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Processes
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Role of Intelligent Control Systems in Industry 5.0
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Role of Intelligent Control Systems in Industry 5.0

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 3129

Special Issue Editors

Dr. Scarlett Chen

E-Mail Website
Guest Editor
Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA
Interests: model predictive control; machine learning; advanced dynamics and control
Dr. Zhe Wu

E-Mail Website
Guest Editor
Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
Interests: chemical process control; model predictive control; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Youzhi Liang

E-Mail Website
Guest Editor Assistant
Department of Computer Science, Stanford University, Stanford, CA 94305, USA
Interests: robotics; advanced dynamics and control

Special Issue Information

Dear Colleagues,

The term “Industry 4.0” was proposed over a decade ago for the interconnectivity of physical and cyber domains in industry processes to enable higher levels of automation, productivity, and efficiency. The fast-growing AI-based technologies have contributed to an increase in productivity and accuracy in many industrial fields. With the launch of ChatGPT and the associated unprecedented worldwide phenomenon of its applications, the rise of linguistic intelligence has indicated a paradigm shift where interactions and collaborations between humans and machines become increasingly important and prevalent. Efforts involved with Industry 4.0 may no longer meet increased demand for the personalization of tasks while aligning with the higher-level goals of sustainability. Therefore, “Industry 5.0” was first proposed to emphasize the need to not just build artificial intelligence as tools but rather build them as collaborators. In interactions with humans, machines should capture and learn human preferences in addition to the technical scope of the task at hand. The human whom the machine interacts with could be a customer, an operator, an environmentalist, a social worker, and so forth. In Industry 5.0, the incorporation of AI in industrial operations will empower a human-centric, sustainable, and resilient environment where mass personalization can be achieved.

Coming from a more holistic perspective, Industry 5.0 recognizes the industry’s power to fulfill societal needs beyond employment and growth, and therefore aims to provide resilient prosperity by making production respect the limitations of our environment and employee capacity. The study of existing tools in Industry 4.0, along with the exploration of new tools in today’s world, are all technologies that will help define the scope and enable the advent of Industry 5.0. The concept of Industry 5.0 remains a novel topic that is yet to be clearly defined; nevertheless, intelligent control systems play an important role, especially when combined with the state-of-the-art artificial and linguistic intelligence. This Special Issue seeks high-quality studies on the latest studies on the design and optimization of intelligent control systems, with topics including but not limited to:

  • Human‒machine interaction;
  • Digitalization (augmented, virtual, and mixed technology);
  • Speech recognition;
  • Collaborative robots;
  • Cyber security;
  • Big data and analytics;
  • Internet of Things;
  • Smart grids.

Dr. Scarlett Chen
Dr. Zhe Wu
Guest Editors

Dr. Youzhi Liang
Guest Editor Assistant

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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • Industry 5.0
  • human‒machine interaction
  • digitalization
  • speech recognition
  • collaborative robots
  • cyber security
  • big data and analytics
  • Internet of Things
  • smart grids

Published Papers (3 papers)

