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arXiv 2024年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. In this paper, we show how universal shifted stability and performance of discrete-time systems can be analyzed by making use of the time-difference dynamics. Moreover, we extend the existing results for incremental dissipativity for discrete-time systems based on dissipativity analysis of the differential dynamics to more general state-dependent storage functions for less conservative results. Finally, we show how both these equilibrium-free notions can be cast as a convex analysis problem by making use of the linear parameter-varying framework, which is also demonstrated by means of an example. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

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Structure design and analysis of a rigid-soft robotic hand with large load capacity 3

Structure design and analysis of a rigid-soft robotic hand w...

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3rd International Conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Wu, Peichen Lan, Fuming Hong, Wen Chen, Xiaoping Shuai, Wei Cui, Guowei Gao, Yang Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Shanghai Institute of Aerospace System Engineering Shanghai201100 China Intelligent Manufacturing Institute of HFUT HeFei230041 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510688179

In recent years, soft grippers have gained significant attention due to their adaptability and safety. However, when performing horizontal object grasping, their limited structural stiffness prevents them from withstanding the torque generated by friction between the gripper and the object, leading to a loss of contact and limiting their horizontal load capacity. To address this limitation while preserving the adaptability of soft grippers, this paper proposes a gripper structure called the rigid-soft robotic hand, which incorporates lateral rigid connecting rods outside the soft joints. The paper analyzes the bending capacity of the rigid-soft joint using kinematic methods and investigates the influence of link design parameters on maximum bending angle. Subsequently, a finite element analysis is conducted to study the finger structure with the added connecting rods, aiming to optimize the link design parameters. Experimental results demonstrate the grasping capabilities of the rigid-soft gripper in both vertical and horizontal orientations. The findings show that the rigid-soft gripper significantly improves the load-carrying capacity during horizontal object grasping while maintaining the adaptability of the soft gripper. © The Authors.

关键词： Robotic arms

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Enhancing Efficiency and Safety Through Autonomous Navigation in the Intensive Care Unit 8

Enhancing Efficiency and Safety Through Autonomous Navigatio...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Ponzi, Valerio Puglisi, Adriano Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The use of autonomous systems in Intensive Care Units (ICUs) has become incredibly important, especially during the COVID-19 pandemic. This period has overwhelmed both ICUs and hospitals, halting many other medical activities and causing significant challenges. This project aims to develop a navigation system tailored specifically for the ICU environment, adapting it to the unique procedures and regulations of that setting. Due to the critical conditions of ICU patients, strict rules dictate precise requirements for navigation, necessitating a context-specific approach. This work will propose a comprehensive navigation system capable of safely guiding from point A to point B within an ICU while addressing the critical issues present in such environments. Unlike traditional Nav2 systems, it will feature specialized collision avoidance components designed specifically for ICU settings, taking into account both contextual demands and the chosen approach. This will involve implementing a multilayered protection technique and employing active movements to prevent collisions with dynamic obstacles. © 2023 Copyright for this paper by its authors.

关键词： Reinforcement learning

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Exploring Brain Tumor Segmentation and Patient Survival: An Interpretable Model Approach 8

Exploring Brain Tumor Segmentation and Patient Survival: An ...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Ponzi, Valerio De Magistris, Giorgio Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

Detecting and delineating brain tumors from MRI images using artificial intelligence presents a complex challenge in medical AI. Recent progress has seen a variety of techniques employed to assist medical professionals in this task. Despite the effectiveness of machine learning algorithms in segmenting tumors, their lack of transparency in decision-making hinders trust and validation. In our project, we constructed an interpretable U-Net Model specifically tailored for brain tumor segmentation, leveraging both the Gradient-weighted Class Activation Mapping (Grad-CAM) Algorithm and the SHapley Additive exPlanations (SHAP) library. We relied on the BraTS2020 benchmark dataset for training and evaluation purposes. The U-Net model we employed yielded promising results. We then utilized Grad-CAM to visualize the crucial features attended to by the model within an image. Additionally, we enhanced interpretability by utilizing the SHAP library to elucidate the predictions made by various models (including Random Forest, KNN, SVC, and MLP) utilized for predicting patient survival days. © 2023 Copyright for this paper by its authors.

