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arXiv 2018年

作者： Wu, Shuang Ding, Kemi Cheng, Peng Shi, Ling Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong School of Electrical Computer and Energy Engineering Arizona State University Tempe United States State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

This work considers the sensor scheduling for multiple dynamic processes. We consider n linear dynamic processes. The state of each process is measured by a sensor, which transmits its local state estimate over one wireless channel to a remote estimator with certain communication costs. At each time step, only a portion of the sensors are allowed to transmit data to the remote estimator and the packet might be lost due to unreliability of the wireless channels. Our goal is to find a scheduling policy which coordinates the sensors in a centralized manner to minimize the total expected estimation error of the remote estimator and the communication costs. We formulate the problem as a Markov decision process. We develop an algorithm to check whether there exists a deterministic stationary optimal policy. We show the optimality of monotone policies, which saves the computational effort of finding an optimal policy and facilitates practical implementation. Nevertheless, obtaining an exact optimal policy still suffers from curse of dimensionality when the number of processes is large. We further provide an index-based heuristic to avoid brute force computation. We derive analytic expressions of the indices and show that this heuristic is asymptotically optimal. Numerical examples are presented to illustrate the theoretical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Markov processes

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Optimal design of SWIPT-aware fog computing networks

arXiv

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arXiv 2019年

作者： Liu, Jingxian Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing network, where a multiple antenna fog function integrated hybrid access point (F-HAP) transfers information and energy to multiple heterogeneous single-antenna sensors and also helps some of them fulfill computing tasks. By jointly optimizing energy and information beamforming designs at the F-HAP, the bandwidth allocation and the computation offloading distribution, an optimization problem is formulated to minimize the required energy under communication and computation requirements, as well as energy harvesting constraints. Two optimal designs, i.e., fixed offloading time (FOT) and optimized offloading time (OOT) designs, are proposed. As both designs get involved in solving non-convex problems, there are no known solutions to them. Therefore, for the FOT design, the semidefinite relaxation (SDR) is adopted to solve it. It is theoretically proved that the rank-one constraints are always satisfied, so the global optimal solution is guaranteed. For the OOT design, since its non-convexity is hard to deal with, a penalty dual decomposition (PDD)-based algorithm is proposed, which is able to achieve a suboptimal solution. The computational complexity for two designs are analyzed. Numerical results show that the partial offloading mode is superior to binary benchmark modes. It is also shown that if the system is with strong enough computing capability, the OOT design is suggested to achieve lower required energy;Otherwise, the FOT design is preferred to achieve a relatively low computation complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing

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Optimal online transmission policy in wireless powered networks with urgency-aware age of information

arXiv

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arXiv 2019年

作者： Lu, Yang Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with time between two received updates. Due to EH, some waiting time is required at the sensor before transmitting the status update. To find the optimal transmission policy, an optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. As the problem is non-convex and with no known solution, a two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and in the inner loop, a semi-closed-form expression of the optimal waiting time policy is derived based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results shows that our proposed optimal transmission policy outperforms the the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are non-congested. It is also observed that in order to achieve the lower U-AoI, the sensor should transmit the next update without waiting when the network is congested while should wait a moment before transmitting the next update when the network is non-congested. Additionally, it also shows that the system U-AoI first decreases and then keep unchanged with the increments of EH circuit's saturation level and the energy outage probability. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Initial-Parameter-Criterion based Gaussian Mixture Model Monitoring Method for Non-Gaussian Process

Initial-Parameter-Criterion based Gaussian Mixture Model Mon...

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第37届中国控制会议

作者： Ying Tian Wenli Du Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Gaussian mixture model(GMM) monitoring method is commonly used for non-Gaussian process monitoring. In GMM monitoring method, the final number of Gaussian components and the statistical distribution parameters are estimated through the improved Figueiredo-Jain(F-J) algorithm in the modeling phase. However, the data dimensionality of modern industry is high, the number of initial Gaussian components is large for the accuracy of the algorithm. Under this condition, the relative small number of samplings makes the denominator in the weight estimation formula of the improved F-J algorithm become zero and the algorithm suspends. To make the algorithm work well in an acceptable modeling time, we deduce the Initial-ParameterCriterion among initial Gaussian components, data dimension, sampling number and propose the Initial-Parameter-Criterion based GMM(IPC-GMM) monitoring method. The proposed monitoring method is used for TE process monitoring, simulation results reveal the effectiveness and necessity of IPC-GMM monitoring method.

