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Applied Sciences
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Intelligent Data Analysis with the Evolutionary Computation Methods
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Intelligent Data Analysis with the Evolutionary Computation Methods

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 4441

Special Issue Editor

Dr. Nianyin Zeng

E-Mail Website
Guest Editor
Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 61102, China
Interests: intelligent data analysis; machine learning and computer vision; computational intelligent; artificial intelligence in biomedical and industry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect high-quality papers in the field of computational intelligence with contributions to the development, improvement, and application of the evolutionary algorithms for imperfect data analysis. To be specific, the evolutionary algorithms such as the particle swarm optimizer have played important roles in various industrial domains due to their advantages such as convenient implementation and fast convergence, but due to the imperfect property of related data, the algorithms may suffer from the premature problem at the same time. Therefore, how to endow the algorithm with considerable comprehensive performance so as to realize effective data analysis deserves further attention. Topics of interest for this Special Issue include, but are not limited to:

  • Strategy for enhancing global searching ability of evolutionary algorithms;
  • Development of novel heuristic algorithms;
  • Processing of the imperfect data;
  • Applications of evolutionary algorithms in cross-disciplinary studies;
  • Optimization theory in engineering;
  • Learnable optimizer with assistance of other advanced techniques.

Dr. Nianyin Zeng
Guest Editor

Manuscript Submission Information

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Keywords

  • computational intelligence
  • evolutionary algorithm
  • intelligent data analysis
  • imperfect data processing

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Published Papers (3 papers)

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Research

18 pages, 2232 KiB  
Article
Optimizing sEMG Gesture Recognition: Leveraging Channel Selection and Feature Compression for Improved Accuracy and Computational Efficiency
by Yinxi Niu, Wensheng Chen, Hui Zeng, Zhenhua Gan and Baoping Xiong
Appl. Sci. 2024, 14(8), 3389; https://doi.org/10.3390/app14083389 - 17 Apr 2024
Viewed by 1057
Abstract
In the task of upper-limb pattern recognition, effective feature extraction, channel selection, and classification methods are crucial for the construction of an efficient surface electromyography (sEMG) signal classification framework. However, existing deep learning models often face limitations due to improper channel selection methods [...] Read more.
In the task of upper-limb pattern recognition, effective feature extraction, channel selection, and classification methods are crucial for the construction of an efficient surface electromyography (sEMG) signal classification framework. However, existing deep learning models often face limitations due to improper channel selection methods and overly specific designs, leading to high computational complexity and limited scalability. To address this challenge, this study introduces a deep learning network based on channel feature compression—partial channel selection sEMG net (PCS-EMGNet). This network combines channel feature compression (channel selection) and feature extraction (partial block), aiming to reduce the model’s parameter count while maintaining recognition accuracy. PCS-EMGNet extracts high-dimensional feature vectors from sEMG signals through the partial block, decoding spatial and temporal feature information. Subsequently, channel selection compresses and filters these high-dimensional feature vectors, accurately selecting channel features to reduce the model’s parameter count, thereby decreasing computational complexity and enhancing the model’s processing speed. Moreover, the proposed method ensures the stability of classification, further improving the model’s capability of recognizing features in sEMG signal data. Experimental validation was conducted on five benchmark databases, namely the NinaPro DB4, NinaPro DB5, BioPatRec DB1, BioPatRec DB2, and BioPatRec DB3 datasets. Compared to traditional gesture recognition methods, PCS-EMGNet significantly enhanced recognition accuracy and computational efficiency, broadening its application prospects in real-world settings. The experimental results showed that our model achieved the highest average accuracy of 88.34% across these databases, marking a 9.96% increase in average accuracy compared to models with similar parameter counts. Simultaneously, our model’s parameter size was reduced by an average of 80% compared to previous gesture recognition models, demonstrating the effectiveness of channel feature compression in maintaining recognition accuracy while significantly reducing the parameter count. Full article
(This article belongs to the Special Issue Intelligent Data Analysis with the Evolutionary Computation Methods)
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19 pages, 1649 KiB  
Article
Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition
by Wensheng Chen, Yinxi Niu, Zhenhua Gan, Baoping Xiong and Shan Huang
Appl. Sci. 2023, 13(24), 13332; https://doi.org/10.3390/app132413332 - 18 Dec 2023
Cited by 3 | Viewed by 1241
Abstract
Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across [...] Read more.
Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features. Full article
(This article belongs to the Special Issue Intelligent Data Analysis with the Evolutionary Computation Methods)
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15 pages, 2547 KiB  
Article
Research on the Reconfiguration Method of Space-Based Exploration Satellite Constellations for Moving Target Tracking at Sea
by Yao Wang, Junren Luo, Xueqiang Gu and Wanpeng Zhang
Appl. Sci. 2023, 13(18), 10103; https://doi.org/10.3390/app131810103 - 7 Sep 2023
Cited by 2 | Viewed by 1324
Abstract
In addressing the challenge of tracking moving targets at sea, our focus has been directed towards the development of a reconstruction methodology founded upon satellite orbital manoeuvres. This endeavour has led us to devise a predictive model for manoeuvres within a geographic coordinate [...] Read more.
In addressing the challenge of tracking moving targets at sea, our focus has been directed towards the development of a reconstruction methodology founded upon satellite orbital manoeuvres. This endeavour has led us to devise a predictive model for manoeuvres within a geographic coordinate system, alongside the creation of a three-phase orbital manoeuvre model. A Non-dominant Sorting Adaptive Memetic (NSAM) algorithm is proposed in this paper, which is a two-layer multi-objective optimization algorithm that retains the advantages of evolutionary algorithms based on the population’s evolution and has an excellent local optimization ability of local search algorithms. The proposed algorithm can be used to solve multi-objective optimization problems. By comparing the target observation results before and after the satellite reconstruction simulation, it can be concluded that the orbital manoeuvring can effectively improve the observation probability and observation duration of the target at a certain speed. The orbital manoeuvre model created in this paper provides a certain methodical support for the tracking problem of moving targets at sea. Full article
(This article belongs to the Special Issue Intelligent Data Analysis with the Evolutionary Computation Methods)
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