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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Computer Modeling in engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

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2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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Research on a Hyperplane Decomposition NMF Algorithm Applied to Speech Signal Separation 22

Research on a Hyperplane Decomposition NMF Algorithm Applied...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Chaofeng Lan Han Ye Yan Liu Xiaoxia Guo Yulan Han School of Measurement and Communication Engineering Harbin University of Science and Technology China

ISBN: (纸本)9781450397810

Non-negative Matrix Factorization (NMF) solves the single-channel speech separation problem. However, problem solving mostly separates the pure speech signal from the mixed speech signal with noise. This paper proposes an NMF algorithm based on hyperplane decomposition to separate speech for this problem. Based on the NMF algorithm, the observed signal is decomposed into different hyperplanes, a nonlinear mapping between the amplitude of the observed signal and the amplitude of the signal to be separated is constructed, and the speech signal is reconstructed according to the amplitude spectrum of the pure speech signal to realize the separation of the two original speech signals. The correlation coefficients between the reconstructed speech signal and the original speech signal after decomposition into different numbers of hyperplanes are compared and analyzed. The effects of different numbers of hyperplanes on the separation effect of speech signals are discussed. The study shows that when the number of hyperplanes is 20, the similarity coefficient after speech separation is 0.8356, and the separation effect is better. Meanwhile, the algorithm proposed in this paper can obtain more extracted features, which can be used as the training data of deep learning algorithm and provide some technical reference for speech separation.

关键词： Hyperplane decomposition Speech separation NMF algorithm

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Blockchain-Assisted Electronic Medical Data-Sharing:Developments,Approaches and Perspectives

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Computers, Materials & Continua 2024年第12期81卷 3421-3450页

作者： Chenquan Gan Xinghai Xiao Qingyi Zhu Deepak Kumar Jain Akanksha Saini School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Artificial IntelligenceDalian University of TechnologyDalian116024China Symbiosis Institute of Technology Symbiosis International UniversityPune412115India College of Business and Law RMIT UniversityMelbourneVIC 3000Australia

Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical *** blockchain is a decentralized digital ledger that ensures data-sharing security,transparency,and traceability through cryptographic technology and consensus ***,medical blockchain data-sharing methods have garnered significant attention and research ***,current methods have different storage and transmission measures for original data in the medical blockchain,resulting in large differences in performance and ***,we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage *** them,off-chain sharing can be subdivided into on-cloud sharing and local sharing according to whether the data is ***,we provide a detailed analysis of basic processes and research content for each ***,we summarize the challenges posed by the current methods and discuss future research directions.

关键词： Electronic medical data data-sharing medical blockchain cloud federated learning

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Robust Target Detection and Localization for UAV Autonomous Landing with Partially Occluded Targets 15th

Robust Target Detection and Localization for UAV Autonomous ...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shao, Mingmei Liu, Yanyan Tang, Jiaze Liu, Huanyu Li, Junbao Faculty of Computing Harbin Institute of Technology Harbin150080 China National Key Laboratory of Electromagnetic Space Security Tianjin300308 China Electrical Measurement Technology and Intelligent Control Institute School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

This paper presents an approach for robust target detection and localization in the context of UAV autonomous landing with partially occluded targets. Since the performance of the autonomous landing system largely depends on the accurate detection and positioning of the landing target, it is very important to deal with the local occlusion caused by obstacles or environmental factors. In order to improve the overall performance of the system, we propose a fault-tolerant target detection method and target completion strategy. The proposed target detection method combines image processing technology with feature extraction algorithm, which can recognize and locate the landing target even in the case of occlusion. The target completion strategy aims to recover the missing part of the occluded target and make the detection and location algorithm execute more reliably. We perform experiments on image data sets with different horizontal object occlusion to evaluate the performance of the proposed method. The results show that our method successfully detects and locates the landing target under different occlusion levels, and significantly improves the performance of UAV autonomous landing system in the real scene. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Graph Convolutional Networks with Visual Masking and Social Interaction Models for Pedestrian Trajectory Prediction

Graph Convolutional Networks with Visual Masking and Social ...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Guopei Zeng Luping Wang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd Zhejiang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Pedestrian trajectory prediction is critical for autonomous driving, crowd analysis, and urban surveillance. To address insufficient interaction modeling in existing methods, we propose two enhancements. First, a field-of-view(FoV)-aware masking mechanism filters irrelevant interactions by dynamically adjusting to pedestrian distances and motion directions. Second, we introduce a more detailed modeling of the influence among pedestrians, replacing the traditional modeling based on reciprocal of distance. This model, integrated with FoV masks to construct sparse adjacency matrices for graph convolution. A temporal convolutional network then predicts trajectories as bivariate Gaussian distributions. Evaluations on ETH and UCY benchmarks demonstrate the effectiveness of our method.

关键词： Analytical models Visualization Pedestrians Filters Surveillance Predictive models Benchmark testing Vectors Trajectory Sparse matrices

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Monitoring and Early Warning System for Bolt Looseness of the Electric Power Tower Based on Capacitance Sensor and LoRa Network 42

Monitoring and Early Warning System for Bolt Looseness of th...

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42nd Chinese control Conference, CCC 2023

作者： Zhao, Liang Chen, Changxin Zhang, Qianru North University of China State Key Laboratory of Dynamic Measurement Technology Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

ISBN: (纸本)9789887581543

In order to solve the problem of the electric power tower collapsed caused by bolt loosening, a monitoring and early warning system for bolt loosening of the electric power tower based on LoRa network is designed in this manuscript. A non-contact measurement method and height-dividing area management for bolt is used, and its rationality is verified by simulation with Ansoft Maxwell or experiment. The results show that the capacitance of the bolt looseness test module can change obviously when the bolt or nut rotates 10 degrees, and the error of the height measurement module is 6.4%. The LoRa network can realize remote monitoring, so a monitoring system designed can meet the requirements of remote on-line monitoring of bolts to reduce accidents by bolt looseness. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Capacitance

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MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT images via a Deep Learning Architecture

MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT ...

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Tang, Xiaoyu Chai, Lin Song, Chengze School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China School of Sino-french Engineering Institute Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781837242689

Computer-aided diagnosis has emerged as a crucial trend in modern medicine. Recently, CT-based automatic detection algorithms for lung infectious diseases suffer from issues of low accuracy and complex architecture. In this paper, three MoB-NetV2 models (MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL) for the detection of COVID-19 in CTs are proposed. the MoB-NetV2 model is proposed by inserting batch normalization layers between bottlenecks in MobileNetV2 and substituting the fully connected layer with the Net-1 model (SNN, ELM, RVFL), i.e. MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL. After comparison, it is found that the proposed MoB-NetV2-RVFL achieves an average accuracy of 93.42% on a private dataset comprising of 2392 images, while the precision is reached at 97.33%. Furthermore, it is demonstrated that our MoB-NetV2-RVFL outperforms other existing algorithms in the literature. High classification accuracy and precision and low computing amount are achieved, thereby enhancing the potential application of the model in the field of medical image-aided diagnosis of COVID-19 virus. © The Institution of engineering & technology 2024.

关键词： COVID-19

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