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Electronics
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State-of-the-Art Artificial Intelligence Technology
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State-of-the-Art Artificial Intelligence Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7154

Special Issue Editors

Prof. Dr. Cheng Siong Chin

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Guest Editor
Intelligent Systems Design, Newcastle University, Singapore 038986, Singapore
Interests: intelligent systems design of complex systems in uncertain environments (underwater/electric vehicle, battery, PV system, acoustic enclosure, and water distribution network) involving predictive analytics (data mining, predictive modeling, and machine learning)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kalyana C. Veluvolu

E-Mail Website
Guest Editor
NCBS Lab, School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
Interests: onlinear estimation and filtering; sliding-mode control; vehicle dynamics and control; autonomous vehicle control; AI; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Mazdak Zamani

E-Mail Website
Guest Editor
School of Business and Information Sciences, Felician University, 1 Felician Way, Rutherford, NJ 07070, USA
Interests: networks; artificial Intelligence; computer Science & engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Len Gelman

E-Mail Website
Guest Editor
School of Computing and Engineering, Department of Engineering and Technology, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Interests: digital signal processing; structural health monitoring; condition monitoring; artificial intelligence; vibration analysis; motor current signature analysis; adaptation of diagnosis systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electronics is pleased to announce its forthcoming Special Issue, with the aim of presenting state-of-the-art research and reviews in the field of Artificial Intelligence Technology in various countries and territories around the world.

We welcome scholars to collaborate with us by submitting their contributions to this Special Issue.

Any topic within the scope of Artificial Intelligence Technology is appropriate, but contributions should address new areas or consolidate existing areas with significant recent progress.

Prof. Dr. Cheng Siong Chin
Prof. Dr. Kalyana C. Veluvolu
Dr. Mazdak Zamani
Prof. Dr. Len Gelman
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.

Published Papers (3 papers)

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Research

21 pages, 6415 KiB  
Article
CoroTrans-CL: A Novel Transformer-Based Continual Deep Learning Model for Image Recognition of Coronavirus Infections
by Boyuan Wang, Du Zhang and Zonggui Tian
Electronics 2023, 12(4), 866; https://doi.org/10.3390/electronics12040866 - 08 Feb 2023
Cited by 3 | Viewed by 1896
Abstract
The rapid evolution of coronaviruses in respiratory diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a significant challenge for deep learning models to accurately detect and adapt to new strains. To address this challenge, we propose a novel Continuous Learning approach, [...] Read more.
The rapid evolution of coronaviruses in respiratory diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a significant challenge for deep learning models to accurately detect and adapt to new strains. To address this challenge, we propose a novel Continuous Learning approach, CoroTrans-CL, for the diagnosis and prevention of various coronavirus infections that cause severe respiratory diseases using chest radiography images. Our approach is based on the Swin Transformer architecture and uses a combination of the Elastic Weight Consolidation (EWC) and Herding Selection Replay (HSR) methods to mitigate the problem of catastrophic forgetting. We constructed an informative benchmark dataset containing multiple strains of coronaviruses and present the proposed approach in five successive learning stages representing the epidemic timeline of different coronaviruses (SARS, MERS, wild-type SARS-CoV-2, and the Omicron and Delta variants of SARS-CoV-2) in the real world. Our experiments showed that the proposed CoroTrans-CL model achieved a joint training accuracy of 95.34%, an F1 score of 92%, and an average accuracy of 83.40% while maintaining a balance between plasticity and stability. Our study demonstrates that CoroTrans-CL can accurately diagnose and detect the changes caused by new mutant viral strains in the lungs without forgetting existing strains, and it provides an effective solution for the ongoing diagnosis of mutant SARS-CoV-2 virus infections. Full article
(This article belongs to the Special Issue State-of-the-Art Artificial Intelligence Technology)
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17 pages, 3102 KiB  
Article
Typhoon Tracks Prediction with ConvLSTM Fused Reanalysis Data
by Peng Lu, Mingyu Xu, Ao Sun, Zhenhua Wang and Zongsheng Zheng
Electronics 2022, 11(20), 3279; https://doi.org/10.3390/electronics11203279 - 12 Oct 2022
Cited by 4 | Viewed by 1630
Abstract
Typhoon occurrences pose a great threat to people’s lives and property; therefore, it is important to predict typhoon tracks accurately for disaster prevention and reduction. In recent years, research using traditional machine learning methods has struggled to include temporal and spatial features. Moreover, [...] Read more.
Typhoon occurrences pose a great threat to people’s lives and property; therefore, it is important to predict typhoon tracks accurately for disaster prevention and reduction. In recent years, research using traditional machine learning methods has struggled to include temporal and spatial features. Moreover, research that has been conducted using satellite images only does not consider the influence of physical factors on typhoon movement; therefore, this paper proposes to add a convolutional layer to the Convolutional LSTM (ConvLSTM) model to improve the ability of the model to extract images. The previous positions of the typhoon’s center are marked on subsequent reanalysis images. The subsequent coordinates of the typhoon’s center are found by fitting the predicted coordinates of each physical variable. The research method in this paper required selecting the physical variables group which was most correlated with the direction and distance of the typhoon movement from 11 physical variables; this was achieved using Canonical Correlation Analysis (CCA) and Grey Relation Analysis (GRA). Then, reanalysis data is transformed into images and a continuous series of reanalysis image sequences is inputted into the ConvLSTM model so that it can make predictions. The mean absolute error of distance used for the ERA5 dataset, using the method proposed, was 54.69 km; thus, the validity of the model was proven. Full article
(This article belongs to the Special Issue State-of-the-Art Artificial Intelligence Technology)
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18 pages, 5926 KiB  
Article
A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm
by Yong Tao, Yufang Wen, He Gao, Tianmiao Wang, Jiahao Wan and Jiangbo Lan
Electronics 2022, 11(8), 1192; https://doi.org/10.3390/electronics11081192 - 08 Apr 2022
Cited by 7 | Viewed by 1732
Abstract
A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path [...] Read more.
A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot. Full article
(This article belongs to the Special Issue State-of-the-Art Artificial Intelligence Technology)
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