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arXiv 2024年

作者： Xie, Jinxia Zhong, Bineng Mo, Zhiyi Zhang, Shengping Shi, Liangtao Song, Shuxiang Ji, Rongrong Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education China Guangxi Key Lab of Multi-Source Information Mining & Security China Guangxi Normal University Guilin541004 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China School of Computer Science and Technology Harbin Institute of Technology China Media Analytics and Computing Lab School of Informatics Xiamen University China

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently, the spatio-temporal information is far away from being fully explored. To alleviate this issue, we propose an adaptive tracker with spatio-temporal transformers (named AQATrack), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then, we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally, based on the initial target template and learnt autoregressive queries, a spatio-temporal information fusion module (STM) is designed for spatio-temporal formation aggregation to locate a target object. Benefiting from the STM, we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker’s performance on six popular tracking benchmarks: LaSOT, LaSOText, TrackingNet, GOT-10k, TNL2K, and *** and models will be here. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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SAGPCN: Self-Attention Graph Pooling Convolutional Network for Battery State of Health Estimation

SAGPCN: Self-Attention Graph Pooling Convolutional Network f...

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IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)

作者： Jiusi Zhang Jilun Tian Yuchen Jiang Hao Luo Cong-Sheng Huang Mo-Yuen Chow Shen Yin Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan R.O.C. Department of Electrical and Computer Engineering North Carolina State University Raleigh NC U.S.A. Faculty of Engineering Norwegian University of Science and Technology Trondheim Norway

Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches.

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EdgePro: Edge Deep Learning Model Protection via Neuron Authorization

arXiv

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arXiv 2023年

作者： Chen, Jinyin Zheng, Haibin Liu, Tao Li, Rongchang Cheng, Yao Zhang, Xuhong Ji, Shouling Institute of Cyberspace Security the College of Information Engineering Zhejiang University of Technology Hangzhou310023 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Huawei International Singapore College of Control Science and Engineering Zhejiang University Hangzhou310007 China College of Computer Science and Technology Zhejiang University Hangzhou310007 China

With the development of deep learning processors and accelerators, deep learning models have been widely deployed on edge devices as part of the Internet of Things. Edge device models are generally considered as valuable intellectual properties that are worth for careful protection. Unfortunately, these models have a great risk of being stolen or illegally copied. The existing model protections using encryption algorithms are suffered from high computation overhead which is not practical due to the limited computing capacity on edge devices. In this work, we propose a light-weight, practical, and general Edge device model Protection method at neuron level, denoted as EdgePro. Specifically, we select several neurons as authorization neurons and set their activation values to locking values and scale the neuron outputs as the "passwords" during training. EdgePro protects the model by ensuring it can only work correctly when the "passwords" are met, at the cost of encrypting and storing the information of the "passwords" instead of the whole model. Extensive experimental results indicate that EdgePro can work well on the task of protecting on datasets with different modes. The inference time increase of EdgePro is only 60% of state-of-the-art methods, and the accuracy loss is less than 1%. Additionally, EdgePro is robust against adaptive attacks including fine-tuning and pruning, which makes it more practical in real-world applications. EdgePro is also open sourced to facilitate future research: https://***/Leon022/EdgePro. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurons

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Exact Algorithm for Batch Scheduling with Many-to-Many Job-Tool Matching and Job Release Time Constraints

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 16499-16511页

作者： Li, Shengchao Liu, Shixin Zhao, Ziyan Zhou, Mengchu Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang 110819 China Northeastern University at Qinhuangdao Hebei Key Laboratory of Industrial Software for Advanced Planning and Scheduling of Complex Industrial Processes School of Control Engineering Qinhuangdao 066004 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark 07102 NJ United States

Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switch bring great challenge for high-performance production scheduling in BPSs. In this study, we present a novel batch scheduling problem derived from a satellite vibration test system. Different from basic scheduling problems in BPSs with tool switching, it has complications caused by many-to-many task-tool matching and job release time constraints. Specifically, each job has predetermined candidate tools. One of them should be selected and equipped to process a given job. Unlike sequence-dependent setup time that is only related to two adjacent jobs, tool switching time is additionally dependent on the tools selected by both jobs from their respective candidate tools. Both job sequence and tool selections need to be determined to minimize makespan. Mixed-integer linear programming (MILP) and constraint programming (CP) models are developed for formulating the problem. We prove that the problem is NP hard. To obtain exact solutions, we propose a novel branch-and-bound algorithm (B&B) based on the analysis and extraction of problem properties. It integrates a domain reduction mechanism, branching strategies, and dominance rules. Among them, the domain reduction mechanism reduces the number of jobs to be scheduled;the branching strategies accelerate solving speed;and the dominance rules save the memory consumption. Ablation studies and key influencing factor experiments are performed. The results show that the proposed B&B can optimally solve much larger instances than state-of-the-art methods (including some heuristics). It can optimally solve industrial-size problems in a short time, implying that it can well satisfy the performance demand of a satellite vibration test system and thus advancing the filed of optimal scheduling for BPSs. © 2004-2012 IEEE.

