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Special Issue "Intelligent Control and Digital Twins for Industry 4.0"

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

Deadline for manuscript submissions: 15 October 2022 | Viewed by 4468

Special Issue Editor

Dr. Aleksei Tepljakov
E-Mail Website
Guest Editor
Centre for Intelligent Systems, Department of Computer Systems, Tallinn University of Technology, Akadeemia tee 15, 12618 Tallinn, Estonia
Interests: modeling and control of complex dynamic systems; evolutionary algorithms and computational intelligence; computer vision; extended reality and digital twins; intelligent immersive virtual environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Looking at the industrial and manufacturing landscape, one finds it in a transitional state towards the so-called Industry 4.0 today. In a nutshell, Industry 4.0 follows the continued trend of digitalization of products and services on a global scale, including both industrial and consumer markets.

An important concept which forms a part of Industry 4.0 is the digital twin—a digital representation of real-life systems and phenomena. An intrinsic part of a digital twin is data. In industry, massive amounts of data can be collected through sensing technology that describe process dynamics, related process trends, etc. Digital twins allow putting these data to beneficial use through the application of advanced modeling techniques resulting in accurate process descriptions that can then be used to design intelligent control systems ensuring optimal performance and energy efficiency of industrial systems.

Some subtopics may include

- Modeling, analysis, and control of complex industrial processes;
- Applications of digital twins in Industry 4.0;

- Sensing technologies for digital twins;
- Artificial intelligence and machine-learning-based applications for industrial processes. 

Dr. Aleksei Tepljakov
Guest Editor

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 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 4.0
  • machine learning
  • artificial intelligence
  • computational intelligence
  • digital twin
  • computer vision
  • industrial application
  • intelligent control system
  • communications and signal processing
  • Internet of Things

Published Papers (7 papers)

