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On the conceptualization of total disturbance and its profound implications

science China(Information sciences) 2020年第2期63卷 221-223页

作者： Sen CHEN Wenyan BAI Yu HU Yi HUANG Zhiqiang GAO Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing Aerospace Automatic Control Institute Center for Advanced Control Technologies Department of Electrical Engineering and Computer ScienceCleveland State University

Dear editor,How to deal with uncertainties and/or disturbances is a central issue pushing the development of both control science and control technology. Among various approaches, the active disturbance rejection control (ADRC) has been successfully implemented in various industrial practices because of its uniqueness in concepts, simplicity

关键词： ESO On the conceptualization of total disturbance and its profound implications

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Decentralized control of Multi-Agent systems Under Acyclic Spatio-Temporal Task Dependencies

Decentralized Control of Multi-Agent Systems Under Acyclic S...

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IEEE Conference on Decision and control

作者： Gregorio Marchesini Siyuan Liu Lars Lindemann Dimos V. Dimarogonas Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We introduce a novel distributed sampled-data control method tailored for heterogeneous multi-agent systems under a global spatio-temporal task with acyclic dependencies. Specifically, we consider the global task as a conjunction of independent and collaborative tasks, defined over the absolute and relative states of agent pairs. Task dependencies in this form are then represented by a task graph, which we assume to be acyclic. From the given task graph, we provide an algorithmic approach to define a distributed sampled-data controller prioritizing the fulfilment of collaborative tasks as the primary objective, while fulfilling independent tasks unless they conflict with collaborative ones. Moreover, communication maintenance among collaborating agents is seamlessly enforced within the proposed control framework. A numerical simulation is provided to showcase the potential of our control framework.

关键词： Scalability Decentralized control Collaboration Numerical simulation Robustness Maintenance Multi-agent systems

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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth systems sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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Challenges in Cyber Physical Social systems and Internet of Things

Challenges in Cyber Physical Social Systems and Internet of ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Revathi J. Raja M Mohanraj K. Malathi Balasubbareddy Mallala R.G. Vidhya Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Eshwar college of engineering Coimbatore India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of ECE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research analyses the complex dynamics of Cyber-Physical-Social systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.

关键词： Data privacy Social networking (online) Shape Media Real-time systems Internet of Things Protection Predictive analytics Monitoring Resilience

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Optimizing Load and Routes in Supply Chain Management with Travelling Salesman Problem and CapsNet Attention

Optimizing Load and Routes in Supply Chain Management with T...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Rajasekar P M Shabeena Begam Shouvik Chattopadhyay S. Chand Basha Shanker Chandre Vijay Kumar Dwivedi Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of MBA SNS College of Technology Coimbatore India Department of MBA Institute of Engineering & Management Kolkata India University of Engineering & Management Kolkata India Department of Business Administration St Ann's College of Engineering and Technology Chirala India Department of Computer Science & Artificial Intelligence School of Computer Science & Artificial Intelligence SR University Warangal India Department of Mathematics Vishwavidyalaya Engineering College Ambikapur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Ever since the industrial revolution, industries have partnered to solve marketing problems and create new channels. Because they don't understand their clients' needs, sales centers often pursue conflicting aims. Lack of information has extended delivery times. Due to supply time, quality, or bulk freight depreciation agreements, facility expenses rise to satisfy demand, resulting in excess inventory. Supply chain management, particularly regional inventory management, has considerably impacted beer manufacturing profitability. The recommended method involves training the model and extracting features. Dynamic and PCA feature extraction are different. While PCA's main purpose is to dissociate correlated from uncorrelated data, dynamic PCA considers auto- and cross-correlation in time-series observations. A revised CapsNet approach trained the models. The Improved CapsNet model outperformed the Attention and baseline models in every test. The model solved brewing supply chain issues with 94.68% accuracy. The most successful advanced machine learning approaches for improving SCM include augmented CapsNet, PCA, and dynamic PCA, according to research. These techniques boost industrial profitability when applied to major issues like excess stock and supply unpredictability. The suggested strategy is more reliable and superior than present solutions, according to results.

关键词： Training Surveys Supply chain management Accuracy Profitability Time series analysis Feature extraction Integrated circuit modeling Surges Principal component analysis

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Exploring Advancements in 6G Wireless Networks for the Future of High-Speed Connectivity

Exploring Advancements in 6G Wireless Networks for the Futur...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Suresh J V Sheeja Kumari N. Muthuvairavan Pillai P. Jesu Jayarin D. Beulah David Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India Department of Computer Science and Business Systems R.M.D Engineering College Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

High-speed connectivity technologies that aim to be faster and more secure have been explored so that there will be a change of the very nature of wireless communication. This article presents advances concerning the high-speed connection component of the sixth-generation wireless networks, which is simply denoted as 6G. Different performance parameters of 6G are compared with 4G (LTE) and 5G (5G) through multiplicity of research analyses done across disciplines. Our findings suggested that 6G networks are incredibly dynamic and will change how the internet is used. These SixG networks are due to accompany massive data transmission rates of 100 Gbps, five times higher than 5G’s 20 Gbps. Latency also approaches same level as 6G where on average 1 millisecond is noticed compared to 5G's 10 and 4G's 50. Server speed and its response time in milliseconds will expose room for a number of real-time applications from only augmented reality imaging to the world of driverless automobiles and interactive games. Our study outlines the major consequences of the coming health shift. 4G allowed only for slow/limited improvements in healthcare whereas the 5G made telemedicine possible. On the other hand, 6G reportedly offers "precision remote surgery", allowing surgeons to be located remotely of a patient while still performing a high precision surgery. Attitude of people becomes completely unaware when they are briefed about concepts of latest generation known as 6G. Before studying about 6G, the number of those are eagerly delighted are 20%, having no idea on how it will be is 50%, while the other 30% are skeptics. Meanwhile, once people are better cognizant of 6G, fanatics raise up from 60 percent and sceptics drop till 10 percent. The significant shift is a reflection of 6G technology in the stakeholders’ mind.

