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arXiv 2024年

作者： Pan, Qingtao Qiao, Wenhao Lou, Jingjiao Ji, Bing Li, Shuo School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it often suffers from error supervision from low-quality pseudo labels. Vision-Language Model (VLM) has great potential to enhance pseudo labels by introducing text prompt guided multimodal supervision information. It nevertheless faces the cross-modal problem: the obtained messages tend to correspond to multiple targets. To address aforementioned problems, we propose a Dual Semantic Similarity-Supervised VLM (DuSSS) for SSMIS. Specifically, 1) a Dual Contrastive Learning (DCL) is designed to improve cross-modal semantic consistency by capturing intrinsic representations within each modality and semantic correlations across modalities. 2) To encourage the learning of multiple semantic correspondences, a Semantic Similarity-Supervision strategy (SSS) is proposed and injected into each contrastive learning process in DCL, supervising semantic similarity via the distribution-based uncertainty levels. Furthermore, a novel VLM-based SSMIS network is designed to compensate for the quality deficiencies of pseudo-labels. It utilizes the pretrained VLM to generate text prompt guided supervision information, refining the pseudo label for better consistency regularization. Experimental results demonstrate that our DuSSS achieves outstanding performance with Dice of 82.52%, 74.61% and 78.03% on three public datasets (QaTa-COV19, BM-Seg and MoNuSeg). Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Stability of Internet-Based control Systems with Uncertainties and Multiple Time-Varying Delays

Stability of Internet-Based Control Systems with Uncertainti...

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IEEE Conference on Decision and control

作者： Zhi-Hong Guan Hao Zhang Shuang-Hua Yang Hua O. Wang Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science Loughborough University Loughborough UK Department of AeroSpace Mechanical Engineering Boston University Boston MA USA

ISBN: (纸本)1424401704;9781424401703

In this paper, based on remote control and local control strategy, a class of hybrid multi-rate control models with uncertainties and multiple time-varying delays is formulated and their robust stability properties are investigated. By Lyapunov-Krasovskii functions and apply it to a descriptor model transformation, some new criteria of robust stability for such Internet-based control systems are established. Numerical example and simulation are given to illustrate the effectiveness of the theoretical results

关键词： Internet control systems Uncertainty Time varying systems Delay effects Communication system control Stability criteria Robust stability control system synthesis Frequency

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BACKLASH COMPENSATION USING RECEDING HORIZON control

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IFAC Proceedings Volumes 2005年第1期38卷 508-513页

作者： María Seron Kyung Sang Yoo Graham Goodwin Centre for Complex Dynamic Systems and Control School of Electrical Engineering and Computer Science The University of Newcastle Callaghan 2308 NSW Australia Department of Electrical Engineering Doowon Technical College Ansung 456-718 Korea

We present a receding horizon control algorithm for compensation of backlash at the input of a stable linear system under control rate constraints. The problem is first posed as a receding horizon optimal control problem by modelling backlash as a piecewise affine system with a state space partition consisting of three regions. This optimal control problem involves solving, at each step, 3 N quadratic programmes, where N is the optimisation horizon. As an alternative to solving the quadratic programmes, we propose a strategy based on a recently devised suboptimal receding horizon control algorithm which utilises a singular value decomposition of the Hessian of the quadratic programme. This alternative strategy leads, at the cost of some performance degradation, to much smaller computational load since a feasible rather than optimal solution has to be obtained at each step.

关键词： Receding horizon control backlash piecewise affine systems rate constraints model predictive control SVD algorithm

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Constrained time-optimal control for nonlinear systems: A fast explicit approximation

Constrained time-optimal control for nonlinear systems: A fa...

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4th IFAC Conference on Nonlinear Model Predictive control, NMPC'12

作者： Raimondo, D.M. Huber, O. Schulze Darup, M. Mönnigmann, M. Morari, M. Department of Computer Engineering and Systems Science University of Pavia Pavia 27100 Italy Inria Grenoble Rĥone-Alpes 655 avenue de l'Europe Inovallée 38334 Saint Ismier France Automatic Control and Systems Theory Ruhr-Universität Bochum 44780 Bochum Germany Automatic Control Laboratory ETH Zurich 8092 Zurich Switzerland

ISBN: (纸本)9783902823076

We present a method for the calculation of approximate explicit solutions to constrained time-optimal control problems for nonlinear discrete-time systems. We aim to pre-compute offline a low complexity control law that provides feasibility guarantees and fast online computation. Related approaches build on hyperrectangular state space partitions that, unfortunately, lead to a large number of regions. We introduce a method based on a hierarchy of Voronoi diagrams, which leads to partitions with fewer elements. Thereby, we use the aforementioned hyperrectangular partitions as initializations and try to systematically reduce the number of regions. © 2012 IFAC.

关键词： Discrete time control systems

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Continuous-Time Damping-Based Mirror Descent for a Class of Non-Convex Multi-Player Games with Coupling Constraints

Continuous-Time Damping-Based Mirror Descent for a Class of ...

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International Conference on control and Automation (ICCA)

作者： Guanpu Chen Kun Cao Karl Henrik Johansson Yiguang Hong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Control Science and Engineering Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

We study the computation of the global generalized Nash equilibrium (GNE) for a class of non-convex multi-player games, where players' actions are subject to both local and coupling constraints. Due to the non-convex payoff functions, we employ canonical duality to reformulate the setting as a complementary problem. Under given conditions, we reveal the relation between the stationary point and the global GNE. According to the convex-concave properties within the complementary function, we propose a continuous-time mirror descent to compute GNE by generating functions in the Bregman divergence and the damping-based design. Then, we devise several Lyapunov functions to prove that the trajectory along the dynamics is bounded and convergent.

