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arXiv 2025年

作者： Ma, Xiaochuan Fu, Jia Liao, Wenjun Zhang, Shichuan Wang, Guotai School of Computer Science and Engineering China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital & Institute Sichuan Cancer Center School of Medicine University of Electronic Science and Technology of China Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China

Brain tumor segmentation is important for diagnosis of the tumor, and current deep-learning methods rely on a large set of annotated images for training, with high annotation costs. Unsupervised segmentation is promising to avoid human annotations while the performance is often limited. In this study, we present a novel unsupervised segmentation approach that leverages the capabilities of foundation models, and it consists of three main steps: (1) A vision-language model (i.e., CLIP) is employed to obtain image-level pseudo-labels for training a classification network. Class Activation Mapping (CAM) is then employed to extract Regions of Interest (ROIs), where an adaptive masking-based data augmentation is used to enhance ROI identification. (2) The ROIs are used to generate bounding box and point prompts for the Segment Anything Model (SAM) to obtain segmentation pseudo-labels. (3) A 3D segmentation network is trained with the SAM-derived pseudo-labels, where low-quality pseudo-labels are filtered out in a self-learning process based on the similarity between the SAM’s output and the network’s prediction. Evaluation on the BraTS2020 dataset demonstrates that our approach obtained an average Dice Similarity Score (DSC) of 85.60%, outperforming five state-of-the-art unsupervised segmentation methods by more than 10 percentage points. Besides, our approach outperforms directly using SAM for zero-shot inference, and its performance is close to fully supervised learning. © 2025, CC BY.

关键词： Self-supervised learning

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A Study of Robustness of a Closed-Loop System for Blood Pressure Control

A Study of Robustness of a Closed-Loop System for Blood Pres...

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American Control Conference (ACC)

作者： Taylor E. Baum Bharadwaj Satchidanandan Munther A. Dahleh Emery N. Brown Electrical Engineering and Computer Science Department Massachusetts Institute of Technology MA USA Department of Electrical Engineering and Computer Science The Laboratory for Information & Decision Systems The Institute for Data Systems and Society at the Massachusetts Institute of Technology MA USA Department of Anesthesia Critical Care and Pain Medicine The Massachusetts General Hospital MA USA The Picower Institute for Learning and Memory The Massachusetts Institute of Technology MA USA The Division of Sleep Medicine Harvard Medical School MA USA

Wide variability of patient response to vasoactive drugs, and mechanical limits of infusion pumps lead to difficulties in achieving robust Closed-Loop Blood Pressure Control (CLBPC). Our goal is to refine a previously proposed CLBPC system which emphasizes the incorporation of mechanistic cardiovascular models, design simple Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) controllers, and assess their robustness to estimation errors in physiological parameters of patients that are expected to vary widely within a patient population. A pharmacokinetic-pharmacodynamic (PK-PD) model that quantifies the impact of drugs, phenylephrine and nicardipine, on a patient’s blood pressure is provided. A state space model is then formulated which incorporates the full dynamics of a patient’s blood pressure. We then implement MPC and PID control to simulate regulation of blood pressure and explore the robustness of such controllers to errors in estimates of patient physiological parameters.

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Attack Impact Evaluation for Stochastic Control systems through Alarm Flag State Augmentation

arXiv

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arXiv 2023年

作者： Sasahara, Hampei Tanaka, Takashi Sandberg, Henrik The Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8552 Japan The Department of Aerospace Engineering and Engineering Mechanics Cockrell School of Engineering The University of Texas AustinTX78712 United States The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

This note addresses the problem of evaluating the impact of an attack on discrete-time nonlinear stochastic control systems. The problem is formulated as an optimal control problem with a joint chance constraint that forces the adversary to avoid detection throughout a given time period. Due to the joint constraint, the optimal control policy depends not only on the current state, but also on the entire history, leading to an explosion of the search space and making the problem generally intractable. However, we discover that the current state and whether an alarm has been triggered, or not, is sufficient for specifying the optimal decision at each time step. This information, which we refer to as the alarm flag, can be added to the state space to create an equivalent optimal control problem that can be solved with existing numerical approaches using a Markov policy. Additionally, we note that the formulation results in a policy that does not avoid detection once an alarm has been triggered. We extend the formulation to handle multi-alarm avoidance policies for more reasonable attack impact evaluations, and show that the idea of augmenting the state space with an alarm flag is valid in this extended formulation as well. © 2023, CC BY-NC-ND.

关键词： Stochastic systems

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Channel Estimation Refinement for Tethered Aerial Platform Enabled Multi-User Communication systems

Channel Estimation Refinement for Tethered Aerial Platform E...

