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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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H∞ Filtering of Polynomial Fuzzy systems with Fading Measurements 33

H∞ Filtering of Polynomial Fuzzy Systems with Fading Measur...

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第三十三届中国控制会议

作者： JIANG Yong ZHAO Yan Shandong Petroleum Equipment Engineering and Technology Research Center Shengli Oilfield Highland Petroleum Equipment Co.Ltd Advanced Control Systems Laboratory School of Electronics and Information EngineeringBeijing Jiaotong University

ISBN: (纸本)9781479947249

This paper investigates the distributed H∞ filtering problem of the polynomial fuzzy system with fading measurements.A fading channel is assumed to be existing between the plant and the filter,and the Rice channel model is dedicated to describe the imperfect communication *** attention is to design a fuzzy H∞ filter such that the filter error system is stochastically stable guaranteeing the prescribed H∞ *** conditions of the filter are derived in terms of sum of square inequalities,which can be solved by the recently developed SOSTOOLS.

关键词： Polynomial fuzzy models,H∞ filter,Sum of Squares

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Improved chicken swarm optimization

Improved chicken swarm optimization

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Dinghui Wu Fei Kong Wenzhong Gao Yanxia Shen Zhicheng Ji Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology Jiangnan University Wuxi Jiangsu China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi Jiangsu China Dept. of Electrical and Computer Engineering University of Denver Denver CO USA

ISBN: (纸本)9781479987313

Considering the problem that the original chicken swarm optimization algorithm is easy to fall into local optimum because of premature convergence for high-dimensional complex problems, an improved chicken swarm optimization was proposed. In this algorithm, the part of chicks learning from the rooster in their subgroup is added to chick's position update equation, and the inertia weight and learning factor are also introduced. Then eight benchmark functions are used to test the proposed algorithm and the comparison with particle swarm optimization, bat algorithm, and original chicken swarm optimization are also performed. The simulated experimental results showed that the proposed algorithm is able to avoid premature convergence and therefore can escape from local optimum. Especially, the proposed method outperforms other evolutionary algorithms in finding the global optimum for high-dimensional problems.

关键词： Convergence Algorithm design and analysis Particle swarm optimization Benchmark testing Optimization Accuracy Standards

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Multi-innovation observer based fault detection for pitch system of wind turbines

Multi-innovation observer based fault detection for pitch sy...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Dinghui Wu Yanjie Zhai Wenzhong Gao Yanxia Shen Zhicheng Ji Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology Jiangnan University Wuxi Jiangsu China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi Jiangsu China Dept. of Electrical and Computer Engineering University of Denver Denver CO USA

ISBN: (纸本)9781479987313

Considering the pitch sensor fault of pitch systems in wind turbines, a multi-innovation observer based fault detection method is proposed. The pitch system can be modeled as a second order differential equation. And then the equation is transformed into a state space model. According to the state space model, a multi-innovation observer is designed to estimate the system state. The pitch sensor fault can be detected through the residual which is generated by comparing the state estimation with the output of an actual system. The simulation results show that the proposed approach is able to detect the pitch sensor fault effectively.

关键词： Wind turbines Observers Fault detection Technological innovation Fault diagnosis Shafts

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[email protected] : Analysis and results of evolving competitions for domestic and service robots

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Artificial Intelligence 2015年 229卷 258-281页

作者： Luca Iocchi Dirk Holz Javier Ruiz-del-Solar Komei Sugiura Tijn van der Zant Department of Computer Control and Management Engineering Sapienza University of Rome Italy Autonomous Intelligent Systems Group University of Bonn Germany Department of Electrical Engineering & Advanced Mining Technology Center Universidad de Chile Chile Information Services Platform Laboratory National Institute of Information and Communications Technology Japan Cognitive Robotics Laboratory University of Groningen The Netherlands

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the [email protected] competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that [email protected] robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in [email protected] for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics.

