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Agrosystem Engineering, technology & Applications (AGRETA), IEEE International Conference on

作者： Abdul Khaliq Jahariah Sampe Fazida Hanim Hashim Huda Abdullah Noor Hidayah Mohd Yunus Muhammad Abuzar Baqir Institute of Microengineering & Nanoelectronics Universiti Kebangsaan Malaysia Bangi Malaysia Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Malaysia Advanced Telecommunication Technology Communication Technology Section Universiti Kuala Lumpur Malaysia Electrical & Computer Engineering Department COMSATS University Islamabad Sahiwal Punjab Pakistan

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

CMOS technology advancements have significantly enhanced smart farming monitoring system (SFMS) miniaturization and efficiency, especially in transceivers using All-Digital Phase-Locked Loops (ADPLLs). The ADPLL's phase-frequency detector (PFD) evaluates two digital inputs to identify frequency and phase errors. A time-to-digital converter (TDC), typically used by the PFD block, has high power usage and slow phase-frequency acquisition. Therefore, the study aims to minimize the high-power consumption and improve the fast phase-frequency acquisition of traditional Phase-Frequency Detectors (PFDs) by developing a Verilog HDL-optimized digital PFD explicitly designed for ultra-low power ADPLLs. The simulation results revealed a duty cycle of 34.2%, no frequency difference, and a time difference of 0.05 ns, suggesting accurate phase error detection and correction. The PFD's fast response time and minimal dead zone impact assure fast phase lock, which is crucial for SFMS in real-time. Furthermore, the ultra-low power and fast locking range required by the Verilog-simulated PFD design make it ideal for SFMS applications. This research highlights how improved PFD designs may develop high-performance, low-power ADPLL transceivers.

关键词： Smart agriculture Radio frequency Time-frequency analysis Low power electronics Phase frequency detectors Transceivers Real-time systems Time factors Hardware design languages Monitoring

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High fusion computers: The IoTs, edges, data centers, and humans-in-the-loop as a computer

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第3期2卷 100075-100075页

作者： Gao, Wanling Wang, Lei Chen, Mingyu Xiong, Jin Luo, Chunjie Zhang, Wenli Huang, Yunyou Li, Weiping Kang, Guoxin Zheng, Chen Xie, Biwei Dai, Shaopeng He, Qian Ye, Hainan Bao, Yungang Zhan, Jianfeng State Key Lab of Processors and Research Center for Advanced Computer Systems Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Road Haidian District Beijing 100190 China Yuquan Road Shijingshan District Beijing 100049 China Guangxi Normal University No. 15 Yucai Road Qixing District Guangxi Guilin541010 China Civil Aviation Flight University of China No. 46 Section 4 Nanchang Road Sichuan Guanghan618307 China Institute of Software Chinese Academy of Sciences No. 4 South 4th Street Beijing Zhongguancun100190 China Beijing Institute of Open Source Chip No. 7 Caoqiao Northwest Corner of Haidian Bridge Haidian District Beijing 100080 China

Emerging and future applications rely heavily upon systems consisting of Internet of Things (IoT), edges, data centers, and humans-in-the-loop. Significantly different from warehouse-scale computers that serve independent concurrent user requests, this new class of computer systems directly interacts with the physical world, considering humans an essential part and performing safety-critical and mission-critical operations;their computations have intertwined dependencies between not only adjacent execution loops but also actions or decisions triggered by IoTs, edge, datacenters, or humans-in-the-loop;the systems must first satisfy the accuracy metric in predicting, interpreting, or taking action before meeting the performance goal under different cases. This article argues we need a paradigm shift to reconstruct the IoTs, edges, data centers, and humans-in-the-loop as a computer rather than a distributed system. We coin a new term, high fusion computers (HFCs), to describe this class of systems. The fusion in the term has two implications: fusing IoTs, edges, data centers, and humans-in-the-loop as a computer, fusing the physical and digital worlds through HFC systems. HFC is a pivotal case of the open-source computer systems initiative. We laid out the challenges, plan, and call for uniting our community's wisdom and actions to address the HFC challenges. Everything, including the source code, will be publicly available from the project homepage: https://***/HFC/. © 2022 The Authors

