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Energies
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Artificial Neural Network in Engineering
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Artificial Neural Network in Engineering

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 6338

Special Issue Editors

Dr. Marcel Luzar

E-Mail Website
Guest Editor
Institute of Control and Computation Engineering, University Of Zielona Góra, 65-246 Zielona Góra, Poland
Interests: artificial neural networks; fault diagnosis; fault diagnose for energy system; health monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Andrzej Czajkowski

E-Mail Website
Guest Editor
Institute of Control and Computation Engineering, University of Zielona Góra, 65-246 Zielona Góra, Poland
Interests: artificial neural networks; machine learning; signal processing; inertial sensors
Prof. Dr. Józef Korbicz

E-Mail Website
Guest Editor
Institute of Control and Computation Engineering, University of Zielona Gora, 65-246 Zielona Góra, Poland
Interests: fault diagnosis, fault tolerant control; soft computing; distributed parameter systems; state and parameter identification

Special Issue Information

Dear Colleagues,

Over several decades, artificial neural networks have been used in many research areas; in some, proving their usefulness in applications, in others, their performance being inferior to other approaches. System modeling, especially non-linear, is an undoubted area in which artificial neural networks are most applicable, owing their position to good approximation abilities, a relatively low computational complexity and ease of implementation. Neural models obtained from such modeling are used, among other things, in the system fault diagnosis, with a lot of research related to this having recently been observed. Additionally, in recent years, there has been a demand for modeling systems related to renewable energy, a result of decisions determined by governments at climate summits, which, in a way, force companies and private individuals to change the technology of obtaining energy. Therefore, using artificial neural networks, energy consumption is forecasted, solar irradiance is predicted, energy efficiency of buildings is optimized and much more.

Considering the above, this Special Issue focuses on the use of artificial neural networks in the modeling of non-linear systems and fault diagnosis and their use in renewable energy systems such as wind turbines and photovoltaic panels. Both theoretical and experimental work and, especially, the combination of these are welcome.

Dr. Marcel Luzar
Dr. Andrzej Czajkowski
Prof. Dr. Józef Korbicz
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. Energies 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 2600 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

  • non-linear system modeling
  • system fault diagnosis
  • renewable energy
  • intelligent control
  • health monitoring
  • industrial and software application

Published Papers (2 papers)

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Research

22 pages, 9358 KiB  
Article
Convolutional Neural Networks for Classifying Electronic Components in Industrial Applications
by Stanisław Hożyń
Energies 2023, 16(2), 887; https://doi.org/10.3390/en16020887 - 12 Jan 2023
Cited by 6 | Viewed by 2304
Abstract
Electronic component classification often constitutes the uncomplicated task of classifying a single object on a simple background. It is because, in many applications, a technological process employs constant lighting conditions, a fixed camera position, and a designated set of classified components. To date, [...] Read more.
Electronic component classification often constitutes the uncomplicated task of classifying a single object on a simple background. It is because, in many applications, a technological process employs constant lighting conditions, a fixed camera position, and a designated set of classified components. To date, there has not been an adequate attempt to develop a method for object classification under the above conditions in industrial applications. Therefore, this work focuses on the classification problem of a particular technological process. The process classifies electronic components on an assembly line using a fixed-mounted camera. The research investigated all the essential steps required to build a classification system, such as image acquisition, database creation, and neural network development. The first part of the experiment was devoted to creating an image dataset utilising the proposed image acquisition system. Then, custom and pre-trained networks were developed and tested. The results indicated that the pre-trained network (ResNet50) attained the highest accuracy (99.03%), which was better than the 98.99% achieved in relevant research on classifying elementary components. The proposed solution can be adapted to similar technological processes, where a defined set of components is classified under comparable conditions. Full article
(This article belongs to the Special Issue Artificial Neural Network in Engineering)
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19 pages, 11001 KiB  
Article
Machine Learning in Creating Energy Consumption Model for UAV
by Krystian Góra, Paweł Smyczyński, Mateusz Kujawiński and Grzegorz Granosik
Energies 2022, 15(18), 6810; https://doi.org/10.3390/en15186810 - 18 Sep 2022
Cited by 9 | Viewed by 2782
Abstract
The growing interest in the utilization of Unmanned Aerial Vehicles (UAVs) demands minimizing the costs of robot maintenance, where one of the main aspects relates to energy consumption. This manuscript presents a novel approach to create an energy consumption model for UAVs. The [...] Read more.
The growing interest in the utilization of Unmanned Aerial Vehicles (UAVs) demands minimizing the costs of robot maintenance, where one of the main aspects relates to energy consumption. This manuscript presents a novel approach to create an energy consumption model for UAVs. The authors prove, based on experimentally collected data using a drone carrying various payloads, that Machine Learning (ML) algorithms allow to sufficiently accurately estimate a power signal. As opposed to the classical approach with mathematical modeling, the presented method does not require any knowledge about the drone’s construction, thus making it a universal tool. Calculated metrics show the Decision Tree is the most suitable algorithm among eight different ML methods due to its high energy prediction accuracy of at least 97.5% and a short learning time which was equal to 2 ms for the largest dataset. Full article
(This article belongs to the Special Issue Artificial Neural Network in Engineering)
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