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Low-Complexity control for a Class of Uncertain MIMO Nonlinear Systems under Generalized Time-Varying Output Constraints (extended version)

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arXiv 2024年

作者： Mehdifar, Farhad Lindemann, Lars Bechlioulis, Charalampos P. Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States Division of Systems and Control Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper introduces a novel control framework to address the satisfaction of multiple time-varying output constraints in uncertain high-order MIMO nonlinear control systems. Unlike existing methods, which often assume that the constraints are always decoupled and feasible, our approach can handle coupled time-varying constraints even in the presence of potential infeasibilities. First, it is shown that satisfying multiple constraints essentially boils down to ensuring the positivity of a scalar variable, representing the signed distance from the boundary of the time-varying output-constrained set. To achieve this, a single consolidating constraint is designed that, when satisfied, guarantees convergence to and invariance of the time-varying output-constrained set within a user-defined finite time. Next, a novel robust and low-complexity feedback controller is proposed to ensure the satisfaction of the consolidating constraint. Additionally, we provide a mechanism for online modification of the consolidating constraint to find a least violating solution when the constraints become mutually infeasible for some time. Finally, simulation examples of trajectory and region tracking for a mobile robot validate the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Feedback control

A Versatile Low-Light Enhancement Module for Improved Workpiece Detection

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A Versatile Low-Light Enhancement Module for Improved Workpi...

Information and Communication technology (ICTech), International Conference of

作者： ZhenYu Yin DaHeng Lin XiaoHui Wang Qi Xin XueMei Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

This study presents a versatile Low-Light Enhancement Module (LLEM) to enhance object detection models, particularly for workpieces in challenging lighting conditions. The LLEM seamlessly integrates into existing models, addressing low-light challenges through a denoising branch, a global branch, and a Structural Perception loss for image quality improvement. The denoising branch utilizes Residual Denoising Modules to effectively reduce noise and preserve low-level features. Meanwhile, the global branch focuses on color recovery and gamma correction. Experimental results show YOLOv3+LLEM significantly improves detection performance, achieving a mAP increase from 88.0% to 91.9%. This enhancement, especially with the introduction of Structural Perception loss, maintains computational efficiency, validating its practicality for industrial workpiece detection in low-light conditions.

关键词： YOLO Measurement Image quality Image color analysis Computational modeling Noise reduction Lighting Computational efficiency Information and communication technology Colored noise

MedFILIP: Medical Fine-grained Language-Image Pre-training

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arXiv 2025年

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of computer and control engineering Northeast Forestry University Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. The code is available in https://***/ PerceptionComputingLab/MedFILIP. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

Internet of things for high-speed railways Block

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Intelligent and Converged Networks 2021年第2期2卷 115-132页

作者： Guidong Zhong Ke Xiong Zhangdui Zhong Bo Ai Railway Safety and IoT Technology Research Institute Jiaxun Feihong Intelligent Technology InstituteBeijing 100044China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China State Key Lab of Rail Traffic Control&Safety Beijing Jiaotong UniversityBeijing 100044China

As a new generation of application infrastructure and strategic emerging technologies,the internet of things(IoT)is an inevitable trend to be integrated into the rapid development of high-speed railways(HSR).This article first introduces the concept and key technologies of the HSR IoT,and then analyzes the HSR’s requirements for the *** on these concepts,a system architecture of the HSR IoT is proposed to expand the in-depth applications of IoT in various fields of the HSR ***,future development trends of the HSR IoT are *** article is expected to provide some useful insights into the study and evolution of HSR IoT.

关键词： internet of things high-speed railway high-speed train intelligent high-speed railway

Reachability Analysis Using Constrained Polynomial Logical Zonotopes

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arXiv 2024年

作者： Hafez, Ahmad Jiang, Frank J. Johansson, Karl H. Alanwar, Amr Technical University of Munich TUM School of Computation Information and Technology Department of Computer Engineering Germany KTH Royal Institute of Technology Stockholm Sweden Division of Decision and Control Systems EECS Digital Futures

In this paper, we propose reachability analysis using constrained polynomial logical zonotopes. We perform reachability analysis to compute the set of states that could be reached. To do this, we utilize a recently introduced set representation called polynomial logical zonotopes for performing computationally efficient and exact reachability analysis on logical systems. Notably, polynomial logical zonotopes address the "curse of dimensionality" when analyzing the reachability of logical systems since the set representation can represent 2ℎ binary vectors using ℎ generators. After finishing the reachability analysis, the formal verification involves verifying whether the intersection of the calculated reachable set and the unsafe set is empty or not. Polynomial logical zonotopes lack closure under intersections, prompting the formulation of constrained polynomial logical zonotopes, which preserve the computational efficiency and exactness of polynomial logical zonotopes for reachability analysis while enabling exact intersections. Additionally, an extensive empirical study is presented to demonstrate and validate the advantages of constrained polynomial logical zonotopes. Copyright © 2024, The Authors. All rights reserved.