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Research

27 pages, 940 KiB  
Article
Detection of Multiplicative False Data Injection Cyberattacks on Process Control Systems via Randomized Control Mode Switching
by Shilpa Narasimhan, Matthew J. Ellis and Nael H. El-Farra
Processes 2024, 12(2), 327; https://doi.org/10.3390/pr12020327 - 02 Feb 2024
Viewed by 567
Abstract
A fundamental problem at the intersection of process control and operations is the design of detection schemes monitoring a process for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify operational data with a multiplicative factor and could be designed to [...] Read more.
A fundamental problem at the intersection of process control and operations is the design of detection schemes monitoring a process for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify operational data with a multiplicative factor and could be designed to be detection evading without in-depth process knowledge. In a prior work, we presented a control mode switching strategy that enhances the detection of multiplicative FDI attacks in processes operating at steady state (when process states evolve within a small neighborhood of the steady state). Control mode switching on the attack-free process at steady-state may induce transients and generate false alarms in the detection scheme. To minimize false alarms, we subsequently developed a control mode switch-scheduling condition for processes with an invertible output matrix. In the current work, we utilize a reachable set-based detection scheme and use randomized control mode switches to augment attack detection capabilities. The detection scheme eliminates potential false alarms occurring from control mode switching, even for processes with a non-invertible output matrix, while the randomized switching helps bolster the confidentiality of the switching schedule, preventing the design of a detection-evading “smart” attack. We present two simulation examples to illustrate attack detection without false alarms, and the merits of randomized switching (compared with scheduled switching) for the detection of a smart attack. Full article
(This article belongs to the Special Issue Role of Intelligent Control Systems in Industry 5.0)
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19 pages, 1969 KiB  
Article
MiAMix: Enhancing Image Classification through a Multi-Stage Augmented Mixed Sample Data Augmentation Method
by Wen Liang, Youzhi Liang and Jianguo Jia
Processes 2023, 11(12), 3284; https://doi.org/10.3390/pr11123284 - 24 Nov 2023
Cited by 2 | Viewed by 1345
Abstract
Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks. While various strategies have been [...] Read more.
Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks. While various strategies have been proposed, Mixed Sample Data Augmentation (MSDA) has shown great potential for enhancing model performance and generalization. We introduce a novel mixup method called MiAMix, which stands for Multi-stage Augmented Mixup. MiAMix integrates image augmentation into the mixup framework, utilizes multiple diversified mixing methods concurrently, and improves the mixing method by randomly selecting mixing mask augmentation methods. Recent methods utilize saliency information and the MiAMix is designed for computational efficiency as well, reducing additional overhead and offering easy integration into existing training pipelines. We comprehensively evaluate MiAMix using four image benchmarks and pitting it against current state-of-the-art mixed sample data augmentation techniques to demonstrate that MiAMix improves performance without heavy computational overhead. Full article
(This article belongs to the Special Issue Role of Intelligent Control Systems in Industry 5.0)
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22 pages, 1011 KiB  
Article
Encrypted Model Predictive Control of a Nonlinear Chemical Process Network
by Yash A. Kadakia, Atharva Suryavanshi, Aisha Alnajdi, Fahim Abdullah and Panagiotis D. Christofides
Processes 2023, 11(8), 2501; https://doi.org/10.3390/pr11082501 - 20 Aug 2023
Cited by 4 | Viewed by 857
Abstract
This work focuses on developing and applying Encrypted Lyapunov-based Model Predictive Control (LMPC) in a nonlinear chemical process network for Ethylbenzene production. The network, governed by a nonlinear dynamic model, comprises two continuously stirred tank reactors that are connected in series and is [...] Read more.
This work focuses on developing and applying Encrypted Lyapunov-based Model Predictive Control (LMPC) in a nonlinear chemical process network for Ethylbenzene production. The network, governed by a nonlinear dynamic model, comprises two continuously stirred tank reactors that are connected in series and is simulated using Aspen Plus Dynamics. For enhancing system cybersecurity, the Paillier cryptosystem is employed for encryption–decryption operations in the communication channels between the sensor–controller and controller–actuator, establishing a secure network infrastructure. Cryptosystems generally require integer inputs, necessitating a quantization parameter d, for quantization of real-valued signals. We utilize the quantization parameter to quantize process measurements and control inputs before encryption. Through closed-loop simulations under the encrypted LMPC scheme, where the LMPC uses a first-principles nonlinear dynamical model, we examine the effect of the quantization parameter on the performance of the controller and the overall encryption to control the input calculation time. We illustrate that the impact of quantization can outweigh those of plant/model mismatch, showcasing this phenomenon through the implementation of a first-principles-based LMPC on an Aspen Plus Dynamics process model. Based on the findings, we propose a strategy to mitigate the quantization effect on controller performance while maintaining a manageable computational burden on the control input calculation time. Full article
(This article belongs to the Special Issue Role of Intelligent Control Systems in Industry 5.0)
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