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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Comment on "Inherent security of phase coding quantum key distribution systems against detector blinding attacks" [Laser Phys. Lett. 15, 095203 (2018)]

arXiv

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arXiv 2018年

作者： Fedorov, Aleksey Gerhardt, Ilja Huang, Anqi Jogenfors, Jonathan Kurochkin, Yury Lamas-Linares, Antía Larsson, Jan-Åke Leuchs, Gerd Lydersen, Lars Makarov, Vadim Skaar, Johannes Russian Quantum Center Skolkovo Moscow143025 Russia QRate Skolkovo Moscow143025 Russia QApp Skolkovo Moscow143025 Russia Institute of Physics University of Stuttgart Institute for Quantum Science and Technology Pfaffenwaldring 57 StuttgartD-70569 Germany Max Planck Institute for Solid State Research Heisenbergstraße 1 StuttgartD-70569 Germany Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden Texas Advanced Computing Center University of Texas at Austin AustinTX United States Max Planck Institute for the Science of Light University of Erlangen-Nürnberg ErlangenD-91058 Germany Kringsjåvegen 3E TrondheimNO-7032 Norway National University of Science and Technology MISIS Moscow119049 Russia Department of Technology Systems University of Oslo Box 70 KjellerNO-2027 Norway

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3D model of dispatchable renewable energy for smart microgrid power system

3D model of dispatchable renewable energy for smart microgri...

引用

Conference on Technologies for Sustainability (Sustech)

作者： Fred Chiou Richard Fry Jake P. Gentle Timothy R. McJunkin Electronics Engineering Technology Weber State University Ogden Utah U.S.A. Department of Computer Science Weber State University Ogden Utah U.S.A. Critical Infrastructure Protection Idaho National Laboratory Idaho Falls Idaho U.S.A. Power & Energy Systems Idaho National Laboratory Idaho Falls Idaho U.S.A.

ISBN: (纸本)9781538604533;9781538604526

The goal of this research is to model a 3D simulation used to investigate the performance of a distribution management system (DMS) of a smart microgrid system that integrates renewable energy and energy storage. The simulation reads recorded data of power generations and loads and attempts to balance the supply and demand to sustain a stable and high quality electricity system. The 3D model is designed to provide a user-friendly game-like educational STEM learning environment. The model simulates the integration of renewable energy (solar, wind, and hydro), energy storage, loads (industrial, commercial, and residential), and the distribution management system with resilient control [1][2] in the microgrid power system. The DMS performance is measured by a proportional, integral, and derivative (PID) controller and analyzed to verify if it can keep the balance of power supply and demand within the allowable limits while sustaining the quality of electricity. This research monitors the frequency of the system as an indicator of the quality of the electricity. The simulation model is implemented using the Unity 3D development platform with C# scripts controlling the algorithms. Users can explore and learn the function of each element in the system by navigating the scenes from a first-person perspective.

关键词： Energy storage Three-dimensional displays Microgrids Wind turbines Solid modeling Load modeling Wind power generation

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Negative exponential behavior of image mutual information for pseudo-thermal light ghost imaging: Observation, modeling, and verification

arXiv

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arXiv 2017年

作者： Li, Junhui Luo, Bin Yang, Dongyue Yin, Longfei Wu, Guohua Guo, Hong State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China

When use the image mutual information to assess the quality of reconstructed image in pseudo-thermal light ghost imaging, a negative exponential behavior with respect to the measurement number is observed. Based on information theory and a few simple and verifiable assumptions, semi-quantitative model of image mutual information under varying measurement numbers is established. It is the Gaussian characteristics of the bucket detector output probability distribution that leads to this negative exponential behavior. Designed experiments verify the model. Copyright © 2017, The Authors. All rights reserved.

关键词： Probability distributions

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Consensus of Second-Order Multi-Agent systems with Time-Varying Delays and Antagonistic Interactions

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Tsinghua Science and technology 2015年第2期20卷 205-211页

作者： Bo Hou Fuchun Sun Hongbo Li Guangbin Liu State Key Laboratory on Intelligent Technology and Systems Department of Computer Science and TechnologyTsinghua University High-Tech Institute of Xi’an

This study investigates the consensus problem of second-order multi-agent systems subject to time- varying interval-like delays. The notion of consensus is extended to networks containing antagonistic interactions modeled by negative weights on the communication graph. A unified framework is established to address both the stationary and dynamic consensus issues in sampled-data settings. Using the reciprocally convex approach, a sufficient condition for consensus is derived in terms of matrix inequalities. Numerical examples are provided to illustrate the effectiveness of the proposed result.