关键词： Batch scheduling branch-and-bound algorithm heterogenerous tool demand mixed-integer linear programming satellite vibration test system

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The Method of Optimal Geometric Parameters Synthesis of Two Mechanisms in the Rehabilitation System on Account of Relative Position 12th

The Method of Optimal Geometric Parameters Synthesis of Tw...

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12th International Conference on Optimization and Applications, OPTIMA 2021

作者： Malyshev, Dmitry Rybak, Larisa Mohan, Santhakumar Diveev, Askhat Cherkasov, Vladislav Pisarenko, Anton Belgorod State Technological University named after V.G. Shukhov Belgorod Russia Indian Institute of Technology Palakkad Palakkad India Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783030927103

The paper presents the method of optimal geometric parameters synthesis for a robotic rehabilitation system of the lower limbs as based on a passive orthosis in the form of a serial RRRR mechanism and an active parallel 3-PRRR mechanism. A mathematical model of the kinematics of the RRRR mechanism is formulated. A numerical method is worked out for determining the workspace of the RRRR mechanism. The workspace description is based on the approximation of many solutions of systems of nonlinear inequalities to provide the required rotation angle of rotation in the joints. Effective numerical methods and algorithms are developed and tested. The paves the way for determining the minimum geometric parameters of an active parallel mechanism that ensure movements of a passive orthosis within the workspace under clinical data constrains when simulating walking. In order to implement the developed methods, an effective algorithm, software package and visualization system are synthesized for exported three-dimensional workspaces in STL. © 2021, Springer Nature Switzerland AG.

关键词： Mechanisms

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Convergence Analysis of the Best Response Algorithm for Time-Varying Games

arXiv

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arXiv 2023年

作者： Wang, Zifan Shen, Yi Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology also with Digital Futures StockholmSE-10044 Sweden the Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

— This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe other agents’ historical actions and choose actions that best respond to other agents’ previous actions;we call this a best response scheme. We start by analyzing the convergence rate of this best response scheme for standard time-invariant games. Specifically, we provide a sufficient condition on the strong monotonicity parameter of the time-invariant games under which the proposed best response algorithm achieves exponential convergence to the static Nash equilibrium. We further illustrate that this best response algorithm may oscillate when the proposed sufficient condition fails to hold, which indicates that this condition is tight. Next, we analyze this best response algorithm for time-varying games where the cost functions of each agent change over time. Under similar conditions as for time-invariant games, we show that the proposed best response algorithm stays asymptotically close to the evolving equilibrium. We do so by analyzing both the equilibrium tracking error and the dynamic regret. Numerical experiments on economic market problems are presented to validate our analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot

Augmenting GRIPS with Heuristic Sampling for Planning Feasib...

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European Conference on Mobile Robots (ECMR)

作者： Brian Angulo Ivan Radionov Konstantin Yakovlev Moscow Institute of Physics and Technology LLC "Integrant" Federal Research Center "Computer Science and Control" RAS

Kinodynamic motion planning for non-holomonic mobile robots is a challenging problem that is lacking a universal solution. One of the computationally efficient ways to solve it is to build a geometric path first and then transform this path into a kinematically feasible one. Gradient-informed Path Smoothing (GRIPS) [1] is a recently introduced method for such transformation. GRIPS iteratively deforms the path and adds/deletes the waypoints while trying to connect each consecutive pair of them via the provided steering function that respects the kinematic constraints. The algorithm is relatively fast but, unfortunately, does not provide any guarantees that it will succeed. In practice, it often fails to produce feasible trajectories for car-like robots with large turning radius. In this work, we introduce a range of modifications that are aimed at increasing the success rate of GRIPS for car-like robots. The main enhancement is adding an additional step that heuristically samples the waypoints along the bottleneck parts of the geometric paths (such as sharp turns). The results of the experimental evaluation provide a clear evidence that the success rate of the suggested algorithm is up to 40% higher compared to the original GRIPS and hits the bar of 90%, while its runtime is lower.

