering， the modified weight of each evidence was determined by the consistency of the evidence at credal level， pignistic level and the uncertainty of the evidence itself. Secondly， the optimal comprehensive weight was obtained by linear combination of each weight vector based on game theory. Finally， the Dempster’s combination rule was used to fuse the modified evidence， and the probability distribution of the product reliability index was obtained through the Pignistic probability transformation formula to complete the product reliability evaluation. The reliability evaluation results of one electronic device show that compared with the results of Jiang’s combination method and Yang’s combination method， which also consider multi-dimensional weight modification， the credibility of the conflict interval given by the proposed method is reduced by 69.6% and 54.6% respectively， and the credibility of the overall frame of discrimination given by the proposed method is reduced by 5.6% and 3.7% respectively. Therefore， in the application of reliability engineering， the performance of the proposed method in solving evidence conflict and reducing the uncertainty of fusion results is better than that of the comparison methods， and this method can fuse multi-source reliability information effectively and improve the credibility of the results of product reliability evaluation.

Since Knowledge Graph（KG） can alleviate the problems of data sparsity and cold start in collaborative filtering algorithm， it has been widely studied and applied in the recommendation field. Many existing recommendation models based on KG confuse the collaborative filtering information in user-item bipartite graph and the association information between entities in KG， resulting in the learned user vector and item vector cannot accurately express the characteristics of users and items， and even introducing wrong information to interfere with recommendation. Regarding the issues above， a model called KG Attention Network fusing Collaborative Filtering information （KGANCF） was proposed. Firstly， the collaborative filtering information of users and items was dug out by the collaborative filtering layer of the network from the user-item bipartite graph， avoiding the interference of the entity information of KG. Then， the graph attention mechanism was applied in the KG attention embedding layer， the attribute information closely related to users and items was extracted from KG. Finally， the collaborative filtering information and the attribute information in KG were merged at the prediction layer to obtain the final vector representations of users and items， and then the scores of users to items were predicted. The experiments were carried out on MovieLens-20M and Last.FM datasets. Compared with the results of Collaborative Knowledge-aware Attentive Network （CKAN）， on Movielens-20M， F1-score of KGANCF improves by 1.1 percentage points while Area Under Curve （AUC） improves by 0.6 percentage points； on Last.FM， F1-score improves by 3.3 percentage points and AUC improves by 8.5 percentage points. Experimental results show that KGANCF can effectively improve the accuracy of recommendation results， and is significantly better than CKE （Collaborative Knowledge base Embedding），KGCN （Knowledge Graph Convolutional Network），KGAT （Knowledge Graph Attention Network） and CKAN models on datasets with sparse KG.

In the reference vector based high-dimensional many-objective evolutionary algorithms， the random selection of parent individuals will slow down the speed of convergence， and the lack of individuals assigned to some reference vectors will weaken the diversity of population. In order to solve these problems， an Improved high-dimensional Many-Objective Evolutionary Algorithm based on Decomposition （IMaOEA/D） was proposed. Firstly， when a reference vector was assigned at least two individuals in the framework of decomposition strategy， the parent individuals were selected for reproduction of offspring according to the distance from the individual assigned to the reference vector to the ideal point， so as to increase the search speed. Then， for the reference vector that was not assigned at least two individuals， the point with the smallest distance from the ideal point along the reference vector was selected from all the individuals， so that at least two individuals and the reference vector were associated. Meanwhile， by guaranteeing one individual was related to each reference vector after environmental selection， the diversity of population was ensured. The proposed method was tested and compared with other four high-dimensional many-objective optimization algorithms based on decomposition on the MaF test problem sets with 10 and 15 objectives. Experimental results show that， the proposed algorithm has good optimization ability for high-dimensional many-objective optimization problems： the optimization results of the proposed algorithm on 14 test problems of the 30 test problems are better than those of the other four comparison algorithms. Especially， the proposed algorithm has certain advantage on the degradation problem optimization.

Concerning the problem that it is complex to detect the surface feature of continuous casting slab, a surface trait extraction method based on complex Contourlet decomposition was developed. Compared to conventional method, the new one has the characteristics of shift invariance, excellent directional selectivity and higher retrieval rate. The image was decomposed in Contourlet domain and then the image's sub-bands directional matrices were extracted by using directional filter bands, then the feature vector was constructed by the energy value, stand deviation and skewness. The support vector machine was trained by the feature vectors, to classify images. Industrial test result shows that the accurate rate of classifying surface features is about 90%, and it can be used in image feature extraction and slab flaw detection.

For estimating and removing the salt-and-pepper noise point accurately in image, a new adaptive median filtering algorithm was proposed.Firstly, if the pixel in the center of n×n (n is an odd integer not less than three) template was the extreme value of all the pixels in the window, it was supposed to be probably a noise point. The pixel gray value in the sequence difference between the two scripts and a template sequence of the slope of the pixel gray value within the region were used to determine the mean quasi-adaptive noise point to be the real noise points. Finally, mean filtering was done on the noised pixels. Compared with median filter, the condition of detecting noises with this method has been largely enhanced. And the method can both effectively restrain noises and maintain details.