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Machine Learning in Cyber Physical Systems
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Machine Learning in Cyber Physical Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 17661

Special Issue Editors

Dr. Azfar Khalid

E-Mail Website
Guest Editor
Department of Engineering, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: robotics; cyber physical production systems; digital twins; human–robot collaboration; precision engineering
Dr. Jamshed Iqbal

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Faculty of Science and Engineering, University of Hull, Hull HU6 7RX, UK
Interests: mechatronics; robotics; control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Reza Vatankhah Barenji

E-Mail Website
Guest Editor
Department of Engineering, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: Industry 4.0; machine learning; cyber physical systems; blockchain
Prof. Dr. Jürgen Pannek

E-Mail Website
Guest Editor
TU Braunschweig, Braunschweig, Germany
Interests: model predictive control; robotics; reinforcement learning; digitalization

Special Issue Information

Dear Colleagues,

Industrial systems worldwide are undergoing a paradigm shift towards Industry 4.0 and Industry 5.0. Self-decision making is the core characteristic of such systems, where machine intelligence is employed to accomplish tasks. Cyber physical systems is one such area of enabling technology necessary to create a seamless integration of cyber and physical components. The digital twinning of physical systems is rapidly developing, and can correlate large real-time sensing and IoT data. Sensor fusion, machine learning, AI, and other advanced techniques are applied to create a dynamic virtual representation of entire systems.

Consequently, this Special Issue seeks innovative works on a wide range of research topics spanning Industry 4.0 and Industry 5.0 related technologies, including but not restricted to the following topics:

  1. All aspects of cyber physical production systems including sensing, robotics, machine learning, big data analytics and system vulnerability.
  2. CPS applications in logistics and vehicular networks, supply chain and blockchain implementation. 
  3. Digital twin, Human centric digital twins, AR/VR and seamless integration with physical systems.
  4. Machine learning, deep learning and reinforcement learning use cases.
  5. Use of neural networks in modelling complex systems.
  6. Advanced control schemes including state space and model predictive control. 

Dr. Azfar Khalid
Dr. Jamshed Iqbal
Dr. Reza Vatankhah Barenji
Prof. Dr. Jürgen Pannek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • cyber physical production systems
  • machine learning
  • deep learning
  • reinforcement learning
  • digital twins
  • control systems
  • predictive control

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Published Papers (2 papers)

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Research

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15 pages, 4083 KiB  
Article
BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems
by Shokhikha Amalana Murdivien and Jumyung Um
Sensors 2023, 23(15), 6928; https://doi.org/10.3390/s23156928 - 3 Aug 2023
Cited by 8 | Viewed by 5449
Abstract
Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement [...] Read more.
Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement Learning is a potential solution to solve more complex problems by introducing artificial neural networks in Reinforcement Learning. In this paper, a game engine was used for Deep Reinforcement Learning training, which allows visualization of view learning and result processes more intuitively than other tools, as well as a physical engine for a more realistic problem-solving environment. The present research demonstrates that a Deep Reinforcement Learning model can effectively address the real-time sequential 3D bin packing problem by utilizing a game engine to visualize the environment. The results indicate that this approach holds promise for tackling complex logistical challenges in dynamic settings. Full article
(This article belongs to the Special Issue Machine Learning in Cyber Physical Systems)
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Review

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27 pages, 8475 KiB  
Review
Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies
by Usman Asad, Madeeha Khan, Azfar Khalid and Waqas Akbar Lughmani
Sensors 2023, 23(8), 3938; https://doi.org/10.3390/s23083938 - 12 Apr 2023
Cited by 55 | Viewed by 11021
Abstract
The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for [...] Read more.
The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs. Full article
(This article belongs to the Special Issue Machine Learning in Cyber Physical Systems)
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