关键词： Decision making

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Cluster Synchronization of Kuramoto Oscillators via Pacemakers and Mean-phase Feedback control 63

Cluster Synchronization of Kuramoto Oscillators via Pacemake...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kokubo, Ryota Matsubara, Mitsuaki Kato, Rui Ishii, Hideaki Tokyo Institute of Technology Department of Computer Science Yokohama226-8502 Japan Tokyo Institute of Technology Department of Systems and Control Engineering Yokohama226-8502 Japan Villeurbanne69622 France The University of Tokyo Department of Information Physics and Computing Tokyo113-8656 Japan

ISBN: (纸本)9798350316339

Brain networks typically exhibit characteristic synchronization patterns where several synchronized clusters coexist. On the other hand, neurological disorders are considered to be related to pathological synchronization such as excessive synchronization of large populations of neurons. Motivated by these phenomena, this paper presents two approaches to control the cluster synchronization of Kuramoto oscillators. One is based on the use of pacemakers to the clusters, and the other is based on feeding back the mean phases to the clusters. We first show conditions on the pacemaker weights and the feedback gains for the network to achieve cluster synchronization. Then, we propose a method to find optimal feedback gains through convex optimization. A numerical example demonstrates the effectiveness of the proposed methods. © 2024 IEEE.

关键词： Pacemakers

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Safe platooning control of connected and autonomous vehicles on curved multi-lane roads

arXiv

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arXiv 2025年

作者： Chen, Xiao Tang, Zhiqi Johansson, Karl H. Mårtensson, Jonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs into two distinct parts: a lateral control law that ensures a geometrical convergence towards the reference path regardless of the translational velocity, and a longitudinal control design for each vehicle to achieve the desired relative arc length and velocity with respect to its neighboring vehicle. The second novelty is exploiting the constructive barrier feedback as an additive term to the nominal tracking control, ensuring both lateral and longitudinal collision avoidance. This constructive barrier feedback acts as a dissipative term, slowing down the relative velocity toward obstacles without affecting the nominal controller’s performance. Consequently, our proposed control method enables safe platoon formation of vehicles on curved multi-lane roads, with theoretical guarantees for safety invariance and stability analysis. Simulation and experimental results on connected vehicles are provided to further validate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback

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Application of deep learning in pothole road recognition: a YOLOv5 method combining ASFF and SimAm 3

Application of deep learning in pothole road recognition: a ...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Lu, Weiquan Zhang, Jiarong Ju, Jinli Liu, Ziyu Zhao, Yuxiang Qin, Rong Lu, Danni Zhou, Xiaolu School of Information Engineering Xi'an Mingde Institute of Technology Xi'an710124 China School of Intelligent Manufacturing and Control Technology Xi'an Mingde Institute of Technology Xi'an710124 China School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan430200 China

ISBN: (纸本)9781510680449

In the application of computer vision recognition, the recognition objects of some tissue-like structures present highly complex and variable characteristics. For this practical usage scenario, traditional classification algorithms usually cannot achieve the desired recognition effect. To achieve better recognition performance in this direction, this study integrates ASFF into the YOLOv5 model and introduces the SimAm attention mechanism module. The improved network structure is more lightweight, has fewer network parameters, and the attention mechanism is more effective. We trained the modified YOLO model on datasets, and experimental results show a significant increase in mAP value and a 20% increase in accuracy. This indicates that improvements to the model can significantly enhance performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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Distance Estimation of Fixed Objects in Driving Environments 9

Distance Estimation of Fixed Objects in Driving Environments

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Leporoni, Giorgio Ponzi, Valerio Pro, Francesco Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

Autonomous driving is a highly relevant topic today, particularly among major car manufacturers attempting to lead in technological innovation and enhance driving safety. An autonomous vehicle must possess the capability to sense its environment and navigate without human intervention. Thus, it serves as both a driver support system and, in some cases, a substitute. A crucial aspect involves identifying the positions of pedestrians, traffic signs, traffic lights, and other vehicles while computing distances from them. This enables the vehicle to emit alerts to the driver in potentially dangerous situations, such as impending obstacles due to external factors or driver distraction. In this paper, we introduce an approach for identifying traffic signs and determining the distance from them. Our method utilizes the YOLOv4 network for identification and a customized network for distance computation. This integration of AI technologies facilitates the timely detection of hazards and enables proactive alert mechanisms, thereby advancing the capabilities of autonomous vehicles and enhancing driving safety. © 2023 Copyright for this paper by its authors.

关键词： Autonomous vehicles

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Multimodal Large Models are Effective Action Anticipators

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IEEE Transactions on Multimedia 2025年 27卷 2949-2960页

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le Xi'an University of Architecture and Technology College of Information and Control Engineering Xi'an710055 China Northwestern Polytechnical University School of Astronautics Xi'an710072 China Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Shenzhen518055 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230022 China University of Science and Technology of China School of Information Science and Technology Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. © 1999-2012 IEEE.

关键词： Semantics

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