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Electronics
Special Issues
New Insights in Computational Intelligence and Its Applications
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New Insights in Computational Intelligence and Its Applications

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 4044

Special Issue Editors

Dr. Cheng He

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: intelligent computing; power equipment condition monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Handing Wang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Xidian University, Xi'an, China
Interests: evolutionary computing; multi-objective optimization; machine learning; data-driven optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Ye Tian

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
Interests: evolutionary computation and its applications in machine learning; data mining; network science; scheduling
Special Issues, Collections and Topics in MDPI journals
Dr. Chuanji Zhang

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: condition monitoring; electrical measurement; online monitoring; power system; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) has emerged as a dynamic and multidisciplinary field that encompasses various computational techniques inspired by nature, human cognition, and collective behavior. This Special Issue aims to showcase the latest advancements and applications in CI, highlighting its significance in solving complex real-world problems across diverse domains.

CI encompasses a wide spectrum of techniques including neural networks, evolutionary algorithms, fuzzy systems, swarm intelligence, and more. These techniques enable machines to learn, adapt, and make decisions in dynamic and uncertain environments. Over the years, CI has demonstrated its effectiveness in tackling intricate challenges such as optimization, pattern recognition, data mining, decision-making, control systems, and beyond.

The integration of CI approaches into various applications has led to remarkable breakthroughs. From healthcare and finance to manufacturing and autonomous systems, CI has played a pivotal role in enhancing efficiency, accuracy, and automation. Moreover, as the body of data continues to grow exponentially, CI techniques are invaluable in extracting meaningful insights from massive datasets, thereby contributing to the advancement of artificial intelligence.

This Special Issue aims to explore the latest advancements and applications of CI across various domains, with a particular focus on power systems. We invite researchers, practitioners, and experts to contribute their original research on the integration of CI techniques in solving complex problems. Topics of interest include, but are not limited to:

  • Automatic CI algorithm design for industry
  • CI for multiobjective/multimodal/multitasking/multifactorial/multi-fidelity optimization
  • CI-enhanced evolutionary computation
  • CI-enhanced neural architecture search/feature selection/community detection
  • CI-based load forecasting and energy management
  • Evolutionary algorithms for optimal power generation and distribution
  • Swarm intelligence for management in smart grids
  • CI-powered fault detection and diagnosis in power systems
  • Hybrid CI approaches for renewable energy integration
  • CI in energy trading and market optimization
  • CI-enhanced cybersecurity for power infrastructure
  • Applications of CI in energy-efficient systems and sustainability
  • CI techniques in engineering design
  • CI-driven smart grid optimization and control

This Special Issue invites researchers, practitioners, and experts from academia and industry to contribute original research, reviews, and case studies that push the boundaries of CI. We encourage submissions that explore novel algorithms, theoretical frameworks, hybrid methodologies, and successful applications of CI in both traditional and emerging fields. We look forward to reading your contributions to this Special Issue, poised to deepen the understanding and application of computational intelligence in overcoming real-world complexities.

Dr. Cheng He
Prof. Dr. Handing Wang
Dr. Ye Tian
Dr. Chuanji Zhang
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
  • evolutionary algorithm
  • machine learning
  • pattern recognition
  • complex optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

