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arXiv 2022年

作者： Zheng, Kaikai Shi, Dawei Chen, Tongwen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

This work attempts to discuss the observability of linear time-invariant systems with event-triggered measurements. A new notion of observability, namely, ǫ-observability is defined with parameter ǫ, which relates to the worst-case performance of inferring the initial state based on not only the received measurement but also the implicit information in the event-triggering conditions at no-event instants. A criterion is developed to test the proposed ǫ-observability of discrete-time linear systems, based on which an iterative event-triggered set-membership observer is designed to evaluate a set containing all possible values of the state. The proposed set-membership observer is designed as the outer approximation of the ellipsoids predicted based on previous state estimates and the ellipsoids inferred by fusing the received measurement and communication conditions, which is optimal in the sense of trace at each step and is proved to be asymptotically bounded. The efficiency of the proposed event-triggered set-membership state observer is verified by numerical experiments. © 2022, CC BY.

关键词： State estimation

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Electron dynamics in planar radio frequency magnetron plasmas: I. The mechanism of Hall heating and the µ-mode

arXiv

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arXiv 2022年

作者： Eremin, Denis Engel, Dennis Krüger, Dennis Wilczek, Sebastian Berger, Birk Oberberg, Moritz Wölfel, Christian Smolyakov, Andrei Lunze, Jan Awakowicz, Peter Schulze, Julian Brinkmann, Ralf Peter Institute of Theoretical Electrical Engineering Ruhr University Bochum Germany Institute of Applied Electromagnetics and Plasma Technology Ruhr University Bochum Germany Institute of Automation and Computer Control Ruhr University Bochum Germany Department of Physics and Engineering Physics University of Saskatchewan Saskatoon Canada

The electron dynamics and the mechanisms of power absorption in radio-frequency (RF) driven, magnetically enhanced capacitively coupled plasmas (MECCPs) at low pressure are investigated. The device in focus is a geometrically asymmetric cylindrical magnetron with a radially nonuniform magnetic field in axial direction and an electric field in radial direction. The dynamics is studied analytically using the cold plasma model and a single-particle formalism, and numerically with the inhouse energy and charge conserving particle-in-cell/Monte Carlo collisions code ECCOPIC1S-M. It is found that the dynamics differs significantly from that of an unmagnetized reference discharge. In the magnetized region in front of the powered electrode, an enhanced electric field arises during sheath expansion and a reversed electric field during sheath collapse. Both fields are needed to ensure discharge sustaining electron transport against the confining effect of the magnetic field. The corresponding azimuthal E×B-drift can accelerate electrons into the inelastic energy range which gives rise to a new mechanism of RF power dissipation. It is related to the Hall current and is different in nature from Ohmic heating, as which it has been classified in previous literature. The new heating is expected to be dominant in many magnetized capacitively coupled discharges. It is proposed to term it the "µ-mode" to separate it from other heating modes. © 2022, CC BY-NC-ND.

关键词： Dynamics

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A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

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computer Modeling in Engineering & Sciences 2020年第11期125卷 579-596页

作者： Xing Deng Haijian Shao Liang Shi Xia Wang Tongling Xie School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China Department of Electrical and Computer Engineering National University of Singapore117583Singapore The People’s Hospital of RUGAO Nantong226500China

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff *** of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation *** this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of *** benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

关键词： COVID-19 detection deep learning transfer learning pre-trained models

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Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

arXiv

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arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Navigating the future:Embracing resilience and interoperability in the industrial internet

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Fundamental Research 2024年第1期4卷 1-2页

作者： Chaojie Gu Chunxiao Jiang Jiming Chen Yang Cong Bin Xiao College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China

In the midst of the fourth industrial revolution,the convergence of the digital,physical,and biological realms is propelling industrial innovation to new *** the heart of this transformative era lies the Industrial In... 详细信息

关键词： internet network shaping

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Maturity Model for AI in Smart Production Planning and control System 55

Maturity Model for AI in Smart Production Planning and Contr...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Colangelo, Eduardo Fries, Christian Hinrichsen, Theresa-Franziska Szaller, Ádám Nick, Gábor Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12 Stuttgart70569 Germany University of Stuttgart Institute of Industrial Manufacturing and Management IFF Allmandring 35 Stuttgart70569 Germany Institute for Computer Science and Control Kende str. 13-17. Budapest1111 Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest1111 Hungary EPIC InnoLabs Nonprofit Ltd. Kende str. 13-17. Budapest1111 Hungary

The utilization of Artificial Intelligence (AI) to improve processes constitutes a main subject for many enterprises. The area of Production Planning and control (PPC) possesses several functions that could profit from such approaches. However, manufacturing companies find themselves often limited in the application of these approaches. This paper concentrates on three elements to assist enterprises: 1) the clarification of what AI is (in the manufacturing context) and its application to the field of PPC;2) a review performed together with manufacturing enterprises in Germany and Hungary in order to understand the obstacles for the implementation of AI;and 3) the proposal of a maturity model to help enterprises understand where they are in regards to AI, as a way to help them create a roadmap to achieve their objectives. © 2022 The Authors. Published by Elsevier B.V.

