In the Radio Frequency IDentification （RFID） real-time defect detection task in industrial scenes， the deep learning target detection algorithms such as You Only Look Once （YOLO） are often adopted in order to ensure the detection precision and speed. However， these algorithms are still difficult to meet the speed requirement of industrial detection， and the corresponding network models cannot be deployed on resource-constrained devices. To solve these problems， the YOLO model must be pruned and compressed. A new network pruning method of the weighted fusion of feature information richness and feature information diversity based on rank information was proposed. Firstly， the unpruned model was loaded and reasoned， and the rank information of the corresponding feature maps of the filters was obtained in forward propagation to measure the feature information richness. Secondly， according to the different pruning rates， the rank information was clustered or the similarity of the rank information was calculated to measure the feature information diversity. Finally， the importance degrees of the corresponding filters were obtained after the weighted fusion and were sorted， and the filters with low importance were cut off. Experimental results show that， for YOLOv4， when the pruning rate is 28.87% and the weight of feature information richness is 0.75， the proposed method has the mean Average Precision （mAP） improved by 2.6% - 8.9% compared with the method that uses rank information of the feature maps alone， and the model pruned by the proposed method even has the mAP increased by 0.4% and the model parameters reduced by 35.0% compared with the unpruned model， indicating that the proposed method is conducive to the model deployment.

Point-Of-Interest (POI) recommendation aims to recommend places that users do not visit but may be interested in, which is one of the important location-based services. In POI recommendation, time is an important factor, but it is not well considered in the existing POI recommendation models. Therefore, the Time-incorporated User-based Collaborative Filtering POI recommendation (TUCF) algorithm was proposed to improve the performance of POI recommendation by considering time factor. Firstly, the users' check-in data of Location-Based Social Network (LBSN) was analyzed to explore the time relationship of users' check-ins. Then, the time relationship was used to smooth the users' check-in data, so as to incorporate time factor and alleviate data sparsity. Finally, according to the user-based collaborative filtering method, different POIs were recommended to the users at different times. Experimental results on real check-in datasets showed that compared with the User-based collaborative filtering (U) algorithm, TUCF algorithm had the precision and recall increased by 63% and 69% respectively, compared with the U with Temporal preference with smoothing Enhancement (UTE) algorithm, TUCF algorithm had the precision and recall increased by 8% and 12% respectively. And TUCF algorithms reduced the Mean Absolute Error (MAE) by 1.4% and 0.5% respectively, compared with U and UTE algorithms.

It is difficult to distinguish the intensity between dim moving targets and background noise in the case of extremely Low Signal-to-Noise Ratio (LSNR). In order to solve the problem, a new extremely dim target search algorithm based on detection and tracking mutual iteration was proposed with a new strategy for combining and iterating the process of temporal domain detection and spatial domain tracking. Firstly, the difference between the signal segment in the detection window and the extracted background estimated feature was calculated during the detection process. Then, the dynamic programming algorithm was adopted to remain the trajectories with the largest trajectory energy accumulation in the tracking process. Finally, the threshold parameters of the detector of the remained trajectory were adaptively adjusted in the next detection process, so that the pixels in this trajectory were able to be retained to the next detection and tracking stage with a more tolerant strategy. Experimental results show that, the dim moving targets with SNR as low as 0 dB can be detected by the proposed algorithm, false alarm rate of 1% - 2% and detection rate of about 70%. It can be seen that the detection ability for dim targets with extremely LSNR can be improved effectively by the proposed algorithm.

To solve the problem of feature extraction and state prediction of intermittent non-stationary time series in the industrial field, a new prediction approach based on Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Support Vector Machine (SVM) was proposed in this paper. Firstly, the intermittent non-stationary time series was analyzed by multiple time scales and decomposed into a couple of IMF components which possessed the different scales by the EEMD algorithm. Then, the noise energy was estimated to determine the cumulative contribution rate adaptively on the basis of 3-sigma principle. The feature dimension and redundancy were reduced and the noise in IMF was removed by using PCA algorithm. Finally, on the basis of the determining of SVM key parameters, the principal components were regarded as input variables to predict future. Instance's testing results show that Mean Average Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Squared Percentage Error (MSPE) were 514.774, 78.216, 12.03% and 1.862%, respectively. It is concluded that the SVM prediction of the time series of output power of wind farm possesses a higher accuracy than not using PCA because the frequency mixing phenomena was inhibited, the non-stationary was reduced and the noise was further eliminated by EEMD algorithm and PCA algorithm.

As a cluster of high-dimensional visualization and unsupervised learning algorithm, Self-Organizing Map (SOM) provided a favorable means for Chinese Web clustering. However, the SOM algorithm has a natural flaw of being sensitive to the network initial power value so as that the accuracy of the cluster made by the SOM has been influenced. To solve the problem, this paper applied genetic algorithm to optimize SOM. This paper made the following contributions. 1) Propose a text clustering method based on GA-SOM-based Text Clustering Algorithms (GSTCA); 2) Make comparison experiment. The result of the experiment shows that the GSTCA has higher accuracy rate than SOM algorithm in the Web Chinese Document Clustering, and the average value of F measure is improved by 14.5% than traditional method. The experiments also show that GSTCA is not sensitive to initial weights of the network, thereby enhancing the stability of the algorithm.