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4th International Symposium on Electrical, electronics and information engineering, ISEEIE 2024

作者： Li, Dongfang Zhang, Yong Ma, Wenying Xue, Yajuan Wang, Liu Li, Juan Zhao, Zhiqin The School of Communication Engineering Chengdu University of Information Technology Chengdu China The School of Electronic Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798350355772

Oil pollution will damage the living environment of marine organisms and destroy the marine ecosystem. Rapid and accurate monitoring of oil spill and extraction of oil spill information are of great significance for disaster assessment and cleanup after the occurrence of oil spill. In this paper, the electromagnetic (EM) scattering characteristics in the process of marine oil spills from shallow sea environment are analyzed. In consideration of the ocean waves are affected by the hydrodynamic modulation and tilt modulation of oil film, a geometric model of oil spill in limited water depth is established from shallow sea environment. The facet-based KA-IIEM hybrid algorithm is used for EM calculation of oil spill in shallow sea environment. The variation of EM scattering intensity during oil leakage is analyzed. The preliminary EM characteristics of the sea surface with oil spill from shallow sea environment are obtained. The EM characteristic of oil spill surface in shallow water environment is analyzed. It provides important technical reference for offshore oil and gas identification and remote sensing. © 2024 IEEE.

关键词： Oil spills

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A Novel Approach of Electromagnetic Compatibility Conducted Susceptibility Analysis Based on Gaussian Even Pulse Signal

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Chinese Journal of electronics 2024年第2期33卷 472-478页

作者： Youwei MENG Yanhua PENG Haoyang ZHANG Lilin LI Donglin SU The School of Electronic and Information Engineering Beihang University The Institute of Electromagnetic Compatibility Technology Beihang University The Research Institute for Frontier Science Beihang University

The purpose of the electromagnetic compatibility conducted susceptibility test for interconnected cables in the system is to evaluate its ability to operate acceptably when subjected to interference. We propose a novel conducted susceptibility analysis approach: by injecting the Gaussian even pulse signal, we find that the susceptibility threshold of the system shows two different patterns with the change of signal parameters; then we locate the cause of the susceptibility of the device by analyzing the threshold level curves. The effectiveness of the proposed approach is verified by testing with devices containing digital modules such as navigation receivers. The proposed approach facilitates a deeper understanding of the susceptibility mechanism of systems and their appropriate electromagnetic compatibility design.

关键词： Electromagnetic compatibility Conducted susceptibility Gaussian even pulse Threshold level curves

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Residual diverse ensemble for long-tailed multi-label text classification

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science China(information sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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CNN-Transformer Architecture Solution for Compound Facial Expression Recognition 9

CNN-Transformer Architecture Solution for Compound Facial Ex...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Ullah, Sana Xie, Yuanlun Ou, Jie Tian, Wenhong University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu61173 China

ISBN: (纸本)9798350317251

True emotions can be indicated by the human facial expression of emotions. Facial expression recognition has vast applications in healthcare, security, artificial intelligence, e-learning, sports, agriculture and various other fields. Although significant research has been conducted on basic emotions, there is currently a surge of interest in recognizing compound facial expressions of emotions in the field of image processing. In this paper, we present a method that employs the vision transformer (ViT) and utilizes the DenseNet-121 as a backbone for recognizing compound facial expressions of emotions on the CFEE and RAFDB datasets. The proposed method has outperformed and improved the recognition accuracy of compound emotions compared to the state-of-the-art (SOTA) models. The obtained results demonstrate the accuracy rates of 66.4% for the CFEE dataset and 72.05% for the RAFDB dataset. The recognition accuracy was enhanced by around 9.05% on the CFEE dataset and approximately 3.62% on the RAFDB dataset in comparison to the current state of the art (SOTA), thanks to the proposed methodology. This approach tackles the current task and paves the way for future investigations in detecting complex emotions using ViT models. © 2023 IEEE.

