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Pattern recognition of UAV flight data based on semi-supervised clustering

Journal of Physics: Conference Series 2019年第1期1195卷

作者： N Wang Z SH Xu SH W Sun Y Liu School of Management Science and Information Engineering Jilin University of Finance and EconomicsChina Laboratory of Logistics Industry Economy and Intelligent Logistics Jilin University of Finance and Economics China Jilin Big Data Research Center for Business Jilin University of Finance and EconomicsChina Jilin Province Key Laboratory of Fintech Jilin University of Finance and EconomicsChina Shenyang Woozoom Technology Co. LtdChina

UAV is an unmanned aerial vehicle controlled by a remote radio signal or a trajectory planning software carried itself. It is widely used in military, civil and scientific research fields. However, due to the lack of real-time decision-making ability, the UAV has high fault rate. The flight quality assessment of UAV and the construction of fault prediction model can be used for debugging and fault-removing to customer's aircraft, and also to increase the added value of the civilian UAV products. Before building a fault prediction model, a very important step is to identify the pattern of sampled data. For each group of flight data, the efficiency and accuracy rate of manual quality evaluation and fault identification are not acceptable. Based on the UAV flight data accumulated in the big data platform of an UAV production company in Shenyang, Liaoning Province of China, this paper proposes a semi-supervised clustering technique to do automatic pattern recognition for the sampling points. According to the characteristics of UAV flight data, two different methods are designed to choose initial centroids. Meanwhile, we use the existing normal flight data to train distance thresholds to combine some clusters to eliminate the resulting error clustering. Real flight data or flight test data with manually added labels are used to run the proposed algorithms to verify the recognition results. The experimental results show that the proposed methods greatly improve the efficiency and accuracy of adding precise labels to the historical flight data and play a role in assisting the manual recognition of sampling points while strengthening the management and statistics.

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Non-rigid 3D models retrieval based on hierarchical matching

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Computer Aided Drafting,Design and Manufacturing 2017年第1期27卷 39-47页

作者： Liu Yujie Li Wei Song Yang Gong Weiqing Li Zongmin Li Hua Department of Computer & Communication Engineering China University of Petroleum ChinaPetro (Karamay) Data Center Sinopec Exploration & Production Research Institute (PEPRIS) Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

In 3D models retrieval, feature description and retrieval of non-rigid model face more complex problems due to the isometry transformation of itself. We introduce the hierarchical combination matching into the feature comparison, and build a map between the divided regions of two models, and then achieve accurate feature matching based on patch-by-patch, which successfully introduces the spatial information into feature matching. Verified by experiment, the 3D model retrieval method proposed in this paper based on hierarchical combination matching can make sure more accurate feature matching, so as to enhance the precision of retrieval.

关键词： non-rigid 3D model shape descriptor spatial distribution hierarchical combination matching

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A parallel evolutionary extreme learning machine scheme for electrical load prediction

A parallel evolutionary extreme learning machine scheme for ...

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Science and Information Conference (SAI)

作者： Peikun Chen Wanhua Li Yuzhong Chen Kun Guo Yuzhen Niu College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou Fuzhou China

ISBN: (纸本)9781509054442

Application of cloud computing technologies in power system has made a great contribution to the establishment of smart grid. Among applications of smart grid, electrical load prediction plays an important role in efficient use of power resource. However, the exponential growth of data has posed a great challenge to the existing algorithms. In this paper, we firstly propose a novel parallel hybrid algorithm, combining the Improved Particle Swarm Optimization (PSO) with ELM, named PIPSO-ELM. Here a modified particle swarm optimization is presented to find the optimal number of hidden neurons as well as the corresponding input weights and hidden biases. Furthermore, in the iterative search process of PSO, an update strategy employs the mutation operator of evolutionary algorithms is introduced for further improving the global search capability and convergence speed of PSO. After that, to handle the large-scale dataset efficiently, the parallel implementation of PIPSO-ELM is achieved using Spark. Finally, extensive experiments on real-life electrical load data and comprehensive evaluation are conducted to verify the performance of PIPSO-ELM in electrical load prediction. Extensive experimental results demonstrate that PIPSO-ELM outperforms the compared algorithms in terms of stability, efficiency and scalability simultaneously.

