Ames Research Center Awarded Contracts - Machine Learning | Federal Compass

Ames Research Center Awarded Contracts - Machine Learning

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NNX13AD49A - THE PROPOSED RESEARCH FOCUSES ON DEVELOPING KEY DATA MANAGEMENT AND MACHINE LEARNING TECHNIQUES FOR ADAPTIVE ENERGY MANAGEMENT, OCCUPANT SATISFACTION AND BUILDING AUTOMATION IN A GREEN OFFICE BUILDING (THE SUSTAINABILITY BASE), AND IN A LARGER SMART COMMUNITY CONSISTING OF CERTAIN NASA AMES LEGACY BUILDINGS, SUCH AS BLDG. 23, FEDERATED WITH THE SUSTAINABILITY BASE. THESE BUILDINGS, IF NOT ALREADY INSTRUMENTED, WILL BE EQUIPPED WITH A VARIETY OF SENSORS, COLLECTING EXTENSIVE DATA FROM THIS DISTRIBUTED WEB OF THINGS . THE GOAL IS FOR THIS LARGE‐SCALE SENSOR‐ENABLED SYSTEM TO INTERACT WITH BUILDING MANAGEMENT SYSTEMS, WITH USERS (VIA THEIR SMART PHONE SENSORS), AND WITH THE CLOUD, TO OFFER OPTIMIZED OVERALL ENERGY CONSUMPTION WHILE MAINTAINING OR INCREASING OCCUPANCY SATISFACTION LEVELS THROUGH LOCALIZED ADAPTIVE BEHAVIORS. THESE INSTRUMENTED BUILDINGS WILL PRODUCE A STREAM OF REAL‐TIME DATA TO BE ANALYZED AND USED FOR A VARIETY OF PURPOSES, INCLUDING THE DESIRED ADAPTIVE CONTROL AND (FUTURE) STATE ESTIMATION CAPABILITIES. ONE EXAMPLE OF STATE ESTIMATION IS OCCUPANCY SENSING AND MODELING. HEATING, VENTILATION AND AIR CONDITIONING (HVAC) IN SELECTED ZONES OF A BUILDING CAN BE TURNED ON OR OFF IN ANTICIPATION OF OCCUPANCY‐ PEOPLE ARRIVING OR LEAVING AT VARIABLE TIMES. UNLIKE LIGHTING, WHICH CAN TURN ON OR OFF IMMEDIATELY BY DETECTING PRESENCE OR ABSENCE OF PEOPLE, HVAC IS ASSOCIATED WITH TIME DELAYS, BASED ON ROOM SIZE, TYPE OF HVAC SYSTEM AND NUMBER OF PEOPLE IN THE ROOM. FURTHERMORE, OPTIMAL HVAC ACTIONS DEPEND ON ENVIRONMENTAL DATA SUCH CURRENT OR PREDICTED WEATHER, INTERNAL HUMIDITY, TRAFFIC CONDITIONS AFFECTING PEOPLE ARRIVING OR LEAVING, KNOWN SPECIAL EVENTS OR CONFERENCES, MEETINGS, ETC. HVAC IS JUST ONE EXAMPLE OPPORTUNITY. MORE GENERALLY, THE BUILDING MANAGEMENT SYSTEMS NEED TO "MAKE SENSE" OF THE DATA STREAM TO ENABLE STATE ESTIMATION, SITUATION AWARENESS, AND PREDICTIONS FOR A RANGE OF APPLICATIONS. IN THE PROPOSED RESEARCH, WE WILL DEVELOP SCALABLE, ROBUST SOFTWARE SYSTEMS TO FUSE THE SENSOR DATA WITH THE ADDITIONAL ENVIRONMENTAL INFORMATION, AND DISTRIBUTE ACTIONABLE INFORMATION TO PEOPLE AND SYSTEMS IN A CONTEXT‐AWARE WAY.
