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Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Nature communications 2023年第1期14卷 3043页

作者： Jianxin Shi Kouya Shiraishi Jiyeon Choi Keitaro Matsuo Tzu-Yu Chen Juncheng Dai Rayjean J Hung Kexin Chen Xiao-Ou Shu Young Tae Kim Maria Teresa Landi Dongxin Lin Wei Zheng Zhihua Yin Baosen Zhou Bao Song Jiucun Wang Wei Jie Seow Lei Song I-Shou Chang Wei Hu Li-Hsin Chien Qiuyin Cai Yun-Chul Hong Hee Nam Kim Yi-Long Wu Maria Pik Wong Brian Douglas Richardson Karen M Funderburk Shilan Li Tongwu Zhang Charles Breeze Zhaoming Wang Batel Blechter Bryan A Bassig Jin Hee Kim Demetrius Albanes Jason Y Y Wong Min-Ho Shin Lap Ping Chung Yang Yang She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young-Chul Kim Neil E Caporaso Jiang Chang James Chung Man Ho Michiaki Kubo Yataro Daigo Minsun Song Yukihide Momozawa Yoichiro Kamatani Masashi Kobayashi Kenichi Okubo Takayuki Honda Dean H Hosgood Hideo Kunitoh Harsh Patel Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Hidehito Horinouchi Masahiro Tsuboi Ryuji Hamamoto Koichi Goto Yuichiro Ohe Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Megumi Hara Yuichiro Nishida Kenji Takeuchi Kenji Wakai Koichi Matsuda Yoshinori Murakami Kimihiro Shimizu Hiroyuki Suzuki Motonobu Saito Yoichi Ohtaki Kazumi Tanaka Tangchun Wu Fusheng Wei Hongji Dai Mitchell J Machiela Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Gee-Chen Chang Ying-Huang Tsai Kuan-Yu Chen Ming-Shyan Huang Wu-Chou Su Yuh-Min Chen Adeline Seow Jae Yong Park Sun-Seog Kweon Kun-Chieh Chen Yu-Tang Gao Biyun Qian Chen Wu Daru Lu Jianjun Liu Ann G Schwartz Richard Houlston Margaret R Spitz Ivan P Gorlov Xifeng Wu Ping Yang Stephen Lam Adonina Tardon Chu Chen Stig E Bojesen Mattias Johansson Angela Risch Heike Bickeböller Bu-Tian Ji H-Erich Wichmann David C Christiani Gadi Rennert Susanne Arnold Paul Brennan James McKay John K Field Sanjay S Shete Loic Le Marchand Geoffrey Liu Angeline Andrew Lambertus A Kiemeney Shan Zienolddiny-Narui Kjell Grankvist Mikael Johansson Angela Cox Fiona Taylor Jian-Min Yuan Philip Lazarus Matthew B Schabath Melinda C Aldrich Hyo-Sung Jeon Shih Sheng Jiang Jae Sook Sung C Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. jianxin.shi@nih.gov. Division of Genome Biology National Cancer Research Institute Tokyo Japan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Division of Cancer Epidemiology and Prevention Aichi Cancer Center Research Institute Nagoya Japan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Jiangsu Key Lab of Cancer Biomarkers Prevention and Treatment Collaborative Innovation Center for Cancer Medicine Nanjing Medical University Nanjing China. Prosserman Centre for Population Health Research Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada. Department of Epidemiology and Biostatistics National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Tianjin China. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology Cancer Institute and Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Department of Epidemiology School of Public Health China Medical University Shenyang China. Department of Clinical Epidemiology and Center of Evidence Based Medicine The First Hospital of China Medical University Shenyang China. Department of Oncology Shandong Cancer Hospital and Institute Shandong Academy of Medical Sciences Jinan China. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China. S

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

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Bus Priority system Using Radio Frequency Identification (RFID) Detection in Singapore

Bus Priority System Using Radio Frequency Identification (RF...