关键词： Convolutional neural networks

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Design of Virtual Reality Teaching System for Image Fourier Transforms 9

Design of Virtual Reality Teaching System for Image Fourier ...

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9th International Symposium on Computer and information Processing technology, ISCIPT 2024

作者： Fan, Di Liu, Hongyun Wang, Yongfei Ma, Xiulong Shandong University of Science and Technology School of Electronic Information Engineering China

ISBN: (纸本)9798350388404

This paper gives the design of a Fourier transform virtual reality teaching system. The system contains modules of theoretical teaching, experimental teaching, comprehensive assessment and information management. The system produces suburban adventure scenes and laboratory scenes in 3DsMax and Unity3D;builds user interfaces such as registration and login interfaces and task enactment by using UGUI interaction technology;realizes character bone animation and character movement by using collision detection technology, character controllers, and animation systems;realizes perspective switching based on 3D mathematical design scripts;realizes data storage and reading based on Json formatting to save the learning progress;Socket communication mechanism to establish the server and client to build online chat rooms. The application shows that the system has a strong sense of immersion, good interactivity, and better realizes Fourier transform gamification teaching. © 2024 IEEE.

关键词： Animation

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Virtual Assembly Collision Detection Algorithm Using Backpropagation Neural Network

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Computers, Materials & Continua 2024年第10期81卷 1085-1100页

作者： Baowei Wang Wen You School of Computer Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment TechnologyDigital Forensics Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

As computer graphics technology continues to advance,Collision Detection(CD)has emerged as a critical element in fields such as virtual reality,computer graphics,and interactive *** is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments,particularly within complex scenarios like virtual assembly,where both high precision and real-time responsiveness are *** ongoing developments,current CD techniques often fall short in meeting these stringent requirements,resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly *** address these limitations,this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network(BPNN)to optimize the structural composition of the Hybrid Bounding Volume Tree(HBVT).Through this optimization,the research proposes a refined Hybrid Hierarchical Bounding Box(HHBB)framework,which is specifically designed to enhance the computational efficiency and precision of CD *** HHBB framework strategically reduces the complexity of collision detection computations,thereby enabling more rapid and accurate responses to collision *** experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD,particularly in handling complex *** optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates,presenting a robust and scalable solution for real-time applications in intricate virtual *** findings suggest that the proposed approach offers a substantial advancement in CD technology,with broad implications for its application in virtual reality,computer graphics,and related fields.

关键词： Collision detection virtual assembly backpropagation neural network real-time interactivity

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Memristive Binary Firefly Algorithm for White Lung Images Segmentation

Memristive Binary Firefly Algorithm for White Lung Images Se...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Shen, Jiarun Yu, Yongbin Wang, Xiangxiang Feng, Xiao Zhong, Xuefeng Wang, Jingya Han, Xinyi University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China

ISBN: (纸本)9798350316889

This paper investigates the feasibility of designing and employing swarm intelligence algorithms using memristors. As a representative swarm intelligence algorithm, the firefly algorithm is binarized and implemented through a memristor crossbar array to facilitate image threshold segmentation. Firstly, a memristor crossbar array with an external control circuit is meticulously designed, presenting both elevated device density and the potential for parallel computing. Secondly, the binary firefly algorithm is enacted through this memristor crossbar array. Finally, the method is put to practical use by applying it to the segmentation of medical images, the threshold can be directly calculated from the current and charge within the crossbar array. Notably, the error rate of our crossbar-based binary firefly algorithm implementation is significantly low compared to the brute-force method. © 2023 IEEE.

