Neuro-inspired Framework for Cognitive Manufacturing Control
DOI:
https://doi.org/10.15837/ijccc.2021.6.4519Keywords:
Perception, Internet of Things, Control Architecture, Bio-inspired manufacturing controlAbstract
There are currently certain categories of manufacturing enterprises whose structure, organization and operating context have an extremely high degree of complexity, especially due to the way in which their various components interact and influence each other. For them, a series of paradigms have been developed, including intelligent manufacturing, smart manufacturing, cognitive manufacturing; which are based equally on information and knowledge management, management and interpretation of data flows and problem solving approaches. This work presents a new vision regarding the evolution of the future enterprise based on concepts and attributes acquired from the field of biology. Our approach addresses in a systemic manner the structural, functional, and behavioral aspects of the enterprise, seen as a complex dynamic system. In this article we are proposing an architecture and management methodology based on the human brain, where the problem solving is achieved by Perception — Memory — Learning and Behavior Generation mechanisms. In order to support the design of such an architecture and to allow a faster learning process, a software modeling and simulation platform was developed and is briefly presented.
References
[2] Christopher Baldassano. Visual Scene Perception in the Human Brain: Connections to Memory, Categorization, and Social Cognition. Stanford University, 2015.
[3] Zdzislaw Bubnicki. Analysis and Decision Making in Uncertain Systems. Communications and Control Engineering. Springer-Verlag, London, 2004. https://doi.org/10.1007/978-1-4471-3760-3
[4] Zdzislaw Bubnicki. Modern Control Theory. Springer-Verlag, Berlin Heidelberg, 2005.
[5] Kyungyong Chung, Hyun Yoo, Doeun Choe, and Hoill Jung. Blockchain network based topic mining process for cognitive manufacturing. Wireless Personal Communications, 105(2):583-597, 2019. Publisher: Springer. https://doi.org/10.1007/s11277-018-5979-8
[6] Greg Cline. IoT and Analytics: Better Manufacturing Decisions in the era of Industry 4.0, August 2017.
[7] Ioan Dumitrache and Simona Iuliana Caramihai. The intelligent manufacturing paradigm in knowledge society. Knowledge Management, pages 36-56, 2010. Publisher: InTech Rijeka. https://doi.org/10.5772/9533
[8] Ioan Dumitrache, Simona Iuliana Caramihai, Octavian Arsene, Mihnea Moisescu, and Ioan Sacala. A new framework for human perception modelling. In ICON4N 2018: 1st International Conference on Neuroscience, Neuroinformatics, Neurotechnology and Neuro-Psycho- Pharmacology, Bucharest, November 2018.
[9] Ioan Dumitrache, Simona Iuliana Caramihai, Mihnea Alexandru Moisescu, Ioan Stefan Sacala, Luige Vladareanu, and Dragos Repta. A perceptive interface for intelligent cyber enterprises. Sensors, 19(20):4422, 2019. Publisher: Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/s19204422
[10] Ioan Dumitrache, Simona Iuliana Caramihai, and Aurelian Stanescu. From mass production to intelligent cyber-enterprise. In 2013 19th international conference on control systems and computer science, pages 399-404, 2013. tex.organization: IEEE. https://doi.org/10.1109/CSCS.2013.100
[11] R Ezry, M Haydock, B Tyler, and R Shockley. Analytics: Dawn of the cognitive era-How early adopters have raised the bar for data-driven insights, 2016. Publisher: October.