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Research

Article
An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications
Sensors 2022, 22(10), 3836; https://doi.org/10.3390/s22103836 - 18 May 2022
Viewed by 243
Abstract
Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search [...] Read more.
Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Article
Performance Portrait Method: An Intelligent PID Controller Design Based on a Database of Relevant Systems Behaviors
Sensors 2022, 22(10), 3753; https://doi.org/10.3390/s22103753 - 14 May 2022
Viewed by 344
Abstract
The article deals with a computer-supported design of optimal and robust proportional-integral-derivative controllers with two degrees of freedom (2DoF PID) for a double integrator plus dead-time (DIPDT) process model. The particular design steps are discussed in terms of intelligent use of all available [...] Read more.
The article deals with a computer-supported design of optimal and robust proportional-integral-derivative controllers with two degrees of freedom (2DoF PID) for a double integrator plus dead-time (DIPDT) process model. The particular design steps are discussed in terms of intelligent use of all available information extracted from a database of control tracking and disturbance rejection step responses, assessed by means of speed and shape-related performance measures of the process input and output signals, and denoted as a performance portrait (PP). In the first step, the performance portrait method (PPM) is used as a verifier, for whether the pilot analytical design of the parallel 2DoF PID controller did not omit practically interesting settings and shows that the optimality analysis can easily be extended to the series 2DoF PID controller. This is important as an explicit observer of equivalent input disturbances based on steady-state input values of ultra-local DIPDT models, while the parallel PID controller, allowing faster transient responses, needs an additional low-pass filter when reconstructed equivalent disturbances are required. Next, the design efficiency and conciseness in analyzing the effects of different loop parameters on changing the optimal processes are illustrated by an iterative use of PPM, enabled by the visualization of the dependence between the closed-loop performance and the shapes of the control signals. The main contributions of the paper are the introduction of PPM as an intelligent method for controller tuning that mimics an expert with sufficient experience to select the most appropriate solution based on a database of known solutions. In doing so, the analysis in this paper reveals new, previously undiscovered dimensions of PID control design. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Article
Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems
Sensors 2022, 22(10), 3751; https://doi.org/10.3390/s22103751 - 14 May 2022
Viewed by 276
Abstract
Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous [...] Read more.
Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, the problem of the development of image representation, which provides compression and protection features in combination with the ability to perform low complexity analysis, is relevant for EC-based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. To solve this problem, we propose to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. A lossless image compression algorithm based on DAT is developed, and its performance is studied for different structures of DAT. This algorithm, which combines low computational complexity, efficient lossless compression, and reliable protection features with convenient image representation, is the main contribution of the paper. It is shown that a sufficient reduction of memory expenses can be obtained. Additionally, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that the variation of DAT structure produces a minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure or file for storing the compressed and protected data is proposed, and its properties are considered. Multi-level structure for the application of atomic functions in image processing and protection for EC in IoT systems is suggested and analyzed. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Article
An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition
Sensors 2022, 22(3), 941; https://doi.org/10.3390/s22030941 - 26 Jan 2022
Viewed by 727
Abstract
The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. [...] Read more.
The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Article
Proposal for an IIoT Device Solution According to Industry 4.0 Concept
Sensors 2022, 22(1), 325; https://doi.org/10.3390/s22010325 - 02 Jan 2022
Cited by 3 | Viewed by 726
Abstract
Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features [...] Read more.
Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features they provide; therefore, we decided to design an IIoT device taking advantage of the benefits arising from OPC UA. The design procedure was based on the creation of sequences of steps resulting in a workflow that was transformed into a finite state machine (FSM) model. The FSM model was transformed into an OPC UA object, which was implemented in the proposed IIoT. The OPC UA object makes it possible to monitor events and provide important information based on a client’s criteria. The result was the design and implementation of an IIoT device that provides improved monitoring and data acquisition, enabling improved control of the manufacturing process. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Article
When Digital Twin Meets Network Softwarization in the Industrial IoT: Real-Time Requirements Case Study
Sensors 2021, 21(24), 8194; https://doi.org/10.3390/s21248194 - 08 Dec 2021
Cited by 1 | Viewed by 943
Abstract
The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole [...] Read more.
The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole lifecycle. Meanwhile, Digital Twinning technology has been increasingly used to optimize the performances of industrial systems and has been ranked as one of the top ten most promising technological trends in the next decade. Many Digital Twins of industrial systems exist nowadays but only few are destined to networks. In this paper, we propose a holistic digital twinning architecture for the IIoT where the network is integrated along with the other industrial components of the system. To do so, the concept of Network Digital Twin is introduced. The main motivation is to permit a closed-loop network management across the whole network lifecycle, from the design to the service phase. Our architecture leverages the Software Defined Networking (SDN) paradigm as an expression of network softwarization. Mainly, the SDN controller allows for setting up the connection between each Digital Twin of the industrial system and its physical counterpart. We validate the feasibility of the proposed architecture in the process of choosing the most suitable communication mechanism that satisfies the real-time requirements of a Flexible Production System. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Article
Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
Sensors 2021, 21(22), 7529; https://doi.org/10.3390/s21227529 - 12 Nov 2021
Cited by 2 | Viewed by 444
Abstract
The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. [...] Read more.
The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Digital Image Representation by Atomic Functions: Compression and Protection of Data for Edge Computing in IoT Systems
Authors: Viktor Makarichev; Vladimir Lukin; Oleg Illiashenko; Vyacheslav Kharchenko
Affiliation: National Aerospace University “KhAI”, Kharkiv, Ukraine
Abstract: Digital images are used in various technological, financial, economic and social processes. Huge datasets of high-resolution images require protected storage and low resource intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, a problem of development of image representation, which provides compression and protection features in combination with ability to perform low complexity analysis, is relevant for EC based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. In order to solve this problem, it is proposed to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. Lossless image compression algorithm based on the procedure DAT is developed and its performance is researched for different structures of DAT. It is shown that a sufficient reduction of memory expenses can be obtained. Also, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that variation of DAT structure leads to minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure of file for storing the compressed and protected data is proposed and its peculiarities are considered.

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