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Uncertainty-Guided Diffusion Model for Camouflaged Object Detection

IEEE Transactions on Multimedia 2025年

作者： Yang, Jinsheng Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Song, Shuxiang Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China

Recently, diffusion models have significantly improved the performance of Camouflaged Object Detection (COD) by adding noise to a mask and iteratively denoising it to match the target distributions. Due to the direct extraction of features from noisy masks and the lack of conditional constraints on a prediction area, the diffusion model may deviate from a correct prediction range and produces mispredictions in regions with high uncertainty. To address this issue, we propose an uncertainty-guided diffusion model (UGDNet) for COD, which explicitly quantifies uncertainty and integrates it as an anchor condition into the diffusion models to provide an initialization of the diffusion regions. The core idea is first to utilize a probability representation and transformer to explicitly model uncertainty, aiming to identify areas where a model may generate overconfident mispredictions. Then, we use the uncertainty as an anchor condition to provide a reference prediction range for the diffusion model, guiding each step of the diffusion process. Furthermore, we use uncertainty to guide feature aggregation, prompting the model to pay extra attention to the semantic features of regions with high uncertainty to refine the segmentation results further. The experimental results indicate that our proposed UGDNet achieves higher accuracy than existing state-of-the-art models on five COD benchmarks, including COD10K, NC4K, CAMO, CHAMELEON, and CDS2K. © 1999-2012 IEEE.

关键词： Semantics

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Research on Train Operation control Strategy Based on Particle Swarm Optimization Algorithm 25

Research on Train Operation Control Strategy Based on Partic...

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Proceedings of the 2025 International Conference on Intelligent Systems, Automation and control

作者： Youbing Zhang Haihong E Ling Liu Zhiqiang Chen Jianmin Wang Pengyu Bao Research Institute of Safety Control Technology Beijing National Railway Research & Design Institute of Signal & Communication Co. Ltd. Beijing China Graduate College for Engineers Beijing University of Posts and Telecommunications Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China Beijing National Railway Research & Design Institute of Signal & Communication Co. Ltd. Beijing China Research Institute of Safety Control Technology Beijing National Railway Research & Design Institute of Signal & Communication Co. Ltd. Beijing China

ISBN: (纸本)9798400715198

To solve the problems of long running time and high energy consumption of existing high-speed railways, intercity railways and other trains between stations. The study adopts a four-phase operation control strategy to drive trains, and investigates the impact of operating condition transition points on running time and energy consumption. The particle swarm optimization algorithm is used to find the global optimal solution that balances running time and energy consumption in the solution space of operating condition transition points. The research results indicate that a method has been designed to determine the four-phase train operation control strategy by only solving the speed of two points: acceleration-cruising transition point and coasting-braking transition point. This reduces the particle dimension of particle swarm optimization algorithm and improves the efficiency of using particle swarm optimization algorithm to solve the four-phase train operation control strategy. An equivalent method for calculating energy consumption during the cruising phase is proposed to ensure more accurate calculation of energy consumption during train running. By configuring appropriate time and energy consumption coefficients and using particle swarm optimization algorithm, the four-phase train operation control strategy can be solved to obtain the optimal position and speed of the operating condition transition point. This can achieve dual objective optimization of running time and energy consumption, reducing train running energy consumption while ensuring on-time operation. Using the line data from Xinmin North Station to Heishan North Station on the Beijing Shenyang High speed Railway, with CR400BF-A high-speed multiple unit parameters as input, a time coefficient of 0.6 and an energy consumption coefficient of 0.4, the four-phase train operation control strategy solved by particle swarm optimization algorithm was used to drive the train. The running time was 1010 seconds

关键词： Four-phase control strategy

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Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty

arXiv

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arXiv 2022年

作者： Zhang, Yifei Ukil, Sourav Kumar Sperilă, Andrei Sabău, Şerban Department of Electrical and Computer Engineering Stevens Institute of Technology NJ07030 United States Department of Automatic Control and Computer Science "Politehnica" University of Bucharest Bucharest Romania

This paper addresses the end-to-end sample complexity bound for learning in closed loop the state estimator-based robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estimators and the celebrated Youla parameterization. Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter. The robust synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant, such that a min-max optimization problem is formulated for the robust H2 controller via an observer approach. The closed-loop identification scheme follows Zhang et al. (2021), where the nominal model of the true plant is identified by constructing a Hankel-like matrix from a single time-series of noisy, finite length input-output data by using the ordinary least squares algorithm from Sarkar et al. (2020). Finally, a H∞ bound on the estimated model error is provided, as the robust synthesis procedure requires bounded additive uncertainty on the coprime factors of the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Additives

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Curiosity and Affect-Driven Cognitive Architecture for HRI

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IEEE Transactions on Affective Computing 2025年

作者： Berto, Leticia Tanevska, Ana Cirne, Azamor Costa, Paula Simoes, Alexandre Gudwin, Ricardo Rea, Francesco Colombini, Esther Sciutti, Alessandra State University of Campinas Institute of Computing Campinas Brazil Italian Institute of Technology CONTACT Unit Genoa Italy Uppsala University Department of Information Technology Uppsala Sweden Federal University of Paraiba Department of Finance and Accounting João Pessoa Brazil State University of Campinas School of Electrical and Computer Engineering Campinas Brazil São Paulo State University Department of Control and Automation Engineering Sorocaba Brazil

This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants. © 2025 IEEE. All rights reserved.

