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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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MDGCN-Lt:Fair Web API Classification with Sparse and Heterogeneous Data Based on Deep GCN

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1294-1314页

作者： Boyuan Yan Yankun Zhang Wenwen Gong Haoyang Wan Wenwei Wang Weiyi Zhong Caixia Bu Department of School of Computer Science Qufu Normal UniversityRizhao 276500China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China Department of Univalsoft Joint Stock Co. Ltd.Weifang 261000China

Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that *** develop edge applications,developers search API categories to select APIs that meet specific ***,the accurate classification of APIs becomes critically ***,existing approaches,as evident on platforms like ***,face significant ***,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API ***,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data ***,the long-tailed distribution of API data introduces biases,compromising the fairness of classification *** these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous *** tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API ***-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data *** results affirm our approach's superiority over existing methods.

关键词： bidirectional encoder representations from transformers deep graph convolutional networks logit adjustment web application programming interface classification application programming interface correlation graph

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Automobile Entry System Based on Face Recognition

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 573-585页

作者： Fang, Weidong Yu, Zuoxi Pan, Jeng-Shyang School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Chaoyang University of Technology Taichung413310 Taiwan

With the development of technology, the automobile has become an indispensable part of people’s daily lives. People’s needs for automobile entry systems have also changed, in automobile safety and ease of use have become more and more important. In recent years, face recognition technology has made significant progress, and face recognition technology has been widely used in various fields, especially face recognition based on deep learning has great advantages in accuracy, recognition speed, and security, and can provide a more secure and reliable way of identity verification. Traditional automobile entry systems usually use mechanical keys and remote control keys, which do not remove the key and have certain shortcomings in security and user experience. Face recognition-based car entry systems can make up for these shortcomings and provide a more convenient and intuitive user experience. Combining face recognition with automobiles is also a hot topic in current scientific research. In this paper, a set of automobile entry systems with high efficiency and security is designed according to the face recognition method research, using three deep learning models: face detection, live body detection, and face recognition. In face detection, the RetinaFace lightweight model is used the network structure is improved, and the detection speed is increased by 14.9%. For face live detection and face recognition, the MobileFaceNet lightweight network is used as the base network for live detection and face recognition, achieving a 98.9% accuracy rate on the CelebA Spoof live detection dataset. In face recognition, feature extraction is performed on the detected faces after face detection, and the recognition results are output by comparing with the recorded faces. Improvements to its network improved the recognition accuracy by 0.18%, 0.77%, and 0.73% on the LFW, CFP FP, and AgeDB30 datasets, respectively. The model was deployed on Raspberry Pi and connected to CANoe via CAN bu

关键词： Face recognition

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PIOD: An Efficient Parallel Iterative Algorithm for Solving Over-determined Equations 29

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Wei, Zhenguo Li, Yanlong Du, Wei Xiao, Lei Xu, Chaoshuai China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%. © 2023 IEEE.

关键词： conjugate gradient method MPI over-determined equations parallel algorithms parallel iterative methods

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Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

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Tsinghua science and technology 2024年第4期29卷 1206-1218页

作者： Yuwen Liu Yulan Zhang Xingyuan Mao Xucheng Zhou Jingwen Chang Wenwei Wang Pan Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Modern Posts Nanjing University of Posts and TelecommunicationsNanjing 210023China

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

关键词： attention mechanism facies classification GRU MLP(multilayer perceptron)

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Learning Unbiased Rewards with Mutual Information in Adversarial Imitation Learning 48

Learning Unbiased Rewards with Mutual Information in Adversa...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Lihua Liu, Quan Huang, Zhigang Wu, Lan Soochow University School of Computer Science and Technology Suzhou China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou China

ISBN: (纸本)9781728163277

A powerful method for automated decision systems is Adversarial Imitation Learning (AIL). It is based on a generative adversarial framework that alternately optimizes a generator (learner) and a discriminator (reward function). In the popular mind, a high-accuracy discriminator results in informative rewards, thus AIL must balance the performance between a generator and a discriminator. However, we find that the AIL reward function is biased and also results in informative rewards. Thus, we theoretically analyze the bias in the AIL reward function and find that balancing the performance of a generator and a discriminator is not necessary when we recover an unbiased reward function. Further, we propose a mutual information based auxiliary reward function. Experiments on continuous control tasks indicate that MI-GAIL is able to address the bias problem of the AIL reward function and further improve sample efficiency and training stability compared with up-to-date algorithms. © 2023 IEEE.

