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On a novel tracking differentiator design based on iterative learning in a moving window

control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Enhanced Single Pixel Imaging in Atmospheric Turbulence

Enhanced Single Pixel Imaging in Atmospheric Turbulence

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Computational Optical Sensing and Imaging, COSI 2024 - Part of Optica Imaging Congress

作者： Cheng, Yin Liao, Yusen Zhou, Shuaijun Chen, Jingan Lam, Edmund Y. Ke, Jun School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Beijing Aerospace Automatic Control Institute China Aerospace Science and Technology Corporation Beijing100070 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong

The imaging quality of single pixel imaging in atmospheric turbulence is enhanced by combining a deep learning model with classical methods. The effectiveness of this method was confirmed by optical experiments. ©... 详细信息

关键词： Pixels

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Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

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Brain Informatics 2021年第1期8卷 1页

作者： Ein Shoka, Athar A. Alkinani, Monagi H. El-Sherbeny, A.S. El-Sayed, Ayman Dessouky, Mohamed M. Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake. © 2021, The Author(s).

关键词： Electroencephalography

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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Achieving near-optimal convergence for distributed minimax optimization with adaptive stepsizes 24

Achieving near-optimal convergence for distributed minimax o...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yan Huang Xiang Li Yipeng Shen Niao He Jinming Xu College of Control Science and Engineering Zhejiang University China Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9798331314385

In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to employ an adaptive stepsize tracking protocol involving the transmission of two extra (scalar) variables. This protocol ensures the consistency among stepsizes of nodes, eliminating the steady-state error due to the lack of coordination of stepsizes among nodes that commonly exists in vanilla distributed adaptive methods, and thus guarantees exact convergence. For nonconvex-strongly-concave distributed minimax problems, we characterize the specific transient times that ensure time-scale separation of stepsizes and quasi-independence of networks, leading to a near-optimal convergence rate of Õ(ε-(4+δ)) for any small δ > 0, matching that of the centralized counterpart. To our best knowledge, D-AdaST is the first distributed adaptive method achieving near-optimal convergence without knowing any problem-dependent parameters for nonconvex minimax problems. Extensive experiments are conducted to validate our theoretical results.

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A Novel Conv-Mamba-Hybrid Network for Medical Image Segmentation

A Novel Conv-Mamba-Hybrid Network for Medical Image Segmenta...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qiao, Sibo Zhao, Zhiyuan Xie, Pengfei Yin, Wenjing Wang, Min Zhang, Yuanyuan Pang, Shanchen Tiangong University The College of Software Tianjin China China University of Petroleum The College of Computer Science and Technology Qingdao China Khalifa University of Science and Technology The Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tiangong University The College of Life Sciences Tianjin China Qingdao University of Technology The College of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

In the rapidly advancing landscape of intelligent medical technology, high-precision and widely applicable medical image segmentation techniques are pivotal in advancing personalized treatment plans and enhancing patient experiences. To address inherent obstacles, such as complex structures and features that are difficult to capture in segmentation tasks, we first propose a CMH module that merges convolutional neural networks (CNNs) with state space models (e.g., Mamba). This module hierarchically integrates CNN's local priors into the Mamba layer, adeptly capturing specific details of target objects and broader contextual information from different perspectives. Subsequently, we introduce an information fusion module based on Mamba (MIH), aimed at dynamically fusing feature information from different paths and levels to enhance feature expression and discrimination. Building upon the abovementioned modules, we develop an unexplored segmentation model, CMHNet, capable of flexibly capturing feature maps of varying scales and angles from medical images. In the experiments, we present CMHNet's distinguished performance in segmentation tasks through massive experiments on four medical datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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An Access control Scheme Based on Decentralized Identifiers and Verifiable Credentials in IoT 3

An Access Control Scheme Based on Decentralized Identifiers ...

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3rd International Conference on computer science and Management Technology, ICCSMT 2022

作者： Li, Yunliang Fu, Yanfang Du, Zhiqiang Cai, Zhipeng Xi'An Technological University School of Armament Science and Technology Xi'an China Xi'An Technological University School of Computer Science and Engineering Xi'an China North Automatic Control Technology Institute Taiyuan China

ISBN: (纸本)9798350320404

With the rapid development of information technology, the Internet of Things (IoT) has become more complex, and more and more devices and users are connected to the IoT system. While improving data interactivity and expanding the scope of data sharing, secure access to IoT resources is also facing great risks. Therefore, it is necessary to design an appropriate access control scheme. Firstly, this paper introduces Decentralized Identifiers (DID) and Verifiable Credentials. It is used for authentication and rights management of users and devices, respectively, which makes identity and authority unforgeable. Secondly, this paper uses blockchain technology to support DID and Verifiable Credentials, which can ensure the decentralization, invariance and traceability of data. Finally, using smart contracts enables automatic processing of requester authorization and access requests. The test results show that the scheme has good performance. © 2022 IEEE.

关键词： Smart contract

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A DRL-Based Cluster Scheduling Algorithm Configuration Optimization Approach for Artificial Intelligence Jobs

A DRL-Based Cluster Scheduling Algorithm Configuration Optim...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Shilin Wen Wei Liang Siyan Zhu Ke Qiu Xiaoning Wu Department Three of Command and Control North Automatic Control Technology Institute Taiyuan China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the widespread application of machine learning technology across multiple domains, artificial intelligence (AI) jobs have become significant workloads for cloud service providers. However, current cloud platforms rely on pre-set scheduling algorithms, making it difficult to adjust configurations in real-time according to characteristics of AI jobs, thereby limiting job completion efficiency. Additionally, the heterogeneity and dynamism of AI jobs increase the difficulty of resource prediction and scheduling. In order to address this issue, this paper proposes an effective approach based on Deep Reinforcement Learning (DRL), by using DRL technology to adjust scheduling algorithm configurations online, converting the optimization process into a Markov process for model training, so as to optimize configurations in real-time and reduce job completion time. Simultaneously, to shorten the data sampling time for DRL training, this paper develops a cluster simulation scheduler that can efficiently simulate cluster running states, which can accelerate DRL training. Experimental results demonstrate that the simulation scheduler exhibits fast simulation rates and high simulation accuracy, and our proposed approach effectively reduces job completion time in small-scale clusters, thereby enhancing cluster resource utilization.

关键词： Training Electrical engineering Limiting Scheduling algorithms Clustering algorithms Markov processes Real-time systems Resource management Artificial intelligence Optimization

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WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

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Autonomous Intelligent Systems 2021年第1期1卷 104-134页

作者： Olov Andersson Patrick Doherty Mårten Lager Jens-Olof Lindh Linnea Persson Elin A.Topp Jesper Tordenlid Bo Wahlberg Department of Computer and Information Science Linköping UniversityLinköpingSweden Saab Kockums MalmöSweden Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Department of Computer Science Lund UniversityLundSweden Saab Combitech AB LinköpingSweden Division of Automatic Control Department of Electrical EngineeringLinköping UniversityLinköpingSweden

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is *** objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal *** enabler is a computational infrastructure with a core system architecture for industrial and academic *** includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial *** motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also *** first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface *** second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine *** research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test *** would be most difficult to do experiments on this large scale without the WARA-PS research ***,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.

关键词： Autonomous systems Intelligent system architectures Research demonstration arena Autonomous drones Autonomous marine vessels Public safety and security Collaborative robotics

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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