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AIAA SPACE 2014 Conference and Exposition

作者： McNutt, Leslie Johnson, Les Clardy, Dennon Castillo-Rogez, Julie Frick, Andreas Jones, Laura NASA Marshall Space Flight Center Huntsville AL 35812 United States Jet Propulsion Laboratory Pasadena CA 91109 United States Exploration and Space Transportation Development Office MSFC/FP30 United States Advanced Concepts Office MSFC/ED04 United States MSFC/FP01 United States Planetary Science Section Jet Propulsion Laboratory California Institute of Technology United States Assigned Pre-Project Systems Engineering Group Jet Propulsion Laboratory California Institute of Technology United States Guidance and Control Systems Group Jet Propulsion Laboratory California Institute of Technology United States

ISBN: (纸本)9781624102578

Near-Earth Asteroids (NEAs) are an easily accessible object in Earth's vicinity. Detections of NEAs are expected to grow in the near future, offering increasing target opportunities. As NASA continues to refine its plans to possibly explore these small worlds with human explorers, initial reconnaissance with comparatively inexpensive robotic precursors is necessary. Obtaining and analyzing relevant data about these bodies via robotic precursors before committing a crew to visit a NEA will significantly minimize crew and mission risk, as well as maximize exploration return potential. The Marshall Space Flight Center (MSFC) and Jet Propulsion laboratory (JPL) are jointly examining a mission concept, tentatively called 'NEA Scout,' utilizing a low-cost CubeSats platform in response to the current needs for affordable missions with exploration science value. The NEA Scout mission concept would be a secondary payload on the Space Launch System (SLS) Exploration Mission 1 (EM-1), the first planned flight of the SLS and the second un-crewed test flight of the Orion Multi-Purpose Crew Vehicle (MPCV).

关键词： Robotics

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Computation of boundary controls using a Gauss pseudospectral method

Computation of boundary controls using a Gauss pseudospectra...

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2007 AAS/AIAA Astrodynamics Specialist Conference

作者： Huntington, Geoffrey T. Benson, David A. How, Jonathan P. Kanizay, Nicole Darby, Christopher Rao, Anil V. Charles Stark Draper Laboratory Inc. Cambridge MA 02139 United States Massachusetts Institute of Technology Cambridge MA 02139 United States University of Florida Gainesville FL 32611-6250 United States Department of Aeronautics and Astronautics United States Draper Laboratory United States Technical Staff Guidance Navigation and Control Systems Division Department of Mechanical and Aerospace Engineering P.O. Box 116250 Gainesville FL 32611-6250 United States

ISBN: (纸本)9780877035435

A computational approach is presented that improves the solution to an optimal control problem solved via the Gauss pseudospectral method. The approach developed in this paper is a post-optimality computation where the Pontryagin minimum principle is applied at the boundaries to obtain a more accurate control approximation at the initial and final times. In particular, the initial state (obtained from the primal solution of the nonlinear program) and initial costare (obtained from the costate mapping property of the Gauss pseudospectral method) are used in the boundary control computation. The accuracy of the approach is illustrated on three examples. Furthermore, it is shown that including the boundary controls significantly improves the accuracy of the propagated trajectories, thus making the approach potentially worthwhile in the context of real-time optimal control.

关键词： Optimal control systems

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ECONOMICS OF SAMPLE COMPOSITING AS A SCREENING TOOL IN GROUND-WATER QUALITY MONITORING

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GROUND WATER MONITORING AND REMEDIATION 1989年第1期9卷 186-192页

作者： RAJAGOPAL, R WILLIAMS, LR R. Rajagopal received a B.Sc. in mathematics and graduate level training in statistics from the University of Bombay a master's degree in industrial and systems engineering (operations research) from the University of Florida and a Ph.D. in resource management with an emphasis on ground water pollution control from the University of Michigan. He is a professor of geography and civil and environmental engineering at the University of Iowa. In 1985 Rajagopal received the National Association of Environmental Professional's Distinguished Service Award. He is currently a visiting scientist in the Advanced Monitoring Systems Division Environmental Monitoring Systems Laboratory U.S. Environmental Protection Agency (Las Vegas NV 89193). Llewellyn R. Williams received a Ph.D. from Rutgers University in 1971 and has served the U.S. EPA for the past 16 years. He is currently acting director Quality Assurance Division of the U.S. EPA's Environmental Monitoring Systems Laboratory in Las Vegas Nevada (89193-3478). He has authored or co-authored approximately 100 technical articles. The subjects of these works include water quality monitoring bioassay and chemical analysis method development and validation and quality assurance for field and laboratory studies. Recently Dr. Williams arranged to bring together a panel of experts consisting of regulators chemists and statisticians to develop guidance for applying composite sampling to achieve cost savings in environmental monitoring programs.