关键词： Medical imaging

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BiPath-CNet: Optimizing R-Peak Detection Leveraging Bi-Pathway Cardio Network with Spectral Temporal Attention Mechanism 10

BiPath-CNet: Optimizing R-Peak Detection Leveraging Bi-Pathw...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Arhin, Joseph Roger Zhang, Xiaoling Ansah, Ernest Owusu Ewusi, Eric George Coker, Kenneth Sam, Francis School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China School of Medical Sciences University of Bolton Bolton United Kingdom School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Ho Technical University Department of Electrical Electronic Engineering Ho Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331532895

R-peak detection is critical for accurate cardiac signal analysis, and traditional models often struggle with varying signal complexities. This study proposes a Bi-pathway cardio network (BiPath-CNet), an innovative deep learning (DL) model augmented with a Spectral Temporal Attention Mechanism (STAM) to enhance R-peak detection in electrocardiogram (ECG) signals. The proposed BiPath-CNet leverages STAM to dynamically capture spectral and temporal dependencies in ECG data, ensuring more precise feature extraction and robust peak detection. Extensive experiments demonstrate that BiPath-CNet performed better than several conventional DL models offering significant improvements in detection accuracy, recall, and precision. Integrating frequency-aware routing and temporal gating in STAM empowers the model to highlight critical signal components minimizing detection errors. This architecture offers a promising advancement in ECG signal analysis, enhancing the application of DL practices in biomedical research. © 2024 IEEE.

关键词： Deep learning

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RF Fingerprint Recognition Method Based on Lightweight Deep Learning for Doppler Resilience 20

RF Fingerprint Recognition Method Based on Lightweight Deep ...

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20th International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2023

作者： Zeng, Xin Liu, D.I. Yu, Can Lin, D.I. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Radio Frequency (RF) Fingerprint Recognition has emerged as an area of significant promise within the research community. However, its effective deployment in scenarios characterized by vehicular movement or pedestrian traffic remains a challenge, primarily due to the Doppler effect inducing frequency shifts in RF signals. To counter this, a novel Channel-Independent Spectrum Construction methodology has been proposed, aimed at mitigating the influence of Doppler frequency shifts. This study integrates this innovative approach with advanced data augmentation techniques, thereby pioneering an enhanced methodology for bolstering the resistance of RF signal fingerprint features against Doppler interference. Employing sophisticated deep learning algorithms, this research effectively extracts complex, nonlinear characteristics, thereby capturing the nuances of Doppler variations present within signals. Furthermore, this paper conducts a comprehensive comparison between traditional Convolutional Neural Networks, Deep Residual Networks, and our optimized lightweight PruMobileNet model, an adaptation of MobileNetV1. The empirical results from our study underscore the robustness of our approach in handling Doppler channels, while simultaneously achieving a paradigm shift towards low-cost, environmentally sustainable communication through model lightweighting. Notably, this methodology substantially boosts inference speed and computational efficiency, establishing a new benchmark for reliability in RF fingerprint recognition, particularly in environments with low-speed Doppler effects. © 2023 IEEE.

关键词： Doppler effect

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Research on UAV Ad Hoc Network Coverage Optimization Strategy 24

Research on UAV Ad Hoc Network Coverage Optimization Strateg...

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4th International Conference on Artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： Wang, Yijun Chen, Jiaqi Zhuang, Dawei School of Electronic Information Engineering Changchun University of Science and Technology China

ISBN: (纸本)9798400710049

In order to improve the coverage of UAV AD hoc network, reduce the loss of UAV in operation and the number of UAV involved, a network coverage optimization algorithm based on improved moth extinguishing algorithm is proposed. Firstly, the artificial potential energy method is applied to avoid obstacles before the global search, secondly, the nonlinear dynamic adaptive step factor is introduced to expand the moth search range at the beginning of the search stage, and finally, the dynamic adjustment strategy of flight direction is integrated to improve the global optimization ability during the search process, and then the optimal solution is modified by Cauchy. The simulation results show that the convergence and stability of the proposed algorithm are superior to the other four algorithms, and the connectivity between UAV nodes is improved, and the coverage rate is 18.32%, 5.48%, 5.09% and 10.6% higher than that of the other four algorithms, respectively, which effectively reduces the loss and the number of UAVs used and reduces the cost. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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