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2015年南京第十四届亚太智能交通论坛

作者： Tatsuya Higuchi Lincoln Tan Musthafa Ibrahim Joanne Cheong Traffic Control Systems Engineering Section Electronic Control Technology Department ICT Solution He Traffic Control & Transportation Division Mitsubishi Heavy Industries Engine System Asia Pie LtdSin Intelligent Transport Systems Development Land Transport AuthoritySingapore City219428Singapore

Making public transport a choice mode is one of the three key strategic thrusts listed in Land Transport Master Plan (LTMP) *** achieve that, it is important to make the public transport system attractive for use by the *** of such application identified is to provide signal priority for busses at traffic light *** scan and assessment of various detection technologies have indicated that RFID is the appropriate, open standards and economical technology for the bus *** is a wireless non-contact system that uses radio-frequency electromagnetic fields to transfer data from a tag attached to an object, for the purposes of automatic identification and *** tags has been widely used in various industries, including transportation for payment services such as for transit fares, highway tolls and parking fares, as well as in the areas of vehicle identification and access control in many *** the cost of RFID tags has decreased significantly over time, its detection capability has also improved to allow longer range *** this, a concerted initiative between the local authorities and the industry to conduct a technical trial for traffic light priority for buses using radio frequency identification technology (RFID) was executed at selected test locations in *** paper discusses on the technical trial conducted to assess the accuracy and reliability of Bus Priority system using RFID in providing traffic light priority for buses in Singapore.

关键词： ITS signal priority technical trial radio frequency identification

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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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Partial discharge classification using principal component analysis combined with self-organizing map

Partial discharge classification using principal component a...

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IEEE Region 10 International Conference TENCON

作者： Norasage Pattanadech Phethai Nimsanong Electrical Engineering Dept King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Operation and Control Section Power System Control Dept Metropolitan Electricity Authority Bangkok Thailand

This document proposes a statistical classification model using principal component analysis (PCA) for a data reduction approach combined with self-organizing map (SOM) for a classification purpose, so called, PCA-SOM model compared with SOM model to classify partial discharge pattern (PD) into four categories listed as corona at high voltage side, corona at low voltage side, surface discharge, and internal discharge. PD signals were investigated by using ultra high frequency (UHF) measurement technique. 12 independent parameters, skewness and kurtosis of each period of the measured electromagnetic signal, were calculated. 80 experiments in total were performed. PCA-SOM PD classification model was constructed. Then, 60% of the experimented data was used as a training data for the PD classification model. Another 40% experimented data was utilized to evaluate the performance of the designed PD classification model. Besides, noise signals were generated with a computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed SOM model and PCA-SOM model can predict PD patterns without noise signal with the accuracy 100% of classification. The prediction ability of SOM for PD classification models decreased sharply when this model was tested by the mixed-PD signals with the noise level of 30% of the peak value of the PD signal. Whereas the PCA-SOM provided some degree accuracy reducing for PD pattern classification when it was verified with such data.

关键词： Partial discharges Noise Discharges (electric) Corona Mathematical model Data models Vectors

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Partial discharge classification using learning vector quantization network model

Partial discharge classification using learning vector quant...

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IEEE Region 10 International Conference TENCON

This paper represents a partial discharge (PD) classification technique by using learning vector quantization (LVQ) network model. LVQ model was not only implemented with the PD test data but also combined with the principal component analysis (PCA), so called, PCA-LVQ for a data reduction. In this research work, both LVQ model and PCA-LVQ model were investigated. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of the experimented data was used as a training data for the PD classification models. Another 40% experimented data was used to evaluate the performance of the designed PD classification models both LVQ and PCA-LVQ models. Besides, noise signals were generated with computer program for testing the efficiency of these models. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed LVQ model can predict PD patterns without noise signal with the accuracy 100% whereas the PCA-LVQ model provided more than 87.5% accuracy of PD classification. The prediction ability of LVQ PD classification models decreased sharply especially for CHV when this model was tested by the mixed noise-PD signals. Whereas the prediction accuracy of PCA-LVQ model was more robust to the noise signals than LVQ model.

关键词： Partial discharges Mathematical model Noise Discharges (electric) Predictive models Data models Corona

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A second update on mapping the human genetic architecture of COVID-19

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Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Navigation filter simulator development for small body proximity operation 24

Navigation filter simulator development for small body proxi...

