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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Jin, Tao Feng, Shuo Zhou, Hongjuan Wang, Chenxu Zhou, Zhiquan Harbin Institute of Technology Weihai China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai China Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems Weihai264209 China

ISBN: (纸本)9798350351019

With the further development of marine resources, more and more attention has been paid to the research of marine communication systems, accurate and timely prediction of radio wave propagation loss under different wind speeds is of great significance to the practical application of marine wireless communication equipment. This paper is based on the parabolic equation method and machine learning algorithms to predict the radio wave propagation loss under different sea conditions, in order to achieve the performance prediction of maritime communication systems. Through comparative analysis, this paper finds that the prediction performance index of back propagation (BP) neural network method is better than that of random forest (RF) model and long short-term memory (LSTM) model under different wind speeds. However, with the increase of wind speed, the prediction performance decreases, and further model optimization is needed or explore other methods. © 2024 IEEE.

关键词： Marine communication

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Side-channel analysis attacks based on deep learning network

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Frontiers of Computer Science 2022年第2期16卷 35-45页

作者： Yu OU Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

There has been a growing interest in the sidechannel analysis(SCA)field based on deep learning(DL)*** DL network or model has been developed to improve the efficiency of ***,few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate *** on the convolutional neural network and the autoencoder,this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three *** TAPDC model detects the periodicity of power trace,relating power to the intermediate values and mining the deeper features by the multi-layer convolutional *** implement the TAPDC model and compare it with two classical models in a fair *** evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption ***,Using the classifier layer,this model links power information to the probability of intermediate *** completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

关键词： side-channel analysis template attack machine learning deep learning

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Lightweight Multilevel Feature Fusion Network for Hyperspectral Image Classification 5

Lightweight Multilevel Feature Fusion Network for Hyperspect...

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5th International Conference on information Communication and Signal processing, ICICSP 2022

作者： Huang, Quanyu Tang, Haojin Xie, Weixin Li, Yanshan Shenzhen University Atr National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781665485890

Hyperspectral image (HSI) classification is the key technology of remote sensing image processing. In recent years, convolutional neural network (CNN), which is a powerful feature extractor, has been introduced into the field of HSI classification. Since the features of HSI are the basis of HSI classification, how to effectively extract the spectral-spatial features from HSI with CNN has become a research hotspot. The HSI feature extraction network, based on two-dimensional (2D) and three-dimensional (3D) CNN which can extract both spectral and spatial information, may lead to the increase of parameters and computational cost. Compared with 2D CNN and 3D CNN, the number of parameters and computational cost of one-dimensional (1D) CNN will be greatly reduced. However, 1D CNN based algorithms can only extract the spectral information without considering the spatial information. Therefore, in this paper, a lightweight multilevel feature fusion network (LMFFN) is proposed for HSI classification, which aims to achieve efficient extraction of spectral-spatial features and to minimize the number of parameters. The main contributions of this paper are divided into the following two points: First, we design a hybrid spectral-spatial feature extraction network (HSSFEN) to combine the advantages of 1D, 2D and 3D CNN. It introduces the idea of depthwise separable convolution method, which effectively reduces the complexity of the proposed HSSFEN. Then, a multilevel spectral-spatial feature fusion network (MSSFFN) is proposed to further obtain more effective spectral-spatial features, which effectively fuses the bottom spectral-spatial features and the top spectral-spatial features. To demonstrate the performance of our proposed method, a series of experiments are conducted on three HSI datasets, including Indian Pine, University of Pavia, and Salinas Scene datasets. The experimental results indicate that our proposed LMFFN is able to achieve better performance than the manual f

关键词： Convolution

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SERGCN: Graph Convolutional Neural Networks with Skip-Edge Residual Connection for Node Classification Tasks 6

SERGCN: Graph Convolutional Neural Networks with Skip-Edge R...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Qin, Aqian He, Guoqing Wang, Huiwei College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing Chongqing404100 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798350353594

Graph Convolutional Networks (GCNs) have been widely used for node categorization, graph embedding, and link prediction, etc. Two main challenges regarding developing deep GCNs for node classifying are overfitting and oversmoothing. The main causes of oversmoothing are overfitting and training barriers, which is directly caused by gradient disappearance. To resolve this issue, we propose the Skip-Edge Residual Graph Con-volution Network (SERGCN), which extracts a certain proportion of edge information at each layer in the novel way and integrates the initial information and the information of the previous layer in the convolution process, so as to prevent and slow down the gradient disappearance and further oversmoothing. Based on extensive experiments and real-world data sets used in several benchmarks, our proposed solution incorporates the benefits of a deep architecture while equaling or even exceeding the accuracy of existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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Online Identification of Boost Converter Components with Multiple Updating RLS Algorithm 42

Online Identification of Boost Converter Components with Mul...

