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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of computer Science and engineering The University of AizuAizuwakamatsuFukushima965-8580Japan department of computer Science&engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

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Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

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computers, Materials & Continua 2024年第4期79卷 449-471页

作者： Yongjiang Zhao Haoyi Zhong Chang Cyoon Lim department of computer engineering Chonnam National UniversityYeosu59626South Korea

This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature in modern power *** tackle the unique challenges of voltage control in distributed renewable energy networks,researchers are increasingly turning towards multi-agent reinforcement learning(MARL).However,MARL raises safety concerns due to the unpredictability in agent actions during their exploration *** unpredictability can lead to unsafe control *** mitigate these safety concerns in MARL-based voltage control,our study introduces a novel approach:Safety-ConstrainedMulti-Agent Reinforcement Learning(SC-MARL).This approach incorporates a specialized safety constraint module specifically designed for voltage control within the MARL *** module ensures that the MARL agents carry out voltage control actions *** experiments demonstrate that,in the 33-buses,141-buses,and 322-buses power systems,employing SC-MARL for voltage control resulted in a reduction of the Voltage Out of Control Rate(%***)from0.43,0.24,and 2.95 to 0,0.01,and 0.03,***,the Reactive Power Loss(Q loss)decreased from 0.095,0.547,and 0.017 to 0.062,0.452,and 0.016 in the corresponding systems.

关键词： Power quality control multi-agent reinforcement learning safety-constrained MARL

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Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

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computer Modeling in engineering & Sciences 2024年第8期140卷 1757-1781页

作者： Haoyi Zhong Yongjiang Zhao Chang Gyoon Lim department of computer engineering Chonnam National UniversityYeosu59626South Korea

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series *** the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy *** are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP ***,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission *** methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB *** response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB *** method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal *** is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time *** tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

关键词： Lithium-ion battery abnormal state detection autoencoder virtual power plants LSTM

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Robust control of isopropyl benzene production process using H_(∞) loop shaping control scheme

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Journal of Control and Decision 2024年第1期11卷 153-163页

作者： Vinila Mundakkal Lakshmanan Aparna Kallingal Sreepriya Sreekumar department of Chemical engineering National Institute of Technology CalicutKozhikodeKeralaIndia department of Robotics and automation Adi Shankara Institute of Engineering and TechnologyKaladyIndia

This paper discusses robust control strategy for isopropyl benzene production process using the method of loop shaping H_(∞) *** cumene production process is a part of phenol plant in HOCL,Kochi.H_(∞) control of the propylene concentration is done *** H_(∞) controller is derived from the linearised model of the *** optimal H_(∞) controller is obtained by simplifying two algebraic Riccati *** proposed PID-like H_(∞) controller provides a single tuning parameter which makes the controller design more *** proposed controller has been compared with other robust controllers like H_(2) and *** H_(∞) controller is found to be superior in a wide frequency range and has a feature of low distortion and good regulating *** reactor model has been developed in COMSOL Multiphysics with the parameters obtained from HOCL plant,*** model extracted is reduced using model order reduction for the controller design.

关键词： Cumene reactor H_(∞)loop shaping model order reduction Riccati equation PID control stability robustness

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Improved Subsynchronous Oscillation Parameter Identification with Synchrophasor Based on Matrix Pencil Method in Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 22-33页

作者： Xiaoxue Zhang Fang Zhang Wenzhong Gao Jinghan He the School of Electrical engineering Beijing Jiaotong UniversityBeijing 100044China the department of Electrical and computer engineering University of DenverDenverUSA

The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power *** realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method(MPM),this paper propos es an improved MPM-based parameter identification with syn *** MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coeffi cients of the matrix pencil to ensure the accuracy of the identi fied parameters,since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation ***,the identification and recovery of bad data are proposed by uti lizing the difference in temporal continuity of the synchropha sors before and after noise *** proposed parameter identification is verified with synthetic,simulated,and actual measured phase measurement unit(PMU)*** with the existing MPM,the improved MPM achieves better accuracy for parameter identification of each component in SSOs,better real-time performance,and significantly reduces the effect of bad data.

关键词： Subsynchronous oscillations(SSOs) synchrophasor parameter identification matrix pencil method bad data

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Improved dynamic regret of distributed online multiple Frank-Wolfe convex optimization

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Science China(Information Sciences) 2024年第11期67卷 164-179页

作者： Wentao ZHANG Yang SHI Baoyong ZHANG Deming YUAN School of automation Nanjing University of Science and Technology department of Mechanical engineering University of Victoria

In this paper, we explore a distributed online convex optimization problem over a time-varying multi-agent network. The network aims to minimize a global loss function through local computation and communication with neighboring agents. To effectively handle the optimization problem which involves highdimensional and structural constraint sets, we develop a distributed online multiple Frank-Wolfe algorithm that circumvents the expensive computational cost associated with projection operations. The dynamic regret bounds are established as O(T1-γ+ HT) with the linear oracle number O(T1+γ), which depends on the horizon（total iteration number） T, the function variation HT, and the tuning parameter 0 < γ < *** particular, when the prior knowledge of HTand T is available, the bound can be enhanced to O（1 +HT）. Moreover, we explore the significant advantages provided by the multiple iteration technique and reveal a trade-off between dynamic regret bound, computational cost, and communication cost. Finally,the performance of our algorithm is validated and compared through the distributed online ridge regression problems with two constraint sets.

关键词： distributed online convex optimization multiple iterations Frank-Wolfe algorithm dynamic regret gradient tracking method

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Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

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China Communications 2024年第3期21卷 1-18页

作者： Mao Haobin Liu Yanming Zhu Lipeng Mao Tianqi Xiao Zhenyu Zhang Rui Han Zhu Xia Xianggen School of Electronic and Information engineering Beihang UniversityBeijing 100191China department of Electrical and computer engineering National University of SingaporeSingapore 117583Singapore School of Science and engineering Shenzhen Research Institute of Big Datathe Chinese University of Hong KongShenzhenGuangdong 518172China department of Electrical and computer engineering University of HoustonHoustonTX 77004USA department of computer Science and engineering Kyung Hee UniversitySeoulSouth Korea446-701 department of Electrical and computer engineering University of DelawareNewarkDE 19716USA

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

关键词： artificial intelligence(AI) sixth generation(6G) space-air-ground integrated networks(SAGINs) space/air covert communications

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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of computer Science and engineering Central South UniversityChangshaChina Hunan engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of automation Chinese Academy of SciencesBeijingChina department of computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar department of computer Science and engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan department of computer Science National University of PakistanRawalpindiPunjab46000Pakistan department of computer Software engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan department of computer engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia department of computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK department of computer engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

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computers, Materials & Continua 2024年第2期78卷 2767-2786页

作者： Asma Hassan Alshehri department of computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or *** Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their *** academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for *** primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real *** paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other *** reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are ***,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave *** performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics.

关键词： Security fake review semi-supervised learning ML algorithms review detection

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