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

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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Analysis of CNN and Feed-Forward ANN Model for the Evaluation of ECG Signal

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Current Signal Transduction Therapy 2023年第1期18卷 1-8页

作者： Mathur, Prerak Sharma, Tanu Veer, Karan Department of Instrumentation and Control Engineering Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar India

Background: Heart disease is considered one of the complex diseases that has affected a large number of people around the world. It is important to detect and identify cardiac diseases at early stages. Objective: A large number of methods are already present that detect various heart diseases;how-ever, there are some limitations to these methods, that have degraded their overall performance. Methods: In this paper, an effective and efficient method based on a convolutional neural network (CNN) and a feed-forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purposes. Results: The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices. Conclusion: The simulated outcomes have proved the proposed CNN-FFANN model to be more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications. © 2023 Bentham science Publishers.

关键词： artificial intelligence artificial neural network biomedical applications cardiac diseases ECG signal health monitoring system

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Transit search optimized least squares support vector machine for the state of health estimation of lithium-ion batteries

Transit search optimized least squares support vector machin...

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2024 China Automation Congress, CAC 2024

作者： Wang, Kangjie Cui, Fangshu Shi, Yuanhao Wang, Bohui School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

The State of Health (SOH) of lithium-ion batteries is a critical parameter that mirrors the performance of their health monitoring systems. To achieve accurate prediction of SOH, we propose a novel prediction model. The model combines the advantages of variational mode decomposition (VMD) and transit search optimization- least squares support vector machine (TSOA-LSSVM). The constant-current charging time (CCCT), constant-current discharging time (CCDT), and Constant voltage charging time(CVCT) with Internal resistance(IR) are extracted as health indicators (HIs), and the correlations between HIs and SOH are evaluated by using Spearman correlation coefficient (spearman) respectively. With the VMD method, the most correlated HI is decomposed into multiple intrinsic modal functions (IMFs), and each IMF is fitted by the LSSVM. TSOA is used to obtain the most appropriate hyperparameters. The efficacy of the newly developed VMD-TSOA-LSSVM model is assessed by utilizing battery aging data sourced from the Center for Advanced Life Cycle engineering (CALCE). Its performance is then compared to that of the solo LSSVM, the VMD-LSSVM, and the VMD-TSOA-SVM methods. The laboratory outcomes demonstrate that the VMD-TSOA-LSSVM model outperforms other models in prediction and has better robustness and generalization ability. © 2024 IEEE.

关键词： Variational mode decomposition

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State of health estimation of lithium-ion batteries based on TiDE

State of health estimation of lithium-ion batteries based on...

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2024 China Automation Congress, CAC 2024

作者： Li, Hui Cui, Fangshu Shi, Yuanhao Wang, Bohui School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350368604

Given the widespread use of lithium-ion batteries, accurately forecasting their State of Health (SOH) is crucial for ensuring the secure and reliable operation of equipment. The local capacity regeneration during battery degradation can undermine prediction accuracy. This research introduces an MDT-FOA+TiDE approach for predicting SOH in lithium-ion batteries using multidimensional time series data. Initially, capacity and various feature data from the degradation process were collected and health indicators were derived. Features with high correlation to capacity were integrated into multidimensional time series. Subsequently, the Time-series Dense Encoder (TiDE) model was employed for training and prediction, while the Fox Optimization Algorithm (FOA) was used for model optimization. Experimental results using the NASA dataset demonstrate that the proposed approach outperforms SVR and LSTM models that utilize univariate data and MDT-TiDE model without FOA optimization. © 2024 IEEE.

关键词： lithium-ion batteries local capacity regeneration multidimensional time series state of health

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Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Fall Detection using NAO Robot Pose Estimation in RoboCup SPL Matches 9

Fall Detection using NAO Robot Pose Estimation in RoboCup SP...

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9th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2022

作者： Zampino, Cristian Biancospino, Flavio Brienza, Michele Laus, Francesco Di Stefano, Gianluca Romano, Rocchina Pennisi, Andrea Suriani, Vincenzo Bloisi, Domenico Daniele Department of Mathematics Computer Science and Economics University of Basilicata Potenza Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

RoboCup is an International robotics initiative whose aim is to promote robotics and AI research. RoboCup's long-term goal is to create a fully autonomous humanoid robot team capable of competing and winning a soccer game against the human World champion team, in compliance with the official rules of FIFA, by 2050. In this paper, we describe a two-step method for action recognition. In the first step, we extract the pose of the robots using a pose detector trained on a novel dataset for pose estimation called UNIBAS NAO Pose Dataset, which is a contribution of this work. In the second step, a Spatial-Temporal Graph Convolutional Network is used for modeling the gameplay, with particular regard to fall-down detection. Experimental results show the effectiveness of our approach in detecting falls for humanoid robots. © 2022 Copyright for this paper by its authors.

关键词： Sports

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Weighted syncmer sampling improves long-read mapping

Weighted syncmer sampling improves long-read mapping

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Quan, Wei Kang, Jinjun Deng, Yanfei Liu, Zhuang Zhu, Xiao Quan, Guangri Yantai University School of Computer and Control Engineering Yantai China Xinjiang University of Political Science and Law School of Information and Network Security Tumxuk China

ISBN: (纸本)9798350386226

Syncmers represent a novel class of methods for selecting k-mers that exhibit robustness against mutations in flanking sequences and demonstrate superior conservation in mutated sequences. Nevertheless, syncmers may generate a higher frequency of repetitive seed matches compared to alternative techniques, such as minimizers, which can result in increased computational time. In this article, we introduce weighted minimizer sampling, which integrates weighted minimizer sampling and syncmer sampling to enhance the sensitivity and accuracy of long-read mapping. We modified two state-of-the-art long-read mappers, Minimap2 and Winnowmap, by substituting the sketching sampling methods with weighted minimizer sampling. We assessed their sensitivity and accuracy using simulated and real datasets. The experimental results indicate that weighted minimizer sampling significantly improves the sensitivity and accuracy of long-read mapping. The source code is available at GitHub: https://***/hexinkaoan/weighted-syncmer. © 2024 IEEE.

关键词： Mapping

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Sequential Masking Imitation Learning for Handling Causal Confusion in Autonomous Driving 8th

Sequential Masking Imitation Learning for Handling Causal C...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Zhang, Huanghui Zheng, Zhi College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789819975891

Training agents for autonomous driving using imitation learning seems like a promising way since its only requirement is the demonstration from expert drivers. However, causal confusion is a problem existing in imitation learning, which is that with more features offered, an agent may perform even worse. Here, we aim to augment agents’ imitation ability in driving scenarios under sequential setting, by a novel method we proposed: Sequential Masking Imitation Learning(SEMI). First, we train a Vector Quantised-Variational AutoEncoder(VQ-VAE) to encode a sequence of images into a latent representation with discrete codes. After that we deploy several masks on the encoded images, the masks here will randomly hide some semantic objects in the encoded images. Finally, we design the behavior clone network as a predictor of expert action, using an encoded and masked image sequence as input, encouraging the network to make expert-like predictions when some partition of information about the environment is missing. The masking procedure in SEMI helps the imitator identify the contribution of each encoded feature to the expert’s prediction. We demonstrate that this method could alleviate causal confusion in driving simulation by deploying it to the CARLA simulator, and compared it with other methods. Experimental results show that SEMI can effectively reduce confusion in autonomous driving. The agent trained with SEMI method reduces the collision rate by 45 % compared to methods without masking procedure, and obtain the highest average survival timesteps among competing methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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