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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Lin, Xiaohui Li, Zuoyong Huang, Xunhua Chen, Xinwei Fan, Haoyi Fuzhou University College of Computer and Data Science Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Minjiang University Fujian Engineering and Research Center of New Chinese Lacquer Materials Fuzhou350121 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

ISBN: (纸本)9798350310153

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE. © 2022 IEEE.

关键词： Contamination

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Neighborhood Combination Search for Single-Machine Scheduling with Sequence-Dependent Setup Time

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Journal of computer Science & Technology 2024年第3期39卷 737-752页

作者：刘晓路徐宏云陈嘉铭苏宙行吕志鹏丁俊文 College of System Engineering National University of Defense TechnologyChangsha 410015China School of Artificial Intelligence Jianghan UniversityWuhan 430056China Xi’an Satellite Control Center Xi’an 710043China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China CCF

In a local search algorithm,one of its most important features is the definition of its neighborhood which is crucial to the algorithm's *** this paper,we present an analysis of neighborhood combination search for solv-ing the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness(SMSWT).First,We propose a new neighborhood structure named Block Swap(B1)which can be con-sidered as an extension of the previously widely used Block Move(B2)neighborhood,and a fast incremental evaluation technique to enhance its evaluation ***,based on the Block Swap and Block Move neighborhoods,we present two kinds of neighborhood structures:neighborhood union(denoted by B1UB2)and token-ring search(denoted by B1→B2),both of which are combinations of B1 and ***,we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms:the Iterated Local Search Algorithm(ILSnew)and the Hybrid Evolutionary Algorithm(HEA_(new))to investigate the performance of the neighborhood union and token-ring ***-sive experiments show the competitiveness of the token-ring search combination mechanism of the two *** on the 120 public benchmark instances,our HEA_(new)has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent *** have also tested the HEA,new algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup *** is able to match the optimal or the best known results for all the 64 *** particular,the computational time for reaching the best well-known results for five chal-lenging instances is reduced by at least 61.25%.

关键词： single-machine scheduling sequence-dependent setup time neighborhood combination search token-ring search hybrid evolutionary algorithm

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the university of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Comparison of pulse rate variability from post-auricula and heart rate variability during different body states for healthy subjects

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Journal of Medical engineering and Technology 2023年第3期47卷 179-188页

作者： Qi, Yusheng Zhang, Aihua Ma, Yurun Chang, Tingting Xu, Jianwen College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology Lanzhou China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology Lanzhou China College of Computer and Communication Lanzhou University of Technology Lanzhou China

Heart rate variability (HRV) extracted from the electrocardiogram (ECG) is an essential indicator for assessing the autonomic nervous system in clinical. Some scholars have studied the feasibility of pulse rate variability (PRV) instead of HRV. However, there is little qualitative research in different body states. In this paper, the photoplethysmography (PPG) of postauricular and finger and the ECG of fifteen subjects were synchronously collected for comparative analysis. The eleven experiments were designed according to the daily living state, including the stationary state, limb movement state, and facial movement state. The substitutability of nine variables was investigated in the time, frequency, and nonlinearity domain by Passing Bablok regression and Bland Altman analysis. The results showed that the PPG of the finger was destroyed in the limb movement state. There were six variables of postauricular PRV, which showed a positive linear relationship and good agreement (p > 0.05, ratio (Formula presented.) 0.2) with HRV in all experiments. Our study suggests that the postauricular PPG could retain the necessary information of the pulse signal under the limb movement state and facial movement state. Therefore, postauricular PPG could be a better substitute for HRV, daily PPG detection, and mobile health than finger PPG. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Photoplethysmography

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ASLiquid: Non-Intrusive Liquid Counterfeit Identification with Your Earphones 24

ASLiquid: Non-Intrusive Liquid Counterfeit Identification wi...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Li, Zhaohui Luo, Wei Zhang, Yongmin Chen, Jianxi Shu, Yuanchao Zhang, Yaoxue School of Computer Science and Engineering Central South University China College of Control Science and Engineering Zhejiang University China Department of Computer Science and Technology BNRist Tsinghua University China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798400706974

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density. © 2024 Copyright is held by the owner/author(s).