关键词： Internet of things

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High Fusion computers: The IoTs, Edges, Data Centers, and Humans-in-the-loop as a computer

arXiv

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arXiv 2022年

作者： Gao, Wanling Wang, Lei Chen, Mingyu Xiong, Jin Luo, Chunjie Zhang, Wenli Huang, Yunyou Li, Weiping Kang, Guoxin Zheng, Chen Xie, Biwei Dai, Shaopeng He, Qian Ye, Hainan Bao, Yungang Zhan, Jianfeng State Key Lab of Processors Research Center for Advanced Computer Systems Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Road Haidian District Beijing100190 China Yuquan Road Shijingshan District Beijing100049 China Guangxi Normal University No. 15 Yucai Road Qixing District Guangxi Guilin541010 China Civil Aviation Flight University of China No. 46 Section 4 Nanchang Road Sichuan Guanghan618307 China Institute of Software Chinese Academy of Sciences No. 4 South 4th Street Zhongguancun Beijing100190 China Beijing Institute of Open Source Chip No. 7 Caoqiao northwest corner of Haidian Bridge Haidian District Beijing100080 China

Emerging and future applications rely heavily upon systems consisting of Internet of Things (IoT), edges, data centers, and humans-in-the-loop. Significantly different from warehouse-scale computers that serve independent concurrent user requests, this new class of computer systems directly interacts with the physical world, considering humans an essential part and performing safety-critical and mission-critical operations;their computations have intertwined dependencies between not only adjacent execution loops but also actions or decisions triggered by IoTs, edge, datacenters, or humans-in-the-loop;the systems must first satisfy the accuracy metric in predicting, interpreting, or taking action before meeting the performance goal under different cases. This article argues we need a paradigm shift to reconstruct the IoTs, edges, data centers, and humans-in-the-loop as a computer rather than a distributed system. We coin a new term, high fusion computers (HFCs), to describe this class of systems. The fusion in the term has two implications: fusing IoTs, edges, data centers, and humans-in-the-loop as a computer, fusing the physical and digital worlds through HFC systems. HFC is a pivotal case of the open-source computer systems initiative. We laid out the challenges, plan, and call for uniting our community’s wisdom and actions to address the HFC challenges. Everything, including the source code, will be publicly available from the project homepage: https://***/HFC/. © 2022, CC BY-NC-ND.

关键词： Internet of things

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Compressive and energy absorption properties of titanium hybrid lattice structures for bioimplant applications fabricated via LPBF

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Materials and Design 2025年 254卷

作者： Karmiris-Obratański, Panagiotis Georgakopoulos-Soares, Ilias Cichocki, Kamil Lisiecka-Graca, Paulina Papazoglou, Emmanouil L. Advanced Manufacturing Laboratory Department of Manufacturing Systems Faculty of Mechanical Engineering and Robotics AGH University of Krakow Krakow Poland School of Chemical Engineering Department of Materials Science and Engineering National Technical University of Athens Heroon Polytechniou 9 Athens15780 Greece Institute for Personalized Medicine Department of Biochemistry and Molecular Biology The Pennsylvania State University College of Medicine HersheyPA United States Faculty of Metals Engineering and Computer Science AGH University of Krakow Adama Mickiewicza Ave. 30 Kraków30-059 Poland School of Mechanical Engineering Section of Manufacturing Technology National Technical University of Athens Heroon Polytechniou 9 Athens15780 Greece

The compressive properties of lattice structures are strongly influenced by lattice design, density, material, and manufacturing method. Recent advancements in manufacturing, particularly Additive Manufacturing, have enabled the fabrication of complex lattice structures from advanced materials. Building on this progress, the present study investigates the compressive properties and energy absorption capabilities of four distinct lattice structures at three relative densities (20 %, 30 %, and 40 %), specifically tailored for implant applications. The lattices, fabricated from Ti6Al4V ELI alloy using the Laser Powder Bed Fusion (LPBF) method, include a hybrid Fluorite-BCC lattice, a SplitP TPMS skeletal lattice, a stochastic Voronoi lattice, and a hybrid Voronoi-TPMS Gyroid lattice. Compressive properties, including the Elastic Modulus of Compression (EMC), Compressive Yield Strength, and Ultimate Compressive Strength, were systematically analyzed alongside energy absorption capabilities. The Gibson-Ashby model was employed to quantify the relationship between compressive properties and relative density. Additionally, Digital Image Correlation and high-resolution imaging during compression tests provided detailed insights into deformation mechanisms and collapse behavior. The findings offer valuable insights into the compressive performance of these novel hybrid lattices, highlighting their potential applicability in bioimplant applications, with the Fluorite-BCC lattice exhibits the highest EMC and the Fluorite-BCC and SplitP lattices exhibit higher energy absorption capabilities. © 2025 The Author(s)

