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Bridging human and machine learning for the needs of collective intelligence development

Mariia Gavriushenko, Olena Kaikova, Vagan Terziyan
2020 Procedia Manufacturing  
In this paper we point out the need for bridging the gap between the human and machine learning, so that some approaches used in machine learning will be useful for humans and vice-versa, some knowledge  ...  In this paper we point out the need for bridging the gap between the human and machine learning, so that some approaches used in machine learning will be useful for humans and vice-versa, some knowledge  ...  Bridging the gap between human and AI learning As our generic goal is bridging the gap between human and machine learning for their mutual benefits and coevolution, we are looking for answers to three  ... 
doi:10.1016/j.promfg.2020.02.092 fatcat:bc2xg2jyjvhulmcju2wnqwdezy

Integration of digital learning in industry 4.0

Nina Tvenge, Kristian Martinsen
2018 Procedia Manufacturing  
Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient.  ...  Abstract The emerging advances in sensor systems, automation and Information and Communication Technology (ICT) for manufacturing opens new possibilities for lifelong learning utilizing data from production  ...  The authors wish to thank the Research Council for funding and the companies participating in the discussions for this paper.  ... 
doi:10.1016/j.promfg.2018.04.027 fatcat:tuiev67tqrfpdcg7kdtvemaqsa

Cloning and training collective intelligence with generative adversarial networks

Vagan Terziyan, Mariia Gavriushenko, Anastasiia Girka, Andrii Gontarenko, Olena Kaikova
2021 IET Collaborative Intelligent Manufacturing  
This study tries to bridge the gap between human and AI learning addressing human-machine co-working that is inevitable for the knowledge management in Industry 4.0 [23] .  ...  | BRIDGING THE GAP BETWEEN HUMAN AND 'AI' Learning The need for training the autonomous AI systems in the same way as humans (in addition to traditional ML) was recently discussed in [8] .  ... 
doi:10.1049/cim2.12008 fatcat:k2x7aorfivasnci6cspgxjupvq

Industry 4.0: contributions of holonic manufacturing control architectures and future challenges

William Derigent, Olivier Cardin, Damien Trentesaux
2020 Journal of Intelligent Manufacturing  
Over-connectivity and data management abilities targeted by Industry 4.0 paradigm enable the emergence of more flexible and reactive control systems, based on the cooperation of autonomous and connected  ...  From most relevant articles extracted from existing literature, a list of 10 key enablers for Industry 4.0 is first presented.  ...  Acknowledgements The authors would like to thank the reviewers for their valuable time and constructive comments that allowed to improve this research paper.  ... 
doi:10.1007/s10845-020-01532-x fatcat:bbkxsy4ygvcpxosmevhwrnokpm

Service Interface Translation. An Interoperability Approach

Cristina Paniagua
2021 Applied Sciences  
Interoperability plays an important role in Industry 4.0.  ...  In this paper, the autonomous consumer interface generation process, the system architecture, and the generated interface instance are described.  ...  Acknowledgments: The authors thank the European Commission and the Arrowhead Tools project (EU ECSEL Joint Undertaking under grant agreement number 826452) for funding.  ... 
doi:10.3390/app112411643 fatcat:6gouqa6zlbh4zlsir4a5m2evdm

Industry 4.0 for the Construction Industry—How Ready Is the Industry?

Raihan Maskuriy, Ali Selamat, Kherun Nita Ali, Petra Maresova, Ondrej Krejcar
2019 Applied Sciences  
Industry 4.0 is expected to augment both the quality and productivity of construction and attract domestic and foreign investors.  ...  The growth of the construction industry is a subset of the universal set of the gross domestic product value; thus, Industry 4.0 has a spillover effect on the engineering and construction industry.  ...  Acknowledgments: We are also grateful for the support of student Jan Hruska and Sebastien Mambou in consultations regarding application aspects.  ... 
doi:10.3390/app9142819 fatcat:6xtgnkeat5hwnm7xfz7lvjbb7i

A Collaboration-Oriented M2M Messaging Mechanism for the Collaborative Automation between Machines in Future Industrial Networks

2017 Sensors  
connectivity and discovery, ubiquitous messaging, and semantic interoperability that are well suited for the production line-scale interoperability of manufacturing applications.  ...  Although the crucial technical issues have been addressed, there exists an underlying gap between model-based service architectures and mechanical operations.  ...  Z.W. and J.G provided supervisions and supports for the research work.  ... 
doi:10.3390/s17112694 pmid:29165347 pmcid:PMC5712802 fatcat:vkjvtzixcbge5pckutoi2vvlna

