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Electronics
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Emerging Technologies in Computational Intelligence
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Emerging Technologies in Computational Intelligence

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 3211

Special Issue Editors

Dr. Hao Li

E-Mail Website
Guest Editor
School of Electronic Engineering, Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an 710071, China
Interests: artificial intelligence (in particular, machine learning, multiagent systems and their applications) and formal methods (in particular, machine learning-based model checking)
Special Issues, Collections and Topics in MDPI journals
Dr. Mingyang Zhang

E-Mail Website
Guest Editor
Key Laboratory of Collaborative Intelligence Systems of Ministry of Education, School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: artificial intelligence (in particular, machine learning, multiagent systems and their applications); formal methods (in particular, machine-learning-based model checking)
Special Issues, Collections and Topics in MDPI journals
Dr. Shiguo Chen

E-Mail Website
Guest Editor
School of Physics, Xidian University, Xi’an 710071, China
Interests: infrared scene simulation and intelligent learning algorithm
Dr. Bingqi Yu

E-Mail Website
Guest Editor
School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471000, China
Interests: semantic segmentation; object detection; pose estimation

Special Issue Information

Dear Colleagues,

Computational intelligence is a branch of artificial intelligence that deals with creating algorithms and systems that can learn from data and make decisions based on what they have learned. This includes tasks such as machine learning, neural networks, fuzzy systems, and evolutionary computation. Numerous current technologies, including artificial intelligence, machine learning, data mining, and optimization algorithms, are built upon by the notion of computational intelligence. We can create solutions that are not just faster than before, but also far more precise, by merging these disciplines. Furthermore, because of its adaptability, it can be applied across a wide range of industries from finance to healthcare. In recent years, lots of Emerging Technologies have been developed in the field of computational intelligence, such as deep learning, graph neural networks, multi-task/many-task/transfer/large-scale evolutionary optimization.

Researchers are invited to submit high-quality research papers and surveys for this Special Issue on any topic related to computational intelligence. Papers that highlight the effectiveness and significance of computational intelligence applications are particularly encouraged. Topics of interest include, but are not limited to, the following:

  1. Machine learning for object detection, segmentation and categorization;
  2. Multi-task/many-task/transfer/large-scale evolutionary optimization;
  3. Deep leaning, graph neural networks, diffusion model;
  4. Underwater/remote sensing/infrared target detection, identification and tracking;
  5. Infrared/radar scene simulation and intelligent learning algorithm;
  6. Applications in computational intelligence.

Dr. Hao Li
Prof. Dr. Mingyang Zhang
Dr. Shiguo Chen
Dr. Bingqi Yu
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

  • deep learning
  • deep neural networks
  • classification
  • object detection
  • segmentation
  • evolutionary algorithm
  • fuzzy systems

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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