Cooperative Agreement
Contractor
Carnegie Mellon University (CARNEGIE MELLON UNIVERSITY)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/16/2013
Obligated Amount
$359.6k
NNX12AL30A - COMMON ISSUES IN DATA ANALYSIS INVOLVING VERY COMPLEX AND VERY LARGE DATA SETS - PETABYTES IN SIZE- FOR WHICH MACHINE LEARNING, DATA MINING, AND OTHER AUTOMATED ANALYSIS TECLMIQUES WILL BE NEEDED TO EXPAND CURRENT KNOWLEDGE AS WELL AS TO UNCOVER NEW PHENOMENA. DEEP STATISTICAL ISSUES INVOLVING COMPUTATIONAL EFFICIENCY, ACCOUNTING FOR FALSE POSITIVES, AUTOMATED OBJECTIVE ANALYSIS PROCEDURES, LOOM HIGH ON THE LIST OF CHALLENGES IN THIS NEW ERA. FOR EXAMPLE, X-RAY FREE ELECTRON LASER (FEL) FACILITIES SUCH AS THE LINAC COHERENT LIGHT SOURCE PROVIDE A NEW SOURCE OF LARGE AND FERTILE DATA SETS ASSOCIATED WITH PHOTON SCIENCE. IN THE CASE OF SERIAL FEMTOSECOND XRAY DIFFRACTION EXPERIMENTS USED TO STUDY BIOLOGICAL SAMPLES, ENGINEERED NANOMATERIALS AND AEROSOLS ARE PRODUCING UP TO 10 TB OF DATA PER 12 HOUR SHIFT WITH LCLS OPERATED AT 120HZ. FUTURE X-RAY FEL FACILITIES SCHEDULED TO BE OPERATIONAL BEFORE 2020 HAVE PULSE DELIVERY RATES EXCEEDING 1 00 KHZ . EXTRAPOLATING EXISTING EXPERIMENTS TO FUTURE FACILITY CAPABILITIES INDICATE THAT DATA RATES OF>2 PETABYTES PER DAY CAN BE EXPECTED. THIS PATTERN OF VERY LARGE AND RICH DATA SETS IS REPEATED FOR THE LHC AND LSST, AND THERE ARE IMPORTANT PARALLELS THAT MIGHT BE EXPLOITED IN COMMON STATISTICAL APPROACHES TO DATA ANALYSIS. THIS CONFERENCE WILL BRING TOGETHER PRACTITIONERS FROM ASTROPHYSICS, PARTICLE PHYSICS AND PHOTON SCIENCE AS WELL AS STATISTICIANS TO PRESENT THE CURRENT STATE OF THE ART AND EXAMINE SOME OF THE OPTIONS FOR THE FUTURE. EXISTING DATA SETS WILL BE SHOWCASED, STATISTICS AND COMPUTER SCIENCE ISSUES IN MACHINE LEARNING AND DATA MINING EFFORTS WILL BE HIGHLIGHTED, AND ONLINE DATABASES FOR OPEN ACCESS TO DATA SETS WILL BE DISCUSSED.
Cooperative Agreement
Contractor
Stanford University (LELAND STANFORD JUNIOR UNIVERSITY, THE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
08/06/2012
Obligated Amount
$7.3k
NNX11CD50P - AUTOMATED TOOLS ARE QUICKLY MAKING INROADS INTO CASUAL COMPUTING ENVIRONMENTS, SOLVING PROGRESSIVELY MORE COMPLEX TASKS. HOWEVER, THESE ADVANCEMENTS STILL REQUIRE TRADING RELIABILITY FOR CONVENIENCE. FREQUENT MINOR FAILURES ARE ACCEPTABLE IN CASUAL ENVIRONMENTS, BUT CRITICAL SYSTEMS CANNOT MAKE THE SAME EXCHANGE. THE SOFTWARE SYSTEMS THAT NASA DEVELOPS COULD GREATLY BENEFIT FROM MACHINE LEARNING TECHNOLOGIES THAT HAVE BEEN APPLIED TO CASUAL COMPUTING, IF THE SOFTWARE DEVELOPED BY LEARNING ALGORITHMS COULD BE VERIFIED. WE PROPOSE TO APPLY FORMAL METHODS TO MACHINE LEARNING, AND SPECIFICALLY PROGRAMMING BY DEMONSTRATION (PBD). THE EXISTING TECHNOLOGY READINESS LEVEL IS VERY LOW: NO KNOWN VERIFIABLE PBD SYSTEMS HAVE BEEN DEPLOYED AND THE EXISTING RESEARCH IN THE AREA IS LIMITED. THE PHASE I EFFORT WILL RESULT IN PUBLICATIONS AND A PROTOTYPE IMPLEMENTATION OF A VERIFIABLE PBD SYSTEM USING SMT SOLVERS. PHASE II WILL BUILD ON PHASE I PUBLICATIONS AND PROTOTYPES TO DEMONSTRATE INCREASED VERIFICATION CAPABILITIES THROUGH THE APPLICATION OF MORE COMPLEX FORMAL METHODS, SUCH AS MODEL CHECKING. VERIFIABLE MACHINE LEARNING WOULD ALLOW FOR TRAINABLE SYSTEMS THAT MEET CRITICAL PROPERTIES, BUT ARE STILL ADAPTABLE TO SPECIFIC USE CASES. SUCH TOOLS COULD BE RE-PURPOSED FOR MULTIPLE APPLICATIONS WITHOUT INCURRING THE DEVELOPMENT COSTS ASSOCIATED WITH MANUAL AUTOMATION TECHNIQUES TODAY.