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24th AAS/AIAA Space Flight Mechanics Meeting, 2014

作者： Lai, Peter C. Bayard, David S. Data Handling System - Satellite Engineering Globalstar Inc. 461 S. Milpitas Blvd MilpitasCA95035 United States Guidance and Control Section Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States

ISBN: (纸本)9780877036111

A simulator is developed to test a vision-based on-board navigation filter algorithm relevant to space exploration of small bodies such as asteroids and comets. The navigation filter algorithm was developed in earlier references and includes many data types relevant to small body proximity operations. These data types include the Landmark Table, Paired Feature Table, and Range Measurement Table. The first two data types use vision-based camera measurements, while the third consists of multi-beam altimeter measurements. The paper presents the simulation architecture and test scenarios to validate the simulation code, along with the derivation of a range dependent measurement noise. The MATLAB simulation environment was developed in the form of modules for portability and reusability, and is intended for future integration into a robotic testbed with coding into C/C++ for real-time applications.

关键词： Space research

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Author Correction: Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

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Nature genetics 2022年第3期54卷 361页

作者： Wouter van Rheenen Rick A A van der Spek Mark K Bakker Joke J F A van Vugt Paul J Hop Ramona A J Zwamborn Niek de Klein Harm-Jan Westra Olivier B Bakker Patrick Deelen Gemma Shireby Eilis Hannon Matthieu Moisse Denis Baird Restuadi Restuadi Egor Dolzhenko Annelot M Dekker Klara Gawor Henk-Jan Westeneng Gijs H P Tazelaar Kristel R van Eijk Maarten Kooyman Ross P Byrne Mark Doherty Mark Heverin Ahmad Al Khleifat Alfredo Iacoangeli Aleksey Shatunov Nicola Ticozzi Johnathan Cooper-Knock Bradley N Smith Marta Gromicho Siddharthan Chandran Suvankar Pal Karen E Morrison Pamela J Shaw John Hardy Richard W Orrell Michael Sendtner Thomas Meyer Nazli Başak Anneke J van der Kooi Antonia Ratti Isabella Fogh Cinzia Gellera Giuseppe Lauria Stefania Corti Cristina Cereda Daisy Sproviero Sandra D'Alfonso Gianni Sorarù Gabriele Siciliano Massimiliano Filosto Alessandro Padovani Adriano Chiò Andrea Calvo Cristina Moglia Maura Brunetti Antonio Canosa Maurizio Grassano Ettore Beghi Elisabetta Pupillo Giancarlo Logroscino Beatrice Nefussy Alma Osmanovic Angelica Nordin Yossef Lerner Michal Zabari Marc Gotkine Robert H Baloh Shaughn Bell Patrick Vourc'h Philippe Corcia Philippe Couratier Stéphanie Millecamps Vincent Meininger François Salachas Jesus S Mora Pardina Abdelilah Assialioui Ricardo Rojas-García Patrick A Dion Jay P Ross Albert C Ludolph Jochen H Weishaupt David Brenner Axel Freischmidt Gilbert Bensimon Alexis Brice Alexandra Durr Christine A M Payan Safa Saker-Delye Nicholas W Wood Simon Topp Rosa Rademakers Lukas Tittmann Wolfgang Lieb Andre Franke Stephan Ripke Alice Braun Julia Kraft David C Whiteman Catherine M Olsen Andre G Uitterlinden Albert Hofman Marcella Rietschel Sven Cichon Markus M Nöthen Philippe Amouyel Bryan J Traynor Andrew B Singleton Miguel Mitne Neto Ruben J Cauchi Roel A Ophoff Martina Wiedau-Pazos Catherine Lomen-Hoerth Vivianna M van Deerlin Julian Grosskreutz Annekathrin Roediger Nayana Gaur Alexander Jörk Tabea Barthel Erik Theele Benjamin Ilse Beatrice Stubendorff Otto W Witte Robert Steinbach Chris Department of Neurology UMC Utrecht Brain Center University Medical Center Utrecht Utrecht University Utrecht the Netherlands. w.vanrheenen-2@umcutrecht.nl. Department of Neurology UMC Utrecht Brain Center University Medical Center Utrecht Utrecht University Utrecht the Netherlands. Department of Genetics University of Groningen University Medical Centre Groningen Groningen the Netherlands. Department of Genetics University Medical Center Utrecht Utrecht University Utrecht the Netherlands. University of Exeter Medical School College of Medicine and Health University of Exeter Exeter UK. Department of Neurosciences Experimental Neurology and Leuven Brain Institute (LBI) KU Leuven-University of Leuven Leuven Belgium. Laboratory of Neurobiology VIB Center for Brain & Disease Research Leuven Belgium. Department of Neurology University Hospitals Leuven Leuven Belgium. Translational Biology Biogen Boston MA USA. MRC Integrative Epidemiology Unit (IEU) Population Health Sciences University of Bristol Bristol UK. Institute for Molecular Bioscience University of Queensland Brisbane Queensland Australia. Illumina San Diego CA USA. Complex Trait Genomics Laboratory Smurfit Institute of Genetics Trinity College Dublin Dublin Ireland. Academic Unit of Neurology Trinity Biomedical Sciences Institute Trinity College Dublin Dublin Ireland. Maurice Wohl Clinical Neuroscience Institute Department of Basic and Clinical Neuroscience Institute of Psychiatry Psychology and Neuroscience King's College London London UK. Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London UK. National Institute for Health Research Biomedical Research Centre and Dementia Unit South London and Maudsley NHS Foundation Trust and King's College London London UK. Department of Neurology Stroke Unit and Laboratory of Neuroscience Istituto Auxologico Italiano IRCCS Milan Italy. Department of Pathophysiology and

