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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 18442

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Special Issue Editors

Prof. Dr. Imre J. Rudas

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Guest Editor

University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary
Interests: intelligent systems; robotics; control; systems and system of systems

Dr. György Eigner

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Guest Editor

Physiological Controls Research Center, Óbuda University, 1034 Budapest, Hungary
Interests: biomedical and control systems; machine learning-based applications

Special Issue Information

Dear Colleagues,

Computational cybernetics (CC) is the synergetic integration of cybernetics and computational intelligence. Computational cybernetics covers the areas of system of systems, biological and physiological systems, signal processing, information technology, and the theory of complex systems and computer sciences, where the application of advanced solutions of artificial intelligence, control theory, concepts and demands of Industry 4.0 and intelligent robotics is becoming a must these days. The purpose of this Special Issue is to provide a wide range introduction of the latest developments on the field of computational cybernetics through specific applications of the advanced methodologies in practice.

The papers considered for possible publication may focus on but not necessarily be limited to the following areas:

Machine learning techniques in robotics, IoT, and manufacturing industries;
Advanced control and estimator solutions for industrial, physiological systems;
Application of the fuzzy theorem on the field of computational cybernetics;
Machine learning, deep learning, and reinforcement learning in computational cybernetics;
Novel applications and case studies related to computational cybernetics.

Prof. Dr. Imre J. Rudas
Dr. György Eigner
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. Electronics 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

computational intelligence
computational cybernetics
machine learning
deep learning
control and estimation theorems
intelligent robotics
fuzzy systems
adaptive systems
operational systems

Published Papers (7 papers)

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Research

9 pages, 953 KiB

Open AccessFeature PaperArticle

Intelligent Computing Methods for Contextual Driving in Smart Engineering Model Systems

by László Horváth and Imre J. Rudas

Electronics 2022, 11(11), 1728; https://doi.org/10.3390/electronics11111728 - 30 May 2022

Cited by 1 | Viewed by 1373

Abstract

An engineering model system (EMS) is a complex purposeful structure of representation and description type content that is developed during development and application; in other words, it represents the lifecycle of an industrial product. The essential improvements for systems operated that are autonomous, situation controlled, and include cyber physical features of products dramatically changed requirements relative to EMS. Engineering modeling platforms (EMPs) experienced major improvements to cope with new requirements by smart products. These improvements were related to system level representation, autonomous active model features, situation level reaction to changed contexts, system and physical level virtual execution, and the support of situation-level human intervention. The lifecycle support of smart product engineering requires EMS, which behaves as a digital twin of a physically existing products and has an active connection with its contextual world, particularly with cyber units of the cyber physical system that it represents. The work in this paper aimed to develop new methods that can be implemented in EMS under developments in EMP. This is a new area of research that is motivated and at the same time enforced by moving engineering activities into highly integrated and automated complex model spaces. This paper focuses on context driving and the autonomous features of EMS as well as utilization-organized intellectual-property-powered intelligent computing relative to the definition and application of contextually connected object parameters in EMS. It introduces several latest results by the authors as a contribution for EMS to cope with the new requirements. These contributions include a new scenario called the lifecycle representation of contexts (LRC) to enhance integration in EMS, handling contexts in contextually connected autonomous unit (CCAU), organizing the intelligent content of EMS in proposed level-sublevel structure of LRC, and examining findings about situation-level cooperation between human and autonomous process. Full article

(This article belongs to the Special Issue Computational Cybernetics)

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21 pages, 7380 KiB

Open AccessArticle

Experimental and Simulation-Based Performance Analysis of a Computed Torque Control (CTC) Method Running on a Double Rotor Aeromechanical Testbed

by Árpád Varga, György Eigner, Imre Rudas and József Kázmér Tar

Electronics 2021, 10(14), 1745; https://doi.org/10.3390/electronics10141745 - 20 Jul 2021

Cited by 5 | Viewed by 1666

Abstract

Concept of closed loop control appears in many fields of engineering sciences, where the output quantity of some physical system must be forced to follow some prescribed function over time, e.g., when a robotic arm endpoint must track a desired trajectory or path given as timed series of spatial coordinates. The classic approach for solving this kind of problem involves a PID compensation block, and the necessary input signal for keeping the controlled process in the vicinity of the desired trajectory is calculated as the weighted sum of momentary deviation, deviation integral, and deviation derivative relative to the reference path. However, despite the obvious advantages, practical usability, and simplicity of the PID controllers, their performance is limited when they are utilized for controlling nonlinear systems. Even with linear systems, their proper operation requires an accurate system model and precise tuning process for finding the best weight values for the proportional, integral, and derivative effects, and the planned closed loop behavior might change significantly as the parameters of the controlled plant change over time. In this article, a computed torque-based controller is presented, which has only one adjustable parameter ensuring precise trajectory tracking even with significantly alternated model constants. The practical usability of the offered algorithm is evaluated and verified by simulations and experiments performed on a simple mechanical bi-rotor testbed playing the role of controlled plant. Full article

(This article belongs to the Special Issue Computational Cybernetics)

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13 pages, 3659 KiB

Open AccessArticle

Multi-Agent System Observer: Intelligent Support for Engaged E-Learning

by Igor Vuković, Kristijan Kuk, Petar Čisar, Miloš Banđur, Đoko Banđur, Nenad Milić and Brankica Popović

Electronics 2021, 10(12), 1370; https://doi.org/10.3390/electronics10121370 - 08 Jun 2021

Cited by 5 | Viewed by 2252

Abstract

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms. Full article

(This article belongs to the Special Issue Computational Cybernetics)

