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Performance-Guaranteed Finite-Time Tracking of Strict-Feedback Systems with Unknown Control Directions: A Novel Switching Mechanism

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IEEE Transactions on Automatic Control 2025年第6期70卷 4061-4068页

作者： Mao, Bing Wu, Xiaoqun Fan, Ziye Lu, Jinhu Chen, Guanrong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Wuhan University School of Mathematics and Statistics Hubei430072 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

The problem of achieving performance-guaranteed finite-time exact tracking for uncertain strict-feedback nonlinear systems with unknown control directions is addressed. A novel logic switching mechanism with monitoring functions is designed to deal with unknown control directions. This approach is different from existing methods like the Nussbaum gain technique and smooth orientation functions, which are inapplicable to finite-time exact tracking. The main challenge lies in designing monitoring functions and determining switching rules, which is well resolved through three steps. Most significantly, in contrast to existing works that solely guarantee bounded tracking or asymptotic exact tracking, the new mechanism incorporates novel integral sliding mode techniques to achieve finite-time exact tracking with guaranteed performance. Finally, simulations are presented with comparison to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Feedback

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Cooperative and Dynamic UAV-Based Mobile Sink Deployment using Deep Reinforcement Learning 11

Cooperative and Dynamic UAV-Based Mobile Sink Deployment usi...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Shao, Mingyue Luo, Hanjiang Ruby, Rukhsana Dong, Pengyan Tao, Hang Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350345346

In maritime emergency response operations, autonomous underwater vehicles (AUVs) can perform underwater search and transmit emergency data in real-time. To collect the sensed data from AUVs across the water-air interface, the traditional method is to deploy surface relays. However, the deployment of surface relays has some disadvantages, such as poor and imbalanced communication coverage of real-time connections with AUVs. Since the deployment of static relays may limit the dynamic search area of AUVs, whereas the deployment of free surface drifting relays may disrupt the connections while the relays are drifting away with the ocean current. To address these challenges, in this paper, we propose a novel method by leveraging the flexibility of multiple unmanned aerial vehicles (UAVs) equipped with underwater hydrophones and design a dynamic deployment scheme based on a multi-agent deep deterministic policy gradient (MADDPG) approach. By this scheme, multiple UAVs are able to deploy in a cooperative manner to deal with the coverage imbalance issue and provide maximum coverage service. The numerical simulations demonstrate that the proposed scheme is effective in terms of communication coverage, fairness, and energy consumption. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Hyperspectral Image Classification Method Based on Global Space-Spectral Attention Mechanism

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Journal of Shanghai Jiaotong university (science) 2025年 1-11页

作者： Qin, Rui Wu, Benze Liu, Xinfu Wu, Yirui College of Computer Science and Software Engineering Hohai University Nanjing211100 China Nangjing Research Institute of Electronic Engineering Nanjing210007 China College of Artificial Intelligence and Automation Hohai University Jiangsu Changzhou213200 China

In hyperspectral remote sensing imagery, pixel interactions within defined spatial extents result in the mixing of adjacent pixels. Additionally, the high similarity of adjacent spectra leads to information redundancy, which hinders the extraction of global spatial and spectral correlations. In order to solve the problems of mixed adjacent pixels and redundant adjacent spectra, this work offers a hyperspectral image classification approach that uses a global space-spectral attention mechanism. First, the proposed method’s global spatial attention module uses multi-scale dilated convolution to produce a bigger receptive field to be capable of capturing global spatial correlation and obtain unmixed pixel information. Then, the global spectral attention module designs a spectral domain partition algorithm, using the combination of regional density as well as information entropy as the threshold to divide spectrum into dispersed subsets and eliminate redundant information. The global context information for entire spectral band is fully exploited, and correlation of the global spectral information is extracted. Finally, the two modules combine to provide a global correlation of space and spectrum. Experiments demonstrate that the suggested method obtains overall accuracies of 97.28%, 94.73%, and 95.76% on the three WHU-Hi hyperspectral datasets, surpassing comparison methods. © Shanghai Jiao Tong university 2024.

关键词： Pixels

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Generative AI — The End of Systematic Reviews in PhD Projects?

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ACM Inroads 2024年第2期15卷 48-50页

作者： Zamani, Sanaz Sinha, Roopak Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Information Technology Deakin University Melbourne Australia

In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the ubiquitous availability of Generative AI tools shape PhD research and supervision, and how can we capitalize on such disruptions? We report our reflections about early-stage PhD research from 10+ PhD supervisions completed by Sinha, and a recently started PhD project with Zamani as the student. We conducted a systematic literature review following traditional methods for the first four months and then revisited the task using ChatGPT and other related tools. We opine that while SRs still have value for new students, Generative AI must be incorporated, with care, to significantly enhance student learning, reinvest time savings into other, often-ignored skills, and improve the quality (breadth and depth) of SRs. © 2024 Association for Computing Machinery. All rights reserved.