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IEEE International Conference on Communications in China (ICCC)

作者： Puguang An Peng Yang Xianbin Cao Chaoqun You Q. S. Tony Quek Dapeng Oliver Wu School of Electronic Information Engineering Beihang University Beijing China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen Guangdong China School of Computer Science Fudan University Shanghai China Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Computer Science City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This paper investigates the three-dimensional (3D) downlink sparse channel estimation for tethered aerial platformenabled multi-user communication systems operating in a frequency division duplexing mode with large-scale antenna arrays. To this end, we design a non-identical Bernoulli-Gaussian distribution-based channel model that reflects the potential common sparsity caused by distant scatterers in a low-altitude environment. A low-complexity channel estimation algorithm without the need for prior knowledge of channel sparsity is proposed. It first applies a greedy pursuit algorithm to roughly estimate the common support set (CSS). Given the initial CSS, multi-user channels are then iteratively estimated and refined using a novel neighborhood-based channel estimation refinement scheme, which includes a decentralized sparse channel estimator to recover sparse channels accurately under non-i.i.d channel sparsity priors with low computation burden. Simulation results indicate that the proposed algorithm outperforms its existing counterparts and approaches the performance limit with perfect knowledge of the CSS.

关键词： Three-dimensional displays Communication systems Simulation Channel estimation Clustering algorithms Downlink Frequency conversion

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Digital Twins for Marine Operations: A Brief Review on Their Implementation

arXiv

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arXiv 2023年

作者： Zocco, Federico Wang, Hsueh-Cheng Van, Mien Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom Taiwan Taiwan Labs Hsinchu Taiwan

While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore, in this review we show how different authors implemented their digital twins, discuss our findings, and finally give insights on future research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent systems

Data-driven Optimal Cooperative Output Regulation of Unknown...

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Chinese Control and Decision Conference, CCDC

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. To remove this condition, the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is unknown. First, a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external inputs. Second, the unknown regulator equations are solved using the iterative recurrent neural network approach. Third, the state-based data-driven distributed control law is synthesized to solve the problem. The optimal gains are derived by solving convex optimization problems using input and state data. Finally, a numerical example is presented to verify the feasibility of the proposed framework.

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Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning

arXiv

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arXiv 2023年

作者： Duan, Yue Zhao, Zhen Qi, Lei Zhou, Luping Wang, Lei Shi, Yinghuan National Key Laboratory for Novel Software Technology Nanjing University China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Computer Science and Engineering Southeast University China School of Computing and Information Technology University of Wollongong Australia

While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e.g., fine-grained visual classification in the context of SSL (SS-FGVC). The increased recognition difficulty on fine-grained unlabeled data spells disaster for pseudo-labeling accuracy, resulting in poor performance of the SSL model. To tackle this challenge, we propose Soft Label Selection with Confidence-Aware Clustering based on Class Transition Tracking (SoC) by reconstructing the pseudo-label selection process by jointly optimizing Expansion Objective and Shrinkage Objective, which is based on a soft label manner. Respectively, the former objective encourages soft labels to absorb more candidate classes to ensure the attendance of ground-truth class, while the latter encourages soft labels to reject more noisy classes, which is theoretically proved to be equivalent to entropy minimization. In comparisons with various state-of-the-art methods, our approach demonstrates its superior performance in SS-FGVC. Checkpoints and source code are available at https://***/NJUyued/SoC4SS-FGVC. Copyright © 2023, The Authors. All rights reserved.

关键词： Shrinkage

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Read, Spell and Repeat: Scene Text Recognition with Vision-Language Circular Refinement

Read, Spell and Repeat: Scene Text Recognition with Vision-L...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Taiwei Zhang Zhenghui Hu Weixin Li Qingjie Liu Yunhong Wang Hangzhou Innovation Institute Beihang University Hangzhou China Shanghai Artificial Intelligence Laboratory Shanghai China School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

Scene Text Recognition (STR) has long been considered an important yet challenging task in the field of computer vision. Recent works have demonstrated that utilizing language information is effective for the visually difficult images, like ones with occultation or blurring. However, the use of language information sometimes leads to the over-correction problem. For out-of-vocabulary samples (e.g. "hou" and "0x4a"), some methods have tended to be biased to language side and over-corrected (e.g. over-correct "hou" to "hot"). This imbalance of vision and language has limited the usage of models in practical scenarios, yet it is rarely occurs for human. To address this issue, we rethink the human’s recognition process and propose a model behaving in the order of "Read, Spell and Repeat". It refines the recognition process circularly with vision and language information. With this mechanism, our model integrates vision and language information in a more effective manner, achieving higher accuracy with less parameters compared to baseline and competitive performance with SOTA methods in the standard benchmarks.

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Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects

arXiv

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arXiv 2023年

作者： Yan, Feng Jiang, Xiaoheng Lu, Yang Cui, Lisha Li, Shupan Cao, Jiale Xu, Mingliang Tao, Dacheng School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China School of Electrical and Information Engineering Tianjin University Tianjin China the Sydney AI Centre the School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depthwise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% Fβw, 93.55% Sα, and 97.35% E) on SD-saliency-900 while running 272fps on a single gpu. Copyright © 2023, The Authors. All rights reserved.

关键词： Defects

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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