关键词： Artificial Intelligence Maturity Model Production Planning and control Smart Production

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Dynamic programming method for traffic distribution in lorawan network 20th

Dynamic programming method for traffic distribution in loraw...

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20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020

作者： Ali Muthanna, Mohammed Saleh Wang, Ping Wei, Min Al-Mughalles, Waleed Rafiq, Ahsan School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg St. Petersburg Russia School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9783030657253

Internet of things (IoT) allows millions of devices to be connected, measured, monitored to automate processes, and support better decision making. On the other hand, these IoT devices demand cost-effective, long-range, and power-efficient sensors and actuators. LoRaWAN can be considered as a promising way to overcome these issues. By leveraging LoRaWAN protocol into IoT systems, it will help to optimize energy consumption, capacity, cost, and coverage of the system. This study proposes a scheme for modeling the LoRAWANnetwork based on the availability of several independent radio frequency channels. As part of this approach, we also offer a model and calculation method. Numerical results show the efficacy of our proposed scheme. © Springer Nature Switzerland AG 2020.

关键词： Internet of things

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An Adaptive Data Rate-Based Task Offloading Scheme in Vehicular Networks

An Adaptive Data Rate-Based Task Offloading Scheme in Vehicu...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chaofan Chen Wendi Nie Yaoxin Duan Victor C.S. Lee Kai Liu Huamin Li School of Automation Chongqing University Chongqing China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China Department of Automation Chongqing University of Posts and Telecommunications Chongqing China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong China College of Computer Science Chongqing University Chongqing China

As an important application of Internet of Things (IoT), Internet of Vehicles (IoVs) can provide various valuable services which may require computation-intensive tasks under strict time constraints. Most traditional vehicles may not be able to process all these computation-intensive tasks locally because of the limitation of computing resources. Therefore, task offloading has been proposed, which allows vehicles to offload computation-intensive tasks to Mobile Edge Computing (MEC) servers. With the arising and development of intelligent vehicles, the concept of Vehicle as a Resource (VaaR) has been proposed as an important supplement to MEC, which enables intelligent vehicles to share computation resources with nearby vehicles. Most studies in VaaR generally assume that the transmission data rate of offloading tasks from one vehicle to another is fixed. However, in VaaR, due to the high mobility of vehicles, the communication distance between vehicles may change over time, resulting in changing data rate. Therefore, it is challenging to make offloading decisions (i.e., selecting proper vehicles as computation resource providers) while considering adaptive data rate. In this paper, we study task offloading in vehicular networks while considering adaptive data rate. We propose an Adaptive Data Rate-based Offloading algorithm named ADRO, which can not only achieve minimum energy consumption while satisfying time constraints, but also take adaptive data rate into consideration. Comprehensive experiments have been conducted to demonstrate the efficiency of the ADRO algorithm.

关键词： Energy consumption Adaptive systems Multi-access edge computing Intelligent vehicles Sensors Time factors Servers

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries

Exponential Weighted Moving Average-Based Variational Mode D...

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Data Driven control and Learning Systems (DDCLS)

作者： Penghao Wu Hongfeng Tao Engang Tian Tangwen Yin Hongtian Chen Yiyang Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods.

关键词： Fault diagnosis Learning systems Employee welfare Sensitivity Power supplies Noise Electric vehicles

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Prediction of Drug-Disease Associations Based on Long Short-Term Memory Network and Gaussian Interaction Profile Kernel 15th

Prediction of Drug-Disease Associations Based on Long Short-...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Jiang, Han-Jing Wang, Yan-Bin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computer Science and Technology Zhejiang University Hangzhou315000 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Drug-disease association depicts the accessibility landscape of the association at the molecular level, thereby revealing the intermolecular reaction in the process of drug reposition. However, the scarcity of drug-disease association data often complicates the drug reposition. In this work, we present a method for predicting drug-disease association, which is called drug-disease association prediction via Gaussian interaction profile kernel and long short-term memory network (G-LSTM). G-LSTM combines a recurrent network framework with a Gaussian interaction profile kernel to learn underlying features that accurately characterize drug-disease-association data. We have verified G-LSTM models on the two benchmark datasets Fdataset and Cdataset, respectively. The experimental results show that the predictive performance was significantly improved, with AUC of 0.9527 and 0.9705, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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