Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach
出版年份 2021 全文链接
标题
Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach
作者
关键词
Industrial knowledge graph, Graph embedding, Cognitive manufacturing, Graph neural network, Reinforcement learning
出版物
JOURNAL OF MANUFACTURING SYSTEMS
Volume 61, Issue -, Pages 16-26
出版商
Elsevier BV
发表日期
2021-08-12
DOI
10.1016/j.jmsy.2021.08.002
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network
- (2020) Liang Hu et al. JOURNAL OF MANUFACTURING SYSTEMS
- Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network
- (2020) Dingcheng Zhang et al. MEASUREMENT
- A digital twin-enhanced system for engineering product family design and optimization
- (2020) Kendrik Yan Hong Lim et al. JOURNAL OF MANUFACTURING SYSTEMS
- Smart Grid for Industry Using Multi-Agent Reinforcement Learning
- (2020) Martin Roesch et al. Applied Sciences-Basel
- Deep reinforcement learning based preventive maintenance policy for serial production lines
- (2020) Jing Huang et al. EXPERT SYSTEMS WITH APPLICATIONS
- A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0
- (2020) Jiewu Leng et al. JOURNAL OF CLEANER PRODUCTION
- A data-driven reversible framework for achieving Sustainable Smart product-service systems
- (2020) Xinyu Li et al. JOURNAL OF CLEANER PRODUCTION
- A Comprehensive Survey on Transfer Learning
- (2020) Fuzhen Zhuang et al. PROCEEDINGS OF THE IEEE
- A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks
- (2020) Youyang Qu et al. IEEE Transactions on Industrial Informatics
- A Deep Swarm-Optimized Model for Leveraging Industrial Data Analytics in Cognitive Manufacturing
- (2020) Akshi Kumar et al. IEEE Transactions on Industrial Informatics
- Using graphs to link data across the product lifecycle for enabling smart manufacturing digital threads
- (2019) Thomas D. Hedberg et al. JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
- A survey of smart product-service systems: Key aspects, challenges and future perspectives
- (2019) Pai Zheng et al. ADVANCED ENGINEERING INFORMATICS
- A novel data-driven graph-based requirement elicitation framework in the smart product-service system context
- (2019) Zuoxu Wang et al. ADVANCED ENGINEERING INFORMATICS
- A graph-based context-aware requirement elicitation approach in smart product-service systems
- (2019) Zuoxu Wang et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Contextual self-organizing of manufacturing process for mass individualization: a cyber-physical-social system approach
- (2018) Jiewu Leng et al. Enterprise Information Systems
- Exploiting semantic similarity for named entity disambiguation in knowledge graphs
- (2018) Ganggao Zhu et al. EXPERT SYSTEMS WITH APPLICATIONS
- Industrial Internet of Things: Challenges, Opportunities, and Directions
- (2018) Emiliano Sisinni et al. IEEE Transactions on Industrial Informatics
- Structured modeling of heterogeneous CAM model based on process knowledge graph
- (2018) Xiuling Li et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A novel approach for analysing evolutional motivation of empirical engineering knowledge
- (2018) Xinyu Li et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Deep learning for smart manufacturing: Methods and applications
- (2018) Jinjiang Wang et al. JOURNAL OF MANUFACTURING SYSTEMS
- Data-driven smart manufacturing
- (2018) Fei Tao et al. JOURNAL OF MANUFACTURING SYSTEMS
- Graph embedding techniques, applications, and performance: A survey
- (2018) Palash Goyal et al. KNOWLEDGE-BASED SYSTEMS
- Cognitive Computing: Architecture, Technologies and Intelligent Applications
- (2018) Min Chen et al. IEEE Access
- Reconfigurable manufacturing systems: Literature review and research trend
- (2018) Marco Bortolini et al. JOURNAL OF MANUFACTURING SYSTEMS
- Deep Reinforcement Learning: A Brief Survey
- (2017) Kai Arulkumaran et al. IEEE SIGNAL PROCESSING MAGAZINE
- Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor
- (2017) Yingfeng Zhang et al. IEEE Transactions on Industrial Informatics
- Personalized product configuration framework in an adaptable open architecture product platform
- (2017) Pai Zheng et al. JOURNAL OF MANUFACTURING SYSTEMS
- A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line
- (2017) Hao Zhang et al. IEEE Access
- Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination
- (2016) Shiyong Wang et al. Computer Networks
- Knowledge reuse integrating the collaboration from experts in industrial maintenance management
- (2013) Paula Andrea Potes Ruiz et al. KNOWLEDGE-BASED SYSTEMS
- Cognitive computing
- (2011) Dharmendra S. Modha et al. COMMUNICATIONS OF THE ACM
- Design of reconfigurable manufacturing systems
- (2011) Yoram Koren et al. JOURNAL OF MANUFACTURING SYSTEMS
- From cloud computing to cloud manufacturing
- (2011) Xun Xu ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Design for mass personalization
- (2010) M.M. Tseng et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Ontology-based reconfiguration agent for intelligent mechatronic systems in flexible manufacturing
- (2010) Yazen Alsafi et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Community detection in graphs
- (2009) Santo Fortunato PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
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