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20 pages, 8245 KiB

Open AccessArticle

Redundant Photo-Voltaic Power Cell in a Highly Reliable System

by Bertalan Beszédes, Károly Széll and György Györök

Electronics 2021, 10(11), 1253; https://doi.org/10.3390/electronics10111253 - 24 May 2021

Cited by 7 | Viewed by 1853

Abstract

The conversion of solar energy into electricity makes it possible to generate a power resource at the relevant location, independent of the availability of the electrical network. The application of the technology greatly facilitates the supply of electricity to objects that, due to their location, cannot be connected to the electrical network. Typical areas of use are nature reserves, game management areas, large-scale agricultural areas, large-scale livestock areas, industrial pipeline routes, water resources far from infrastructure, etc. The protection of such areas and assets and the detection of their functionality are of particular importance, sectors classified as critical infrastructure are of paramount importance. This article aims to show the conceptual structure of a possible design of a high-reliability, redundant, modular, self-monitoring, microcontroller-controlled system that can be used in the outlined areas. Full article

(This article belongs to the Special Issue Computational Cybernetics)

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24 pages, 3076 KiB

Open AccessArticle

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

by Ján Vaščák, Ladislav Pomšár, Peter Papcun, Erik Kajáti and Iveta Zolotová

Electronics 2021, 10(7), 809; https://doi.org/10.3390/electronics10070809 - 29 Mar 2021

Cited by 10 | Viewed by 1802

Abstract

Development of accessible and cheap sensors as well as the possibility to transfer and process huge amounts of data offer new possibilities for many areas utilizing till now conventional approaches. Navigation of robots and autonomous vehicles is no exception in this aspect and Internet of Things (IoT), together with the means of computational intelligence, represents a new way for construction and use of robots. In this paper, the possibility to move sensors from robots to their surroundings with the help of IoT is presented and the modification of the IoT concept in the form of intelligent space as well as the concept of ubiquitous robot are shown in the paper. On an example of route tracking, we will clarify the potential of distributed networked sensors and processing their data with the use of fuzzy cognitive maps for robotic navigation. Besides, two modifications of adaptation approaches, namely particle swarm optimization and migration algorithm, are presented here. A series of simulations was performed, which are discussed and future research directions are proposed. Full article

(This article belongs to the Special Issue Computational Cybernetics)

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15 pages, 6315 KiB

Open AccessArticle

A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map

by Ádám Wolf, Péter Troll, Stefan Romeder-Finger, Andreas Archenti, Károly Széll and Péter Galambos

Electronics 2020, 9(12), 2091; https://doi.org/10.3390/electronics9122091 - 08 Dec 2020

Cited by 4 | Viewed by 3715

Abstract

The fast evolution in computational and sensor technologies brings previously niche solutions to a wider userbase. As such, 3D reconstruction technologies are reaching new use-cases in scientific and everyday areas where they were not present before. Cost-effective and easy-to-use solutions include camera-based 3D scanning techniques, such as photogrammetry. This paper provides an overview of the available solutions and discusses in detail the depth-image based Real-time Appearance-based Mapping (RTAB-Map) technique as well as a smartphone-based solution that utilises ARCore, the Augmented Reality (AR) framework of Google. To qualitatively compare the two 3D reconstruction technologies, a simple length measurement-based method was applied with a purpose-designed reference object. The captured data were then analysed by a processing algorithm. In addition to the experimental results, specific case studies are briefly discussed, evaluating the applicability based on the capabilities of the technologies. As such, the paper presents the use-case of interior surveying in an automated laboratory as well as an example for using the discussed techniques for landmark surveying. The major findings are that point clouds created with these technologies provide a direction- and shape-accurate model, but those contain mesh continuity errors, and the estimated scale factor has a large standard deviation. Full article

(This article belongs to the Special Issue Computational Cybernetics)

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16 pages, 3525 KiB

Open AccessArticle

Deep Learning Models for Automated Diagnosis of Retinopathy of Prematurity in Preterm Infants

by Yo-Ping Huang, Spandana Vadloori, Hung-Chi Chu, Eugene Yu-Chuan Kang, Wei-Chi Wu, Shunji Kusaka and Yoko Fukushima

Electronics 2020, 9(9), 1444; https://doi.org/10.3390/electronics9091444 - 04 Sep 2020

Cited by 28 | Viewed by 4487

Abstract

Retinopathy of prematurity (ROP) is a disease that can cause blindness in premature infants. It is characterized by immature vascular growth of the retinal blood vessels. However, early detection and treatment of ROP can significantly improve the visual acuity of high-risk patients. Thus, early diagnosis of ROP is crucial in preventing visual impairment. However, several patients refrain from treatment owing to the lack of medical expertise in diagnosing the disease; this is especially problematic considering that the number of ROP cases is on the rise. To this end, we applied transfer learning to five deep neural network architectures for identifying ROP in preterm infants. Our results showed that the VGG19 model outperformed the other models in determining whether a preterm infant has ROP, with 96% accuracy, 96.6% sensitivity, and 95.2% specificity. We also classified the severity of the disease; the VGG19 model showed 98.82% accuracy in predicting the severity of the disease with a sensitivity and specificity of 100% and 98.41%, respectively. We performed 5-fold cross-validation on the datasets to validate the reliability of the VGG19 model and found that the VGG19 model exhibited high accuracy in predicting ROP. These findings could help promote the development of computer-aided diagnosis. Full article

(This article belongs to the Special Issue Computational Cybernetics)

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