12 pages, 1253 KiB  
Article
Modeling and Simulation of Wide-Frequency Characteristics of Electromagnetic Standard Voltage Transformer
by Lewei Wang, Zhenhua Li, Heping Lu, Feng Zhou and Yinglong Diao
Electronics 2024, 13(21), 4206; https://doi.org/10.3390/electronics13214206 - 27 Oct 2024
Viewed by 949
Abstract
To achieve the broadband applicability of standard voltage transformers in “dual high” power systems, an equivalent circuit model of the standard voltage transformer is first established. Using the complex magnetic permeability method and utilizing existing core parameters, the excitation impedance values are obtained. [...] Read more.
To achieve the broadband applicability of standard voltage transformers in “dual high” power systems, an equivalent circuit model of the standard voltage transformer is first established. Using the complex magnetic permeability method and utilizing existing core parameters, the excitation impedance values are obtained. Next, based on the equivalent circuit model, the no-load error function of the standard voltage transformer is analyzed, and through simulation, the no-load error response curve of the standard voltage transformer in the frequency range of 20 Hz to 3000 Hz is derived. The simulation results indicate that within the 20 Hz to 700 Hz range, both the no-load ratio error and the no-load angular error meet the accuracy requirements, with the ratio error within ±0.05% and the angular error within 2′. Additionally, the derivation of the error transfer function demonstrates the correlation between the no-load error values and the number of turns and cross-sectional area of the standard voltage transformer. Simulation results, obtained by increasing and decreasing the number of turns and cross-sectional area by 10%, provide valuable insights for the error compensation and structural design of standard voltage transformers. Full article
(This article belongs to the Special Issue New Insights in Computational Intelligence and Its Applications)
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24 pages, 9567 KiB  
Article
Recognition of Street Landscape Patterns in Kunming City Based on Intelligent Decision Algorithm and Regional Cultural Expression
by Xingxiao Zhu, Zhizhong Xing, Xia Chen, Jing Wang, Xinyue Yang, Lei Yang, Lin Wang, Ruimin Li and Yayu Wang
Electronics 2024, 13(21), 4183; https://doi.org/10.3390/electronics13214183 - 25 Oct 2024
Viewed by 858
Abstract
The integration of intelligent decision-making algorithms with urban cultural expression is becoming a hot topic in both academic and practical fields for exploring urban street landscapes. Exploring the application strategies of intelligent decision-making algorithms and regional cultural expression in street landscape pattern recognition [...] Read more.
The integration of intelligent decision-making algorithms with urban cultural expression is becoming a hot topic in both academic and practical fields for exploring urban street landscapes. Exploring the application strategies of intelligent decision-making algorithms and regional cultural expression in street landscape pattern recognition and innovative design is a key step. The single layout of urban street construction, cultural deficiency, ecological imbalance, and low resident participation seriously constrain the overall quality improvement of the city. To address this dilemma, this study delved into Kunming City and selected the ten “most beautiful streets”, such as Dianchi Road, for research. By using the Analytic Hierarchy Process, a comprehensive evaluation system covering multiple dimensions, such as the street layout, plant landscape, and historical culture, was constructed to analyze the street landscape of Kunming. The research results indicate that the top four roads in terms of weight evaluation scores are Cuihu Ring Road, Jiaochang Middle Road, Qingnian Road, and Beijing Road, with values of 0.2076, 0.1531, 0.1274, and 0.1173. The weight reveals that each street has its unique landscape factors, such as the profound cultural heritage of Cuihu Ring Road and the beautiful plant landscape of Jiaochang Middle Road. Further analysis also reveals the close relationship between various factors in the evaluation model, emphasizing the importance of supplementing material and cultural elements in street landscape design. The significance of this study goes beyond a single analysis of the street landscape in Kunming City. Drawing a regional street landscape pattern map sets an example for other cities to build distinctive, eco-friendly, culturally rich, and highly humanized street spaces, providing reference and inspiration. More importantly, this study promotes the application and development of intelligent decision-making algorithms in the field of urban landscapes. Future research will further optimize algorithms to improve their adaptability and accuracy in complex environments. Full article
(This article belongs to the Special Issue New Insights in Computational Intelligence and Its Applications)
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14 pages, 721 KiB  
Article
Adaptive Gaussian Kernel-Based Incremental Scheme for Outlier Detection
by Panpan Zhang, Tao Wang, Hui Cao and Siliang Lu
Electronics 2023, 12(22), 4571; https://doi.org/10.3390/electronics12224571 - 8 Nov 2023
Viewed by 1389
Abstract
An outlier, known as an error state, can bring valuable cognitive analytic results in many industrial applications. Aiming at detecting outliers as soon as they appear in data streams that continuously arrive from data sources, this paper presents an adaptive-kernel-based incremental scheme. Specifically, [...] Read more.
An outlier, known as an error state, can bring valuable cognitive analytic results in many industrial applications. Aiming at detecting outliers as soon as they appear in data streams that continuously arrive from data sources, this paper presents an adaptive-kernel-based incremental scheme. Specifically, the Gaussian kernel function with an adaptive kernel width is employed to ensure smoothness in local measures and to improve discriminability between objects. The dynamical Gaussian kernel density is presented to describe the gradual process of changing density. When new data arrives, the method updates the relevant density measures of the affected objects to achieve outlier computation of the arrived object, which can significantly reduce the computational burden. Experiments are performed on five commonly used datasets, and experimental results illustrate that the proposed method is more effective and robust for incremental outlier mining automatically. Full article
(This article belongs to the Special Issue New Insights in Computational Intelligence and Its Applications)
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