关键词： Adversarial imitation learning Continuous control Mutual information Potential-based reward function Reward bias

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A Perturbation-Based Policy Distillation Framework with Generative Adversarial Nets 48

A Perturbation-Based Policy Distillation Framework with Gene...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Lihua Liu, Quan Zhang, Xiongzhen Xu, Yapeng Soochow University School of Computer Science and Technology Suzhou China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou China

ISBN: (纸本)9781728163277

We study the problem of imitation learning in automated decision systems, in which a learner is trained to imitate an expert demonstrator. A widely used method is adversarial imitation learning that alternately optimizes a generator (learner) and a discriminator (reward function). However, the discriminator is biased during the initial and intermediate training stages. Consequently, the gradient descent direction of the learner is misguided, which leads to unstable training and sample complexity. In this paper, we propose deep imitation learning through a guidance-based policy distillation (GIL) algorithm. First, GIL proposes a teacher model, the guidance-based variational autoencoder, which is pre-trained with expert demonstrations. Then, GIL proposes a perturbation-based policy distillation method that uses the teacher model to guide the learner in the correct optimization direction, enabling the learner to imitate the expert policy with fewer detours. The experimental results show that our approach achieve higher sample efficiency compared with multiple baselines. © 2023 IEEE.

关键词： Distillation

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Control Flow Divergence Optimization by Exploiting Tensor Cores 24

Control Flow Divergence Optimization by Exploiting Tensor Co...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Pang, Weiguang Jiang, Xu Liu, Songran Qiao, Lei Fu, Kexue Gao, Longxiang Yi, Wang University of Electronic Science and Technology of China China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Engineering Northeastern University China Beijing Institute of Control Engineering Beijing China Uppsala University Sweden

ISBN: (纸本)9798400706011

Kernels are scheduled on Graphics Processing Units (GPUs) in the granularity of GPU warp, which is a bunch of threads that must be scheduled together. When executing kernels with conditional branches, the threads within a warp may execute different branches sequentially, resulting in a considerable utilization loss and unpredictable execution time. This problem is known as the control flow divergence. In this work, we propose a novel method to predict threads' execution path before the launch of the kernel by deploying a branch prediction network on the GPU's tensor cores, which can efficiently parallel run with the kernels on CUDA cores, so that the divergence problem can be eased in a large extent with the lowest overhead. Combined with a well-designed thread data reorganization algorithm, this solution can better mitigate GPUs' control flow divergence problem. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Tensors

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FedTC:A Personalized Federated LearningMethod with Two Classifiers

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computers, Materials & Continua 2023年第9期76卷 3013-3027页

作者： Yang Liu Jiabo Wang Qinbo Liu Mehdi Gheisari Wanyin Xu Zoe L.Jiang Jiajia Zhang School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055China Research Center for Cyberspace Security Peng Cheng LaboratoryShenzhen518055China

Centralized training of deep learning models poses privacy risks that hinder their *** learning(FL)has emerged as a solution to address these risks,allowing multiple clients to train deep learning models collaborativelywithout sharing ***,FL is vulnerable to the impact of heterogeneous distributed data,which weakens convergence stability and suboptimal performance of the trained model on local *** is due to the discarding of the old local model at each round of training,which results in the loss of personalized information in the model critical for maintaining model accuracy and ensuring *** this paper,we propose FedTC,a personalized federated learning method with two classifiers that can retain personalized information in the local model and improve the model’s performance on local *** divides the model into two parts,namely,the extractor and the classifier,where the classifier is the last layer of the model,and the extractor consists of other *** classifier in the local model is always retained to ensure that the personalized information is not *** receiving the global model,the local extractor is overwritten by the globalmodel’s extractor,and the classifier of the globalmodel serves as anadditional classifier of the localmodel toguide local *** FedTCintroduces a two-classifier training strategy to coordinate the two classifiers for local model *** results on Cifar10 and Cifar100 datasets demonstrate that FedTC performs better on heterogeneous data than current studies,such as FedAvg,FedPer,and local training,achieving a maximum improvement of 27.95%in model classification test accuracy compared to FedAvg.

关键词： Distributed machine learning federated learning data hetero-geneity non-independent identically distributed

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