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Non-intrusive monitoring and disaggregation of industrial load based on Petri net theory

IOP Conference Series: Materials Science and engineering 2020年第6期768卷

作者： Wei Zheng Jie Yu Hongjie Tian Peng Li Chenning Wu Mengfu Tu School of electrical engineering Southeast University Nanjing 210096 China Ningxia Power Exchange Center Company Yinchuan 750001 China Nanjing SAC Power Grid Automation Co. Ltd Nanjing 210096 China NARI Group Corporation (State Grid Electric Power Research Institute) Nanjing 211106 China State Key Laboratory of Smart Grid Protection and Control Nanjing 211106 China

In this paper, the non-intrusive monitoring and disaggregation method of industrial load based on Petri net theory is studied. First, the basic concepts of Petri nets are introduced. On the basis of prototype Petri nets, time factor is added. Then, the Petri net with time factor is combined with industrial load production process. Considering the production sequence, start-stop time and production time constraints of industrial load, a non-intrusive monitoring and disaggregation model of industrial load is constructed. Finally, according to the actual production process of industrial load, the electricity consumption situation of each production link is calculated, and the detailed analysis results of industrial user's electricity consumption behavior are obtained. The research could provide an important reference for realization of friendly interaction between industrial users and power grid.

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A Spiking Neural Network with Visible Light Communications

A Spiking Neural Network with Visible Light Communications

引用

International Symposium on Communication Systems, Networks and Digital Signal Processing, CNSDSP

作者： Mircea Hulea Zabih Ghassemlooy Sujan Rajbhandari Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania Optical Communications Research Group Northumbria University Newcastle UK Institute for Future Transport and Cities Coventry University UK

Spiking neural networks benefit from the real-time response when implemented using the analogue hardware since each electronic neuron operates independently from each other. The main problem of such neural networks is when the neural areas are non-static and are at a significant distance from each other. One solution to solve this problem is to use wireless based connectivity with parallel information transmission capability between neurons. This work presents the design and implementation of an all-optical synapse, based on the wavelength division multiplexed visible light communications between neurons, which has been adopted to overcome the problem of real-time weights adjustment. The results showed that by direct conversion of the electronic spikes into optical pulses, the spiking neurons are able to communicate through the optical channel with very low influence on the neural network normal spiking activity.

关键词： Neurons Optical filters Optical receivers Synapses Optical pulses Optical amplifiers Stimulated emission

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Relaying Strategy Based on Estimated Information for Multi-Antenna Cooperative Networks

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China Communications 2017年第8期14卷 157-165页

作者： Shuangshuang Han Peng Zhang Feijin Shi Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijingChina and Qingdao Academy of Intelligent Industries School of Computer Engineering Weifang University Communications Headquarters Ministry of Foreign Affairs of the People's Republic of China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina and Research Center of Computational Experiments and Parallel SystemsThe National University of Defense Technology

A sphere-based list forwarding scheme for multiple-input multiple-output(MIMO) relay networks is proposed and analyzed. Firstly, an estimate forwarding(EF) method is proposed, which forwards the minimum mean squared error(MMSE) estimate of the source data to the destination. Since it performs like amplify-and-forward(AF) and decode-and-forward(DF) for the low and high signal-to-noise ratio(SNR) regions, respectively, the EF relay thus outperforms conventional AF and DF across all SNRs without the need for switching algorithms for different SNRs. Because computational complexity is however high for relays with a large number of antennas(large MIMO) and/or high order constellations, list EF for large MIMO relay networks is proposed. It computes a list sphere decoder based MMSE estimate and retains the advantages of the exact EF relay at a negligible performance loss. The proposed list EF could offer a flexible trade-off between the performance and computational complexity.