关键词： Robotic competitions Artificial intelligence and robotics Benchmarking

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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The status of KAGRA underground cryogenic gravitational wave telescope

arXiv

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arXiv 2017年

作者： Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Barton, M.A. Cannon, K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Furuhata, T. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, B.H. Inoue, Y. Ioka, K. Itoh, Y. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kambara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kim, C. Kim, H. Kim, J. Kim, Y. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kumar, R. Lee, H. Lee, H. Liu, Y. Luca, N. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Miyakawa, O. Miyamoto, T. Miyamoto, A. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Narikawa, T. Nguyen Quynh, L. Ni, W.-T. Ochi, T. Oh, J. Oh, S. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Peña Alleano, F.E. Pinto, I. Sago, N. Saijo, M. Saito, Y. Sakai, K. Sakai, Y. Sasaki, Y. Sasaki, M. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shoda, A. Somiya, K. Son, E. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueki, S. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Vocca, H. Wada, S. Wakamatsu, T. Yamada, T. Yamamoto, S. Yamamoto, T. Yamamoto, K. Yamamoto, A. Yokoyama, J. Yokozawa, T. Yoon, T.H. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo113-0032 Japan Department of Advanced Physics Hirosaki University Hirosaki Aomori036-8561 Japan National Astronomical Observatory of Japan Hida Gifu506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa Chiba277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama Toyama930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka558-8585 Japan Department of Physics Osaka University Toyonaka Osaka560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon34141 Korea Republic of Daejeon Yuseong34055 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Computer Simulation Inje University Gimhae Gyeongsangnam50834 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of High Energy Accelerator Research Organization Tsukuba Ibaraki305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka814-0180 Japan Department of Advanced Materials Science University of Tokyo Kashiwa Chiba277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma00185 Italy D

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described. Copyright © 2017, The Authors. All rights reserved.

关键词： Interferometers

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Construction of KAGRA: An underground gravitational wave observatory

arXiv

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arXiv 2017年

作者： Akutsu, T. Ando, M. Araki, S. Araya, A. Arima, T. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Baiotti, L. Barton, M.A. Chen, D. Cho, K. Craig, K. DeSalvo, R. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujibayashi, S. Fujii, Y. Fujimoto, M.-K. Fukushima, M. Furuhata, T. Hagiwara, A. Haino, S. Harita, S. Hasegawa, K. Hasegawa, M. Hashino, K. Hayama, K. Hirata, N. Hirose, E. Ikenoue, B. Inoue, Y. Ioka, K. Ishizaki, H. Itoh, Y. Jia, D. Kagawa, T. Kaji, T. Kajita, T. Kakizaki, M. Kakuhata, H. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kasuya, J. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawamura, S. Kawazoe, F. Kim, C. Kim, J. Kim, J.C. Kim, W. Kimura, N. Kitaoka, Y. Kobayashi, K. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kubo, K. Kumar, R. Kume, T. Kuroda, K. Kuwahara, Y. Lee, H.-K. Lee, H.-W. Lin, C.-Y. Liu, Y. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsumoto, N. Matsushima, F. Michimura, Y. Mio, N. Miyakawa, O. Miyake, K. Miyamoto, A. Miyamoto, T. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Muraki, Y. Murakoshi, M. Musha, M. Nagano, K. Nagano, S. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakano, M. Nakao, H. Nakao, K. Narikawa, T. Ni, W.-T. Nonomura, T. Obuchi, Y. Oh, J.J. Oh, S.-H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohmae, N. Okino, K. Okutomi, K. Ono, K. Ono, Y. Oohara, K. Ota, S. Park, J. Peña Arellano, F.E. Pinto, I.M. Principe, M. Sago, N. Saijo, M. Saito, T. Saito, Y. Saitou, S. Sakai, K. Sakakibara, Y. Sasaki, Y. Sato, S. Sato, T. Sato, Y. Sekiguchi, T. Sekiguchi, Y. Shibata, M. Shiga, K. Shikano, Y. Shimoda, T. Shinkai, H. Shoda, A. Someya, N. Somiya, K. Son, E.J. Starecki, T. Suemasa, A. Sugimoto, Y. Susa, Y. Suwabe, H. Suzuki, T. Tachibana, Y. Tagoshi, H. Takada, S. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. National Astronomical Observatory of Japan Tokyo181-8588 Japan Graduate School of Science University of Tokyo Tokyo113-0033 Japan Department of Physics Graduate School of Science University of Tokyo Tokyo113-0033 Japan Ibaraki305-0801 Japan Earthquake Research Institute University of Tokyo Tokyo113-0032 Japan Department of Physics Graduate School of Science Osaka City University Osaka558-8585 Japan Graduate School of Science and Technology Hirosaki University Aomori036-8561 Japan Department of Physics Graduate School of Science and Engineering Tokyo Institute of Technology Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Chiba277-8582 Japan Institute for Cosmic Ray Research University of Tokyo Gifu506-1205 Japan Graduate School of Science Osaka University Osaka560-0043 Japan Department of Physics Sogang University Seoul121-742 Korea Republic of University of Sannio at Benevento BeneventoI-82100 Italy Sezione di Napoli NapoliI-80126 Italy University of Toyama Toyama930-8555 Japan Department of Physics Graduate School of Science Kyoto University Kyoto606-8502 Japan Institute of Physics Academia Sinica Taipei11529 Taiwan University of Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Albert-Einstein-Institut Max-Planck-Institut für Gravitationsphysik HannoverD-30167 Germany Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of Daejeon34055 Korea Republic of Department of Physics Myongji University Yongin449-728 Korea Republic of Department of Computer Simulation Inje University Gimhae-shi50834 Korea Republic of Daejeon34047 Korea Republic of Department of Physical Science Graduate School of Science Hiroshima University Hiroshima903-0213 Japan Department of Applied Physics Graduate School of Science Fukuoka University Fukuoka814-0180 Japan Graduate School of Science and Engineer