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Effect of noise signals on partial discharge classification models

Effect of noise signals on partial discharge classification ...

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IEEE Region 10 International Conference TENCON

作者： Norasage Pattanadech Phethai Nimsanong Electrical Engineering Dept King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Operation and Control Section 2 Metropolitan Electricity Authority Bangkok Thailand

This document proposes the comparison of four statistical classification models for partial discharge (PD) classification as follows: k-nearest neighbors (KNN) model, probabilistic neural network (PNN) model, and other two statistical models using principal component analysis (PCA) for a data reduction approach combined with KNN and PNN models, so called, PCA-KNN model and PCA-PNN model. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated and measured in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF, and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of experimented data was used as a training data for the PD classification model. Another 40% experimented data was used to evaluate the performance of the designed PD classification models. Besides, noise signals were generated with computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed KNN, PNN, PCA-KNN and PCA - PNN model can predict PD patterns without noise signal with the accuracy 100%. Noise signals with amplitude of 20% or more of peak value of PD signal have obviously influence the accuracy of PD pattern classification. The combination of PCA with KNN model can improve the ability of PD classification compared with KNN PD classification model. However, it seems that PCA provided negative effect when PCA was c

关键词： Partial discharges Noise Principal component analysis Discharges (electric) Atmospheric modeling Data models Training

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Effect of training methods on the accuracy of PCA-KNN partial discharge classification model

Effect of training methods on the accuracy of PCA-KNN partia...

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IEEE Region 10 International Conference TENCON

作者： Norasage Pattanadech Phethai Nimsanong Electrical Engineering Dept King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Operation and Control Section 2 Metropolitan Electricity Authority Bangkok Thailand

The aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were experimented in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 PD experiments in total were performed. The original independent variables for the classification model, skewness and kurtosis of each period of the captured signals, were calculated. To study the effect of training methods: two patterns for data training, odd/even and block training methods were investigated. In case of the block training method, the effect of training data number can be examined as well. Besides, noise signals were generated with the computer program and trained into the PD classification models. The peak of noise signal was set up at 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PCA-KNN PD classification model. It was found that the block data training method provided the higher accuracy PD classification compared with the odd/event data training method. The block training method with 80% training data/20% testing data gave the highest accuracy (95% correction) for PD classification without noise signal. However, this training technique provided the lowest accuracy (56.25% correction) for PD classification with the mixed noise-PD signals.

关键词： Partial discharges Training Noise Accuracy Discharges (electric) Training data Testing

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