检索结果-内蒙古大学图书馆

Pinning control of chimera states in systems with higher-order interactions

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Muolo, Riccardo Gambuzza, Lucia Valentina Nakao, Hiroya Frasca, Mattia Department of Systems and Control Engineering Institute of Science Tokyo former Tokyo Institute of Technology Tokyo152-8552 Japan Department of Electrical Electronics and Computer Science Engineering University of Catania Catania95125 Italy Research Center for Autonomous Systems Materialogy Institute of Innovative Research Institute of Science Tokyo former Tokyo Institute of Technology Kanagawa226-8501 Japan

Understanding and controlling the mechanisms behind synchronization phenomena is of paramount importance in nonlinear science. In particular, the emergence of chimera states, patterns in which order and disorder coexist simultaneously, continues to puzzle scholars, due to its elusive nature. Recently, it has been shown that higher-order (many-body) interactions greatly enhance the presence of chimera states, which are easier to be found and more persistent. In this work, we show that the higher-order framework is fertile not only for the emergence of chimera states, but also for its control. Via pinning control, a technique consisting in applying a forcing to a subset of the nodes, we are able to trigger the emergence of chimera states with only a small fraction of controlled nodes, at striking contrast with the case without higher-order interactions. We show that our setting is robust for different higher-order topologies and types of pinning control and, finally, we give a heuristic interpretation of the results via phase reduction theory. Our numerical and theoretical results provide further understanding on how higher-order interactions shape collective behaviors in nonlinear dynamics. © 2024, CC BY.

关键词： Synchronization

Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Xu, Jianye Alrifaee, Bassam Betz, Johannes Mokhtarian, Armin Mittal, Archak Cai, Mengchi Mangharam, Rahul Shehata, Omar M. Elias, Catherine M. Zaech, Jan-Nico Scheffe, Patrick Jahncke, Felix Ulhas, Sangeet Sankaramangalam Arfvidsson, Kaj Munhoz Department of Computer Science RWTH Aachen University Germany Department of Aerospace Engineering University of the Bundeswehr Munich Germany Professorship of Autonomous Vehicle Systems Technical University of Munich Germany Department of Civil Engineering Indian Institute of Technology Bombay India School of Vehicle and Mobility Tsinghua University China Department of Electrical and Systems Engineering University of Pennsylvania United States Mechatronics Engineering Department Faculty of Engineering and Materials Science The German University in Cairo Egypt Department of Computer Science Engineering The German University in Cairo Egypt INSAIT Sofia University "St. Kliment Ohridski" Bulgaria School for Engineering of Matter Transport and Energy Arizona State University United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effort of participants in the workshop "1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms," held on June 2 at the IEEE Intelligent Vehicles Symposium (IV) 2024 in Jeju, South Korea. The roadmap contains three parts: 1) enhancing accessibility and diversity, especially for underrepresented communities, 2) sharing best practices for the development and maintenance of testbeds, and 3) connecting testbeds through an abstraction layer to support collaboration. The workshop features eight invited speakers, four contributed papers [1]–[4], and a presentation of a survey paper on testbeds [5]. The survey paper provides an online comparative table of more than 25 testbeds, available at https://***/testbeds-survey. The workshop’s own website is available at https://***/iv24-workshop. Copyright © 2025, The Authors. All rights reserved.

关键词： Testbeds

Discrete-Time Stress Matrix-Based Formation control of General Linear Multi-Agent Systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Onuoha, Okechi Kurawa, Suleiman Tang, Zezhi Dong, Yi School of Computing Engineering and Intelligent Systems Ulster University United Kingdom School of Electrical and Electronic Engineering The University of Manchester United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom School of Electronics & Computer Science University of Southampton United Kingdom

This paper considers the distributed leader-follower stress-matrix-based affine formation control problem of discrete-time linear multi-agent systems with static and dynamic leaders. In leader-follower multi-agent formation control, the aim is to drive a set of agents comprising leaders and followers to form any desired geometric pattern and simultaneously execute any required manoeuvre by controlling only a few agents denoted as leaders. Existing works in literature are mostly limited to the cases where the agents’ inter-agent communications are either in the continuous-time settings or the sampled-data cases where the leaders are constrained to constant (or zero) velocities or accelerations. Here, we relax these constraints and study the discrete-time cases where the leaders can have stationary or time-varying velocities. We propose control laws in the study of different situations and provide some sufficient conditions to guarantee the overall system stability. Simulation study is used to demonstrate the efficacy of our proposed control laws. © 2024, CC BY.