Major construction and initial-phase operation of a second-generation gravitational-wave detector KAGRA has been completed. The entire 3-km detector is installed underground in a mine in order to be isolated from background seismic vibrations on the surface. This allows us to achieve a good sensitivity at low frequencies and high stability of the detector. Bare-bones equipment for the interferometer operation has been installed and the first test run was accomplished in March and April of 2016 with a rather simple configuration. The initial configuration of KAGRA is named iKAGRA. In this paper, we summarize the construction of KAGRA, including the study of the advantages and challenges of building an underground detector and the operation of the iKAGRA interferometer together with the geophysics interferometer that has been constructed in the same tunnel. Copyright © 2017, The Authors. All rights reserved.

关键词： Interferometers

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Flight testing a real-time hazard detection system for safe lunar landing on the rocket-powered morpheus vehicle

Flight testing a real-time hazard detection system for safe ...

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AIAA Guidance, Navigation, and control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015

作者： Trawny, Nikolas Huertas, Andres Luna, Michael E. Villalpando, Carlos Y. Martin, Keith E. Carson, John M. Johnson, Andrew E. Restrepo, Carolina Roback, Vincent E. GN&C Hardware and Testbed Development Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Maritime & Aerial Perception Systems Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Guidance & Control Operations Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Advanced Computer Systems & Technologies Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Simulation & Support Equipment Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Integrated GN&C Analysis Branch NASA Johnson Space Center HoustonTX77058 United States Remote Sensing Flight Systems Branch NASA Langley Research Center HamptonVA23681 United States

ISBN: (纸本)9781510801097

The Hazard Detection System (HDS) is a component of the ALHAT (Autonomous Landing and Hazard Avoidance Technology) sensor suite, which together provide a lander Guidance, Navigation and control (GN&C) system with the relevant measurements necessary to enable safe precision landing under any lighting conditions. The HDS consists of a stand-alone compute element (CE), an Inertial Measurement Unit (IMU), and a gimbaled ash LIDAR sensor that are used, in real-time, to generate a Digital Elevation Map (DEM) of the landing terrain, detect candidate safe landing sites for the vehicle through Hazard Detection (HD), and generate hazard-relative navigation (HRN) measurements used for safe precision landing. Following an extensive ground and helicopter test campaign, ALHAT was integrated onto the Morpheus rocket-powered terrestrial test vehicle in March 2014. Morpheus and ALHAT then performed five successful free flights at the simulated lunar hazard field constructed at the Shuttle Landing Facility (SLF) at Kennedy Space Center, for the first time testing the full system on a lunar-like approach geometry in a relevant dynamic environment. During these flights, the HDS successfully generated DEMs, correctly identified safe landing sites and provided HRN measurements to the vehicle, marking the first autonomous landing of a NASA rocket-powered vehicle in hazardous terrain. This paper provides a brief overview of the HDS architecture and describes its in-flight performance. © 2015, E-flow American Institute of Aeronautics and Astronautics (AIAA). All rights reserved.

关键词： Hazards

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