Purchase Order - 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
Contractor
GALOIS, INC.
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
02/18/2011
Obligated Amount
$99.9k
NNX10CF50P - COMPLEX SPACE SYSTEMS SUCH AS LUNAR HABITATS GENERATE HUGE AMOUNTS OF DATA. FOR EXAMPLE, THE INTERNATIONAL SPACE STATION (ISS) HAS OVER 250,000 INDIVIDUALLY IDENTIFIED PIECES OF LOW-LEVEL TELEMETRY AND COMMANDS. INNOVATIVE ALGORITHMS FOR COLLECTING AND ANALYZING THIS DATA ARE LEADING TO NEW TECHNOLOGIES FOR MANAGING LARGE, COMPLEX AND DISTRIBUTED SYSTEMS. LUNAR HABITATS WILL HAVE MULTIPLE INTERACTING SUBSYSTEMS THAT GOVERN THEIR BEHAVIOR AND PERFORMANCE. ASSESSING THE HEALTH OF THE DIFFERENT SUBSYSTEMS AND THEIR EFFECT ON THE OVERALL SYSTEM WILL BE CRUCIAL TO EFFECTIVE AND SAFE CONTROL AND OPERATION OF LUNAR HABITATS. THERE ARE THREE COMPLEMENTARY APPROACHES TO DIAGNOSIS, PROGNOSIS, AND RECOVERY: 1) MODEL-BASED APPROACHES THAT RELY ON A PRIORI MODELS OF THE SYSTEMS; 2) DATA-DRIVEN APPROACHES THAT MINE SENSOR AND COMMAND DATA USING MACHINE LEARNING AND STATISTICAL METHODS; AND 3) PROCEDURE-DRIVEN APPROACHES THAT PERFORM SYSTEM TESTS AND BRANCH ON THE RESULTS UNTIL A ROOT CAUSE IS FOUND AND A RECOVERY STRATEGY EXECUTED. WE ARE PROPOSING TO BUILD A COMPREHENSIVE AND INTEGRATED APPROACH TO FAULT DIAGNOSIS, PROGNOSIS AND RECOVERY THAT COMBINES ALL THREE OF THESE APPROACHES EMPHASIZING THEIR STRENGTHS AND NEGATING THEIR WEAKNESSES. THE RESULTING SYSTEM WILL MONITOR SPACECRAFT SYSTEMS, DETECT AND DIAGNOSE FAILURES AND RESPOND TO MITIGATE THOSE FAILURES.
Purchase Order - 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
Contractor
TRACLABS INC.
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/20/2010
Obligated Amount
$99.9k
NNA07CN64A - MACHINE LEARNING METHODS WITH APPLICATIONS TO COMPOSITES AND OTHER MECHANICAL SYSTEMS - THE DEVELOPMENT OF ROBUST DIAGNOSTIC ALGORITHMS AND ANALYSIS TOOLS FOR MONITORING THE ANOMALOUS BEHAVIORS OF COMPOSITE STRUCTURES AND OTHER MECHANICAL SYSTEMS IS PROPOSED. RESEARCH WILL BE CONDUCTED TO CHARACTERIZE THE STATE OF THE SYSTEMS UNDER VARIOUS LOADING AND SERVICE CONDITIONS TO ESTABLISH STATE AWARENESS MODELS. THIS WILL INCLUDE INVESTIGATION OF EXISTING TECHNIQUES AND DEVELOPMENT OF IMPROVED FEATURE EXTRACTION METHODOLOGIES, DATA MINING ROUTINES AND KNOWLEDGE DISCOVER SCHEMES. THE APPLICABILITY OF THE DEVELOPED PROCEDURES WILL BE VALIDATED USING A VARIETY OF EXPERIMENTAL DATA AND TEST ARTICLES. THE RESEARCH OUTCOME WILL LEAD TO THE ESTABLISHMENT OF DETAILED GUIDELINES ON FEATURE SELECTION OF SYSTEM OR SUB-SYSTEM RESPONSES THAT COULD MAXIMIZE THE DESCRIPTION OF ANOMALY OR FAILURE EFFECTS IN THE MOST EFFICIENT WAY AND DEVELOPMENT OF SYSTEM PROGNOSIS METHODOLOGY BASED ON STATE AWARENESS MODELS AND INFRASTRUCTURE MONITORING.