Recent advances in high throughput/automated compositing with robotics/field-screening methods offer seldom-tapped opportunities for achieving cost-reduction in ground water quality monitoring programs. An economic framework is presented in this paper for the evaluation of sample compositing as a screening tool in ground water quality monitoring. When the likelihood of occurrence of a contaminant in a well is very small, the use of sample compositing instead of routine exhaustive sampling will lead to reduction in analytical efforts. Such reduction will be maximum when there are no contaminated wells in the network. An N-fold reduction will result when none of the wells in a network of N wells are contaminated. When 25 percent or more wells in a network are contaminated, the use of sample compositing will require, at the most, an additional 50 percent analytical effort compared to exhaustive sampling. A quantitative measure of the cost-effectiveness of sample compositing as a screening tool is shown to be dependent on two factors: a ratio (f1) of laboratory analytical cost to that of well installation and field sampling costs and a ratio (f2) of the expected number of contaminated wells to that of the total number of wells in the network. Several useful mathematical results of primary interest are derived and illustrated with case examples in the paper. Selected areas for further research are also outlined.

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Annual business and news:: Beginning the 31st year of the Journal of guidance, control, and Dynamics

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JOURNAL OF guidance control AND DYNAMICS 2008年第1期31卷 1-1页

作者： Schmidt, George T. Aeronautics and Astronautics Massachusetts Institute of Technology (MIT) AIAA Institute of Electrical and Electronics Engineers Russian Federation Academy of Navigation and Motion Control Department of Mechanical and Aerospace Engineering and Engineering Mechanics University of Missouri-Rolla(UMR) Lockheed Electronics Company Center for Space Research University of Texas Austin United States Wright Laboratory Eglin Air Force Base UMR AIAA Guidance Navigation and Control Technical Committee American Automatic Control Council Lockheed Martin Aeronautics Company Palmdale CA United States C4ISR and Unmanned Air Vehicle (UAV) Programs for Air Vehicle Sciences and Systems Accreditation Board Engineering and Technology Inc./Aeronautical National Technical Committee on Guidance Navigation and Control Department of Mechanical and Aerospace Engineering University at Buffalo State University of New York (SUNY) AIAA Technical Committee on GN and C Air Vehicles Directorate Air Force Research Laboratory (AFRL) Wright-Patterson Air Force Base Space Access and Hypersonic Vehicle Guidance and Control Group Control Science Center of Excellence AFRL AIAA Technical Committee on Guidance Navigation and Control Department of Mechanical and Aeronautical Engineering University of California (UC) Davis United States Boeing Company Modernized Flight Control System for the Apache Longbow AIAA Institute of Electrical and Electronics Engineers AHS Jet Propulsion Laboratory California Institute of Technology Boeing Company Flight Sciences and Advanced Design Group Guidance Navigation and Control (GN and C) Systems Engineer Distributed Space Systems NASA Goddard Space Flight Center (GSFC) AIAA GN and C Technical Committee Department of Aerospace Engineering and Engineering Mechanics University of Texas Austin United States Institute of Navigation American Astronautical Society AIAA Guidance Navigation (GN and C) and Control Technical Committee Department of Aerospace Engineering Iowa State University Iowa

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Comparative study of coherent, non-coherent, and semi-coherent integration schemes for GNSS receivers

Comparative study of coherent, non-coherent, and semi-cohere...