关键词： MIMO system cooperative network soft information

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Early Non-Small Cell Lung Cancer Heterogeneity and Recurrence Assessed by Tissue Next-Generation Sequencing Genotyping and Circulating Tumor Cell EZH2 Characterization

Journal of Liquid Biopsy, The

引用

Journal of Liquid Biopsy, The 2023年 1卷 100096-100096页

作者： Diaz, A. Garcia Moyano, M. de Miguel Perez, D. Bayarri-Lara, C. Exposito-Hernandez, J. Ortuño, F. Malapelle, U. Garcia-Moreno, A. Landeira, D. Lorente, J. Rolfo, C. Garrido-Navas, M. Serrano, M. GENYO Centre for Genomics and Oncological Research: Pfizer University of Granada Andalusian Regional Government Liquid Biopsy and Cancer Interception Group Granada Spain Department of Thoracic Surgery Virgen de las Nieves University Hospital Av de las Fuerzas Armadas Granada Spain Center for Thoracic Oncology Tisch Cancer Institute Mount Sinai Medical System & Icahn School of Medicine Mount Sinai New York USA IBS Granada Instituto de Investigacion Biosanitaria de Granada Granada Spain Computational Medicine Platform Andalusian Public Foundation Progress and Health-FPS Sevilla Spain Department of Computer Engineering Automation and Robotics University of Granada Granada Spain Department of Public Health University of Naples Federico II Naples Italy Bioinformatics Unit GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government PTS Granada - Avenida de la Ilustración 114 - 18016 Granada Spain Centre for Genomics and Oncological Research (GENYO) Avenida de la Ilustración 114 Granada Spain Department of Biochemistry and Molecular Biology II Faculty of Pharmacy University of Granada Granada Spain Lab. of Genetic Identification Department of Legal Medicine & Toxicology and Physical Anthropology School of Medicine University of Granada 18016 Granada Spain Genetics Department Faculty of Sciences University of Granada 18071 Granada Spain Comprehensive Oncology Division Virgen de las Nieves University Hospital Av de las Fuerzas Armadas 2 18014 Granada Spain

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature ecology & evolution 2025年 2025 May 22页

作者： Stephen Pringle Martin Dallimer Mark A Goddard Léni K Le Goff Emma Hart Simon J Langdale Jessica C Fisher Sara-Adela Abad Marc Ancrenaz Fabio Angeoletto Fernando Auat Cheein Gail E Austen Joseph J Bailey Katherine C R Baldock Lindsay F Banin Cristina Banks-Leite Aliyu S Barau Reshu Bashyal Adam J Bates Jake E Bicknell Jon Bielby Petra Bosilj Emma R Bush Simon J Butler Dan Carpenter Christopher F Clements Antoine Cully Kendi F Davies Nicolas J Deere Michael Dodd Rosie Drinkwater Don A Driscoll Guillaume Dutilleux Mads Dyrmann David P Edwards Mohammad S Farhadinia Aisyah Faruk Richard Field Robert J Fletcher Chris W Foster Richard Fox Richard M Francksen Aldina M A Franco Alison M Gainsbury Charlie J Gardner Ioanna Giorgi Richard A Griffiths Salua Hamaza Marc Hanheide Matt W Hayward Marcus Hedblom Thorunn Helgason Sui P Heon Kevin A Hughes Edmund R Hunt Daniel J Ingram George Jackson-Mills Kelly Jowett Timothy H Keitt Laura N Kloepper Stephanie Kramer-Schadt Jim Labisko Frédéric Labrosse Jenna Lawson Nicolas Lecomte Ricardo F de Lima Nick A Littlewood Harry H Marshall Giovanni L Masala Lindsay C Maskell Eleni Matechou Barbara Mazzolai Alistair McConnell Brett A Melbourne Aslan Miriyev Eric Djomo Nana Alessandro Ossola Sarah Papworth Catherine L Parr Ana Payo-Payo Gad Perry Nathalie Pettorelli Rajeev Pillay Simon G Potts Miranda T Prendergast-Miller Lan Qie Persie Rolley-Parnell Stephen J Rossiter Marcus Rowcliffe Heather Rumble Jon P Sadler Christopher J Sandom Asiem Sanyal Franziska Schrodt Sarab S Sethi Adi Shabrani Robert Siddall Simón C Smith Robbert P H Snep Carl D Soulsbury Margaret C Stanley Philip A Stephens P J Stephenson Matthew J Struebig Matthew Studley Martin Svátek Gilbert Tang Nicholas K Taylor Kate D L Umbers Robert J Ward Patrick J C White Mark J Whittingham Serge Wich Christopher D Williams Ibrahim B Yakubu Natalie Yoh Syed A R Zaidi Anna Zmarz Joeri A Zwerts Zoe G Davies Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK. s.pringle@kent.ac.uk. Centre for Environmental Policy Imperial College London London UK. Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK. Synthotech Ltd Milner Court Hornbeam Square Harrogate UK. Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK. Mechanical Engineering Department University College London London UK. HUTAN SWD Kota Kinabalu Malaysia. Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brasil. Department of Engineering Harper Adams University Newport UK. Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge UK. Centre for Ecology and Hydrology Penicuik UK. Department of Life Sciences Imperial College London Silwood Park Campus Ascot UK. Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria. Greenhood Nepal Kathmandu Nepal. Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire UK. School of Biological and Environmental Sciences Liverpool John Moores University Liverpool UK. Lincoln Centre for Autonomous Systems University of Lincoln Lincoln UK. Royal Botanic Garden Edinburgh Edinburgh UK. School of Biological Sciences University of East Anglia Norwich Research Park Norwich UK. Digital Ecology Limited Bristol UK. School of Biological Sciences University of Bristol Bristol UK. Department of Computing Imperial College London London UK. Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA. Faculty of Science Technology Engineering and Mathematics (STEM) The Open University Walton Hall Milt