Cooperative Agreement
Contractor
Arizona State University (ARIZONA STATE UNIVERSITY)
Contracting Agency/Office
National Aeronautics and Space Administration»Ames Research Center
Effective date
01/30/2007
Obligated Amount
$95k
NNX07CA22P - TO BE USED NATURALLY IN DESIGN OPTIMIZATION, PARAMETRIC STUDY AND ACHIEVE QUICK TOTAL TIME-TO-SOLUTION, SIMULATION MUST NATURALLY AND PERSONALLY BE AVAILABLE TO THE SCIENTIST/ENGINEER, AS EASILY AS EMAIL OR WORD-PROCESSING. ENVIRONMENTS SUCH AS MATLAB/IDL ALLOW EASE OF USE, BUT UNLESS SIMULATIONS ARE EXTREMELY FAST, THEY CANNOT BE USED NATURALLY. MANY LARGE-SCALE NUMERICAL CALCULATIONS REQUIRE STORAGE AND COMPUTATION THAT GROW AS THE SQUARE/CUBE OF THE NUMBER OF VARIABLES, INCLUDING SUCH LINEAR ALGEBRA OPERATIONS AS SOLVING DENSE LINEAR SYSTEMS, COMPUTING EIGEN-VALUES/VECTORS, AND OTHERS. THE USE OF FAST ALGORITHMS SUCH AS THE FAST MULTIPOLE METHOD (FMM) COUPLED WITH ITERATIVE METHODS ALLOWS MANY PROBLEMS OF INTEREST TO BE SOLVED IN NEAR LINEAR TIME AND MEMORY. WE HAVE TAKEN A LEADERSHIP ROLE IN APPLYING AND EXTENDING THE FMM TO VARIOUS PROBLEMS IN ACOUSTICS, FLUID FLOW, ELECTROMAGNETICS, FUNCTION FITTING AND MACHINE LEARNING. GRAPHICAL PROCESSING UNITS (GPUS) ARE NOW UBIQUITOUS IN GAME CONSOLES, IN WORKSTATIONS AND OTHER DEVICES AND ARE SPECIAL PURPOSE PROCESSORS FOR GRAPHICS, THAT ARE PREDICTED TO SHORTLY ACHIEVE PERFORMANCE IN THE HUNDREDS OF GIGAFLOP RANGE FOR SPECIALIZED CALCULATIONS (MUCH FASTER THAN COTS PCS) AT LOW PRICE POINTS. IT IS CONCEIVABLE NOW TO EQUIP PERSONAL WORKSTATIONS WITH SEVERAL CPUS AND GPUS, AND SOLVE PROBLEMS WITH MILLIONS OR BILLIONS OF VARIABLES QUICKLY USING FAST ALGORITHMS. WE WILL TAKE AN IMPORTANT ALGORITHM WITH WIDE APPLICABILITY: THE FMM, AND IMPLEMENT IT ON THE WIDELY AVAILABLE HETEROGENEOUS CPU/GPU ARCHITECTURE, AND PROVE THE FEASIBILITY OF ACCELERATING IT TREMENDOUSLY. A FUNDAMENTAL RECONSIDERATION OF THE ALGORITHM THAT MAPS APPROPRIATE PIECES ON TO THE CORRECT PART OF THE ARCHITECTURE FORMS THE BASIS OF OUR APPROACH. DEVELOPED SOFTWARE WILL BE TESTED, AND BENCHMARK PROBLEMS SOLVED. A LIBRARY OF SOFTWARE THAT WILL SUPPORT THE PORTING OF THE FMM AND OTHER SCIENTIFIC COMPUTING TO THE CPU/GPU ARCHITECTURE WILL BE DEVELOPED.
Purchase Order - 541710 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
FANTALGO LIMITED LIABILITY COMPANY
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/19/2007
Obligated Amount
$100k

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