关键词： non-Gaussian process monitoring Gaussian mixture model Initial-Parameter-Criterion

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Research on Identification Method of Transformer Based on Improved Hu Invariant Moments

Research on Identification Method of Transformer Based on Im...

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International Conference on Mechatronics and Intelligent Robotics, ICMIR 2017

作者： Yu, Hua-dong Ou, Qing-hai Wu, Qing Zhang, Zhe Li, Wen-jing Wang, Yubo Liu, Guohua Lu, Yongling Hu, Chengbo Liu, Na Wang, Rong State Grid Information and Telecommunication Group Co. Ltd. Beijing China State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing100192 China Beijing Engineering Research Center of High-Reliability IC with Power Industrial Grade Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing100192 China State Grid Jiangsu Electric Power Company Research Institute Nanjing21110 China Control and Computer Engineering College North China Electric Power University NCEPU Beijing China

ISBN: (纸本)9783319709895

With the rapid development of electric power industry and the expansion of power grid scale, more and more attentions have been paid to the safe operation of electrical equipment. The target recognition method we used now couldn’t recognize power transformer well, which is based on gray information. In this paper, a transformer identification algorithm based on improved Hu moment invariants is proposed. The transformer identification algorithm proposed in this paper has the advantages of good recognition effect and high recognition accuracy. The correctness and feasibility of the proposed algorithm have been verified by experiments. © 2018, Springer International Publishing AG.

关键词： Template matching

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CONTINUITY-DISCRIMINATION CONVOLUTIONAL NEURAL NETWORK FOR VISUAL OBJECT TRACKING

CONTINUITY-DISCRIMINATION CONVOLUTIONAL NEURAL NETWORK FOR V...

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IEEE International Conference on Multimedia and Expo

作者： Shen Li Bingpeng Ma Hong Chang Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Bejing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN), for visual object tracking. Existing state-of-the-art tracking methods do not deal with temporal relationship in video sequences, which leads to imperfect feature representations. To address this problem, CD-CNN models temporal appearance continuity based on the idea of temporal slowness. Mathematically, we prove that, by introducing temporal appearance continuity into tracking, the upper bound of target appearance representation error can be sufficiently small with high probability. Further, in order to alleviate inaccurate target localization and drifting, we propose a novel notion, object-centroid, to characterize not only objectness but also the relative position of the target within a given patch. Both temporal appearance continuity and object-centroid are jointly learned during offline training and then transferred for online tracking. We evaluate our tracker through extensive experiments on two challenging benchmarks and show its competitive tracking performance compared with state-of-the-art trackers.

关键词： Visual object tracking Temporal appearance continuity Object-centroid discrimination

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Fungal diversity notes 1512–1610: taxonomic and phylogenetic contributions on genera and species of fungal taxa

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Fungal Diversity 2022年第6期117卷 1-272页