关键词： Machinability

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Corrigendum to "Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients" [J. Adv. Res. 37 (2022) 147-168]

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Journal of advanced research 2023年 45卷 193页

作者： O S Albahri A A Zaidan A S Albahri H A Alsattar Rawia Mohammed Uwe Aickelin Gang Kou F M Jumaah Mahmood M Salih A H Alamoodi B B Zaidan Mamoun Alazab Alhamzah Alnoor Jameel R Al-Obaidie Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. Electronic address: aws.alaa@fskik.upsi.edu.my. Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. School of Computing and Information Systems University of Melbourne 700 Swanston Street Victoria 3010 Australia. School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu 611130 China. Department of Advanced Applications and Embedded Systems Intel Corporation Plot 6 Bayan Lepas Technoplex 11900 Georgetown Pulau Pinang Malaysia. Department of Computer Science Computer Science and Mathematics College Tikrit University Tikrit Iraq. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia. Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan ROC. College of Engineering IT and Environment Charles Darwin University NT Australia. School of Management Universiti Sains Malaysia Pulau Pinang Malaysia. Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia.

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Evaluation of the simple safe site selection (S4) hazard detection algorithm using helicopter field test data

Evaluation of the simple safe site selection (S4) hazard det...

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AIAA Guidance, Navigation, and Control Conference, 2017

作者： Luna, Michael E. Almeida, Eduardo Spiers, Gary Villalpando, Carlos Y. Johnson, Andrew E. Trawny, Nikolas Guidance & Control Operations 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 Optics Section Staff 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 Guidance & Control Section Staff Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States GN&C Hardware and Testbed Development Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

ISBN: (数字)9781624104503

ISBN: (纸本)9781624104503

Small scale terrain hazards, such as rocks, slopes, and craters, can pose significant risk to landing spacecraft and rover or payload deployment. Onboard Hazard Detection and Avoidance (HDA) systems scan and analyze the landing area for these hazards in real time during descent, and divert the spacecraft to the safest touchdown site. The computationally efficient Simple Safe Site Selection (S4) algorithm combined with a ash LIDAR is an HDA system geared towards small robotic spacecraft. Rather than creating and analyzing a digital elevation map (DEM) from potentially many overlapping range images, S4 operates directly on a single ash LIDAR image. Extending prior work that has analyzed S4 performance for Mars landing using extensive simulations, this paper evaluates S4 performance using actual ash LIDAR images of an artificial hazard field acquired during a 2014 helicopter field test in Death Valley, CA. In particular, we describe LIDAR characterization and calibration, creation of ground truth elevation and safety maps, creation of ground truth sensor poses, actual S4 algorithm processing, and performance analysis. The results show that the safety cost images produced by S4 are remarkably close to the ground truth safety map (computed offline by an HDA algorithm developed under the Autonomous Landing and Hazard Avoidance (ALHAT) project) at significantly reduced computational cost, confirming S4 as a viable candidate algorithm for onboard spacecraft HDA. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Optical radar

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Overview of KAGRA : Data transfer and management

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Progress of Theoretical and Experimental Physics 2023年第10期2023卷