关键词： multi-agent systems second-order consensus cooperation and competition

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Sidebands suppression of 852 nm Cesium Faraday anomalous dispersion optical filter by argon gas

arXiv

引用

arXiv 2017年

作者： Xiong, Junyu Yin, Longfei Luo, Bin Chen, Jingbiao Guo, Hong State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this work, sidebands suppression induced by the buffer gas argon (Ar) in 852 nm cesium Faraday anomalous dispersion optical filter (FADOF) is investigated. FADOF performances at different Ar pressures (0 torr, 1 torr, 5 torr and 10 torr) are compared, and a single-peak transmittance spectrum with peak transmittance up to 80% is achieved when the pressure of Ar reaches 5 torr. A detailed analysis shows that, this sidebands suppression comes from the depopulation enhancement by the buffer gas. This result can be generalized to other FADOFs with similar level structures such as the D2 lines of other alkali metal atoms. Copyright © 2017, The Authors. All rights reserved.

关键词： Cesium

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Binary sampling ghost imaging: Add random noise to fight quantization caused image quality decline

arXiv

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arXiv 2017年

作者： Li, Junhui Yang, Dongyue Luo, Bin Wu, Guohua Yin, Longfei Guo, Hong State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

When the sampling data of ghost imaging is recorded with less bits, i.e., experiencing quantization, decline of image quality is observed. The less bits used, the worse image one gets. Dithering, which adds suitable random noise to the raw data before quantization, is proved to be capable of compensating image quality decline effectively, even for the extreme binary sampling case. A brief explanation and parameter optimization of dithering are given. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

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Uniform remeshing of 3D meshes of arbitrary topology using discrete constrained centroid voronoi clustering 31

Uniform remeshing of 3D meshes of arbitrary topology using d...

引用

31st International Conference on computers and Their Applications, CATA 2016

作者： Lai, Shuhua He, Gaoqi School of Science and Technology Georgia Gwinnett College LawrencevilleGA30043 United States Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9781943436026

In this paper we propose a remeshing algorithm for altering the mesh geometry and connectivity of a 3D model to improve its uniformality. The new remeshing algorithm replaces an arbitrarily structured 3D mesh with a uniformly structured one, which has a given number of vertices, while approximates the original input mesh well. This is done through an elegant and efficient mesh clustering algorithm, which divides the faces of an input mesh into a given number of clusters for remeshing purpose by approximating the Centroidal Voronoi Tessellation of the mesh. With proliferation of 3D scanners, this remeshing algorithm is particularly useful for reverse engineering applications of 3D models, which in many cases are dense, nonuniform, irregular and arbitrary topology. Examples demonstrating effectiveness of the new algorithm are also included in the paper. Copyright ISCA.

关键词： Clustering algorithms

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Integrated mechatronics design of flexure joint and controller in dual-drive gantry: a constrained H2 optimization approach

Integrated mechatronics design of flexure joint and controll...

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2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017

作者： Ma, Jun Chen, Si-Lu Kamaldin, Nazir Teo, Chek Sing Tay, Arthur Mamun, Abdullah Al Tan, Kok Kiong Department of Electrical and Computer Engineering SIMTech-NUS Joint Lab on Precision Motion Systems National University of Singapore 117582 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Mechatronics Group Singapore Institute of Manufacturing Technology 138634 Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315121 China NUS Grad. School for Inte. Sci. and Eng. and Department of Electrical Computer Engineering National University of Singapore 119077 Singapore

ISBN: (纸本)9781509059980

The dual-drive H gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a novel biaxial gantry with flexure-links is designed to allow a small rotation angle of the cross-Arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-To-point trajectories with same dynamics but different steps. From here, an H2 optimization with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal steps. Comparative experiments results obtained on the testbed are presented to verify the effectiveness of proposed method. © 2017 IEEE.

关键词： Constrained optimization

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