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Learning-based tracking control of AUV:Mixed policy improvement and game-based disturbance rejection

CAAI Transactions on Intelligence technology 2025年第2期10卷 510-528页

作者： Jun Ye Hongbo Gao Manjiang Hu Yougang Bian Qingjia Cui Xiaohui Qin Rongjun Ding State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina School of Information Science and Technology and Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefeiChina Nanyang Technological University SingaporeSingapore Wuxi Intelligent Control Research Institute of Hunan University Hunan UniversityWuxiChina

A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe *** combining prior dynamic knowledge and actual sampled data,the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP ***,the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling ***,the control object interacts with the real environment and continuously gathers adequate sampled data in the *** comprehensively leverage the advantages of model-based and model-free methods during training,an adaptive tuning factor is introduced based on the dataset that possesses model-referenced information and conforms to the distribution of the real-world environment,which balances the influence of model-based control law and data-driven policy gradient on the direction of policy *** a result,the proposed approach accelerates the learning speed compared to data-driven methods,concurrently also enhancing the tracking performance in comparison to model-based control ***,the optimal control problem under disturbances is formulated as a zero-sum game,and the actor-critic-disturbance framework is introduced to approximate the optimal control input,cost function,and disturbance policy,***,the convergence property of the proposed algorithm based on the value iteration method is ***,an example of AUV path following based on the improved line-of-sight guidance is presented to demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming autonomous underwater vehicle game theory optimal control reinforcement learning

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Machine Learning Curriculums Generated by Classifier Ensembles 19

Machine Learning Curriculums Generated by Classifier Ensembl...

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19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023

作者： Huang, Tzu-Jui Kameyama, Keisuke University of Tsukuba Degree Programs in Systems and Information Engineering Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9781665476928

Image classification is a fundamental task that attempts to classify the images into the classes they belong to. CNN is commonly used for the classification due to its flexibility and stability. This paper treats Curriculum Learning as an assistant method that decides the procedure the CNN ought to be trained. Curriculum Learning is generally defined as a method that divides and orders the training datasets into several different levels of learning difficulties. This paper utilizes the idea of ensemble classification that trains according to the order based on the consensus of different machine learning models. As a result, the proposed Ensemble-Generated Curriculum Learning model achieves higher accuracy compared to that of a single-stage CNN learning with dog face recognition and CIFAR-10 datasets. © 2023 IEEE.

关键词： Curricula

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GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection 39

GLCF: A Global-Local Multimodal Coherence Analysis Framework...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

ISBN: (纸本)157735897X

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Public risks

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Decision Tree-Based IoT Botnet Attack Detection 2

Decision Tree-Based IoT Botnet Attack Detection

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors. © 2024 IEEE.

关键词： Forecasting

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Unformer: A Transformer-Based Approach for Adaptive Multiscale Feature Aggregation in Underwater Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1024-1037页

作者： Qing, Yuhao Wang, Yueying Yan, Huaicheng Xie, Xiangpeng Wu, Zhengguang Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

Underwater imaging is often compromised by light scattering and absorption, resulting in image degradation and distortion. This manifests as blurred details, color shifts, and diminished illumination and contrast, thereby hindering advancements in underwater research. To mitigate these issues, we propose Unformer, an innovative underwater image enhancement (UIE) technique that leverages a transformer-based architecture for multiscale adaptive feature aggregation. Our approach employs a multiscale feature fusion strategy that adaptively restores illumination and detail features. We reevaluate the relationship between convolution and transformer to develop a novel encoder structure. This structure effectively integrates both long-range and short-range dependencies, dynamically combines local and global features, and constructs a comprehensive global context. Furthermore, we propose a unique multibranch decoder architecture that enhances and efficiently extracts spatial context information through the transformer module. Extensive experiments on three datasets demonstrate that our proposed method outperforms other techniques in both subjective and objective evaluations. Compared with the latest methods, Unformer has improved the peak signal-to-noise ratio (PSNR) by 19.5% and 14.8% respectively on the LSUI and EUVP datasets. The code is available at: https://***/yhflq/Unformer. © 2024 IEEE.

关键词： Light scattering

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Effect of contact materials on the transient characteristics of vacuum arc plasma and anode erosion

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Plasma science and technology 2024年第11期26卷 170-180页

作者： Ze YANG Dongsheng CAI Qi HUANG Lijun WANG College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059People’s Republic of China Postdoctoral Mobile Station in Nuclear Science and Technology Chengdu University of TechnologyChengdu 610059People’s Republic of China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010People’s Republic of China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong UniversityXi’an 710049People’s Republic of China

In this study, the mechanisms of the anode phenomena and anode erosion with various contact materials were investigated. Arc parameters were calculated, and the anode temperature was predicted with a transient self-consistent model. The simulation results predicted a constricted arc column and obvious anode phenomena in Cu–Cr alloy contacts than in W–Cu alloy *** observation could be the reason for the concentrated anode erosion in Cu–Cr alloys. For the contacts made by pure tungsten(W) and W–Cu alloy, the anode temperature increased rapidly because of the low specific heat of W. However, the maximum energy flux from the arc column to the anode surface was lower than in other cases. The simulation results were compared with experimental results.

关键词： vacuum arc MHD model anode erosion contact materials

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina School of Mechanical Engineering Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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Observer-based stabilization for discrete nonlinear semi-Markov jump singularly perturbed models with mode-switching delay

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science China(information sciences) 2025年第4期68卷 389-390页

作者： Wenhai QI Runkun LI Ju H.PARK Zheng-Guang WU Huaicheng YAN School of Engineering Qufu Normal University Department of Electrical Engineering Yeungnam University Institute of Cyber-Systems and Control Zhejiang University Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology

Semi-Markov jump systems(S-MJMs) not only characterize hybrid systems but also address the limitations of Markov jump systems(MJMs) [1–3]. Due to their ability to exhibit multi-time-scale features, singularly perturbed models(SPMs) effectively model practical systems influenced by multiple time-scale phenomena [4]. In this study, the observer-based output feedback controller is asynchronous with the original system due to the time delay in the controller mode switching. A nonlinear plant with singularly perturbed parameters(SPPs) is represented using an interval type-2(IT2) fuzzy model [5].

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Secrecy Performance Analysis of Hybrid RF-FSO Cooperative Satellite-Aerial-Terrestrial Networks with imperfect CSI 2

Secrecy Performance Analysis of Hybrid RF-FSO Cooperative Sa...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Ma, Yuanyuan Chen, Han Xu, Zhan Zhi, Ruxin Ma, Chunshuai Zheng, Yiyang Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China No.723 Research Institute China State Shipbuilding Corporation Yangzhou China Beijing Information Science and Technology University Beijing China Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

This study examines the secrecy outage performance of uplink transmission within a hybrid cooperative satellite-aerial-terrestrial network (SATN) that integrates radio-frequency (RF) and free-space optical (FSO) technologies. Specifically, in the proposed cooperative SATN, a terrestrial source (S) conveys its information to a satellite receiver (D) with the assistance of an aerial relay (R), while a group of eavesdropping aerial terminals (Eves) attempts to intercept the confidential data. RF and FSO transmission methods are employed over the S-R and R-D links, respectively, both subject to imperfect channel state information (CSI). By considering the randomness of R, D, and Eves, and utilizing a stochastic geometry framework, we investigate the secrecy outage performance of the considered SATN. A closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, simulations are conducted to validate the accuracy of the analysis. © 2024 IEEE.

关键词： Stochastic systems

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