Because of their easy tunability in structure,porosity,and micro-environment,metal-organic frameworks(MOFs)have recently attracted numerous attentions in various fields.The detection of ascorbic acid(AA),dopamine(DA),...Because of their easy tunability in structure,porosity,and micro-environment,metal-organic frameworks(MOFs)have recently attracted numerous attentions in various fields.The detection of ascorbic acid(AA),dopamine(DA),and uric acid(UA)is of great significance not only in biomedicine and neurochemistry but also in disease diagnosis and pathology research.Herein,a series of bimetallic-organic frameworks,MIL-125(Ti-Fe)-x%NH_(2)(x=0,25,50,75,and 100),was successfully synthesized.MIL-125(Ti-Fe)-x%NH_(2)family was employed as electrochemical sensors for the detection of AA,DA,and UA,and MIL-125(Ti-Fe)-100%NH_(2)exhibited the most promising performance with 50%carbon black doping in 0.1 mol·L^(-1)PBS(pH=7.10).In addition,the as-prepared MIL-125(Ti-Fe)-100%NH_(2)/GCE exhibited excellent anti-interference performance and good stability,which provided a promising platform for future utilization in real sample analysis.展开更多
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
Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry.Autonomous operation in semi-structured industrial...Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry.Autonomous operation in semi-structured industrial environments can now be supported by advanced sensor technologies,digital twins,artificial intelligence and novel communication techniques.These enable real-time monitoring of production processes,situation recognition and prediction,automated and adaptive(re)planning,teamwork and performance improvement by learning.This paper summarizes the main requirements towards autonomous industrial robotics and suggests a generic workflow for realizing such systems.Application case studies will be presented from recent practice at HUN-REN SZTAKI in a broad range of domains such as assembly,welding,grinding,picking and placing,and machining.The various solutions have in common that they use a generic digital twin concept as their core.After making general recommendations for realizing autonomous robotic solutions in the industry,open issues for future research will be discussed.展开更多
基金the Natural Science Foundation of Science and Technology Department of Jilin Province(grant No.20210101131JC)the Fundamental Research Funds for the Central Universities(grant No.2412020FZ009 and 2412022ZD048)
文摘Because of their easy tunability in structure,porosity,and micro-environment,metal-organic frameworks(MOFs)have recently attracted numerous attentions in various fields.The detection of ascorbic acid(AA),dopamine(DA),and uric acid(UA)is of great significance not only in biomedicine and neurochemistry but also in disease diagnosis and pathology research.Herein,a series of bimetallic-organic frameworks,MIL-125(Ti-Fe)-x%NH_(2)(x=0,25,50,75,and 100),was successfully synthesized.MIL-125(Ti-Fe)-x%NH_(2)family was employed as electrochemical sensors for the detection of AA,DA,and UA,and MIL-125(Ti-Fe)-100%NH_(2)exhibited the most promising performance with 50%carbon black doping in 0.1 mol·L^(-1)PBS(pH=7.10).In addition,the as-prepared MIL-125(Ti-Fe)-100%NH_(2)/GCE exhibited excellent anti-interference performance and good stability,which provided a promising platform for future utilization in real sample analysis.
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.
基金supported by the European Union within the framework of the“National Laboratory for Autonomous Systems”(No.RRF-2.3.1-212022-00002)the Hungarian“Research on prime exploitation of the potential provided by the industrial digitalisation(No.ED-18-2-2018-0006)”the“Research on cooperative production and logistics systems to support a competitive and sustainable economy(No.TKP2021-NKTA-01)”。
文摘Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry.Autonomous operation in semi-structured industrial environments can now be supported by advanced sensor technologies,digital twins,artificial intelligence and novel communication techniques.These enable real-time monitoring of production processes,situation recognition and prediction,automated and adaptive(re)planning,teamwork and performance improvement by learning.This paper summarizes the main requirements towards autonomous industrial robotics and suggests a generic workflow for realizing such systems.Application case studies will be presented from recent practice at HUN-REN SZTAKI in a broad range of domains such as assembly,welding,grinding,picking and placing,and machining.The various solutions have in common that they use a generic digital twin concept as their core.After making general recommendations for realizing autonomous robotic solutions in the industry,open issues for future research will be discussed.