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63rd Annual Meeting of the Institute of Navigation 2007

作者： Miller, Mikel Nguyen, Thao Yang, Chun Blasch, Erik Air Force Research Lab. MNG Air Force Research Lab. SNRN Sigtem Technology Inc. Air Force Research Lab. SNAA Department of Electrical and Computer Engineering Miami University of Ohio Advanced Guidance Division Munitions Directorate Air Force Research Laboratory Eglin AFB FL Department of EE AFIT Miami University of Ohio ION RTN IEEE AIAA Assessment and Integration Branch Sensors Directorate Air Force Research Laboratory WPAFB OH AFIT SPIE Wright-Patterson AFB OH Reference Systems Branch Sensors Directorate Air Force Research Laboratory WPAFB OH

ISBN: (纸本)1604232862

Three integration schemes, namely, coherent, noncoherent, and semi-coherent integration, and their variants are described in this paper for signal detection and tracking in such applications as pulsed Doppler radar and GPS receiver. We adopt a unified approach to present such integration schemes as best approximations to the likelihood ratio test (LRT) when the signal models are partially known to different extents. Formulas for the conditional probability density functions (pdfs) of the sufficient statistic under alternative hypotheses as well as the probability of detection (Pd) and probability of false alarm (Pfa) are given. Integration improvement factors and integration losses are then formulated. Connections of the semi-coherent integration scheme to the cyclic autocorrelation function, the discrete-time Wigner-Ville transform, and the radar ambiguity function are also outlined. Although most of the results are available in the literature, they are scattered in different textbooks and research papers. By presenting them in a cohesive and concise manner, the paper can serve as (1) an introductory material with key references for those who would like to study the subject further, (2) a quick reference for those who would like to compare these integration schemes, and (3) an integrated approach under varying conditions. Simulation results are presented to compare these integration schemes in terms of detection performance (P d and Pfa) as a function of signal to noise ratio (SNR) and integration interval under two operating conditions. One is for a signal with constant Doppler frequency and the other is for a linear frequency modulation signal (i.e., a chirp signal). We show the curves of the input SNR required under different chirp rates so as to maintain the same detection performance, the resulting LRTs for the different approaches, as well as simulated comparisons between trade-offs of Pd versus SNR.

关键词： Signal receivers

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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

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Journal of Ambient Intelligence and Humanized Computing 2017年 1-11页

作者： Wang, Shui-Hua Muhammad, Khan Phillips, Preetha Dong, Zhengchao Zhang, Yu-Dong Jiangsu Key Laboratory of Advanced Manufacturing Technology HuaiyinJiangsu223003 China School of Computer Science and Technology Henan Polytechnic University JiaozuoHenan454000 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China College of Software Convergence Sejong University Seoul Korea Republic of Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Translational Imaging Division and MRI Unit Columbia University and New York State Psychiatric Institute New YorkNY10032 United States

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Fractal dimension

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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A COMPUTER INTEGRATED engineering SYSTEM FOR DESIGN AND LIFE-CYCLE MANAGEMENT

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NAVAL ENGINEERS JOURNAL 1986年第3期98卷 115-121页

作者： NICKODEMUS, GH YANUS, ID Irwin D. Yanusgraduated from the University of Pennsylvania with a bachelor's degree in arts and science in 1960 and a bachelor of science degree in mechanical engineering in 1961. In 1963 he received a master of science degree in mechanical engineering from Carnegie-Mellon University. Mr. Yanus is presently the manager of naval systems engineering in the Computer Aided Engineering Department of the Plant Engineering Division of Westinghouse Electric Corporation. His prior experience includes 19 years at the Bettis Atomic Power Laboratory where he performed assignments of increasing responsibility in design analysis development and procurement of nuclear reactor components for naval applications. As the manager of reactor mechanical design he was responsible for the mechanical design of theNimitzclass reactor components and control drive mechanism technology development. Mr. Yanus is a member of the American Society of Mechanical Engineers and the Society of Naval Architects and Marine Engineers. Glen H. Nickodemusgraduated from Michigan State University in 1964 with a bachelor of science degree in civil engineering. He joined the Pittsburgh-Des Moines Corporation in July of the same year. A master of science degree in civil engineering was obtained from Carnegie-Mellon University in 1973. In 1969 Mr. Nickodemus joined the Advanced Reactors Division of the Westinghouse Electric Corporation where he performed design and structural analysis functions for the development of the Fast Flux Test Facility and Clinch River Breeder Reactor Plant (CRBRP). As manager of CRBRP reactor engineering stress analysis he was responsible for the reactor vessel internals closure head control rod drivelines and miscellaneous guard vessels and head access area components. He is currently the manager of software development for the Computer Aided Engineering Department of the Plant Engineering Division. Mr. Nickodemus is a member of the American Society of Mechanical Engineers and is a registered professional engineer in Penn