关键词： Polynomials

Distributed Traffic Signal control of Interconnected Intersections: A Two-Lane Traffic Network Model

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arXiv 2024年

作者： Ru, Xinfeng Xia, Weiguo Bai, Ting Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Dalian116024 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

Practical and accurate traffic models play an important role in capturing real traffic dynamics and then in achieving effective control performance. This paper studies traffic signal control in a traffic network with multiple interconnected intersections, where the target is to balance the vehicle density on each lane by controlling the green times of each phase at every intersection. Different from traditional road-based modeling schemes, a two-lane intersection model is first proposed to model the flow propagation in a more accurate way. A distributed model predictive control (MPC) method is then presented to assign the green times. To enable the real-time feasibility of the proposed approach, the alternating direction method of multipliers (ADMM) is incorporated with the distributed MPC scheme for solving the problem. Finally, the simulation studies performed in VISSIM for a six-intersection traffic network in Dalian, China, show the effectiveness and characteristics of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

CorPN: Corner Prediction Network for Visual Tracking

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Qun Zhang, Haijun Yang, Kai Dai, Wei Ma, Jianghong Liu, Yuping the Department of Computer Science Harbin Institute of Technology Shenzhen Xili University Town Shenzhen518055 China the School of Computer Science and Artificial Intelligence WuHan Textile University Wuhan430200 China the School of Information and Control Engineering Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China the School of Microelectronics Fudan University Shanghai200433 China

Object tracking is a well-established task in the field of computer vision. Despite numerous efforts over the last few decades, object tracking remains a formidable challenge, largely due to the complexity of the scenes encountered in video sequences. In line with the powerful capabilities of Transformers for object feature extraction and fusion, as well as drawing inspiration from CornerNet, this paper introduces a new Transformer-based anchor-free method designed for object tracking called CorPN (i.e., Corner Prediction Network). Specifically, a Transformer-based backbone is proposed to extract and fuse deep features in a Siamese network architecture. Furthermore, our methodology aims to emulate the intuitive cognitive process by which humans identify the bounding-box corners of objects. CorPN turns out to be a more effective and robust tracking method because it jointly leverages multi-level feature aggregation, corner prediction, and probabilistic prediction incorporated in the tracking head. In extensive evaluations on public datasets, our method achieves a state-of-the-art performance. In particular, our approach achieves an area-under-the-curve (AUC) of 85.2%, a precision score (P) of 84.5%, and a normalized precision score (Pnorm) of 89.6%, outperforming extant state-of-the-art trackers on these metrics. © 2025 IEEE. All rights reserved,

关键词： Object detection

Road Crack Detection Algorithm Based on Improved YOLOV5 Model

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Road Crack Detection Algorithm Based on Improved YOLOV5 Mode...

Sensors, Electronics and computer Engineering (ICSECE), IEEE International Conference on

作者： Guangda Liang Wenwen Liu Weiwei Li College of Environment and Architectural Arts Tianjin Academy of Fine Arts Tianjin China Beijing Institute of Control and Electronic Technology Beijing China School of Computer Science and Technology Tiangong University Tianjin China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

A lightweight road crack target detection method based on YOLOv5 and GhostNet is proposed to address the problems of complex models and high computational complexity in existing road crack detection methods. The GhostConv and GhostC3 modules of the lightweight network GhostNet are introduced to improve the backbone network of YOLOv5, achieving the goal of significantly reducing model computation while ensuring the network's feature extraction capability. The experimental results show that the improved algorithm proposed in this paper significantly reduces the volume of the model and the number of parameters, and at the same time ensures the high precision of detection. Therefore, the model can be used as an ideal lightweight detection model for road crack images.

关键词： YOLO Training Solid modeling Accuracy Computational modeling Roads Feature extraction Sensors Computational complexity Detection algorithms

Constructing Equivalent Hodgkin-Huxley Neuron Spice Model of the Subthalamic Nucleus Verified by Swine Deep Brain Stimulation

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Constructing Equivalent Hodgkin-Huxley Neuron Spice Model of...

Neural Imaging and Sensing 2025

作者： Wu, Kun-Ta Chiang, Chih-Chuan Chen, Peng-Jye Yan, Yung-Jhe Mang, Ou-Yang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781510683549

Deep brain stimulation (DBS) is an invasive treatment widely used to alleviate motor disorders in patients with Parkinson's disease (PD). DBS can disrupt pathological neural activity and restore functional motor control by delivering electrical impulses to specific brain regions. DBS is generally divided into two types: open-loop and closed-loop DBS. Open-loop DBS provides constant electrical stimulation without real-time feedback, while closed-loop DBS dynamically adjusts stimulation parameters based on real-time detection of biomarkers, offering the potential for more precise and energyefficient therapy. However, ensuring the stability and efficacy of closed-loop DBS systems requires a deeper understanding of the underlying neural dynamics and accurate modeling techniques. To address these needs, this study focuses on developing an Equivalent Hodgkin-Huxley (HH) neuron SPICE model for the subthalamic nucleus (STN), a key target for DBS in PD treatment. The HH model is a well-known framework including ion channel dynamics and membrane potential changes. Integrating the HH model into SPICE enables its compatibility with electrical components, facilitating its application in circuit-based closed-loop DBS systems. First, an equivalent HH neuron SPICE model for the STN was constructed using a computational model. Then, the model parameters were adjusted based on experimental data collected from a swine brain to ensure biological relevance. Finally, an STN Equivalent HH neuron SPICE model with minimized quantization error was obtained. This model not only bridges computational neuroscience and circuit simulation but also provides a robust foundation for enhancing DBS system design, paving the way for personalized and adaptive PD treatments. © 2025 SPIE.

关键词： SPICE