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more 'difficult' taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively.

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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...

引用

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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Second order statistics analysis and comparison between arithmetic and geometric average fusion

arXiv

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arXiv 2019年

作者： Li, Tiancheng Fan, Hongqi Herrero, Jesús G. Corchado, Juan M. Key Laboratory of Information Fusion Technology Ministry of Education School of Automation Northwestern Polytechnical University Xi'an710072 China BISITE research Group University of Salamanca Salamanca37007 Spain Air Institute IoT Digital Innovation Hub Salamanca37188 Spain National Key Laboratory of Science and Technology on ATR National University of Defense Technology Chang-Sha410073 China Department of Computer Science and Engineering Universidad Carlos III de Madrid Calle Madrid 126 Getafe28903 Spain Department of Electronics Information and Communication Osaka Institute of Technology Osaka535-8585 Japan

Two fundamental approaches to information averaging are based on linear and logarithmic combination, yielding the arithmetic average (AA) and geometric average (GA) of the fusing initials, respectively. In the context of target tracking, the two most common formats of data to be fused are random variables and probability density functions, namely v-fusion and f-fusion, respectively. In this work, we analyze and compare the second order statistics (including variance and mean square error) of AA and GA in terms of both v-fusion and f-fusion. The case of weighted Gaussian mixtures representing multitarget densities in the presence of false alarms and misdetection (whose weight sums are not necessarily unit) is also considered, the result of which appears significantly different from that for a single target. In addition to exact derivation, exemplifying analysis and illustrations are provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Mean square error

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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State-based control of process services within modular process plants

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Procedia CIRP 2018年 72卷 1088-1093页

作者： Henry Bloch Stephan Hensel Mario Hoernicke Katharina Stark Anna Menschner (neé Hahn) Alexander Fay Leon Urbas Torsten Knohl Jens Bernshausen Institute of Automation Technology Helmut-Schmidt-University Holstenhofweg 85 22043 Hamburg Germany Chair of Process Control Systems and Process Systems Engineering Group Technische Universität Dresden 01062 Dresden Germany ABB Corporate Research Opertions Management Wallstadter Straße 59 68526 Bayer AG 51386 Leverkusen Germany INVITE GmbH 51386 Leverkusen Germany

The modularization of process plants is an approach to cope with the changing requirements of the process industry, such as highly volatile markets and shorter product lifecycles. Each module offers services as encapsulated process functions to the superior control system for the orchestration. These services are controlled in a state-based way. The encapsulation of these services in a reasonable way is a difficult task, because aspects of process and automation engineering must be considered. This contribution introduces an approach for the encapsulation of process functions in services from an automation point of view. Furthermore, the operation modes of modules, services and field devices are discussed. Additionally, possible solutions for the orchestration based on recipe models are introduced. The developed concepts have been tested within a simulation environment.

关键词： Modular process automation Microservices Orchestration State-based control Operating modes Service-oriented architectures

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