关键词： Linear systems

A Material Identification Approach Based on Wi-Fi Signal

学校读者我要写书评

暂无评论

computers, Materials & Continua 2021年第12期69卷 3383-3397页

作者： Chao Li Fan Li Wei Du Lihua Yin Bin Wang Chonghua Wang Tianjie Luo Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510700China PCL Research Center of Cyberspace Security Peng Cheng LaboratoryShenzhen518052China Department of Computer Science and Computer Engineering University of ArkansasFayetteville72701USA College of Electrical Engineering Zhejiang UniversityHangzhou310058China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

关键词： Internet of Things Wi-Fi signal channel state information material identification noise elimination

HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

AWGUNET: ATTENTION-AIDED WAVELET GUIDED U-NET FOR NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Roy, Ayush Pramanik, Payel Kaplun, Dmitrii Antonov, Sergei Sarkar, Ram Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://***/AyushRoy2001/AWGUNET © 2024, CC BY.

关键词： Image segmentation

Low-cost IoT-Based sensors dashboard for monitoring the state of health of mobile harbor cranes: Hardware and software description

学校读者我要写书评

暂无评论

Heliyon 2024年第22期10卷 e40239页

作者： Awad, A.H. Alsabaan, Maazen Ibrahem, Mohamed I. Saraya, M.S. Elksasy, M.S.M. Ali-Eldin, Amr M.T. Abdelsalam, Mohamed M. Computers Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura 35516 Egypt Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia School of Computer and Cyber Sciences Augusta University Augusta 30912 GA United States Faculty of Engineering Delta University for Science and Technology Gamasa 7731168 Egypt

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is “immersed” in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the pa

关键词： IoT Low-cost Mobile harbor Crane Raspberry pi

Resilient Set-based State Estimation for Linear Time-Invariant Systems Using Zonotopes

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Niazi, Muhammad Umar B. Alanwar, Amr Chong, Michelle S. Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science and Electrical Engineering Jacobs University Bremen Germany Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Netherlands

This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to represent these sets for computational efficiency. However, we show that intelligently designed stealthy attacks may cause exponential growth in the algorithm’s worst-case complexity. We present several strategies to handle this complexity issue and illustrate our resilient zonotope-based state estimation algorithm on a rotating target system. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear time-invariant system

Adaptive Sampling of Algal Blooms Using Autonomous Underwater Vehicle and Satellite Imagery: Experimental Validation in the Baltic Sea

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Fonseca, Joana Bhat, Sriharsha Lock, Matthew Stenius, Ivan Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures Sweden The Department of Aeronautical and Vehicle Engineering School of Engineering Sciences Kth Royal Institute of Technology StockholmSE-100 44 Sweden

This paper investigates using satellite data to improve adaptive sampling missions, particularly for front tracking scenarios such as with algal blooms. Our proposed solution to find and track algal bloom fronts uses an Autonomous Underwater Vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a and satellite data. The proposed method learns the kernel parameters for a Gaussian process (GP) model using satellite images of chlorophyll a from the previous days. Then, using the data collected by the AUV, it models chlorophyll a concentration online. We take the gradient of this model to obtain the direction of the algal bloom front and feed it to our control algorithm. The performance of this method is evaluated through realistic simulations for an algal bloom front in the Baltic sea, using the models of the AUV and the chlorophyll a sensor. We compare the performance of different estimation methods, from GP to curve interpolation using least squares. Sensitivity analysis is performed to evaluate the impact of sensor noise on the methods' performance. We implement our method on an AUV and run experiments in the Stockholm archipelago in the summer of 2022. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles