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SpaceOps 2010 Conference

作者： Henricks, Julia A. Hall, Laverne Fung, Janet G. IOS Instrument Software and Science Data Systems Section Mail Stop 230-260 Pasadena CA 91109 United States Instrument Software and Science Data Systems Section Mail Stop 169-315 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Instrument Software and Science Data Systems Section Mail Stop 168-414 Pasadena CA 91109 United States

ISBN: (纸本)9781624101649

The Multi-mission instrument Processing Laboratory (MIPL) at the Jet Propulsion Laboratory (JPL) supports many deep space planetary (orbiter, fly-by, and in-situ) and observatory missions by processing and distributing instrument data, primarily in the form of science imagery and higher order data products which support operations/observation planning. As the MIPL support has extended to both mission critical and distributed operations, a re-assessment of architectural and hosting options was performed to address risk mitigation, improved reliability, extended availability, and changes in Mission Support Area (MSA) design requirements. In response to this paradigm shift from centralized to distributed operations, this paper will discuss the approach to: 1) re-architect MIPL-hosted data processing/distribution software builds and storage capabilities within an integrated development, test, and operations environment, 2) conversion to a service center to consolidate management of disparate funding sources for infrastructure data systems services, 3) lessons learned from moving MRO science data distribution services from the MIPL to a project managed environment, and 4) the challenges ahead. © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Orbits

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Using onboard clustering to summarize remotely sensed imagery

Using onboard clustering to summarize remotely sensed imager...

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作者： Hayden, David S. Chien, Steve Thompson, David R. Castano, Rebecca United States Artificial Intelligence Group JPL Australia Machine Learning and Instrument Autonomy Group JPL United States Instrument Software and Science Data Systems Section JPL United States

Many current and future NASA missions are capable of collecting enormous amounts of data, of which only a small portion can be transmitted to Earth. Communications are limited due to distance, visibility constraints, and competing mission downlinks. Long missions and highresolution, multispectral imaging devices easily produce data exceeding the available bandwidth. As an example, the HiRISE camera aboard the Mars Reconnaissance Orbiter produces images of up to 16.4 Gbits in data volume but downlink bandwidth is limited to 6 Mbits per second (Mbps) © 2010 IEEE.

关键词： Bandwidth

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Mars helicopter technology demonstrator

Mars helicopter technology demonstrator

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AIAA Atmospheric Flight Mechanics Conference, 2018

作者： Bob Balaram, J. Canham, Timothy Duncan, Courtney Golombek, Matt Grip, Håvard Fjær Johnson, Wayne Maki, Justin Quon, Amelia Stern, Ryan Zhu, David Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States NASA Ames Research Center Moffet Field CA94035 United States Robotics Technologist JPL Mobility and Robotic Systems Section JPL Flight Software and Avionics Systems Section JPL Flight Communications Systems Section JPL Planetary Science Section Robotics Technologist JPL Guidance and Control Section NASA Ames Aeromechanics Office JPL Instrument Software and Science Data Systems Section JPL Spacecraft Mechanical Engineering Section JPL Flight Electronics Section Robotics Electrical Engineer JPL Mobility and Robotic Systems Section

We describe a helicopter that is being developed as a technology demonstrator of Mars aerial mobility. The key design features of the helicopter, associated test infrastructure, and results from a full-scale prototype... 详细信息

ISBN: (数字)9781624105258

ISBN: (纸本)9781624105258

关键词： Antennas

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The benefits of Virtual Presence in Space (VPS) to deep space missions

The benefits of Virtual Presence in Space (VPS) to deep spac...

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SpaceOps 2006 Conference - 9th International Conference on Space Operations

作者： De Jong, Eric M. McGuffie, Barbara A. Levoe, Steven R. Suzuki, Shigeru Gorjian, Zareh Leung, Chris Cordell, Christopher Loaiza, Frank Baldwin, Robert Craig, Jason Kuramura, Koji Stetson, Michael Planetary and Life Detection Section 4800 Oak Grove Drive Mail Stop 168-414 Pasadena CA United States Instrument Software Systems SectionModeling and Data Management Systems Sec.387 4800 Oak Grove Drive Mail Stop 168-414 Pasadena CA United States Instrument Software Systems Section 4800 Oak Grove Drive Mail Stop 168-414 Pasadena CA United States

ISBN: (纸本)9781624100512

Understanding our place in the Universe is one of mankind's greatest scientific and technological challenges and achievements. The invention of the telescope, the Copernican Revolution, the development of Newtonian mechanics, and the Space Age exploration of our solar system;provided us with a deeper understanding of our place in the Universe;based on better observations and models. As we approach the end of the first decade of the new millennium, the same quest, to understand our place in the Universe, remains a great challenge. New technologies will enable us to construct and interact with a "Virtual Universe" based on remote and in situ observations of other worlds. As we continue the exploration that began in the last century, we will experience a "Virtual Presence in Space (VPS)" in this century. This paper describes VPS technology, the mechanisms for VPS product distribution and display, the benefits of this technology, and future plans. Deep space mission stereo observations and frames from stereo High Definition Television (HDTV) mission animations are used to illustrate the effectiveness of VPS technology. © 2006 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： High definition television

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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A revisiting of the anatomy and physiology of the grid 1

A revisiting of the anatomy and physiology of the grid

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1st Workshop on the science of Cyberinfrastructure: Research, Experience, Applications and Models, SCREAM 2015

作者： Mattmann, Chris A. Computer Science Department University of Southern California Henry Salvatori Computer Science Center 338 Los AngelesCA United States Instrument and Science Data Systems Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

ISBN: (纸本)9781450335669

The "Grid" as defined by Foster and Kesselman was a unifying architecture that engendered a new generation of distribution computation, data sharing, and science. Along the way many "grid" technologies were developed, but their mapping to the principal architecture envisaged by the Grid's authors was never concretely specified. We present a decade's long study of 18+ technologies that claim to be "grid";and ask the question "what makes a grid technology a grid?" This question, along with empirical research using topical software architectural recovery techniques;code and program analysis;clustering and information retrieval, allow the definition of an updated grid architecture that more uniformly captures what was built over this timeframe.

关键词： Anatomy Apache Grid Physiology Reverse engineering

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Mars 2020 surface mission performance analysis: Part 2. surface traversability

Mars 2020 surface mission performance analysis: Part 2. surf...

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AIAA Space and Astronautics Forum and Exposition, 2018

作者： Ono, Masahiro Heverly, Matthew Rothrock, Brandon Ishimatsu, Takuto Almeida, Eduardo Calef, Fred Soliman, Tariq Williams, Nathan Gengl, Hallie Nicholas, Austin Stilley, Erisa K. Otsu, Kyohei Trautman, Marshall Lange, Robert Milkovich, Sarah Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Research Technologist Mobility and Robotics Systems Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Mechanical Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Science Data Understanding Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Geophysics and Planetary Geosciences Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Data Visualization Developer Instrument Software and Science Data Systems PasadenaCA91106 United States Software Systems Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Mission Systems Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Science Systems Engineer Jet Propulsion Laboratory California Institute of Technology 394B PasadenaCA91106 United States

ISBN: (纸本)9781624105753

– The Mars 2020 Rover Mission (M2020) is characterized by demanding requirement on the distance and time for traveling between scientific Regions Of Interest (ROIs). As a result, surface traversability is one of the major driving factors for the landing site selection of M2020. With the newly developed Mars Terrain Traversability analysis Tools (MTTT), we performed traversability analysis of the eight candidate landing sites with an unprecedented granularity. This paper describes the MTTT analysis capabilityies, as well as how the MTTT capabilities were used to downselect from eight to three candidate landing sites for further evaluation. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Site selection

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Achieving fast operational intelligence in NASA's deep space network through complex event processing 14th

Achieving fast operational intelligence in NASA's deep space...

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14th International Conference on Space Operations, SpaceOps 2016

作者： Choi, Joshua S. Verma, Rishi Malhotra, Shan Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Mission Control Systems Section 4800 Oak Grove Drive M/S: 301-480 PasadenaCA91109-8099 United States Instrument Software and Science Data Systems Section 4800 Oak Grove Drive M/S: 158-242 PasadenaCA91109-8099 United States Planning & Execution Systems Section 4800 Oak Grove Drive M/S: 301-250D PasadenaCA91109-8099 United States

ISBN: (数字)9781624104268

ISBN: (纸本)9781624104268

NASA’s Deep Space Network (DSN) is a complex, global project, in which the expertise of human operators remain crucial for its successful operation. To find ways to save costs in operations and to improve its services, a number of modernization efforts are underway in the DSN. One such effort is a research and technology development task at the Jet Propulsion Laboratory that is investigating the use of complex event processing (CEP) for intelligent assessment of situations, trend analysis, and advanced automation. The technology leverages the significant business intelligence (BI) and data science advancements made in the enterprise industries over the last several years. The open source big data processing engine Apache SparkTM and the high-throughput, distributed messaging system Apache Kafka form the core of the DSN Complex Event Processing (DCEP) framework. This paper discusses the system engineering perspective of why achieving efficient, lower-cost operations in the DSN is a challenging problem, how the DCEP system handles the use cases that help realize intelligent operations, and how this solution fits into the overall model of the planned DSN Follow-the-Sun Operations (FtSO). © 2016 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： NASA

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Reducing the risk and improving mission success for NASA's human mission to a Near-Earth asteroid: How many robotic surveyors?