[12] Karl J Friston and Christian Büchel. Functional connectivity: eigenimages and multivariate analyses. Statistical parametric mapping: the analysis of functional brain images, pages 492-507, 2006. https://doi.org/10.1016/B978-012372560-8/50037-1
[13] Zan Gao, DY Wang, SH Wan, Hua Zhang, and YL Wang. Cognitive-inspired class-statistic matching with triple-constrain for camera free 3D object retrieval. Future Generation Computer Systems, 94:641-653, 2019. Publisher: Elsevier. https://doi.org/10.1016/j.future.2018.12.039
[14] Peter P Groumpos. Intelligent Control and Cognitive Control for a global coherence and sustainable economic growth for the humankind. IFAC-PapersOnLine, 51(30):660-665, 2018. Publisher: Elsevier. https://doi.org/10.1016/j.ifacol.2018.11.232
[15] Sergii Iarovyi, José L Martinez Lastra, Rodolfo Haber, and Raúl del Toro. From artificial cognitive systems and open architectures to cognitive manufacturing systems. In 2015 IEEE 13th international conference on industrial informatics (INDIN), pages 1225-1232, 2015. tex.organization: IEEE. https://doi.org/10.1109/INDIN.2015.7281910
[16] Dmitry Ivanov, Suresh Sethi, Alexandre Dolgui, and Boris Sokolov. A survey on control theory applications to operational systems, supply chain management, and Industry 4.0. Annual Reviews in Control, 46:134-147, 2018. Publisher: Elsevier. https://doi.org/10.1016/j.arcontrol.2018.10.014
[17] Jay Lee, Behrad Bagheri, and Hung-An Kao. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing letters, 3:18-23, 2015. Publisher: Elsevier. https://doi.org/10.1016/j.mfglet.2014.12.001
[18] Steven Liang, Manik Rajora, Xianli Liu, Caixu Yue, Pan Zou, and Lihui Wang. Intelligent manufacturing systems: a review. International Journal of Mechanical Engineering and Robotics Research, 7(3):324-330, 2018. Publisher: International Journal of Mechanical Engineering and Robotics Research. https://doi.org/10.18178/ijmerr.7.3.324-330
[19] Mihnea Alexandru Moisescu, Ioan Stefan Sacala, Ioan Dumitrache, Simona Iuliana Caramihai, Bogdan Barbulescu, and Marius Danciuc. A cyber-physical systems approach to cognitive enterprise. Periodicals of Engineering and Natural Sciences, 7(1):337-342, 2019. https://doi.org/10.21533/pen.v7i1.378
[20] László Monostori, Botond Kádár, Thomas Bauernhansl, Shinsuke Kondoh, Soundar Kumara, Gunther Reinhart, Olaf Sauer, Gunther Schuh, Wilfried Sihn, and Kenichi Ueda. Cyber-physical systems in manufacturing. Cirp Annals, 65(2):621-641, 2016. Publisher: Elsevier. https://doi.org/10.1016/j.cirp.2016.06.005
[21] Dragos Constantin Popescu and Ioan Dumitrache. Relational Modeling Framework for Complex Systems. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science., 83(1):16-28, 2021.
[22] Jonathan D Power, Damien A Fair, Bradley L Schlaggar, and Steven E Petersen. The development of human functional brain networks. Neuron, 67(5):735-748, 2010. Publisher: Elsevier. https://doi.org/10.1016/j.neuron.2010.08.017
[23] V Pureswaran, S Burnett, and B Anderson. The Business of Things: Designing successful business models in the cognitive Internet of Things. IBM Institute for Business Value, 2015.
[24] A Raikov. Cognitive modelling quality rising by applying quantum and optical semantic approaches. IFAC-PapersOnLine, 51(30):492-497, 2018. Publisher: Elsevier. https://doi.org/10.1016/j.ifacol.2018.11.309
[25] G. Setlak and Sławomir Pieczonka. Intelligent Manufacturing Systems: Design Concept of Intelligent Management Systems. 2009.
[26] Caramihai Simona., Dumitrache Ioan., Moisescu Mihnea., Saru Daniela., and Sacala Ioan. A neuro-inspired approach for a generic knowledge management system of the intelligent cyberenterprise. In Proceedings of the 11th international joint conference on knowledge discovery, knowledge engineering and knowledge management - KEOD,, pages 367-374. SciTePress, 2019. ISSN: 2184-3228 tex.organization: INSTICC. https://doi.org/10.5220/0008348603670374
[27] Janusz A Starzyk, Yongtao Guo, and Zhineng Zhu. Dynamically reconfigurable neuron architecture for the implementation of self-organising learning array. International Journal of Embedded Systems, 2(1-2):95-105, 2006. Publisher: Inderscience Publishers. https://doi.org/10.1504/IJES.2006.010168
[28] Shaohua Wan, Zonghua Gu, and Qiang Ni. Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications, 149:99-106, 2020. Publisher: Elsevier. https://doi.org/10.1016/j.comcom.2019.10.012
[29] Jiani Zhang. Cognitive manufacturing & Industry 4.0, 2017.
[30] Yaoyao Fiona Zhao and Xun Xu. Enabling cognitive manufacturing through automated onmachine measurement planning and feedback. Advanced Engineering Informatics, 24(3):269-284, 2010. Publisher: Elsevier. https://doi.org/10.1016/j.aei.2010.05.009
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