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An AUV-TTN Scheme Applied to AUV Trajectory Tracking Under Noise Disturbance

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IEEE Transactions on Vehicular technology 2025年

作者： Luo, Tangtao Zheng, Mianjie Zhou, Yan Xiao, Xiuchun Pan, Yi Guangdong Ocean University School of Oceanography and Meteorology Zhanjiang China Shenzhen University Shenzhen China Guangdong Ocean University School of Electronic and Information Engineering Zhanjiang China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

In marine engineering, autonomous underwater vehicle (AUV), combining self-navigating, decision-making, and operational skills, is crucial for oceanic missions. And trajectory tracking is a key metric for assessing AUV performance and has received considerable focus. However, existing AUV trajectory tracking methods exhibit limitations in control accuracy, and demands substantial computational resources and time, thereby constraining their real-time applicability in dynamic environments. To tackle these challenges, this paper simplifies the AUV trajectory tracking problem into an inequality and equality constrained dynamic quadratic programming (IE-DQP) problem and introduces the zeroing neural network (ZNN) model to address it. Additionally, to reduce underwater noise disturbances, we propose the projection and elliptical ZNN (PEZNN) model, utilizing the Frobenius norm, and develop two projection functions that align with the physical constraints of AUVs. Collectively, we present the aforementioned approach as the AUV-TNN scheme. To assess its performance, we conduct rigorous mathematical derivations to demonstrate the convergence and robustness of the proposed scheme. Finally, the effectiveness of the proposed approach is confirmed through three comprehensive simulated AUV trajectory tracking experiments. © 1967-2012 IEEE.

关键词： Ocean engineering

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Enhancing Visual Representation for Text-Based Person Searching

SSRN

引用

SSRN 2024年

作者： Shen, Wei Fang, Ming Wang, Yuxia Xiao, Jiafeng Chen, Huangqun Xu, Ling Zhang, Weifeng Metrological Verification and Testing Institute of Jiaxing China School of Artificial Intelligence Zhejiang Sci-Tech University Zhejiang China College of Computer Science and Technology Zhejiang University Zhejiang China College of Information Science and Engineering Jiaxing University Zhejiang China Shandong Center for Disease Control and Prevention China

Text-based person search aims to retrieve the matched pedestrians from a large scale image database according to the text description. The core difficulty of this task is how to extract effective details from pedestrian images and texts, and achieve cross-modal alignment in common latent space. Prior works adopt image and text encoders pre-trained on unimodal data to extract global and local features from image and text respectively, and then global-local alignment is achieved explicitly. However, these approaches still lack the ability of understanding visual details, and the retrieval accuracy is still limited by identity confusion. In order to alleviate the above problems, we rethink the importance of visual features for text-based person search, and propose VFE-TPS, a Visual Feature Enhanced Text-based Person Search model. It introduces a pre-trained multimodal backbone and constructs Text Guided Masked Image Modeling task to enhance the model’s ability of learning local visual details without explicit annotation. In addition, we design Identity Supervised Global Visual Feature Calibration task to guide the model learn identity-aware global visual features. Experimental results on three benchmarks demonstrate that our proposed model exceeds the existing approaches, and the Rank-1 accuracy is significantly improved with a notable margin of about 1% ∼ 9%. Our code has been released at https://***/zhangweifeng1218/VFE_TPS. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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A Practical Cross-Device Federated Learning Framework over 5G Networks

arXiv

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arXiv 2022年

作者： Yang, Wenti Wang, Naiyu Guan, Zhitao Wu, Longfei Du, Xiaojiang Guizani, Mohsen School of Control and Computer Engineering North China Electric Power University China Department of Mathematics & Computer Science Fayetteville State University United States Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States Department of Computer Science and Engineering Qatar University Qatar

The concept of federated learning (FL) was first proposed by Google in 2016. Thereafter, FL has been widely studied for the feasibility of application in various fields due to its potential to make full use of data without compromising the privacy. However, limited by the capacity of wireless data transmission, the employment of federated learning on mobile devices has been making slow progress in practical. The development and commercialization of the 5th generation (5G) mobile networks has shed some light on this. In this paper, we analyze the challenges of existing federated learning schemes for mobile devices and propose a novel cross-device federated learning framework, which utilizes the anonymous communication technology and ring signature to protect the privacy of participants while reducing the computation overhead of mobile devices participating in FL. In addition, our scheme implements a contribution-based incentive mechanism to encourage mobile users to participate in FL. We also give a case study of autonomous driving. Finally, we present the performance evaluation of the proposed scheme and discuss some open issues in federated learning. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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