关键词： 6G mobile communication 5G mobile communication Wireless networks Telemedicine Surgery Medical services Real-time systems Vehicle dynamics Long Term Evolution Augmented reality

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Identifying key nodes of mega LEO satellite network based on node embedding and machine learning 74

Identifying key nodes of mega LEO satellite network based on...

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74th International Astronautical Congress, IAC 2023

作者： Zou, Yi-Wei Deng, Hui-Ting Ye, Zhi-Cong Sun, Shou-Ming Zhang, Tao Luo, Zong-Fu School of Systems Science and Engineering SUN YAT-sen University 135 Xingang West Street Guangdong Province Guangzhou510275 China Department of Technology Xi'an Satellite Control Center China 462 Xianning East Street Shaanxi Province Xi'an710043 China

The development of mega low-Earth orbit constellations has indeed gained significant attention in the field of commercial spaceflight. With the increasing number of satellites in space, there is a growing need for space situational awareness and collision warning systems. Existing methods for space collision warning often face challenges such as high computational demands and lack of real-time capabilities. To overcome these issues, this study proposes a novel approach. It models the LEO satellite constellation as a complex network and applies node embedding and machine learning techniques to approximate the closeness centrality of the nodes within the constellation network. This allows for the identification of key satellite nodes, which can serve as a reliable basis for allocating monitoring resources and conducting proximity rendezvous analysis. Experiment results demonstrate that the proposed approach significantly reduces the computational requirements for collision analysis and monitoring, while also improving the timeliness of space object collision warning. This research contributes to enhancing the overall safety and efficiency of mega LEO constellations in commercial spaceflight endeavours. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Collision Warming Complex Network Graph Neural Network Space Situational Awareness

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Locally Differentially Private Online Federated Learning With Correlated Noise

arXiv

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arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Fay, Dominik Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States

We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To address challenges posed by the correlated noise and local updates with streaming non-IID data, we develop a perturbed iterate analysis that controls the impact of the noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed for several classes of nonconvex loss functions. Subject to an (ϵ,δ)-LDP budget, we establish a dynamic regret bound that quantifies the impact of key parameters and the intensity of changes in the dynamic environment on the learning performance. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning

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ImprovNet: Generating controllable Musical Improvisations with Iterative Corruption Refinement

arXiv

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arXiv 2025年

作者： Bhandari, Keshav Chang, Sungkyun Lu, Tongyu Enus, Fareza R. Bradshaw, Louis B. Herremans, Dorien Colton, Simon Electronic Engineering and Computer Science Department Queen Mary University of London United Kingdom Information Systems Technology and Design Singapore University of Technology and Design Singapore

Deep learning has enabled remarkable advances in style transfer across various domains, offering new possibilities for creative content generation. However, in the realm of symbolic music, generating controllable and expressive performance-level style transfers for complete musical works remains challenging due to limited datasets, especially for genres such as jazz, and the lack of unified models that can handle multiple music generation tasks. This paper presents ImprovNet, a transformer-based architecture that generates expressive and controllable musical improvisations through a self-supervised corruption-refinement training strategy. ImprovNet unifies multiple capabilities within a single model: it can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks. The model’s iterative generation framework allows users to control the degree of style transfer and structural similarity to the original composition. Objective and subjective evaluations demonstrate ImprovNet’s effectiveness in generating musically coherent improvisations while maintaining structural relationships with the original pieces. The model outperforms Anticipatory Music Transformer in short continuation and infilling tasks and successfully achieves recognizable genre conversion, with 79% of participants correctly identifying jazz-style improvisations. Our code and demo page can be found at https://***/keshavbhandari/improvnet. © 2025, CC BY.

关键词： Deep learning

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Incentive Design for Eco-driving in Urban Transportation Networks

Incentive Design for Eco-driving in Urban Transportation Net...

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European control Conference (ECC)

作者： M. Umar B. Niazi Jung-Hoon Cho Munther A. Dahleh Roy Dong Cathy Wu Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks. Acknowledging the persuasive influence of incentives in shaping driver behavior, this paper presents the ‘eco-planner,’ a digital platform devised to promote eco-driving practices in urban transportation. At the outset of their trips, users provide the platform with their trip details and travel time preferences, enabling the eco-planner to formulate personalized eco-driving recommendations and corresponding incentives, while adhering to its budgetary constraints. Upon trip completion, incentives are transferred to users who comply with the recommendations and effectively reduce their emissions. By comparing our proposed incentive mechanism with a baseline scheme that offers uniform incentives to all users, we demonstrate that our approach achieves superior emission reductions and increased user compliance with a smaller budget.

关键词： Transportation Europe Greenhouse gases

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