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A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey, Christian Brecher
2021 Sensors  
A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.  ...  As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service.  ...  ., Fraunhofer IPT, as well as the Chair of Production Metrology and Quality Management, and Production Engineering of the Laboratory for Machine Tools and Production Engineering (WZL) for their permission  ... 
doi:10.3390/s21196340 pmid:34640660 fatcat:qy3qiazvqrejvfuixudd6ywqsq

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey, Christian Brecher
2021 Sensors 21(19)  
., Fraunhofer IPT, as well as the Chair of Production Metrology and Quality Management, and Production Engineering of the Laboratory for Machine Tools and Production Engineering (WZL) for their permission  ...  Acknowledgments: The authors would like to thank the German Research Foundation DFG for the support within the Cluster of Excellence "Internet of Production"-390621612.  ...  Composite Material Processing For similarly novel but not yet matured composite material processing techniques, e.g., lightweight production of fiber-reinforced polymers, hybrid modeling of non-measurable  ... 
doi:10.18154/rwth-2021-09877 fatcat:yjhprcascvfutisat7olust3kq

Table of Contents

2022 IEEE Transactions on Industrial Informatics  
Zhu 1083 Reinforcement Learning-Based Composite Optimal Operational Control of Industrial Systems With Multiple Unit Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Passero 1397 Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tii.2021.3119244 fatcat:uugmz2difrdhrjdz4zzs4ttsci

A City-Wide Real-Time Traffic Management System: Enabling Crowdsensing in Social Internet of Vehicles

Xiaojie Wang, Zhaolong Ning, Xiping Hu, Edith C.-H. Ngai, Lei Wang, Bin Hu, Ricky Y. K. Kwok
2018 IEEE Communications Magazine  
This intelligent framework seamlessly integrates machine learning with sensing and communication, information fusion, and decision making in the city network architecture.  ...  We present a case study of vehicular sensing network for urban environment monitoring, in which the services are supported by various machine learning techniques.  ...  Decision Making Support with Reinforcement Learning In light of the previous discussion, we now investigate the network management strategy for improving the quality of service.  ... 
doi:10.1109/mcom.2018.1701065 fatcat:cr4obkbrznbhxgsnwzjpci33bq

2021 Index IEEE Transactions on Sustainable Computing Vol. 6

2022 IEEE Transactions on Sustainable Computing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Integrated circuit design 2021 710-712 Gaussian processes The Quest of the Ideal Error Detecting Architecture: The GRAAL Architec-M ture.  ...  ., +, TSUSC July-Sept. 2021 441-455 Markov processes Deep Reinforcement Learning for Joint Datacenter and HVAC Load Control in Distributed Mixed-Use Buildings.  ... 
doi:10.1109/tsusc.2021.3136425 fatcat:jpk2jcwejbevfkq6cvgcpylie4

Classification of Textile Polymer Composites: Recent Trends and Challenges

Nesrine Amor, Muhammad Tayyab Noman, Michal Petru
2021 Polymers  
This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic.  ...  Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.  ...  It elaborates recently used advanced machine learning algorithms for textile processes and carbon fiber reinforced composites.  ... 
doi:10.3390/polym13162592 pmid:34451132 pmcid:PMC8398028 fatcat:5hnoal6zprfk5iwljrctggyn6y

Artificial intelligence techniques for fault assessment in laminated composite structure: a review

Sidharth Patro, Trupti Ranjan Mahapatra, Sushmita Dash, Vikram Kishore Murty, S. Tummala, S. Kosaraju, P. Bobba, S. Singh
2021 E3S Web of Conferences  
The present paper aims to bring out a concise review on various methodologies employed for damage/fault detection in composite materials with a special emphasis on supervised and unsupervised machine learning  ...  in order to improve their integrity and order.  ...  Therefore, the identification of composite damage was done with the proposed tool using the Fuzzy Gaussian model and was effective. Kim et al.  ... 
doi:10.1051/e3sconf/202130901083 fatcat:7trsuouhfnd7vd7zjqfwokmoem

A Study of Machine Learning in Wireless Sensor Network

Zaki Ahmad Khan, Abdus Samad
2017 International Journal of Computer Networks And Applications  
Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as  ...  Within this Paper, a concept of machine learning strategies suggested. In this investigation to address the design issues in WSNs is introduced.  ...  Reinforcement Learning This type of learning algorithm for WSNs involves learning by interaction with the environment.  ... 
doi:10.22247/ijcna/2017/49122 fatcat:24ttgh7kqzao5bgd32ptxsnda4

Preface [chapter]

2021 Soft Computing in Smart Manufacturing  
machine vision system, and in integration of Open Computer Numerical Control (CNC) with Service-Oriented Architecture (SOA) for STEP-NC monitoring system.  ...  The demand for product personalization introduces a high level of uncertainty and requires highly adaptive, agile processes and systems.  ...  artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), Gaussian process regression (GPR); and (ii) metaheuristic techniques: a nonevolutionary  ... 
doi:10.1515/9783110693225-202 fatcat:75g7a5fdcfd5pgtbzu2f5elpvu

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TII April 2021 2919-2927 Self-Adaptive Traffic Control Model With Behavior Trees and Reinforcement Learning for AGV in Industry 4.0.  ...  ., +, TII June 2021 4219-4228 Self-Adaptive Traffic Control Model With Behavior Trees and Reinforce-ment Learning for AGV in Industry 4.0.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

Optimising Production through Intelligent Manufacturing

Isaac O. Olalere, Oludolapo A. Olanrewaju, O.P. Malik
2020 E3S Web of Conferences  
This research presents a conceptual approach of an adaptive clustering algorithm (ACA) for advanced manufacturing decision-making for smart machining manufacturing.  ...  Cyber twin of the machine tool is developed which runs on a realtime sequence with the physical asset fussed with smart sensors and controllers enabled with cloud computing, IoT and data analytics.  ...  This approach however integrates product quality learning into the adaptive clustering algorithm for accessing the machine health condition, monitoring the manufacturing process, service distribution and  ... 
doi:10.1051/e3sconf/202015203012 fatcat:7qlapa2pwje5tfl7er42wzfppu

Transformation of Networks through Cognitive Approaches [article]

T.R.Gopalakrishnan Nair, Abhijith, Kavitha Sooda
2010 arXiv   pre-print
The growth in data traffic and the increased demand for quality of service had generated a large demand for network systems to be more efficient.  ...  They are identified to have the potential to deal with the future user related quality and efficiency of service at optimized levels.  ...  Reinforcement Learning problems are typically modeled by means of Markov Decision Processes (MDPs) [18] .  ... 
arXiv:1001.3533v2 fatcat:gkv7s7j7gzgm5er4s6xeimkkqa

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence [article]

Tianqing Zhu and Dayong Ye and Wei Wang and Wanlei Zhou and Philip S. Yu
2020 arXiv   pre-print
With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving  ...  It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI.  ...  The learning process of deep reinforcement learning is similar to regular reinforcement learning in that both are based on trial-and-error.  ... 
arXiv:2008.01916v1 fatcat:ujmxv7eq6jcppndfu5shbzkdom

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Tianqing Zhu, Dayong Ye, Wei Wang, Wanlei Zhou, Philip Yu
2020 IEEE Transactions on Knowledge and Data Engineering  
With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving  ...  It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI.  ...  The learning process of deep reinforcement learning is similar to regular reinforcement learning in that both are based on trial-and-error.  ... 
doi:10.1109/tkde.2020.3014246 fatcat:33rl6jxy5rgexpnuel5rvlkg5a

Table of content

2020 2020 2nd International Conference on Industrial Artificial Intelligence (IAI)  
with Kullback Leibler Divergence and Relative Importance Function for Cement Raw Meal Calcination Process Jinghui Qiao, Feng Tian IAI20-0034 A New Ensemble Learning Method Embedded with PCA-ReliefF  ...  , Hu Ji, Hao Ji, Qi Lou IAI20-0152 NOMA based Efficient Spectrum Sharing for Underwater UAV System with Multi-agent Reinforcement Learning Zhaowei Wang, Fei Qin IAI20-0154 Model Predictive Control  ... 
doi:10.1109/iai50351.2020.9262180 fatcat:7jqkonrv7nef3d2hlstqiu2l6i
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