关键词： Neurons Particle swarm optimization Algorithm design and analysis Prediction algorithms Smart grids Encoding Convergence

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Social contagions on weighted networks

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Physical Review E 2017年第1期96卷 012306-012306页

作者： Yu-Xiao Zhu Wei Wang Ming Tang Yong-Yeol Ahn School of Management Guangdong University of Technology Guangzhou 510520 China School of Big Data and Strategy Guangdong University of Technology Guangzhou 510520 China Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China School of Information Science and Technology East China Normal University Shanghai 200241 China School of Informatics and Computing Indiana University Bloomington Indiana 47408 USA

We investigate critical behaviors of a social contagion model on weighted networks. An edge-weight compartmental approach is applied to analyze the weighted social contagion on strongly heterogenous networks with skewed degree and weight distributions. We find that degree heterogeneity cannot only alter the nature of contagion transition from discontinuous to continuous but also can enhance or hamper the size of adoption, depending on the unit transmission probability. We also show that the heterogeneity of weight distribution always hinders social contagions, and does not alter the transition type.

关键词： Epidemic growth Social networks

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Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients 1

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20th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2017

作者： Chen, Lei Zhang, Han Thung, Kim-Han Liu, Luyan Lu, Junfeng Wu, Jinsong Wang, Qian Shen, Dinggang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Radiology and BRIC University of North Carolina Chapel Hill United States School of Biomedical Engineering Med-X Research Institute Shanghai Jiao Tong University Shanghai China Department of Neurosurgery Huashan Hospital Fudan University Shanghai China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses. Our MIMC model not only uses the training subjects with possibly missing MGMT/IDH1 labels, but also leverages the unlabeled testing subjects as a supplement to the limited training dataset. More importantly, we learn inductive labels, instead of directly using transductive labels, as the prediction results for the testing subjects, to alleviate the overfitting issue in small-sample-size studies. Furthermore, we design an optimization algorithm with guaranteed convergence based on the block coordinate descent method to solve the multivariate non-smooth MIMC model. Finally, by using a precious single-center multi-modality presurgical brain imaging and genetic dataset of primary HGG, we demonstrate that our method can produce accurate prediction results, outperforming the previous widely-used single- or multi-task machine learning methods. This study shows the promise of utilizing imaging-derived brain connectome phenotypes for prognosis of HGG in a non-invasive manner. © Springer International Publishing AG 2017.

关键词： Forecasting

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Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

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Molecular psychiatry 2024年第4期29卷 1216页

作者： Bo-Gyeom Kim Gakyung Kim Yoshinari Abe Pino Alonso Stephanie Ameis Alan Anticevic Paul D Arnold Srinivas Balachander Nerisa Banaj Nuria Bargalló Marcelo C Batistuzzo Francesco Benedetti Sara Bertolín Jan Carl Beucke Irene Bollettini Silvia Brem Brian P Brennan Jan K Buitelaar Rosa Calvo Miguel Castelo-Branco Yuqi Cheng Ritu Bhusal Chhatkuli Valentina Ciullo Ana Coelho Beatriz Couto Sara Dallaspezia Benjamin A Ely Sónia Ferreira Martine Fontaine Jean-Paul Fouche Rachael Grazioplene Patricia Gruner Kristen Hagen Bjarne Hansen Gregory L Hanna Yoshiyuki Hirano Marcelo Q Höxter Morgan Hough Hao Hu Chaim Huyser Toshikazu Ikuta Neda Jahanshad Anthony James Fern Jaspers-Fayer Selina Kasprzak Norbert Kathmann Christian Kaufmann Minah Kim Kathrin Koch Gerd Kvale Jun Soo Kwon Luisa Lazaro Junhee Lee Christine Lochner Jin Lu Daniela Rodriguez Manrique Ignacio Martínez-Zalacaín Yoshitada Masuda Koji Matsumoto Maria Paula Maziero Jose M Menchón Luciano Minuzzi Pedro Silva Moreira Pedro Morgado Janardhanan C Narayanaswamy Jin Narumoto Ana E Ortiz Junko Ota Jose C Pariente Chris Perriello Maria Picó-Pérez Christopher Pittenger Sara Poletti Eva Real Y C Janardhan Reddy Daan van Rooij Yuki Sakai João Ricardo Sato Cinto Segalas Roseli G Shavitt Zonglin Shen Eiji Shimizu Venkataram Shivakumar Noam Soreni Carles Soriano-Mas Nuno Sousa Mafalda Machado Sousa Gianfranco Spalletta Emily R Stern S Evelyn Stewart Philip R Szeszko Rajat Thomas Sophia I Thomopoulos Daniela Vecchio Ganesan Venkatasubramanian Chris Vriend Susanne Walitza Zhen Wang Anri Watanabe Lidewij Wolters Jian Xu Kei Yamada Je-Yeon Yun Mojtaba Zarei Qing Zhao Xi Zhu Paul M Thompson Willem B Bruin Guido A van Wingen Federica Piras Fabrizio Piras Dan J Stein Odile A van den Heuvel Helen Blair Simpson Rachel Marsh Jiook Cha Department of Psychology College of Social Sciences Seoul National University Seoul Republic of Korea. Department of Brain and Cognitive Sciences College of Natural Sciences Seoul National University Seoul Republic of Korea. Graduate School of Medical Science Kyoto Prefectural University of Medicine Department of Psychiatry Kyoto City Japan. Bellvitge Biomedical Research Insitute-IDIBELL Bellvitge University Hospital Barcelona Spain. CIBER of Mental Health (CIBERSAM) Carlos III Health Institute Madrid Spain. Department of Clinical Sciences University of Barcelona Barcelona Spain. The Margaret and Wallace McCain Centre for Child Youth & Family Mental Health and Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto ON Canada. Department of Psychiatry University of Toronto Toronto ON Canada. Program in Neurosciences and Mental Health The Hospital for Sick Children Toronto ON Canada. Department of Psychiatry Yale University School of Medicine New Haven CT 06510 USA. The Mathison Centre for Mental Health Research & Education Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary AB Canada. Departments of Psychiatry and Medical Genetics Cumming School of Medicine University of Calgary Calgary AB Canada. OCD clinic Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore India. Laboratory of Neuropsychiatry Department of Clinical and Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy. Center of Image Diagnostic Hospital Clínic de Barcelona Barcelona Spain. Magnetic Resonance Image Core Facility Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain. Departamento e Instituto de Psiquiatria do Hospital das Clinicas IPQ HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. Department of Methods and Techniques in Psychology Pontifical Catholic University São Paulo SP

