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Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

International Journal of Extreme manufacturing 2024年第3期6卷 175-214页

作者： Zijian Wang Yixian Song Guobin Zhang Qi Luo Kai Xu Dawei Gao Bin Yu Desmond Loke Shuai Zhong Yishu Zhang College of Integrated Circuits Zhejiang UniversityHangzhouZhejiang 3112000People’s Republic of China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027People’s Republic of China Department of Science Mathematics and TechnologySingapore University of Technology and DesignSingapore 487372Singapore Guangdong Institute of Intelligence Science and Technology HengqinZhuhai 519031People’s Republic of China

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial *** the field of embedded memory continues to evolve,innovative strategies are emerging to enhance *** them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(industrial manufacturing and its *** provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor ***,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic ***,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

关键词： embedded resistive random access memory industrial manufacturing intelligent computing advanced process node

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Deep industrial Image Anomaly Detection: A Survey

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Machine Intelligence Research 2024年第1期21卷 104-135页

作者： Jiaqi Liu Guoyang Xie Jinbao Wang Shangnian Li Chengjie Wang Feng Zheng Yaochu Jin Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China NICEGroup Universityof SurreyGuildford GU27YXUK Youtu Lab TencentShanghai 200233China NICE Group Bielefeld UniversityBielefeld 33619Germany

The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection(IAD).In this pa-per,we provide a comprehensive review of deep learning-based image anomaly detection techniques,from the perspectives of neural net-work architectures,levels of supervision,loss functions,metrics and *** addition,we extract the promising setting from indus-trial manufacturing and review the current IAD approaches under our proposed ***,we highlight several opening chal-lenges for image anomaly *** merits and downsides of representative network architectures under varying supervision are ***,we summarize the research findings and point out future research *** resources are available at https://***/M-3LAB/awesome-industrial-anomaly-detection.

关键词： Image anomaly detection defect detection industrial manufacturing deep learning computer vision

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Energy Disaggregation of industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

Energy and AI

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Energy and AI 2024年第3期17卷 342-356页

作者： Philipp Pelger Johannes Steinleitner Alexander Sauer Fraunhofer Institute for manufacturing Engineering and Automation IPA Stuttgart 70569Germany Institute for Energy Efficiency in Production(EEP) University of StuttgartStuttgart 70569Germany

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

关键词： Non-intrusive load monitoring Energy transparency Energy consumption evaluation industrial manufacturing Artificial neural networks

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Application of swarm intelligence algorithm on PFSP

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International English Education Research 2017年第2期 52-55页

作者： LI Minnan LIU Sheng School of Management Shanghai University of Engineering Science Shanghai 200620 China

With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, scheduling reasonable flow shop production systems can improve productivity and equipment utilization rate, reduce production costs. So the production system of flow shop scheduling problem has become one of the core problems ofmanufactaring enterprises the use of more and more.

关键词： PFSP swarm intelligence algorithm combinatorial optimiTafion industrial manufacturing

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