Project Collection "Application of Advanced Computing, Control and Processing in Engineering"

A project collection of Electronics (ISSN 2079-9292). This project collection belongs to the section "Computer Science & Engineering".

Papers displayed on this page all arise from the same project. Editorial decisions were made independently of project staff and handled by the Editor-in-Chief or qualified Editorial Board members.

Editors

Dr. Robertas Damaševičius
E-Mail Website
Guest Editor
Professor at Software Engineering Department, Kaunas University of Technology, K. Donelaičio g. 73, Kaunas 44249, Lithuania; Adjunct Professor at Institute of Mathematics, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland
Interests: human–computer interface; robot programming; sustainable software engineering; assisted living; data mining and machine learning; smart learning
Prof. Dr. Sergio Greco
E-Mail Website
Guest Editor
Department of Computer Engineering, Modelling, Electronics and Systemics—DIMES, University of Calabria, Via P. Bucci Cubo 42/A, 87036 Arcavacata di Rende (CS), Italy
Interests: logic programing; artificial intelligence, data mining; cluster computing; sensor network

Project Overview

Dear friends and colleagues,

Computational concepts and techniques have always played a major role in control engineering since the first computer-based control systems were put into operation over twenty years ago. Today’s research discoveries at the confluence of theoretical, experimental, and computational science and engineering are enabled by the continuing availability of an ecosystem of advanced computational resources. The dramatic growth in practical applications for machine learning over the last twenty years has been accompanied by many important developments in the underlying algorithms and techniques. This role has, in fact, been accelerating over the intervening years as the sophistication of the computing methods and tools available—as well as the complexity of the control problems they have been used to solve—have also increased. In particular, the introduction of the microprocessor, nono-electronics, nanoprocessors, and their use as a low-cost computing element in a distributed computer control system has had a profound effect on the way in which the design and implementation of a advanced processing and control system is carried out and, to some extent, on the theory which underlies the basic design strategies.

Artificial intelligence and human–computer interactions is another area that is receiving increased attention day by day, due to their advanced ability to solved many previously unsolved problems The development of interactive computing has encouraged a substantial growth in the use of computer-aided design methods and robust and efficient numerical algorithms have been produced to support these methods. Major advances have also taken place in the languages used for control system implementation in terms of machine learning. The algorithms of human language and interaction with the system whose design is based on some very fundamental computer science concepts have been derived and developed over the past decade. With the extremely high rate of change in the field of computer science and electronics, the more recent developments have outpaced their incorporation into new control system design and implementation techniques. With wide adoption of new modalities of scientific and engineering discovery, the demand for computing and interface capabilities and services has increased significantly both in terms of the range of capabilities and overall capacity.

Concurrently, in this Special Issue, we hope to showcase the latest results from researchers, computer engineers, and electronics professionals in their attempts to explore new methods and algorithms while constantly adapting to rapid technological evolution, as well as in pursuing transformational discoveries across all processes and in engineering. In addition, we also encouraging submissions which attempt to solve various problems related to environmental sensing.

Dr. Sudip Chakraborty
Dr. Robertas Damaševičius
Prof. Dr. Sergio Greco
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 papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection 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 1800 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

  • nanotechnology
  • machine learning
  • artificial neural network
  • human–computer interaction
  • software engineering
  • artificial intelligence
  • environmental sensing
  • smart learning。

Published Papers (4 papers)

2021

Jump to: 2020

Article
Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer
Electronics 2021, 10(4), 447; https://doi.org/10.3390/electronics10040447 - 11 Feb 2021
Viewed by 721
Abstract
The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) [...] Read more.
The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios. Full article
Show Figures

Figure 1

Article
An Industrial Quadrotor UAV Control Method Based on Fuzzy Adaptive Linear Active Disturbance Rejection Control
Electronics 2021, 10(4), 376; https://doi.org/10.3390/electronics10040376 - 04 Feb 2021
Viewed by 737
Abstract
In this paper, a fuzzy adaptive linear active disturbance rejection control (Fuzzy-LADRC) is proposed for strong coupling and nonlinear quadrotor unmanned aerial vehicle (UAV). At present, UAV conveys new opportunities in the industry, such as power line inspection, petroleum conduit patrolling, and defects [...] Read more.
In this paper, a fuzzy adaptive linear active disturbance rejection control (Fuzzy-LADRC) is proposed for strong coupling and nonlinear quadrotor unmanned aerial vehicle (UAV). At present, UAV conveys new opportunities in the industry, such as power line inspection, petroleum conduit patrolling, and defects detection for the wind turbine, because of its advantages in flexibility, high efficiency, and economy. Usually, the scene of the UAV mission has a high risk, and there are internal sensor noise and unknown external disturbance. Thus, the attitude stability and anti-interference ability of UAV are especially essential. To solve the strong coupling problem of UAV, the dynamics model of UAV is established via the Newton-Euler method, and the coupling part of dynamics is modeled as an internal disturbance. According to the function of linear active disturbance rejection control (LADRC) parameters, a Fuzzy-LADRC is proposed to improve the dynamic performance of the system. The proposed control method makes full use of the adaptive ability of the fuzzy controller and the anti-interference ability of LADRC to the nonlinear and strong coupling systems. As we know, this is the first time that Fuzzy-LADRC has been used in UAV control. In the simulation, the performance indicators of four controllers, including Fuzzy-LADRC, LADRC, PID, and Fuzzy-PID are compared and analyzed. The results indicate that the average response speed of Fuzzy-LADRC is 12.65% faster than LADRC, and it is 29.25% faster than PID. The average overshoot of Fuzzy-LADRC is 17% less than LADRC and 77.75% less than PID. The proposed control method can significantly improve the response speed and anti-interference ability of UAV. Full article
Show Figures

Figure 1

Article
A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network
Electronics 2021, 10(3), 255; https://doi.org/10.3390/electronics10030255 - 22 Jan 2021
Viewed by 594
Abstract
High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and [...] Read more.
High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration. Full article
Show Figures

Figure 1

2020

Jump to: 2021

Article
Safety Risk Assessment of a Pb-Zn Mine Based on Fuzzy-Grey Correlation Analysis
Electronics 2020, 9(1), 130; https://doi.org/10.3390/electronics9010130 - 09 Jan 2020
Cited by 9 | Viewed by 1065
Abstract
Improving safety management and risk evaluation methods is important for the global mining industry, which is the backbone of the industrial development of our society. To prevent any accidental loss or harm to human life and property, a safety risk assessment method is [...] Read more.
Improving safety management and risk evaluation methods is important for the global mining industry, which is the backbone of the industrial development of our society. To prevent any accidental loss or harm to human life and property, a safety risk assessment method is needed to perform the continuous risk assessment of mines. Based on the requirements of mine safety evaluation, this paper proposes the Pb-Zn mine safety risk evaluation model based on the fuzzy-grey correlation analysis method. The model is compared with the risk assessment model based on the fuzzy TOPSIS method. Through the experiments, our results demonstrate that the proposed fuzzy-grey correlation model is more sensitive to risk and has less effect on the evaluation results under different scoring attitudes (cautious, rational, and relaxed). Full article
Show Figures

Figure 1

Back to TopTop