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arXiv 2020年

作者： Yue, Xinwei Liu, Yuanwei Key Laboratory of Modern Measurement and Control Technology Ministry of Education School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Intelligent reflecting surface (IRS) is a promising technology to enhance the coverage and performance of wireless networks. We consider the application of IRS to non-orthogonal multiple access (NOMA), where a base station transmits superposed signals to multiple users by the virtue of an IRS. The performance of an IRS-assisted NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) is investigated by invoking 1-bit coding scheme. In particular, we derive new exact and asymptotic expressions for both outage probability and ergodic rate of the mth user with ipSIC/pSIC. Based on analytical results, the diversity order of the m-th user with pSIC is in connection with the number of reflecting elements and channel ordering. The high signal-to-noise radio (SNR) slope of ergodic rate for the m-th user is obtained. The throughput and energy efficiency of nonorthogonal users for IRS-NOMA are discussed both in delaylimited and delay-tolerant transmission modes. Additionally, we derive new exact expressions of outage probability and ergodic rate for IRS-assisted orthogonal multiple access (IRS-OMA). Numerical results are presented to substantiate our analyses and demonstrate that: i) The outage behaviors of IRS-NOMA are superior to that of IRS-OMA and relaying schemes;ii) With increasing the number of reflecting elements, IRS-NOMA is capable of achieving enhanced outage performance;and iii) The M-th user has a larger ergodic rate compared to IRS-OMA and benchmarks. However, the ergodic performance of the m-th user exceeds relaying schemes in the low SNR regime. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Blockchain Based Domestic Appliances Scheduling in Community Microgrids

Blockchain Based Domestic Appliances Scheduling in Community...

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Afzal, M. Umer, Khalid Amin, Waqas Naeem, M. Cai, Dongshang Zhenyuan, Zhang Huang, Qi Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Pakistan

ISBN: (纸本)9781728135205

This paper proposes an optimization model for community microgrid. The proposed scheme performs the scheduling of domestic appliances in a community microgrid using renewable energy resources. The objective of the scheme is to minimize the total operating cost in the whole community. The optimization problem is solved using the branch and bound algorithm. Blockchain (BC) technology is introduced for secure implementation of the proposed approach. Machine to machine interaction is facilitated using BC technology. The transaction information/ data is stored in the distributed shared ledger. The smart contract is used to automatically trigger the flow of electricity and the movement of funds once the necessary conditions are met. © 2019 IEEE.

关键词： Blockchain

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Eight Become One: the EURECA-PRO University Network

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BHM Berg- und Huttenmannische Monatshefte 2022年第10期167卷 462-471页

作者： Kircher, Volkmar Feiel, Susanne Bongaerts, Jan C. Sishchuk, Julia Drebenstedt, Carsten Lagoudakis, Michail Fragkedaki, Konstantina García-Llamas, Paula Suárez-Corona, Adriana Fernández-Raga, María Baelo-Álvarez, Roberto Górski, Marcin Pawelczyk, Marek Hochstein, Synnöve Walter, Stefanie Speck, Maria Ravas, Oana Van Hellemont, Elke Vancauwenbergh, Sadia Resources Innovation Center Leoben Montanuniversität Leoben Leoben Austria Public Relations Office International Relations and University Cooperation Montanuniversität Leoben Leoben Austria Faculty of Business Administration Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of work package education and studies Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of educational council Technische Universität Bergakademie Freiberg Freiberg Germany School of Electrical and Computer Engineering Technical University of Crete Chania Greece EURECA-PRO European University Technical University of Crete Chania Greece Department of General and Specific Didactics and Educational Theory University of León Leon24071 Spain Department of Mathematical Sciences Universidad de León León24071 Spain Department of Applied Physics University of León León24071 Spain Faculty of Civil Engineering Silesian University of Technology Gliwice Poland Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Fakultät Medien Hochschule Mittweida Mittweida Germany International Relation Office University of Petroşani Petroşani Romania Rector’s Office Hasselt University Hasselt Belgium Directorate Research Library and Internationalisation Hasselt University Hasselt Belgium

The United Nations Sustainable Development Goal 12 is the foundation of the European University on Responsible Consumption and Production consortium (EURECA-PRO). This article introduces the eight EURECA-PRO partner universities: Montanuniversität Leoben (Austria), Technische Universität Bergakademie Freiberg (Germany), Technical University of Crete (Greece), University of León (Spain), Silesian University of Technology (Poland), Mittweida University of Applied Sciences (Germany), University of Petroşani (Romania), and Hasselt University (Belgium). In addition, each university’s role within the alliance and unique research and study programmes are outlined. The synergy created by EURECA-PRO enables the pursuit of an ambitious research agenda with five research "Lighthouse Missions" as well as the implementation of joint study programmes. © 2022, The Author(s).

关键词： Sustainable development

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Robust path-following control design of heavy vehicles based on multiobjective evolutionary optimization

arXiv

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arXiv 2020年

作者： Prudencio de Morais, Gustavo A. Marcos, Lucas Barbosa Barbosa, Filipe Marques Barbosa, Bruno H.G. Terra, Marco Henrique Grassi, Valdir Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden Department of Automatics Federal University of Lavras Lavras Brazil

The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigation. However, uncertainty matrices for this class of systems are usually defined by algebraic methods which demand prior knowledge of the system dynamics. In this case, the control system designer depends on the quality of the uncertain model to obtain an optimal control performance. This work proposes a robust recursive controller designed via multiobjective optimization to overcome these shortcomings. Furthermore, a local search approach for multiobjective optimization problems is presented. The proposed method applies to any multiobjective evolutionary algorithm already established in the literature. The results presented show that this combination of model-based controller and machine learning improves the effectiveness of the system in terms of robustness, stability and *** Codes 93C85 (Primary), 68T40, 70Q05 (Secondary) © 2020, CC BY-NC-ND.

