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Intelligent Monitoring, Control and Optimization in Industries 4.0

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 30730

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Special Issue Editors

Prof. Dr. Mengchu Zhou

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Guest Editor

Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: intelligent automation; robotics; Petri nets; discrete event systems; wireless sensor network; big data; Web service; workflow; energy-efficient systems; semiconductor manufacturing; intelligent transportation; and optimization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bi Jing

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Guest Editor

Associate Professor, Faculty of lnformation Technology, Beijing University of Technology (BJUT), Beijing, China
Interests: cloud and edge collaborative computing; task scheduling and resource optimization; multi-scale data analysis and deep learning; energy-efficient computing and green energy

Dr. Mohammadhossein H. Ghahramani

E-Mail Website
Guest Editor

Computing and Data Science, Birmingham City University, Birmingham B5 5JU, UK
Interests: mobile computing; mobile handsets; pattern clustering; telecommunication industry; Big Data; air pollution control; automobiles; carbon compounds; cloud computing; data analysis; data mining

Special Issue Information

Dear Colleagues,

This special issue aims to provide an overview of emerging intelligent computing technologies by shedding light on the progress made in artificial intelligence (AI), machine learning (ML), bioinformatics, and computational biology. It is also dedicated to emerging and challenging topics in the concerned fields of research. This issue concentrates mainly on theories and methodologies as well as emerging applications of intelligent computing.

The rapid development of AI, big data and ML has promoted the transformation of traditional enterprises from process-driven, centrally controlled organizations to shared platforms and new forms of highly decentralized organizations. Such transition has changed all aspects of business operations and production. Using emerging technologies to innovate has become a key research direction and can lead to breakthrough performance and significant growth. In recent years, intelligence technologies, such as image recognition, speech recognition, and neural networks, have been widely adopted to solve various optimization problems. These technologies cannot only promote innovations and development of industries, but also play an important role in driving innovative development in all relevant aspects of economy and society.

This research topic aims to present the latest advances and developments of new methods, techniques, systems, and tools dedicated to applications of above-mentioned enabling technologies.

Contributions to theories and practice, including but not limited to, the following technical areas are welcomed:

Big Data Analysis for Industry 4.0 wearable sensors
Artificial Intelligence and Evolutionary Techniques for Industry 4.0 volatile organic compounds detection
Intelligent Monitoring & Control Systems
Data-Driven Modelling and Optimization
Machine & Deep Learning for Intelligent Systems
Data Analysis for Scheduling Industrial Processes
Data Analysis for Monitoring and Control of Industrial Operations
Computational Intelligence Technologies for Industries
Machine Learning for Sustainable Industrial Internet Optimization Industrial Applications and Case Studies

Prof. Dr. Mengchu Zhou
Prof. Dr. Bi Jing
Dr. Mohammadhossein H. Ghahramani
Guest Editors

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Published Papers (11 papers)

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Research

25 pages, 1003 KiB

Open AccessArticle

Revisiting Classical Controller Design and Tuning with Genetic Programming

by Carlos A. García, Manel Velasco, Cecilio Angulo, Pau Marti and Antonio Camacho

Sensors 2023, 23(24), 9731; https://doi.org/10.3390/s23249731 - 9 Dec 2023

Cited by 2 | Viewed by 1913

Abstract

This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP’s functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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33 pages, 6787 KiB

Open AccessArticle

Constrained Dynamic Matrix Control under International Electrotechnical Commission Standard 61499 and the Open Platform Communications Unified Architecture

by Sergio Bustos-Pulluquitin, Gustavo Caiza, Mayra Llumitasig-Galarza, Maritza Castro-Mayorga, Clara Sánchez-Benítez and Marcelo V. Garcia

Sensors 2023, 23(15), 6919; https://doi.org/10.3390/s23156919 - 3 Aug 2023

Cited by 7 | Viewed by 1560

Abstract

This paper focuses on the implementation of a constrained Dynamic Matrix Control (DMC) approach within the level processes of the FESTO™ MPS-PA Compact Workstation plant in the context of the Industrial Internet of Things (IIoT) paradigm. The goal is to develop an industrial control application with decentralized logic that optimizes the operation of the plant while adhering to specific constraints. The implementation is carried out using the IEC-61499 standard and the OPC-UA protocol, enabling seamless communication between devices and systems. The authors utilize the 4diac-IDE and 4diac-FORTE as the development and runtime environments, respectively, to enable the execution of the control application on low-cost devices. The Beagle Bone Black (BBB) card is used for data acquisition and actuator control. Three types of constraints are considered: control increment (

Δ u (k)

), output (

y m (k)

), and control (

u (k)

) constraints, to prevent unnecessary stress on the actuator and avoid damage to the plant. The QP algorithm is employed to optimize the objective function and address these constraints effectively. By integrating advanced control strategies into industrial processes in the IIoT paradigm and implementing them on low-cost devices, this paper demonstrates the feasibility and effectiveness of improving system performance, resource utilization, and overall productivity while considering system limitations and constraints. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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16 pages, 1193 KiB