作者： Akutsu, T Ando, M Arai, K Arai, Y Araki, S Araya, A Aritomi, N Asada, H Aso, Y Bae, S Bae, Y Baiotti, L Bajpai, R Barton, M A Cannon, K Cao, Z Capocasa, E Chan, M Chen, C Chen, K Chen, Y Chiang, C-Y Chu, H Chu, Y-K Eguchi, S Enomoto, Y Flaminio, R Fujii, Y Fujikawa, Y Fukunaga, M Fukushima, M Gao, D Ge, G Ha, S Hagiwara, A Haino, S Han, W-B Hasegawa, K Hattori, K Hayakawa, H Hayama, K Himemoto, Y Hiranuma, Y Hirata, N Hirose, E Hong, Z Hsieh, B Huang, G-Z Huang, H-Y Huang, P Huang, Y-C Huang, Y Hui, D C Y Ide, S Ikenoue, B Imam, S Inayoshi, K Inoue, Y Ioka, K Ito, K Itoh, Y Izumi, K Jeon, C Jin, H-B Jung, K Jung, P Kaihotsu, K Kajita, T Kakizaki, M Kamiizumi, M Kanda, N Kang, G Kawaguchi, K Kawai, N Kawasaki, T Kim, C Kim, J Kim, J C Kim, W S Kim, Y-M Kimura, N Kita, N Kitazawa, H Kobayashi, Y Kojima, Y Kokeyama, K Komori, K Kong, A K H Kotake, K Kozakai, C Kozu, R Kumar, R Kume, J Kuo, C Kuo, H-S Kuromiya, Y Kuroyanagi, S Kusayanagi, K Kwak, K Lee, H K Lee, H W Lee, R Leonardi, M Li, K L Lin, L C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H L Liu, G C Luo, L-W Majorana, E Marchio, M Michimura, Y Mio, N Miyakawa, O Miyamoto, A Miyazaki, Y Miyo, K Miyoki, S Mori, Y Morisaki, S Moriwaki, Y Nagano, K Nagano, S Nakamura, K Nakano, H Nakano, M Nakashima, R Nakayama, Y Narikawa, T Naticchioni, L Negishi, R Quynh, L Nguyen Ni, W-T Nishizawa, A Nozaki, S Obuchi, Y Ogaki, W Oh, J J Oh, K Oh, S H Ohashi, M Ohashi, T Ohishi, N Ohkawa, M Ohta, H Okutani, Y Okutomi, K Oohara, K Ooi, C Oshino, S Otabe, S Pan, K Pang, H Parisi, A Park, J Arellano, F E Peña Pinto, I Sago, N Saito, S Saito, Y Sakai, K Sakai, Y Sakuno, Y Sasaki, Y Sato, S Sato, T Sawada, T Sekiguchi, T Sekiguchi, Y Shao, L Shibagaki, S Shimizu, R Shimoda, T Shimode, K Shinkai, H Shishido, T Shoda, A Somiya, K Son, E J Sotani, H Sugimoto, R Suresh, J Suzuki, T Tagoshi, H Takahashi, H Takahashi, R Takamori, A Takano, S Takeda, H Takeda, M Tanaka, H Tanaka, K Tanaka, T Tanioka, S Martin, E N Tapia San Telada, S Tomaru, T Tomigami, Y Tomura, T Travasso, F Trozzo, L Tsang, T Ts Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Hirosaki University 3 Bunkyo-cho Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University No. 19 Xinjiekou Street Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Rd. Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy Nation

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located in Kamioka, Gifu prefecture, Japan. In this article, we describe the KAGRA data management system, i.e., recording of data, transfer from the KAGRA experiment site to computing resources, as well as data distribution to tier sites, including international sites in Taiwan and Korea. The amount of KAGRA data exceeded 1.0 PiB and increased by about 1.5 TB per day during operation in 2020. Our system has succeeded in data management, and has achieved performance that can withstand observations after 2023, that is, a transfer rate of 20 MB s-1or more and file storage of sufficient capacity for petabyte class. We also discuss the sharing of data between the global gravitational-wave detector network with other experiments, namely LIGO and Virgo. The latency, which consists of calculation of calibrated strain data and transfer time within the global network, is very important from the view of multi-messenger astronomy using gravitational waves. Real-time calbrated data delivered from the KAGRA detector site and other detectors to our computing system arrive with about 4–15 seconds of latency. These latencies are sufficiently short compared to the time taken for gravitational wave event search computations. We also established a high-latency exchange of offline calibrated data that was aggregated with a better accuracy compared with real-time data.

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