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.

Concerning the problem that the surface defects of the coin are small， variable in shape， easily confused with the background and difficult to be detected， an improved algorithm of coin surface defect detection named DCA-YOLO （Deformable Convolution and Adaptive space feature fusion-YOLO） was proposed. First of all， due to the different shapes of defects， three network structures with deformable convolution modules added at different positions in the backbone network were designed， and the ability to extract defects was improved through convolution learning offset and adjusting parameters. Then， the adaptive spatial feature fusion network was used to learn the weight parameters to better adapt to targets with different scales by adjusting the contribution of each pixel in the feature maps of different scales. Finally， the anchor ratio was adjusted， the category weights were dynamically adjusted， the comparison network performance was optimized， thus， a model network to add deformable convolution before upsampling for multi-scale fusion of the output features of the backbone network was proposed. Experimental results show that on the coin defect dataset， the detection mAP （mean Average Precision） of DCA-YOLO algorithm reaches 92.8%， which is close to that of Faster-RCNN （Faster Region-based Convolutional Neural Network）； compared with YOLOv3， the proposed algorithm has the detection speed basically the same with 3.3 percentage points improvement on detection mAP， and 3.2 percentage points increase on F1-score.

In the past, the learning for QA pattern relies on the labeled data, and the definition of pattern and the scoring method for the candidate answers are over simplified. The verb and noun sequence was extracted as the skeleton pattern to expand definition of QA pattern. In the learning process, a learning mechanism was established based on self-training. At first, the initial study was completed on a labeled QA pair, then the system would automatically select the reliable data for self training through searching in the Web while the system was running. The scoring method of the candidate answers was also improved by applying several heuristic rules. The experimental results show that the performance of Chinese QA system based on our method is improved significantly.

This paper presented a non-linear dimension reduction algorithm-Self-organizing Isometric Embedding (SIE) to compress high-dimensional document data. The algorithm was then validated in document clustering by being compared with the typical linear dimension reduction algorithm-Latent Semantic Indexing (LSI). Experimental results show that while significantly lowering the complexity, the performance of SIE is better than that of LSI and the benchmark.