Open AccessArticle

An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem

by Leilei Meng, Weiyao Cheng, Biao Zhang, Wenqiang Zou, Weikang Fang and Peng Duan

Sensors 2023, 23(8), 3815; https://doi.org/10.3390/s23083815 - 7 Apr 2023

Cited by 45 | Viewed by 4987

Abstract

In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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10 pages, 1504 KiB

Open AccessArticle

Computation of Eigenvalues and Eigenfunctions in the Solution of Eddy Current Problems

by Theodoros Theodoulidis, Anastassios Skarlatos and Grzegorz Tytko

Sensors 2023, 23(6), 3055; https://doi.org/10.3390/s23063055 - 12 Mar 2023

Cited by 8 | Viewed by 1977

Abstract

The solution of the eigenvalue problem in bounded domains with planar and cylindrical stratification is a necessary preliminary task for the construction of modal solutions to canonical problems with discontinuities. The computation of the complex eigenvalue spectrum must be very accurate since losing or misplacing one of the thereto linked modes will have an important impact on the field solution. The approach followed in a number of previous works is to construct the corresponding transcendental equation and locate its roots in the complex plane using the Newton–Raphson method or Cauchy-integral-based techniques. Nevertheless, this approach is cumbersome, and its numerical stability decreases dramatically with the number of layers. An alternative, approach consists in the numerical evaluation of the matrix eigenvalues for the weak formulation for the respective 1D Sturm–Liouville problem using linear algebra tools. An arbitrary number of layers can thus be easily and robustly treated, with continuous material gradients being a limiting case. Although this approach is often used in high frequency studies involving wave propagation, this is the first time that has been used for the induction problem arising in an eddy current inspection situation. The developed method is implemented in Matlab and is used to deal with the following problems: magnetic material with a hole, a magnetic cylinder, and a magnetic ring. In all the conducted tests, the results are obtained in a very short time, without missing a single eigenvalue. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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19 pages, 433 KiB

Open AccessArticle

Performance Optimization for a Class of Petri Nets

by Weijie Shi, Zhou He, Chan Gu, Ning Ran and Ziyue Ma

Sensors 2023, 23(3), 1447; https://doi.org/10.3390/s23031447 - 28 Jan 2023

Cited by 2 | Viewed by 2543

Abstract

Petri nets (PNs) are widely used to model flexible manufacturing systems (FMSs). This paper deals with the performance optimization of FMSs modeled by Petri nets that aim to maximize the system’s performance under a given budget by optimizing both quantities and types of resources, such as sensors and devices. Such an optimization problem is challenging since it is nonlinear; hence, a globally optimal solution is hard to achieve. Here, we developed a genetic algorithm combined with mixed-integer linear programming (MILP) to solve the problem. In this approach, a set of candidate resource allocation strategies, i.e., the choices of the number of resources, are first generated by using MILP. Then, the choices of the type and the cycle time of the resources are evaluated by MILP; the promising ones are used to spawn the next generation of candidate strategies. The effectiveness and efficiency of the developed methodology are illustrated by simulation studies. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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27 pages, 7733 KiB

Open AccessArticle

Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System

by Thadeu Brito, Beatriz Flamia Azevedo, João Mendes, Matheus Zorawski, Florbela P. Fernandes, Ana I. Pereira, José Rufino, José Lima and Paulo Costa

Sensors 2023, 23(3), 1282; https://doi.org/10.3390/s23031282 - 22 Jan 2023

Cited by 6 | Viewed by 2644

Abstract

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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20 pages, 5255 KiB

Open AccessArticle

An Improved African Vulture Optimization Algorithm for Dual-Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problems

by Zhou He, Biao Tang and Fei Luan

Sensors 2023, 23(1), 90; https://doi.org/10.3390/s23010090 - 22 Dec 2022

Cited by 25 | Viewed by 2600

Abstract

According to the characteristics of flexible job shop scheduling problems, a dual-resource constrained flexible job shop scheduling problem (DRCFJSP) model with machine and worker constraints is constructed such that the makespan and total delay are minimized. An improved African vulture optimization algorithm (IAVOA) is developed to solve the presented problem. A three-segment representation is proposed to code the problem, including the operation sequence, machine allocation, and worker selection. In addition, the African vulture optimization algorithm (AVOA) is improved in three aspects: First, in order to enhance the quality of the initial population, three types of rules are employed in population initialization. Second, a memory bank is constructed to retain the optimal individuals in each iteration to increase the calculation precision. Finally, a neighborhood search operation is designed for individuals with certain conditions such that the makespan and total delay are further optimized. The simulation results indicate that the qualities of the solutions obtained by the developed approach are superior to those of the existing approaches. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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16 pages, 1950 KiB

Open AccessArticle

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems

by Yuhang Liu, Yu Shen, Lili Fan, Yonglin Tian, Yunfeng Ai, Bin Tian, Zhongmin Liu and Fei-Yue Wang