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42nd Chinese Control Conference, CCC 2023

作者： Zhou, Tong Zhang, Zhicheng Zuo, Zhiqiang Wang, Yijing Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789887581543

In this paper, component parameters of the boost converter are identified online using a multiple updating recursive least squares (MURLS) algorithm. The component parameters, such as resistance, inductor inductance and capacitor capacitance, are obtained directly through the identification procedure rather than transfer function coefficients. The MURLS algorithm is applied to improve the rapidity of system identification compared to the traditional recursive least squares (RLS) algorithm, which is verified by a comparative simulation between MURLS and RLS and the simulation of a load-switching scenario. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Parameter estimation

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State-of-Charge Balancing Control for Battery Energy Storage systems of Near-Space Vehicles with an Electric-Thermal-Aging Coupling Model 42

State-of-Charge Balancing Control for Battery Energy Storage...

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42nd Chinese Control Conference, CCC 2023

作者： Zuo, Zhiqiang Du, Yunhao Wang, Yijing Zhang, Zhicheng Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9789887581543

State-of-charge (SoC) balancing is crucial for improving the efficiency and lifetime of the battery energy storage system in near-space vehicles. In this paper, the SoC balancing control problem is investigated by a coupling battery model with electric-thermal-aging dynamics. Firstly, a system identification experiment is carried out to obtain the internal parameters of the battery. Secondly, a weight optimization index is designed by integrating the balancing speed and battery state-of-health (SoH). Then, a receding horizon control algorithm is proposed to realize multi-unit battery SoC balancing with partial swarm optimization (PSO). Finally, the effectiveness of the proposed strategy is verified by simulation results. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Battery management systems

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of intelligent and Fuzzy systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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A Bi-selection Method Based on Consistent Matrix for Large-Scale Datasets

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IEEE Transactions on Fuzzy systems 2025年第6期33卷 1992-2005页

作者： Quan, Jinsheng Qiao, Fengcai Yang, Tian Shen, Shuo Qian, Yuhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Institute of Interdisciplinary Studies Changsha410081 China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410081 China Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematics model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a β-consistent granulation method to generate more accurate and concise fuzzy information granules, reducing the impact of noisy samples. In addition, a novel membership function is employed to distinguish noise while simultaneously evaluating both features and samples. As a result, a biselection algorithm with linear computation complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency. © 2025 IEEE.

关键词： Rough sets Noise Feature extraction Classification algorithms Accuracy Machine learning information systems Data models Computational efficiency Approximation algorithms

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Image Quality Assessment Method Based on Cross-Modal 20th

Image Quality Assessment Method Based on Cross-Modal

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Long, Teng Chen, Li Wang, Yubo School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9789819970247

Deep learning methods have achieved remarkable results in the direction of image quality assessment tasks. However, most of the related studies focus only on image unimodal, ignoring the potential advantages that come with the development of cross-modal techniques. Cross-modal models have implied a wealth of information, which provides new research directions and possibilities in the field of image quality assessment. In this paper, the feasibility of cross-modal models in image quality assessment is first explored for the image quality binary classification task. Subsequently, the optimization prompting method is combined with the tuning of the image encoder in the cross-modal model so that the cross-modal model can be used for image quality assessment scoring. To verify the feasibility of the cross-modal model on the image quality assessment task, an empirical analysis was conducted on the binary image quality dataset PQD, and it was found that the F1 score improved by 18% over the baseline model. Further, we propose an adaptive cross-modal image quality assessment method AC-IQA. On the image quality scoring dataset, compared with the previous optimal methods, AC-IQA improves the PLCC and SROCC metrics on the TID2013 dataset by 5.5% and 9.5%, respectively, and on the KADID dataset by 6.2% and 5.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image quality

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