In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationshi...In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.展开更多
Despite its strong advantages in resource, technology and human resource, China's Northeast Industrial Area is also experiencing problems of unreasonable industrial structure, envi- ronmental pollution, and the de...Despite its strong advantages in resource, technology and human resource, China's Northeast Industrial Area is also experiencing problems of unreasonable industrial structure, envi- ronmental pollution, and the degradation of ecological condition, etc., which prevent this area from achieving a sustainable devel- opment. Through analyzing the resource problem, the present paper proposed a strategy of circular economy for the prosperity of this are, discussed the theories of circular economy and re- source recycling, and finally concluded that improving resource productivity is at the core of circular economy.展开更多
文摘In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.
文摘Despite its strong advantages in resource, technology and human resource, China's Northeast Industrial Area is also experiencing problems of unreasonable industrial structure, envi- ronmental pollution, and the degradation of ecological condition, etc., which prevent this area from achieving a sustainable devel- opment. Through analyzing the resource problem, the present paper proposed a strategy of circular economy for the prosperity of this are, discussed the theories of circular economy and re- source recycling, and finally concluded that improving resource productivity is at the core of circular economy.