Sensors 2022, 22(24), 9930; https://doi.org/10.3390/s22249930 - 16 Dec 2022

Cited by 22 | Viewed by 3588

Abstract

Radar is widely employed in many applications, especially in autonomous driving. At present, radars are only designed as simple data collectors, and they are unable to meet new requirements for real-time and intelligent information processing as environmental complexity increases. It is inevitable that smart radar systems will need to be developed to deal with these challenges and digital twins in cyber-physical systems (CPS) have proven to be effective tools in many aspects. However, human involvement is closely related to radar technology and plays an important role in the operation and management of radars; thus, digital twins’ radars in CPS are insufficient to realize smart radar systems due to the inadequate consideration of human factors. ACP-based parallel intelligence in cyber-physical-social systems (CPSS) is used to construct a novel framework for smart radars, called Parallel Radars. A Parallel Radar consists of three main parts: a Descriptive Radar for constructing artificial radar systems in cyberspace, a Predictive Radar for conducting computational experiments with artificial systems, and a Prescriptive Radar for providing prescriptive control to both physical and artificial radars to complete parallel execution. To connect silos of data and protect data privacy, federated radars are proposed. Additionally, taking mines as an example, the application of Parallel Radars in autonomous driving is discussed in detail, and various experiments have been conducted to demonstrate the effectiveness of Parallel Radars. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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15 pages, 6436 KiB

Open AccessArticle

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

by Yu Shen, Yuhang Liu, Yonglin Tian, Zhongmin Liu and Feiyue Wang

Sensors 2022, 22(23), 9483; https://doi.org/10.3390/s22239483 - 4 Dec 2022

Cited by 4 | Viewed by 2592

Abstract

Computer vision tasks, such as motion estimation, depth estimation, object detection, etc., are better suited to light field images with more structural information than traditional 2D monocular images. However, since costly data acquisition instruments are difficult to calibrate, it is always hard to obtain real-world scene light field images. The majority of the datasets for static light field images now available are modest in size and cannot be used in methods such as transformer to fully leverage local and global correlations. Additionally, studies on dynamic situations, such as object tracking and motion estimates based on 4D light field images, have been rare, and we anticipate a superior performance. In this paper, we firstly propose a new static light field dataset that contains up to 50 scenes and takes 8 to 10 perspectives for each scene, with the ground truth including disparities, depths, surface normals, segmentations, and object poses. This dataset is larger scaled compared to current mainstream datasets for depth estimation refinement, and we focus on indoor and some outdoor scenarios. Second, to generate additional optical flow ground truth that indicates 3D motion of objects in addition to the ground truth obtained in static scenes in order to calculate more precise pixel level motion estimation, we released a light field scene flow dataset with dense 3D motion ground truth of pixels, and each scene has 150 frames. Thirdly, by utilizing the DistgDisp and DistgASR, which decouple the angular and spatial domain of the light field, we perform disparity estimation and angular super-resolution to evaluate the performance of our light field dataset. The performance and potential of our dataset in disparity estimation and angular super-resolution have been demonstrated by experimental results. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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18 pages, 6236 KiB

Open AccessArticle

A Combined Approach to Infrared Small-Target Detection with the Alternating Direction Method of Multipliers and an Improved Top-Hat Transformation

by Tengyan Xi, Lihua Yuan and Quanbin Sun

Sensors 2022, 22(19), 7327; https://doi.org/10.3390/s22197327 - 27 Sep 2022

Cited by 4 | Viewed by 2492

Abstract

In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop). Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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38 pages, 17130 KiB

Open AccessArticle

Fuzzy Strategy Grey Wolf Optimizer for Complex Multimodal Optimization Problems

by Hua Qin, Tuanxing Meng and Yuyi Cao

Sensors 2022, 22(17), 6420; https://doi.org/10.3390/s22176420 - 25 Aug 2022

Cited by 5 | Viewed by 2442

Abstract

Traditional grey wolf optimizers (GWOs) have difficulty balancing convergence and diversity when used for multimodal optimization problems (MMOPs), resulting in low-quality solutions and slow convergence. To address these drawbacks of GWOs, a fuzzy strategy grey wolf optimizer (FSGWO) is proposed in this paper. Binary joint normal distribution is used as a fuzzy method to realize the adaptive adjustment of the control parameters of the FSGWO. Next, the fuzzy mutation operator and the fuzzy crossover operator are designed to generate new individuals based on the fuzzy control parameters. Moreover, a noninferior selection strategy is employed to update the grey wolf population, which makes the entire population available for estimating the location of the optimal solution. Finally, the FSGWO is verified on 30 test functions of IEEE CEC2014 and five engineering application problems. Comparing FSGWO with state-of-the-art competitive algorithms, the results show that FSGWO is superior. Specifically, for the 50D test functions of CEC2014, the average calculation accuracy of FSGWO is 33.63%, 46.45%, 62.94%, 64.99%, and 59.82% higher than those of the equilibrium optimizer algorithm, modified particle swarm optimization, original GWO, hybrid particle swarm optimization and GWO, and selective opposition-based GWO, respectively. For the 30D and 50D test functions of CEC2014, the results of the Wilcoxon signed-rank test show that FSGWO is better than the competitive algorithms. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Control and Optimization in Industries 4.0)

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