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SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

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Physical Review D 2024年第8期110卷 083510页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr T. Schrabback T. M. C. Abbott P. A. R. Ade M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon A. J. Anderson J. Annis B. Ansarinejad J. E. Austermann S. Avila D. Bacon M. Bayliss J. A. Beall K. Bechtol M. R. Becker A. N. Bender B. A. Benson G. M. Bernstein S. Bhargava F. Bianchini M. Brodwin D. Brooks L. Bryant A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. L. Chang C. Chang P. Chaubal R. Chen H. C. Chiang A. Choi T-L. Chou R. Citron C. Corbett Moran J. Cordero M. Costanzi T. M. Crawford A. T. Crites L. N. da Costa M. E. S. Pereira C. Davis T. M. Davis J. DeRose S. Desai T. de Haan H. T. Diehl M. A. Dobbs S. Dodelson C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett W. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. Gallicchio J. García-Bellido M. Gatti E. M. George G. Giannini M. D. Gladders D. Gruen R. A. Gruendl N. Gupta G. Gutierrez N. W. Halverson I. Harrison W. G. Hartley K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr E. M. Huff D. Huterer K. D. Irwin D. J. James M. Jarvis G. Khullar K. Kim L. Knox R. Kraft E. Krause K. Kuehn N. Kuropatkin F. Kéruzoré O. Lahav A. T. Lee P.-F. Leget D. Li H. Lin A. Lowitz N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. J. McMahon J. Mena-Fernández F. Menanteau S. S. Meyer R. Miquel J. Montgomery J. Myles T. Natoli A. Navarro-Alsina J. P. Nibarger G. I. Noble V. Novosad R. L. C. Ogando Y. Omori S. Padin S. Pandey P. Paschos S. Patil A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt J. Roberson R. P. Rollins C. Romero A. Roodman J. E. Ruhl E. S. Rykoff B. R. Saliwanchik L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro K. K. Schaffer L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin C. Sievers G. Smecher M. Smith T. Somboonpanyakul M. Sommer B. Stalder A. A. Stark J. Stephen V. Straz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 85748 Garching Germany Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn Germany Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile School of Physics and Astronomy Cardiff University Cardiff CF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia—LIneA Rua Gal. José Cristino 77 Rio de Janeiro Rio de Janeiro—20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6dataset, we place a 95% upper limit on the sum of neutrino masses ∑mν<0.18 eV. When additionally allowing the dark energy equation of state parameter w to vary, we obtain w=−1.45±0.31 from our cluster-based analysis. In combination with Planck data, we measure w=−1.34−0.15+0.22, or a 2.2σ difference with a cosmological constant. We use the cluster abundance to measure σ8 in five redshift bins between 0.25 and 1.8, and we find the results to be consistent with structure growth as predicted by the ΛCDM model fit to Planck primary CMB data.

关键词： Cosmological parameters Dark energy Large scale structure of the Universe Galaxy clusters

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Semantic interoperability for asset communication within smart factories 22

Semantic interoperability for asset communication within sma...