关键词： Earphones

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Identification of Partial Discharge Fault Type and Sensitivity Analysis of Joint Detection Based on Clustering Algorithm 3

Identification of Partial Discharge Fault Type and Sensitivi...

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3rd International Conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Feng, Qi Shao, Zhenhua Wenzhou University College of Electrical and Electronic Engineering Wenzhou China Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9781665417327

Partial discharge (PD) is the initial manifestation of insulation deterioration in power equipment. When PD accumulates to a certain extent, it will lead to equipment damage. PD detection in advance is an effective method to prevent insulation deterioration. In this paper, AE method, TEV method and UHF method are used to study the internal discharge, suspension discharge and surface discharge respectively, pointing out the limitations of a single detection method, and using clustering method to extract the discharge characteristic parameters of different defect types to classify. The results of K-means clustering show that the accuracy of internal discharge is 83%, surface discharge is 65%, and suspension discharge is 66%. FCM clustering results show that the accuracy of internal discharge is 63%, surface discharge is 60%, and suspension discharge is 50%. The results of hierarchical clustering show that the accuracy of internal discharge is 73%, surface discharge is 70%, and suspension discharge is 67%. From the perspective of sensitivity, TEV method has the highest sensitivity, AE method and UHF method have their advantages in the sensitivity of different discharge defects. In order to overcome the limitation of a single detection method, combined with the experimental results and the actual noise interference and local location requirements, the application of multi-means combined detection is analyzed to provide some reference for the selection of joint detection method. UHF/AE method can avoid electromagnetic interference. UHF/TEV method can avoid acoustic interference;TEV/AE method can improve the localization accuracy. © 2021 IEEE.

关键词： Deterioration

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Myocardial segmentation of 2D echocardiography based on the improved self-attention mechanism 2

Myocardial segmentation of 2D echocardiography based on the ...

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2nd International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2024

作者： Li, Delong Sun, Yixin Han, Xue Dong, Suyu College of Computer and Control Engineering Northeast Forestry University Harbin China The Department of Ultrasound Harbin Medical University Cancer Hospital Harbin China

ISBN: (纸本)9798331543037

The measurement of different clinical indicators, such as the myocardial wall's thickness and the ventricle's volume, depends on accurately segmenting the left ventricle in 2D echocardiography. This segmentation is essential for making various clinical decisions, such as whether surgery is necessary. However, segmenting the myocardium requires clinical expertise and makes accurate segmenting time-consuming due to the poor quality of echocardiograms. It is essential to develop an automatic myocardial segmentation method. Some deep learning methods, especially the U-net structure, were proposed to support the automatic myocardium segmentation in echocardiography. The model must simultaneously capture the local and global dependency relationships in the ultrasound images for myocardial segmentation in echocardiography. Given the inherent attribute of local operation of convolution operations, this poses significant limitations for U-Net based on the traditional CNN as the basic architecture. For example, when dealing with complex myocardial textures and structures, we need to accurately identify the myocardium's global morphological features and overall structural patterns. Moreover, this locality may lead to deviations and errors when segmenting the boundary regions or the myocardial parts closely related to the surrounding tissues. Our primary focus will be redefining the skip connection using the attention mechanism. More integrated attention mechanisms can further enhance the segmentation effect of myocardial in echocardiography. Therefore, we integrated the cross-attention mechanism when concatenating features in the skip connection. Through conducting rigorous ablation studies on multiple echocardiography segmentation datasets, the performance of the proposed mechanism was proven. ©2024 IEEE.

关键词： Echocardiography

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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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Improving Power Response of Single-controllable VSG-Based Active Distribution Network

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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