AI-Based Quality Control of Wood Surfaces with Autonomous Material Handling

Mikael Ericsson, Dahniel Johansson, David Stjern
2021 Applied Sciences  
The theory and applications of Smart Factories and Industry 4.0 are increasing the entry into the industry.  ...  This paper presents a proof of concept of an automated inspection system, for wood surfaces, where concepts found in Industry 4.0, such as industrial Internet of things (IIoT), smart factory, flexible  ...  Acknowledgments: The authors would like to thank Södra for providing CLT plates.  ... 
doi:10.3390/app11219965 fatcat:5lumvbiyorbatpe4ofgcqyx6wi

Evolution of 3C Cyber-Physical Systems Architecture for Industry 4.0 [chapter]

Ahmadzai Ahmadi, Ali Hassan Sodhro, Chantal Cherifi, Vincent Cheutet, Yacine Ouzrout
2018 Studies in Computational Intelligence  
Cyber-Physical System (CPS) is considered as the emerging component for Industry 4.0, and the state-of-the art and standard CPS architecture playing the major role to understand the nature of the industrial  ...  So, to remedy these problems and bridging the gap this paper proposes to enhance the 3C CPS architecture based on the traditional 3C one for industry4.0 by adopting the main interfacing elements such as  ...  The authors would like to thank SmartLink Project and Universite Lumiere Lyon2.  ... 
doi:10.1007/978-3-030-03003-2_35 fatcat:llydt65iufdxzaffgmveiyjukq

Smart manufacturing based on cyber-physical systems and beyond

Xifan Yao, Jiajun Zhou, Yingzi Lin, Yun Li, Hongnian Yu, Ying Liu
2017 Journal of Intelligent Manufacturing  
Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0.  ...  The communication networks refer to intermediate components which are responsible for bridging the cyber space with the physical world.  ...  This study fills the gaps between IoT/IoS and CPS, between CPS and smart manufacturing, and between smart manufacturing and wisdom manufacturing.  ... 
doi:10.1007/s10845-017-1384-5 fatcat:qbbgeyj7sfahzddh3fpj64cszu

Six-Gear Roadmap Towards the Smart Factory

Amr T. Sufian, Badr M. Abdullah, Muhammad Ateeq, Roderick Wah, David Clements
2021 Applied Sciences  
The significance of the proposed roadmap serve as a strategic practical tool for rapid adoption of Industry 4.0 technologies for smart manufacturing and to bridge the gap between the advanced technologies  ...  These criteria are sub-grouped to fit within various stages of the proposed roadmap and attempts to bridge the gap between academia and industry and contributes to a new knowledge in the literature.  ...  The approach attempts to bridge the gap between academia and industry and contributes to a new knowledge in the literature.  ... 
doi:10.3390/app11083568 fatcat:5j4jt6kr5zfoxcl3eaumroreru

Designing Construction 4.0 Activities for AEC Classrooms

Rolando Chacón
2021 Buildings  
All these demonstrators are knitted together in a single path in which an educational attempt to fill the identified Construction 4.0 gaps is proposed.  ...  During the last two years, the project was developed and the design, the development and implementation of several demonstrators were completed.  ...  In between, it is pinpointed how cyber-physical systems represent an accessible and affordable way of bridging the gap in AEC classrooms when it comes to implementing Construction 4.0 activities in which  ... 
doi:10.3390/buildings11110511 fatcat:dlmtyg2x4zd5zminx2vamxnoae

Human in the Loop: Industry 4.0 technologies and scenarios for worker mediation of automated manufacturing

Christopher J Turner, Ruidong Ma, Jingyu Chen, John Oyekan
2021 IEEE Access  
Industry 4.0 derived technologies have the potential to enable a new wave of digital manufacturing solutions for semi and fully automated production.  ...  and autonomous production.  ...  In these works, the need for more research on control loops between Manufacturing Execution Systems and shop floor production via Digital Twin is highlighted.  ... 
doi:10.1109/access.2021.3099311 fatcat:j43i6cht7ba5hehsz4oebpcwde

Big data and natural environment. How does different data support different green strategies?

Francesco Calza, Adele Parmentola, Ilaria Tutore
2020 Sustainable Futures  
The paper wants to explore how firms can capture value from big data to improve green engagement by providing a conceptual model through a comprehensive and panoramic literature that relates big data sources  ...  to the adoption of different green strategies.  ...  However, in the context of Big Data, it is a great challenge to efficiently describe semantic features and to build semantic association models to bridge the semantic gap of various heterogeneous data  ... 
doi:10.1016/j.sftr.2020.100029 fatcat:qgm4ogjt4rhlhj6hl3tx4qcxga

Model-driven Logistics Engineering – Challenges of Model and Object Transformation

Michael Lütjen, Hans-Jörg Kreowski, Marco Franke, Klaus-Dieter Thoben, Michael Freitag
2014 Procedia Technology - Elsevier  
for industry 4.0 Production and logistics are very interdisciplinary.  ...  The precondition is the availability of powerful software techniques, which helps to bridge the gap between real and cyber world [3] .  ... 
doi:10.1016/j.protcy.2014.09.084 fatcat:3j26lesgkfhk3kn5myl63yfr5a
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