作者： Ruvishika S.Jayawardena Kevin D.Hyde Song Wang Ya‑Ru Sun Nakarin Suwannarach Phongeun Sysouphanthong Mohamed A.Abdel‑Wahab Faten A.Abdel‑Aziz Pranami D.Abeywickrama Vanessa P.Abreu Alireza Armand AndréAptroot Dan‑Feng Bao Dominik Begerow Jean‑Michel Bellanger Jadson D.P.Bezerra Digvijayini Bundhun Mark S.Calabon Ting Cao Taimy Cantillo João LVRCarvalho Napalai Chaiwan Che‑Chih Chen Régis Courtecuisse Bao‑Kai Cui Ulrike Damm Cvetomir M.Denchev Teodor T.Denchev Chun Y.Deng Bandarupalli Devadatha Nimali Ide Silva Lidiane Ados Santos Nawal K.Dubey Sylvain Dumez Himashi SFerdinandez André L.Firmino Yusufon Gaforov Achala J.Gajanayake Deecksha Gomdola Sugantha Gunaseelan Shucheng‑He Zin H.Htet Malarvizhi Kaliyaperumal Martin Kemler Kezhocuyi Kezo Nuwan DKularathnage Marco Leonardi Ji‑Peng Li Chunfang Liao Shun Liu Michael Loizides Thatsanee Luangharn Jian Ma Hugo Madrid S.Mahadevakumar Sajeewa S.N.Maharachchikumbura Dimuthu S.Manamgoda María P.Martín Niranjan Mekala Pierre‑Arthur Moreau Yan‑Hong Mu Pasouvang Pahoua Dhandevi Pem Olinto L.Pereira Wiphawanee Phonrob Chayanard Phukhamsakda Mubashar Raza Guang‑Cong Ren Andrea C.Rinaldi Walter Rossi Binu C.Samarakoon Milan CSamarakoon Vemuri V.Sarma Indunil C.Senanayake Archana Singh Maria F.Souza Cristina M.Souza‑Motta Adriano A.Spielmann Wenxin Su Xia Tang XingGuo Tian Kasun M.Thambugala Naritsada Thongklang Danushka S.Tennakoon Nopparat Wannathes DingPeng Wei Stéphane Welti Subodini N.Wijesinghe Hongde Yang Yunhui Yang Hai‑Sheng Yuan Huang Zhang Jingyi Zhang Abhaya Balasuriya Chitrabhanu SBhunjun Timur S.Bulgakov Lei Cai Erio Camporesi Putarak Chomnunti Y.S.Deepika Mingkwan Doilom Wei‑Jun Duan Shi‑Ling Han Naruemon Huanraluek EBGareth Jones NLakshmidevi Yu Li Saisamorn Lumyong Zong‑Long Luo Surapong Khuna Jaturong Kumla Ishara S.Manawasinghe Ausana Mapook Wilawan Punyaboon Saowaluck Tibpromma Yong‑Zhong Lu JiYe Yan Yong Wang Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Innovative Institute for Plant Health Zhongkai University of Agriculture and EngineeringGuangzhou 510225P.R.China Department of Plant Pathology College of AgricultureGuizhou UniversityGuiyang 550025GuizhouChina Research Center of Microbial Diversity and Sustainable Utilization Chiang Mai UniversityChiang Mai 50200Thailand Department of Biology Faculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand Biotechnology and Ecology Institute Ministry of Agriculture and ForestryP.O.Box:811Vientiane CapitalLao PDR Department of Botany and Microbiology Faculty of ScienceSohag UniversitySohag 82524Egypt Beijing Key Laboratory of Environment-Friendly Management on Fruit Diseases and Pests in North China Institute of Plant ProtectionBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Departamento de Fitopatologia Universidade Federal de ViçosaViçosaMinas GeraisBrazil Laboratório de Botânica/Liquenologia Instituto de BiociênciasUniversidade Federal de Mato Grosso do SulAvenida Costa e Silva S/NBairro UniversitárioCampo GrandeMato Grosso do Sul CEP 79070-900Brazil College of Agriculture and Biological Sciences Dali UniversityDali 671003YunnanChina Department of Entomology and Plant Pathology Faculty of AgricultureChiang Mai UniversityChiang Mai 50200Thailand Institute of Plant Science and Microbiology Universität HamburgOrganismic Botany and MycologyOhnhorststr.1822609 HamburgGermany CEFE CNRSUniv.MontpellierEPHEIRDINSERM1919Route de Mende34293 Montpellier Cedex 5France Setor de Micologia Departamento de Biociências e TecnologiaInstituto de Patologia Tropical e Saúde PúblicaUniversidade Federal de GoiásRua 235S/NSetor UniversitárioGoiâniaGO CEP:74605-050Brazil Division of Biological Sciences College of Arts and SciencesUniversity of the Philippines Visayas5023 MiagaoIloiloPhilippines CA