This paper describes a computer integrated engineering system for design and life cycle management of weapons systems, ships and other multidisciplined systems. All engineering data are stored in a central engineering database. Individual application databases define and process information necessary for specific discipline evaluations. Interface modules between the application databases and the engineering database ensure that the entered data are complete, consistent, compatible, and in compliance with requirements. Conflicts are immediately identified and efficiently resolved. Implementation of the system improves design quality and reduces costs by minimizing the number of design iterations, reducing the effort to implement changes, providing effective storage and retrieval, and reducing the need for ship checks prior to modifications and alterations.

关键词： MILITARY engineering

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MODELING GROUND-WATER QUALITY SAMPLING DECISIONS

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GROUND WATER MONITORING AND REMEDIATION 1988年第4期8卷 121-134页

作者： HSUEH, YW RAJAGOPAL, R Ya-Wen Hsueh received undergraduate training in liberal arts at the National Taiwan University and a master's degree in geography with an emphasis in water resources from the University of Iowa. Her major research interests are in the fields of water resources Systems analysis environmental impact assessment and geographic information Systems. Her current affiliation at the National Taiwan University (Department of Environmental Science Taiwan Republic of China) includes research dealing with an assessment of state-of-the-art applications and management of Geographic Information Systems in the Department of Interior of the Government of Taiwan. R. Rajagopal received undergraduate and graduate education in mathematics and statistics from the University of Bombay a master's degree in operations research from the University of Florida and a Ph.D. in resource management with an emphasis on ground water pollution control from the University of Michigan. He is a professor of geography and civil and environmental engineering at the University of Iowa. He is currently a visiting scientist in the Advanced Monitoring Systems Division Environmental Monitoring Systems Laboratory P.O. Box 93478 U.S. Environmental Protection Agency Las Vegas NV 89193.

Questions such as what, where, when, and how often to sample play a central role in the development of monitoring strategies. Limited resources will not permit sampling for many contaminants at the same frequency at all well sites. Therefore, a resource allocation strategy is necessary to arrive at answers for the preceding types of questions. Such a strategy for a ground water quality monitoring program is formulated as an integer programming model (an optimization model). The model will be of use in the process of deciding what constituents to sample and where to sample them so as to maximize a given objective, subject to a set of budget, sampling, and regulatory constraints. The maximization objective in the model is defined as a weighted function of population exposure to a scaled measure of observed chemical concentrations. The sampling constraints are based on the observed variability of contaminants in the aquifer, needed precision in estimates, a chosen level of significance, the available budget for implementing the program, and selected regulatory constraints. The model is tested with field data obtained for 10 selected constituents from more than 650 wells in the Cambrian-Ordovician aquifer in Iowa. Results from two alternative formulations of the model are compared, analyzed, and discussed. Further avenues for research are briefly outlined.

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controlling a Networked SIS Model via a Single Input over Undirected Graphs ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10981-10986页

作者： Dan Wang Ji Liu Philip E. Paré Wei Chen Li Qiu Carolyn L. Beck Tamer Başar Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China Department of Electrical and Computer Engineering Stony Brook University NY 11794 USA Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science & Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing China Coordinated Science Laboratory University of Illinois at Urbana-Champaign IL 61801 USA

This paper formulates and studies the problem of controlling a networked SIS model using a single input in which the network structure is described by a connected undirected graph. A necessary and sufficient condition on the values of curing and infection rates for the healthy state to be exponentially stable is obtained via the analysis of signed Laplacians when the control input is the curing budget of a single agent. In the case when the healthy state is stabilizable, an explicit expression for the minimum curing budget is provided. The utility of the algorithm is demonstrated using a simulation over a network of cities in the northeastern United States.

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