Reducing the risk and improving mission success for NASA's h...

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AIAA SPACE Conference and Exposition 2011

作者： Smith, Jeffrey H. Lincoln, William P. Weisbin, Charles R. Jet Propulsion Laboratory California Institute of Technology Mission Systems Concepts Section 4800 Oak Grove Dr. Mail Stop 301-165 Pasadena CA 91109-8099 United States Jet Propulsion Laboratory California Institute of Technology Instrument Software and Science Data Systems Section 4800 Oak Grove Dr. Mail Stop 171-264 Pasadena CA 91109-8099 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. Mail Stop 202-204 Pasadena CA 91109-8099 United States

ISBN: (纸本)9781600869532

NASA's recent attention and interest in sending a human mission to land on a Near-Earth asteroid raises the question of need for a robotic surveyor. This paper describes a Bayesian approach for comparing the productivity and cost-risk tradeoffs of sending (versus not sending) one or more robotic surveyor missions prior to a human mission to land on an asteroid. The probability of finding an asteroid suitable for landing was derived from an analysis of more than 1200 asteroids in order to define a quantitative estimate of suitability. The low cost of the surveyors relative to the human mission underlined the multi-surveyor strategy as relatively inexpensive insurance against the risks of encountering an unsuitable asteroid for landing on arrival by a human mission. © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Asteroids

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Reports of the AAAI 2010 conference workshops

Reports of the AAAI 2010 conference workshops

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作者： Aha, David W. Boddy, Mark Bulitko, Vadim D'Avila Garcez, Artur S. Doshi, Prashant Edelkamp, Stefan Geib, Christopher Gmytrasiewicz, Piotr Goldman, Robert P. Halevy, Alon Hitzler, Pascal Isbell, Charles Josyula, Darsana Kaelbling, Leslie Pack Kersting, Kristian Kunda, Maithilee Lamb, Luis C. Marthi, Bhaskara McGreggor, Keith Mihalkova, Lilyana Nastase, Vivi Natarajan, Sriraam Provan, Gregory Raja, Anita Ram, Ashwin Riedl, Mark Russell, Stuart Sabharwal, Ashish Smaus, Jan-Georg Sukthankar, Gita Tuyls, Karl Van Der Meyden, Ron Adaptive Systems Section Naval Research Laboratory Washington DC United States Adventium Labs. Minneapolis MN United States Department of Computer Science University of Alberta Canada City University London United Kingdom University of Georgia United States University of Bremen Germany School of Informatics University of Edinburgh United Kingdom University of Illinois Chicago United States Smart Information Flow Technologies LLC Minneapolis MN United States Structured Data Group Google Research United States Department of Computer Science Wright State University Dayton OH United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States Department of Computer Science Bowie State University United States Department of Computer Science and Engineering Massachusetts Institute of Technology United States Statistical Relational Mining Group Fraunhofer IAIS Germany Department of Computer Science Federal University of Rio Grande Do sul Brazil Willow Garage Inc. CA United States Computer Science Department University of Maryland College Park United States Heidelberg Institute for Theoretical Studies Heidelberg Germany Department of Computer Science University of Wisconsin Madison United States University College Cork Ireland Cork Complex Systems Lab. Ireland Department of Software and Information Systems University of North Carolina Charlotte United States Department of Computer Science and Engineering University of California Berkeley United States Department of Computer Science Cornell University United States Institute for Computational Sustainability United States University of Freiburg Germany School of Electrical Engineering and Computer Science University of Central Florida United States Maastricht University Netherlands University of New South Wales NICTA Australia

The AAAI-10 workshop program was held on July 11-12, 2010, at the Westin Peachtree Plaza in Atlanta, Georgia. The workshop program included 13 workshops covering a wide range of topics in artificial intelligence. There were several presentations on particular formalisms for combining the levels of planning from the perspectives of classical planning, motion planning, POMDPs, search-based planning, and temporal logic-based constraints. Henry Lieberman from Massachusetts Institute of Technology Media Laboratory presented work developing a platform for knowledge acquisition and usage from expert users, built upon experience with eliciting information from nonexperts in a collaborative manner. The objective of the AAAI Workshop on Goal-Directed Autonomy (GDA) was to encourage discussion and novel contributions on intelligent agents that can self-select their goals. Jeremy Baxter from QinetiQ focused on user interaction, while Nick Hawes described a goal-directed reasoning framework for controlling a mobile robot.

关键词： Motion planning

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