This article is the 14th in the Fungal Diversity Notes series,wherein we report 98 taxa distributed in two phyla,seven classes,26 orders and 50 families which are described and *** in this study were collected from Australia,Brazil,Burkina Faso,Chile,China,Cyprus,Egypt,France,French Guiana,India,Indonesia,Italy,Laos,Mexico,Russia,Sri Lanka,Thailand,and *** are 59 new taxa,39 new hosts and new geographical distributions with one new *** 59 new species comprise Angustimassarina kunmingense,Asterina lopi,Asterina brigadeirensis,Bartalinia bidenticola,Bartalinia caryotae,Buellia pruinocalcarea,Coltricia insularis,Colletotrichum fexuosum,Colletotrichum thasutense,Coniochaeta caraganae,Coniothyrium yuccicola,Dematipyriforma aquatic,Dematipyriforma globispora,Dematipyriforma nilotica,Distoseptispora bambusicola,Fulvifomes jawadhuvensis,Fulvifomes malaiyanurensis,Fulvifomes thiruvannamalaiensis,Fusarium purpurea,Gerronema atrovirens,Gerronema favum,Gerronema keralense,Gerronema kuruvense,Grammothele taiwanensis,Hongkongmyces changchunensis,Hypoxylon inaequale,Kirschsteiniothelia acutisporum,Kirschsteiniothelia crustaceum,Kirschsteiniothelia extensum,Kirschsteiniothelia septemseptatum,Kirschsteiniothelia spatiosum,Lecanora immersocalcarea,Lepiota subthailandica,Lindgomyces guizhouensis,Marthe asmius pallidoaurantiacus,Marasmius tangerinus,Neovaginatispora mangiferae,Pararamichloridium aquisubtropicum,Pestalotiopsis piraubensis,Phacidium chinaum,Phaeoisaria goiasensis,Phaeoseptum thailandicum,Pleurothecium aquisubtropicum,Pseudocercospora vernoniae,Pyrenophora verruculosa,Rhachomyces cruralis,Rhachomyces hyperommae,Rhachomyces magrinii,Rhachomyces platyprosophi,Rhizomarasmius cunninghamietorum,Skeletocutis cangshanensis,Skeletocutis subchrysella,Sporisorium anadelphiae-leptocomae,Tetraploa dashaoensis,Tomentella exiguelata,Tomentella fuscoaraneosa,Tricholomopsis lechatii,Vaginatispora favispora and Wetmoreana *** new combin

关键词： 59 New taxa 39 New records Ascomycota Basidiomycota Dothideomycetes Lecanoromycetes Leotiomycetes Sordariomycetes Ustilaginomycotina

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Open-closed-loop iterative learning control for a class of nonlinear systems with random data dropouts

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IOP Conference Series: Materials Science and engineering 2018年第1期359卷

作者： X Y Cheng H B Wang Y L Jia YH Dong The Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China

In this paper, an open-closed-loop iterative learning control (ILC) algorithm is constructed for a class of nonlinear systems subjecting to random data dropouts. The ILC algorithm is implemented by a networked control system (NCS), where only the off-line data is transmitted by network while the real-time data is delivered in the point-to-point way. Thus, there are two controllers rather than one in the control system, which makes better use of the saved and current information and thereby improves the performance achieved by open-loop control alone. During the transfer of off-line data between the nonlinear plant and the remote controller data dropout occurs randomly and the data dropout rate is modeled as a binary Bernoulli random variable. Both measurement and control data dropouts are taken into consideration simultaneously. The convergence criterion is derived based on rigorous analysis. Finally, the simulation results verify the effectiveness of the proposed method.

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Fuzzy Sliding Mode controller Design for Active Suspension Systems with Load Variation

Fuzzy Sliding Mode Controller Design for Active Suspension S...

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第六届工程机械与车辆工程新进展国际学术会议

作者： S.Liu Z.G.Wang Y.P.Zhang Z.J.Lin Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

This paper mainly investigates the fuzzy sliding mode control of 1/4 vehicle suspension ***,the dynamical model of suspension system with uncertainty is established for the time-varying of

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