关键词： Robot learning

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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou310023 China ZJUT Binjiang Institute of Artificial Intelligence Hangzhou310056 China Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Technology Shanghai200433 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Shenzhen518066 China National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China

Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct the Transaction SubGraph Network (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%∼6.71% relative improvement over previous state-of-the-art. © 2004-2012 IEEE.

关键词： Phishing

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Network Service Identification Method for Electric Power Cloud

Network Service Identification Method for Electric Power Clo...

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Chinese control and Decision Conference, CCDC

作者： Yilu Chen Tongtong Li Zhiyao Liu Zhichao Xu Zibo Wang Hongri Liu School of Computer Science and Technology Harbin Institute of Technology Weihai China School of Cyber Science and Technology Harbin Institute of Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9781665478977

Electric power cloud data center becomes more and more widely applied, and yet lacks security protection inside it. The most direct solution for this issue is internal network isolation. This paper focuses on the first task of the isolation, namely network service identification, and proposes a network service identification framework based on deep clustering. In order to obtain service stream, a network service stream extraction algorithm is proposed based on the flow metric and traffic pattern. Considering the high dimensionality and complexity of service streams, the denoising and convolutional autoencoders are combined for feature extraction. And then a self-organizing mean maps network is adopted to achieve network service identification. Experiment results illustrate the effectiveness and superiority of the self-organizing mean maps network with regard to the internal evaluation index of clustering. Therefore, the identification framework proposed seems to provide a foundation for subsequent isolation study.

关键词： Measurement Data centers Noise reduction Clustering algorithms Telecommunication traffic Traffic control Feature extraction

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Theme-Based Literature Analysis in the Field of Transportation 16th

Theme-Based Literature Analysis in the Field of Transportati...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Zhang, Sifan Lu, Hao Zhang, Fuquan Zhu, Yifan Wang, Rongrong Computer School Beijing Institute of Technology Beijing10089 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Min-Jiang University Fuzhou350121 China

ISBN: (纸本)9789813367562

Transportation is an important research field of engineering science, and its related research covers multiple fields and links in policies, methods, technologies, and applications. In order to sort out the research context and development status of the transportation field and provide scientific and technological information support for scientific researchers in the field, this article analyzes the literature published in SCI journals in the transportation field from 2008 to 2018 through the topic analysis method. The analysis results show that the current main research results in the field of transportation have formed different clusters around keywords such as cognitive radio, electric vehicles, optimization and simulation, and intelligent transportation systems, and the research development process of different countries is different. For example, the research results of China in recent years have gone from energy efficiency, automobile emissions to intelligent transportation, while the United States has been focusing on the research of intelligent transportation systems. Over time, China has gradually focused on the cutting-edge technology in the field of transportation, and its research results have greatly promoted the development of research in the field of transportation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Energy efficiency

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SP-VSLAM:Monocular Visual-SLAM Algorithm Based on SuperPoint Network

SP-VSLAM:Monocular Visual-SLAM Algorithm Based on SuperPoint...

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International Conference on Communication Software and Networks, ICCSN

作者： Zhenyu Yin Dan Feng Chao Fan Chengen Ju Feiqing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Shenyang China

For scenes with significant changes in light intensity or perspective, the traditional ORB feature extraction method is prone to the problems of local homogenization of feature points and errors arising from the accumulated drift. A monocular visual Simultaneous Localization and Mapping algorithm (SLAM) based on SuperPoint network (SP-VSLAM) is proposed. The SuperPoint network is used to extract feature points and calculate binary descriptors simultaneously, combined with Non-Maximal Suppression methods to achieve uniform detection and description of image feature points. Then construct robust and accurate feature association information. It is fused with the back-end local mapping, loop & map merging modules of the ORB-SLAM3 system to construct a complete monocular vision SLAM system. Using the public measurement datasets TUM, the localization accuracy is calculated for five sequences in monocular mode, and the experimental results show that the proposed method has smaller trajectory error and higher pose estimation accuracy in static, partial low dynamic and high dynamic environments, which can effectively enhance the robustness of tracking.

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Consensus control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Integration of Robust control with Reinforcement Learning for Safe Autonomous Vehicle Motion

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IFAC-PapersOnLine 2023年第2期56卷 1101-1106页

作者： Attila Lelkó Balázs Németh Dániel Fényes Péter Gáspár Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) 13-17. Kende u. H-1111 Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics 2 Stoczek u. H-1111 Budapest Hungary

This paper presents a control design framework for the integration of robust control and reinforcement learning-based (RL) control agent. The proposed integration method is applied for motion control of autonomous road vehicles, providing safe motion. In the integrated motion control, longitudinal and lateral dynamics are incorporated. The high-performance motion of the vehicle, e.g., high-velocity motion, path following, and reduction of lateral acceleration, through the RL-based control agent is achieved. The training through Proximal Policy Optimization during episodes is performed. Safe motion with guaranteed performances, i.e., keeping limits on lateral error, through the robust control and the supervisor is achieved. The robust control is designed through the H ∞ method, and in the supervisor, a constrained quadratic programming task is performed. As a result, lateral and longitudinal control inputs of the vehicle are calculated by the integrated control system. The effectiveness of the proposed control method using simulation scenarios and test scenarios on a small-scaled test vehicle is illustrated.

关键词： Adaptive and robust control of automotive systems Autonomous vehicles

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