关键词： Couplings Damping Automation Games Nash equilibrium Trajectory Mirrors

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Application of the Integrated Micro Acceleration Measurement System in Target Recognition

Application of the Integrated Micro Acceleration Measurement...

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IEEE Instrumentation and Measurement Technology Conference

作者： Jinhui Lan Yixin Yin Tian Lan Department of Measurement and Control Technologies Information Engineering School University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering OGI School of Science and Engineering Oregon Health and Sciences University OR USA

It is studied that an integrated micro acceleration measurement system was used to detect the seismic acceleration signals from moving vehicle targets and recognize these targets in the paper. The seismic signals of typical vehicles have been tested by the system and analyzed in this paper, because seismic properties of vehicle targets are an important index of target recognition. In order to realize the target classification and recognition, a technique of artificial neural networks combined with Dempster-Shafer theory of evidence (ANNCDSTE) is applied to classification of seismic signals. The technique and its architecture have been presented. Through outdoor experiments, it can be proven that seismic properties of target acquired by the micro acceleration measurement system are correct, ANNCDSTE method is effective to solve the problem of target recognition

关键词： Accelerometers Target recognition Vehicle detection Paper technology control systems Artificial neural networks Signal processing Testing Automotive engineering Acceleration

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Feature selection for semi-supervised multi-label learning with application to gene function analysis 10

Feature selection for semi-supervised multi-label learning w...

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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

作者： Li, Guo-Zheng You, Mingyu Ge, Lei Yang, Jack Y. Yang, Mary Qu MOE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Department of Control Science and Engineering Tongji University Shanghai 201804 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China Purdue Univ. ECE West Lafayette IN 47907 United States Indiana Univ. Bioinformatics Center Indianapolis IN 46202 United States

ISBN: (纸本)9781450304382

This paper investigates gene function annotation of Yeast by using semi-supervised multi-label learning. Multi-label learning has been a hot topic in the bioinformatics field, but there are many samples unlabeled. Semi-supervised learning may be employed to utilize the unlabeled data. This paper proposes a novel semi-supervised multi-label learning algorithm COMN by combining Co-Training with ML-kNN to utilize the unlabeled yeast gene data to improve modeling accuracy of function annotation. Furthermore, an embedded feature selection algorithm PRECOMN is proposed to perform feature selection for COMN to remove the irrelevant and redundant features. Experimental results on one benchmark data set of Yeast show COMN and PRECOMN perform better than the original multi-label learning algorithm ML-kNN. Furthermore PRECOMN improves generalization performance of COMN. Copyright © 2010 ACM.

关键词： Feature Selection

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Output feedback stabilization for min-max systems

Output feedback stabilization for min-max systems

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2009 International Conference on Information engineering and computer science, ICIECS 2009

作者： Wei, Hongyun Chen, Wende Wang, Yongji Wei, Qingyun Dept. of Control Science and Engineering Huazhong Univ. of Sci. and Tech. Wuhan China College of Computer Science South-Central Univ. for Nationalities Wuhan China Academy of Mathematics and System Sci. Chinese Academy of Sci. Beijing China Dept. of Control Sci. and Engineering Huazhong Univ. of Sci. and Tech. Wuhan China Dept. of Electronic Information Countermeasures Air Force Rader Academy Wuhan China

ISBN: (纸本)9781424449941

A variety of problems arising in communication networks, computer networks, automated manufacturing plants, etc. can be described by min-max-plus system models. Output feedback stabilization for the nonlinear, non-autonomous min-max-plus system (F, G, H) is studied in this paper. A new coloring graph structure is presented based on the new, complex conditions. The former research on new circuits of closed-loop max-plus projection system is considered with only one feedback edge, but more feedback edges should be included in the new circuits is pointed out in this paper. Finally, a sufficient condition for output feedback stabilization of min-max-plus system (F, G, H) is obtained. ©2009 IEEE.

关键词： Stabilization

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Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret

arXiv

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arXiv 2024年

作者： Hua, Youqing Liu, Shuai Hong, Yiguang Johansson, Karl Henrik Wang, Guangchen School of Control Science and Engineering Shandong University Jinan China Nanyang Technological University Singapore Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper considers the distributed online bandit optimization problem with nonconvex loss functions over a time-varying digraph. This problem can be viewed as a repeated game between a group of online players and an adversary. At each round, each player selects a decision from the constraint set, and then the adversary assigns an arbitrary, possibly nonconvex, loss function to this player. Only the loss value at the current round, rather than the entire loss function or any other information (e.g. gradient), is privately revealed to the player. Players aim to minimize a sequence of global loss functions, which are the sum of local losses. We observe that traditional multi-point bandit algorithms are unsuitable for online optimization, where the data for the loss function are not all a priori, while the one-point bandit algorithms suffer from poor regret guarantees. To address these issues, we propose a novel one-point residual feedback distributed online algorithm. This algorithm estimates the gradient using residuals from two points, effectively reducing the regret bound while maintaining O(1) sampling complexity per iteration. We employ a rigorous metric, dynamic regret, to evaluate the algorithm's performance. By appropriately selecting the step size and smoothing parameters, we demonstrate that the expected dynamic regret of our algorithm is comparable to existing algorithms that use two-point feedback, provided the deviation in the objective function sequence and the path length of the minimization grows sublinearly. Finally, we validate the effectiveness of the proposed algorithm through numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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