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22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017

作者： Diedrich, Christian Belyaev, Alexander Schröder, Tizian Willmann, Alexander Koziolek, Heiko Pethig, Florian Niggemann, Oliver Vialkowitsch, Jens Usländer, Thomas Wende, Jörg Institute for Automation Engineering Otto-von-Guericke-Universität Magdeburg39106 Germany Industrial Internet of Things Group Fraunhofer FOKUS Berlin10589 Germany Corporate Research Center ABB Ladenburg68526 Germany Intelligent Automation Lab Big Data Lemgo32657 Germany Robert Bosch GmbH Stuttgart70442 Germany Information Management and DCS Fraunhofer IOSB Karlsruhe76131 Germany IBM Hybrid Cloud Software IBM Deutschland GmbH Dresden01067 Germany

ISBN: (纸本)9781509065059

Industrie 4.0 (I4.0) aims at a manufacturer-independent, vertical- And horizontal-oriented communication and cooperation within smart factories. This is only manageable using international standards. The so-called reference architecture model for Industrie 4.0 (RAMI4.0) and the requirement specification of an I4.0 component are already available. RAMI4.0 could be the basis for the interoperability of the interactions. The working group "Semantic and interaction model for I4.0 components" (GMA 7.20) made progress in this direction. The language used for the interaction of I4.0 components needs model definitions for the interaction consisting of the structure of the components, syntax for the description of the messages and the means to assign the meaning to the language elements. This paper discusses the results for a broader audience. © 2017 IEEE.

关键词： Semantics

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The persistent shadow of the supermassive black hole of M 87: I. Observations, calibration, imaging, and analysis*

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Astronomy and Astrophysics 2024年 681卷

作者： Akiyama, Kazunori Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Baczko, Anne-Kathrin Ball, David Baloković, Mislav Bandyopadhyay, Bidisha Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blackburn, Lindy Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bronzwaer, Thomas Bustamante, Sandra Carlstrom, John E. Chael, Andrew Chan, Chi-Kwan Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu Dahale, Rohan Davelaar, Jordy De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dexter, Jason Dhruv, Vedant Dihingia, Indu K. Doeleman, Sheperd S. Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Edward Ford, H. Alyson Foschi, Marianna Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fromm, Christian M. Fuentes, Antonio Galison, Peter Gammie, Charles F. García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco Gold, Roman Gómez-Ruiz, Arturo I. Gómez, José L. Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Ikeda, Shiro Violette Impellizzeri, C.M. Inoue, Makoto Issaoun, Sara James, David J. Jannuzi, Buell T. Janssen, Michael Jeter, Britton Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Departament d'Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 València Burjassot46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 València Paterna46980 Spain Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory Onsala43992 Sweden Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New HavenCT06511 United States Astronomy Department Universidad de Concepción Casilla 160 Concepción Chile Department of Physics University of Illinois 1110 West Green Street UrbanaIL61801 United States Fermi National Accelerator Laboratory MS209 PO Box 500 BataviaIL60510 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States East Asian Observatory 660 N. A'ohoku Place HiloHI96720 United States 660 N. A'ohoku Place HiloHI96720 Uni

In April 2019, the Event Horizon Telescope (EHT) Collaboration reported the first-ever event-horizon-scale images of a black hole, resolving the central compact radio source in the giant elliptical galaxy M 87. These images reveal a ring with a southerly brightness distribution and a diameter of ∼42 μas, consistent with the predicted size and shape of a shadow produced by the gravitationally lensed emission around a supermassive black hole. These results were obtained as part of the April 2017 EHT observation campaign, using a global very long baseline interferometric radio array operating at a wavelength of 1.3 mm. Here, we present results based on the second EHT observing campaign, taking place in April 2018 with an improved array, wider frequency coverage, and increased bandwidth. In particular, the additional baselines provided by the Greenland telescope improved the coverage of the array. Multiyear EHT observations provide independent snapshots of the horizon-scale emission, allowing us to confirm the persistence, size, and shape of the black hole shadow, and constrain the intrinsic structural variability of the accretion flow. We have confirmed the presence of an asymmetric ring structure, brighter in the southwest, with a median diameter of 43.3-3.1+1.5 μas. The diameter of the 2018 ring is remarkably consistent with the diameter obtained from the previous 2017 observations. On the other hand, the position angle of the brightness asymmetry in 2018 is shifted by about 30 relative to 2017. The perennial persistence of the ring and its diameter robustly support the interpretation that the ring is formed by lensed emission surrounding a Kerr black hole with a mass ∼6.5× 109M. The significant change in the ring brightness asymmetry implies a spin axis that is more consistent with the position angle of the large-scale jet. © 2023 The Author(s).

关键词： Gravitation

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