关键词： Multiobjective optimization

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Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

arXiv

引用

arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab 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 Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction

arXiv

引用

arXiv 2019年

作者： Amian, Mehdi Soltaninejad, Mohammadreza Control and Intelligent Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran School of Computer Science University of Nottingham United Kingdom

In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classification algorithm based on random forests, for survival prediction is presented. The objective is to segment the glioma area and produce segmentation labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor core, the peritumoral edema, and enhancing tumor. The proposed deep architecture for the segmentation task encompasses two parallel streamlines with two different resolutions. One deep convolutional neural network is to learn local features of the input data while the other one is set to have a global observation on whole image. Deemed to be complementary, the outputs of each stream are then merged to provide an ensemble complete learning of the input image. The proposed network takes the whole image as input instead of patch-based approaches in order to consider the semantic features throughout the whole volume. The algorithm is trained on BraTS 2019 which included 335 training cases, and validated on 127 unseen cases from the validation dataset using a blind testing approach. The proposed method was also evaluated on the BraTS 2019 challenge test dataset of 166 cases. The results show that the proposed methods provide promising segmentations as well as survival prediction. The mean Dice overlap measures of automatic brain tumor segmentation for validation set were 0.84, 0.74 and 0.71 for the whole tumor, core and enhancing tumor, respectively. The corresponding results for the challenge test dataset were 0.82, 0.72, and 0.70, respectively. The overall accuracy of the proposed model for the survival prediction task is %52 for the validation and %49 for the test dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Tumors

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Master Equation Emulation and Coherence Preservation with Classical control of a Superconducting Qubit

arXiv

引用

arXiv 2022年

作者： Vlachos, Evangelos Zhang, Haimeng Maurya, Vivek Marshall, Jeffrey Albash, Tameem Levenson-Falk, E.M. Department of Physics & Astronomy Dornsife College of Letters Arts & Sciences University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Electrical Engineering Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Physics and Astronomy Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States

Open quantum systems are a topic of intense theoretical research. The use of master equations to model a system's evolution subject to an interaction with an external environment is one of the most successful theoretical paradigms. General experimental tools to study different open system realizations have been limited, and so it is highly desirable to develop experimental tools which emulate diverse master equation dynamics and give a way to test open systems theories. In this paper we demonstrate a systematic method for engineering specific system-environment interactions and emulating master equations of a particular form using classical stochastic noise in a superconducting transmon qubit. We also demonstrate that non-Markovian noise can be used as a resource to extend the coherence of a quantum system and counteract the adversarial effects of Markovian environments. © 2022, CC BY.

关键词： Stochastic systems

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Middle East and North African Health Informatics Association (MENAHIA): Technological initiatives for 'One Health'

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Yearbook of Medical Informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health Informatics Association) is the International Medical Informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health Informatics Health Information Technologies International Medical Informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

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Dimensionality Reduction of Volterra Kernels by Tensor Decomposition using Higher-Order SVD

Dimensionality Reduction of Volterra Kernels by Tensor Decom...

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IEEE Conference on Decision and control

作者： Urszula Libal John S. Baras Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Wroclaw University of Science and Technology Wroclaw Poland University of Maryland College Park MD USA

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

The paper proposes a practical method for a significant dimensionality reduction of Volterra kernels, defining a discrete nonlinear model of a signal by Volterra series of higher order. In system identification of Volterra series, the Volterra kernels and nonlinear inputs of the system can be described by super-symmetrical tensors. The reduction of their dimensionality is obtained by a tensor decomposition technique called Higher Order Singular Value Decomposition (HOSVD). The main contribution of the paper is a cascade learning algorithm for the system identification based on residuals of least squares minimization. Numerical examples for Volterra system of order four are used to illustrate the approach.

关键词： Tensors Kernel Indexes Singular value decomposition Nonlinear systems Taylor series Matrix decomposition

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Electricity price-based microgrid energy management system in grid-connected mode

Electricity price-based microgrid energy management system i...

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第三十八届中国控制会议

作者： Xiaoyun Xiong Chen Peng Deliang Zeng Department of Automation School of Mechatronic Engineering and Automation Shanghai University School of Control and Computer Engineering North China Electric Power University

The high permeability of distributed renewable energy resources makes the research on microgrid(MG) urgent. From the perspective of the economic and environmental protection of the MG system, this paper considers the optimal distribution of the outputs of distributed generations(DGs) in the MG on the basis of real-time electricity prices. A general model of microgrid energy management in grid-connected mode is proposed. The objective function and constraints are accurately described, with a novel method of weights of the function. And an innovative optimization algorithm is proposed that combines the Yalmip toolbox with particle swarm optimization(PSO) algorithm to achieve the optimal management. Finally simulation results show that the model and algorithm are effective.

关键词： Microgrid energy management particle swarm optimization electricity prices

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