19 pages, 4031 KiB  
Article
MSTrans: Multi-Scale Transformer for Building Extraction from HR Remote Sensing Images
by Fei Yang, Fenlong Jiang, Jianzhao Li and Lei Lu
Electronics 2024, 13(23), 4610; https://doi.org/10.3390/electronics13234610 - 22 Nov 2024
Viewed by 746
Abstract
Buildings are one of the most important goals of human transformation of the Earth’s surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Computational intelligence techniques based on [...] Read more.
Buildings are one of the most important goals of human transformation of the Earth’s surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Computational intelligence techniques based on convolutional neural networks (CNNs) and Transformers have begun to be of interest in BE, and have made some progress. However, the BE methods based on CNNs are limited by the difficulty in capturing global long-range relationships, while Transformer-based methods are often not detailed enough for pixel-level annotation tasks because they focus on global information. To conquer the limitations, a multi-scale Transformer (MSTrans) is proposed for BE from high-resolution remote sensing images. In the proposed MSTrans, we develop a plug-and-play multi-scale Transformer (MST) module based on atrous spatial pyramid pooling (ASPP). The MST module can effectively capture tokens of different scales through the Transformer encoder and Transformer decoder. This can enhance multi-scale feature extraction of buildings, thereby improving the BE performance. Experiments on three real and challenging BE datasets verify the effectiveness of the proposed MSTrans. While the proposed approach may not achieve the highest Precision and Recall accuracies compared with the seven benchmark methods, it improves the overall metrics F1 and mIoU by 0.4% and 1.67%, respectively. Full article
(This article belongs to the Special Issue Emerging Technologies in Computational Intelligence)
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21 pages, 10985 KiB  
Article
A Novel Multi-Scale Feature Enhancement U-Shaped Network for Pixel-Level Road Crack Segmentation
by Jing Wang, Benlan Shen, Guodong Li, Jiao Gao and Chao Chen
Electronics 2024, 13(22), 4503; https://doi.org/10.3390/electronics13224503 - 16 Nov 2024
Cited by 1 | Viewed by 845
Abstract
Timely and accurate detection of pavement cracks, the most common type of road damage, is essential for ensuring road safety. Automatic image segmentation of cracks can accurately locate their pixel positions. This paper proposes a Multi-Scale Feature Enhanced U-shaped Network (MFE-UNet) for pavement [...] Read more.
Timely and accurate detection of pavement cracks, the most common type of road damage, is essential for ensuring road safety. Automatic image segmentation of cracks can accurately locate their pixel positions. This paper proposes a Multi-Scale Feature Enhanced U-shaped Network (MFE-UNet) for pavement crack detection. This network model uses a Residual Detail-Enhanced Block (RDEB) instead of a conventional convolution in the encoder–decoder process. The block combines Efficient Multi-Scale Attention to enhance its feature extraction performance. The Multi-Scale Gating Feature Fusion (MGFF) is incorporated into the skip connections, enhancing the fusion of multi-scale features to capture finer crack details while maintaining rich semantic information. Furthermore, we created a pavement crack image dataset named China_MCrack, consisting of 1500 images collected from road surfaces using smartphone-mounted motorbikes. The proposed network was trained and tested on the China_MCrack, DeepCrack, and Crack-Forest datasets, with additional generalization experiments on the BochumCrackDataset. The results were compared with those of the U-Net model, ResUNet, and Attention U-Net. The experimental results show that the proposed MFE-UNet model achieves accuracies of 82.95%, 91.71%, and 69.02% on three datasets, namely, China_MCrack, DeepCrack, and Crack-Forest datasets, respectively, and the F1_score is improved by 1–4% compared with other networks. Experimental results demonstrate that the proposed method is effective in detecting cracks at the pixel level. Full article
(This article belongs to the Special Issue Emerging Technologies in Computational Intelligence)
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20 pages, 3532 KiB  
Article
A Fault Identification Method of Hybrid HVDC System Based on Wavelet Packet Energy Spectrum and CNN
by Yan Liang, Junwei Zhang, Zheng Shi, Haibo Zhao, Yao Wang, Yahong Xing, Xiaowei Zhang, Yujin Wang and Haixiao Zhu
Electronics 2024, 13(14), 2788; https://doi.org/10.3390/electronics13142788 - 16 Jul 2024
Cited by 6 | Viewed by 878
Abstract
Aiming at the shortcomings of traditional fault identification methods in fault information acquisition, In the scenario of hybrid HVDC transmission system, a new fault identification method is proposed by using wavelet packet energy spectrum and convolutional neural network (CNN), which effectively solves the [...] Read more.
Aiming at the shortcomings of traditional fault identification methods in fault information acquisition, In the scenario of hybrid HVDC transmission system, a new fault identification method is proposed by using wavelet packet energy spectrum and convolutional neural network (CNN), which effectively solves the problem of complex fault feature extraction of hybrid HVDC transmission system. This method effectively improves the accuracy of fault identification. Firstly, tThe frequency-domain characteristics of the fault transient signal are extracted by wavelet packet transform, and the feature differences are reflected in the form of energy spectrum. Secondly, according to the extracted energy feature information, the order of fault line and fault type is identified by CNN. Finally, through example verification and algorithm comparison, it is concluded that, the mentioned model has a strong ability to identify faults, and has strong anti-noise interference and tolerance to transition resistance. Full article
(This article belongs to the Special Issue Emerging Technologies in Computational Intelligence)
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