On-line detection of fabric defects is a major problem faced by textile industry. Aiming at the problems such as high false positive rate, high false negative rate and low real-time in the existing detection of fabric defects, an on-line detection algorithm for fabric defects based on deep learning was proposed. Firstly, based on GoogLeNet network architecture, and referring to classical algorithm of other classification models, a fabric defect classification model suitable for actual production environment was constructed. Secondly, a fabric defect database was set up by using different kinds of fabric pictures marked by quality inspectors, and the database was used to train the fabric defect classification model. Finally, the images collected by high-definition camera on fabric inspection machine were segmented, and the segmented small images were sent to the trained classification model in batches to realize the classification of each small image. Thereby the defects were detected and their positions were determined. The model was validated on a fabric defect database. The experimental results show that the average test time of each small picture is 0.37 ms by this proposed model, which is 67% lower than that by GoogLeNet, 93% lower than that by ResNet-50, and the accuracy of the proposed model is 99.99% on test set, which shows that its accuracy and real-time performance meet actual industrial demands.

In the distributed storage system, Disk space Utilization (DU) is generally used to measure the load balance of each storage node. When given the equal disk space utilization to each node, the balance of storage load is achieved in the whole distributed storage system. However, in practice, the storage node with relatively low disk I/O speed and reliability becomes a bottleneck for the performance of data I/O in the whole storage system. Therefore in heterogeneous distributed storage system and specially the system which has great differences in disk I/O speed and reliability of each storage node, the speed of data I/O is definitely limited when disk space utilization is the only evaluation criteria of storage load balance. A new idea based on read-write efficiency was proposed to measure the storage load balance in the distributed storage system. According to the definition of Storage Entropy (SE) given by the theory of load balance and entropy, a kind of load balance algorithm based on SE was proposed. With system load and single node load determination as well as load shifting, the quantitative adjustment for storage load of the distributed storage system was achieved. The proposed algorithm was tested and compared with the load balance algorithm based on disk space utilization. Experimental results show that the proposed algorithm can balance storage load well in the distributed storage system, which effectively restrains the system load imbalance and improves the overall efficiency of reading and writing of the distributed storage system.