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Stability of partially unstable discrete-time switched positive nonlinear systems with delays

Applied Mathematics and Computation 2025年 507卷

作者： Jinyuan Ni Yin Sheng Qiang Xiao Zhigang Zeng Tingwen Huang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

This paper addresses the stability issue of discrete-time switched positive nonlinear systems (SPNSs) characterized by partial unstable subsystems and constant delays. Firstly, we use a parameter to constrain the ratio of active durations of the stable and unstable modes, and derive the exponential stability conditions for SPNSs based on a key function. Then we analyze the relationship between convergence speed and the constant delay. Additionally, we obtain a stability criterion of SPNSs under a particular sequence of switching signals. Finally, we validate the results through a numerical example.

关键词： Exponential stability Switched positive nonlinear systems Time delay Unstable subsystems

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Thermal-aware Energy Efficient Task Scheduling Framework

Thermal-aware Energy Efficient Task Scheduling Framework

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jian Nong Jia Chen Yinqing Wang Wei Qin Xi He Faculty of Innovation Engineering-School of Computer Science and Engineering Macau University of Science and Technology Macau China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China High Performance Computing Lab Wuzhou University Wuzhou China

Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware resource management while considering energy efficiency. The framework consists of a layered architecture and integrates a set of easy-to-use client tools and a thermal-aware task management middleware to schedule tasks based on thermal conditions within a cluster and among different data centers. As part of this paper we focus on the development of a thermal-aware task scheduling component for a single data center. This component is fundamental to our future activities, while considering to balance the temperature distribution in a single data center, thus implicitly minimizing energy cost in data centers.

关键词： Data centers Temperature distribution Schedules Processor scheduling Thermal management Ubiquitous computing Energy efficiency

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3dvar Sectoral Emission Inversion Based on Source Apportionment and Machine Learning

SSRN

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SSRN 2024年

作者： Huang, Congwu Niu, Tao Wang, Tijian Ma, Chaoqun Li, Mengmeng Li, Rong Wu, Hao Qu, Yawei Liu, Hongli Liu, Xu Faculty of Resources and Environmental Science Hubei University Wuhan430062 China School of Atmospheric Sciences Nanjing University Nanjing210023 China State Key Laboratory of Severe Weather Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological Sciences Beijing100081 China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric Sciences Nanjing210041 China College of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

The air quality model is increasingly important in air pollution forecasting and controlling. Sectoral emissions significantly impact the accuracy of air quality models and source apportionment. This paper studied the 3DVar (three-dimensional variational) emission inversion method based on machine learning and then expanded to sectoral emission inversion combined with source apportionment. Two machine learning conversion matrixes were established to implement this method, the matrix converting the total pollutant concentration to sectoral source apportionment results and the matrix converting the sectoral source apportionment results to corresponding emissions. Combined with the O3 concentration contributed by VOCs and NOx precursors in source apportionment, the inversion ability for O3-NOx-VOCs nonlinear processes is improved. Taking BTH (Beijing-Tianjin-Hebei) region from January 15 to 30, 2019, as an example, the results showed that the regional errors of PM2.5 and O3 in the inversion experiment were reduced by 47%, 45%, and the temporal errors were reduced by 44%, 16%. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Fast Coding Mode Prediction for Intra Prediction in VVC SCC

Fast Coding Mode Prediction for Intra Prediction in VVC SCC

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IEEE International Conference on Image Processing

作者： Dayong Wang Junyi Yu Xin Lu Frederic Dufaux Hongwei Guo Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des signaux et systèmes School of Engineering Honghe University Mengzi Yunnan China University of Electronic Science and Technology of China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by $34.95 \%$ on average while maintaining almost the same coding efficiency.

关键词： Video coding Deep learning Image coding Modulation Prediction algorithms Complexity theory Copper

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Robust Voltage control in DC Networks with Uncertain Nonlinear Load Dynamics

Robust Voltage Control in DC Networks with Uncertain Nonline...

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IEEE Conference on Decision and control

作者： Amirreza Silani Giacomo Casadei Michele Cucuzzella Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen AG Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratoire Ampere Dpt. EEA of the Ecole Centrale de Lyon Université de Lyon Ecully France Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this letter, we propose a control scheme for regulating the voltage in Direct Current (DC) power networks. In contrast with other works in the literature where the loads are assumed to be constant, we consider uncertain time-varying loads described as the outputs of nonlinear dynamical exosystems with uncertain parameters. Based on the output regulation methodology, the proposed control scheme ensures the robust stability of the overall network and achieves voltage regulation in presence of impedance (Z), current (I), and power (P) loads. The simulation results illustrate excellent performance of the proposed control scheme.

关键词： Robust stability Simulation Regulation Impedance Voltage control

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Cross-heterogeneous Domain Authentication Scheme Based on Blockchain

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 92-100页

作者： Jing Liu Yixin Liu Yingxu Lai Rongchen Li Siyu Wu Sami Mian College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Shaanxi Key Laboratory of Network and System Security Xi’an 710071China Science and Technology on Information Assurance Laboratory Beijing 100072China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Fan Gongxiu Honors College Beijing University of TechnologyBeijing 100124China University of Pittsburgh PittsburghPA 15260USA

With the rising popularity of the Internet and the development of big data technology,an increasing number of organizations are opting to cooperate across domains to maximize their *** organizations use public key infrastructure to ensure security in accessing their data and ***,with the continuous development of identity-based encryption(IBE)technology,small-and medium-sized enterprises are increasingly using IBE to deploy internal authentication *** solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process,we propose using blockchain technology to establish a reliable cross-domain authentication *** the distributed and tamper-resistant characteristics of the blockchain,we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on *** model does not change the internal trust structure of each authentication domain and is highly ***,on the premise of ensuring security,the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate,thereby improving the authentication *** developed prototype exhibits generality and simplicity compared to previous methods.

关键词： public key infrastructure identity-based encryption cross-domain authentication blockchain

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Radiomics Model Based on Computed Tomography Images for Prediction of Radiation-Induced Optic Neuropathy Following Radiotherapy of Brain and Head and Neck Tumors

SSRN

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SSRN 2024年

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Noraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Computer Science University of Copenhagen Copenhagen Denmark Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and Methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. The radiomics features were extracted from the area segmented by Swin UNETR version 2, which included the right and left optic nerves and chiasm. We ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31% of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97% and 95% respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision can predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Distributed Optimal Load Frequency control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

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European control Conference (ECC)

作者： Amirreza Silani Michele Cucuzzella Jacquelien M. A. Scherpen Mohammad Javad Yazdanpanah Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran University of Pavia Pavia Italy

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network.

关键词： Renewable energy sources Wind speed Decentralized control Stochastic processes Doubly fed induction generators Differential equations Wind power generation

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Distributed optimal load frequency control with stochastic wind power generation

arXiv

引用

arXiv 2021年

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M.A. Yazdanpanah, Mohammad Javad Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

关键词： Stochastic systems

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

引用

arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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