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The role of ChatGPT in sports trauma: a mini review on strengths and limits of open AI application

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Discover Artificial Intelligence 2023年第1期3卷 40页

作者： Hasnain, Muhammad Mehboob, Bilal Imran, Shahid Department of Computer Science Lahore Leads University Lahore54000 Pakistan Department of Software Engineering Superior University Lahore Lahore54000 Pakistan

Purpose: This paper is focused on the role of ChatGPT an artificial intelligence (AI) language model in the area of sports trauma. Sports trauma represents some significant concerns due to its prevalence and impacts. The objective of this study is to present an overview of the literature on how ChatGPT handles information about sports trauma, considering both its strengths and limitations. Methods: A review method is used in this study. Well-known online databases such as PubMed, scienceDirect, Springer and Google Scholar were searched for the relevant studies. In addition, ChatGPT application was accessed to provide the concise information on the research topic. Results: Search strategy resulted in 30 articles on the topic. Among them only seven studies revealed the potential applications of ChatGPT in sports. The other five studies presented the current status on ChatGPT and sports trauma. The results show that ChatGPT generates information on several types of sports trauma that align with the published literature. However, some limitations of ChatGPT are identified such as its tendency to provide general information about sprains and lack of updated statistics on sports trauma. This study also identified some serious concerns such ethical considerations, data privacy and security regarding the ChatGPT application in sports industry. Conclusion: Despite having some limitations, the ChatGPT application has potential to be used in healthcare, and particularly in sports trauma. The implications of this study guide scholars for the development of enhanced AI systems, which are tailored to redress the challenges of sports trauma. © The Author(s) 2023.

关键词： Sports

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MVDC: A Multi-view Dental Completion Model Based on Contrastive Learning

MVDC: A Multi-view Dental Completion Model Based on Contrast...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Xunyu Deng, Qingxin Huang, Minghan Jiang, Landu Zhang, Dian College of Computer Science and Software Engineering Shenzhen University Guangdong China Guangdong China

ISBN: (纸本)9798350368741

Restoring the patient's occlusal function of broken teeth is a challenging task since tooth texture is very complex, a slight deviation may affect the patient's chewing function and temporomandibular joint function. Therefore, how to efficiently repair the complete shape and real surface of the crown is a critical problem. Traditional technologies are hard to restore complete shape of the dental crown or lack inlay surface details, due to dataset limitations and complexity of missing parts. In this paper, we propose a multi-view crown restoration framework MVDC based on contrastive learning. Specifically, MVDC contains: 1) a multi-view generator with a specially designed loss measurement by using contrastive learning;2) a multi-scale discriminator mechanism able to consider relation and consistency between teeth from different scales;3) an occlusal groove extraction network to extract the occlusal details. We conducted extensive experiments on existing public datasets. The results showcase the superior performance of MVDC. © 2025 IEEE.

关键词： Contrastive learning Multi-view Teeth completion

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the university of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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AI-Powered Approaches to Unmasking Fake News: A Simple Comparison of ML and DL Techniques

AI-Powered Approaches to Unmasking Fake News: A Simple Compa...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Bormon, Sajib Prova, Jebin Ahmed Ahmad, Md Hasan Rimon Sohag, Sohanur Rahman Shishir, Hasanur Rahman Daffodil International University Department of Computer Science and Engineering Ashulia Dhaka Bangladesh Noakhali Science and Technology University Department of Software Engineering Noakhali Bangladesh

ISBN: (纸本)9798331529765

False news spreads quickly due to the extensive distribution of incorrect or misleading information across digital channels, which is a global problem. This bias undermines the credibility of information, promotes the spread of misleading information. Utilizing machine learning and deep learning models this study examines the detection of inaccurate information. Natural Language Processing methods are used for data preprocessing on the text datasets. Five machine learning models are used in this study: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Passive Aggressive Classifier. After preprocessing, the accuracy values of these models are 95%, 99.7%, 99.3%, 98.9%, and 99.5%, respectively. The study demonstrates that the decision tree model has superior performance, with the LSTM algorithm following closely after. The PAC and LSTM model have also demonstrated commendable results of 99.5% accuracy. The accuracies offer valuable information regarding the constraints of each model, aiding researchers in selecting suitable ways for identifying counterfeit news. The findings assist individuals in making informed decisions on the establishment of precise and effective ways for identifying false information, which is essential to maintaining the integrity of information distribution. © 2024 IEEE.

关键词： Adversarial machine learning

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Adversarial Training for Supervised Relation Extraction

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Tsinghua science and Technology 2022年第3期27卷 610-618页

作者： Yanhua Yu Kanghao He Jie Li School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Most supervised methods for relation extraction(RE) involve time-consuming human annotation. Distant supervision for RE is an efficient method to obtain large corpora that contains thousands of instances and various relations. However, the existing approaches rely heavily on knowledge bases(e.g., Freebase), thereby introducing data noise. Various relations and noisy labeling instances make the issue difficult to solve. In this study, we propose a model based on a piecewise convolution neural network with adversarial training. Inspired by generative adversarial networks, we adopt a heuristic algorithm to identify noisy datasets and apply adversarial training to RE. Experiments on the extended dataset of SemEval-2010 Task 8 show that our model can obtain more accurate training data for RE and significantly outperforms several competitive baseline models. Our model has an F1 score of 89.61%.

关键词： relation extraction piecewise convolution neural network adversarial training generative adversarial network

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Navigating the Digital Twin Network landscape:A survey on architecture,applications,privacy and security

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High-Confidence Computing 2024年第4期4卷 129-149页

作者： Akshita Maradapu Vera Venkata Sai Chenyu Wang Zhipeng Cai Yingshu Li Department of Computer and Information Sciences Towson UniversityTowson21252USA Department of Software Engineering and Game Development Kennesaw State UniversityMarietta30060USA Department of Computer Science Georgia State UniversityAtlanta30303USA

In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity *** has led to the development and commercialization of Digital Twin(DT)*** widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical *** create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT *** high volume of user data and the ubiquitous communication systems in DTNs come with their own set of *** most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data ***,currently,there is not enough literature that focuses on privacy and security issues in DTN *** this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their ***,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective *** then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the *** also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the ***,we provide some open research issues and problems in the field of DTN privacy and security.

关键词： Digital Twin Networks Network architecture Privacy and security Federated learning Blockchain Internet of Vehicles 6G Supply chain

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