Sensor fusion display evaluation using information integration models in enhanced/synthetic vision applications

NASA Technical Reports Server (NTRS)

Foyle, David C.

1993-01-01

Based on existing integration models in the psychological literature, an evaluation framework is developed to assess sensor fusion displays as might be implemented in an enhanced/synthetic vision system. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The pilot's performance with the sensor fusion image is compared to models' predictions based on the pilot's performance when viewing the original component sensor images prior to fusion. This allows for the determination as to when a sensor fusion system leads to: poorer performance than one of the original sensor displays, clearly an undesirable system in which the fused sensor system causes some distortion or interference; better performance than with either single sensor system alone, but at a sub-optimal level compared to model predictions; optimal performance compared to model predictions; or, super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays.

A Demand-Driven Approach for a Multi-Agent System in Supply Chain Management

NASA Astrophysics Data System (ADS)

Kovalchuk, Yevgeniya; Fasli, Maria

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit.

Process for predicting structural performance of mechanical systems

DOEpatents

Gardner, David R.; Hendrickson, Bruce A.; Plimpton, Steven J.; Attaway, Stephen W.; Heinstein, Martin W.; Vaughan, Courtenay T.

1998-01-01

A process for predicting the structural performance of a mechanical system represents the mechanical system by a plurality of surface elements. The surface elements are grouped according to their location in the volume occupied by the mechanical system so that contacts between surface elements can be efficiently located. The process is well suited for efficient practice on multiprocessor computers.

Wind Prediction Accuracy for Air Traffic Management Decision Support Tools

NASA Technical Reports Server (NTRS)

Cole, Rod; Green, Steve; Jardin, Matt; Schwartz, Barry; Benjamin, Stan

2000-01-01

The performance of Air Traffic Management and flight deck decision support tools depends in large part on the accuracy of the supporting 4D trajectory predictions. This is particularly relevant to conflict prediction and active advisories for the resolution of conflicts and the conformance with of traffic-flow management flow-rate constraints (e.g., arrival metering / required time of arrival). Flight test results have indicated that wind prediction errors may represent the largest source of trajectory prediction error. The tests also discovered relatively large errors (e.g., greater than 20 knots), existing in pockets of space and time critical to ATM DST performance (one or more sectors, greater than 20 minutes), are inadequately represented by the classic RMS aggregate prediction-accuracy studies of the past. To facilitate the identification and reduction of DST-critical wind-prediction errors, NASA has lead a collaborative research and development activity with MIT Lincoln Laboratories and the Forecast Systems Lab of the National Oceanographic and Atmospheric Administration (NOAA). This activity, begun in 1996, has focussed on the development of key metrics for ATM DST performance, assessment of wind-prediction skill for state of the art systems, and development/validation of system enhancements to improve skill. A 13 month study was conducted for the Denver Center airspace in 1997. Two complementary wind-prediction systems were analyzed and compared to the forecast performance of the then standard 60 km Rapid Update Cycle - version 1 (RUC-1). One system, developed by NOAA, was the prototype 40-km RUC-2 that became operational at NCEP in 1999. RUC-2 introduced a faster cycle (1 hr vs. 3 hr) and improved mesoscale physics. The second system, Augmented Winds (AW), is a prototype en route wind application developed by MITLL based on the Integrated Terminal Wind System (ITWS). AW is run at a local facility (Center) level, and updates RUC predictions based on an optimal interpolation of the latest ACARS reports since the RUC run. This paper presents an overview of the study's results including the identification and use of new large mor wind-prediction accuracy metrics that are key to ATM DST performance.

Digital Troposcatter Performance Model. Users Manual.

DTIC Science & Technology

1983-11-01

and Information Systems - .,- - - UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (When Data Entered) S REPORT DOCUIAENTATION PAGE READ...Diffraction Multipath Prediction MD-918 Modem Error Rate Prediction AN/TRC-170 Link Analysis 20. ABSTRACT (Continue en reverse esie If neceseay end...configurations used in the Defense Communications System (DCS), and prediction of the performance of both the MD-918 and AN/TRC-170 digital troposcatter modems

Process for predicting structural performance of mechanical systems

DOEpatents

Gardner, D.R.; Hendrickson, B.A.; Plimpton, S.J.; Attaway, S.W.; Heinstein, M.W.; Vaughan, C.T.

1998-05-19

A process for predicting the structural performance of a mechanical system represents the mechanical system by a plurality of surface elements. The surface elements are grouped according to their location in the volume occupied by the mechanical system so that contacts between surface elements can be efficiently located. The process is well suited for efficient practice on multiprocessor computers. 12 figs.

Iowa calibration of MEPDG performance prediction models.

DOT National Transportation Integrated Search

2013-06-01

This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

PubMed

Kiral-Kornek, Isabell; Roy, Subhrajit; Nurse, Ewan; Mashford, Benjamin; Karoly, Philippa; Carroll, Thomas; Payne, Daniel; Saha, Susmita; Baldassano, Steven; O'Brien, Terence; Grayden, David; Cook, Mark; Freestone, Dean; Harrer, Stefan

2018-01-01

Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Development of a Wake Vortex Spacing System for Airport Capacity Enhancement and Delay Reduction

NASA Technical Reports Server (NTRS)

Hinton, David A.; OConnor, Cornelius J.

2000-01-01

The Terminal Area Productivity project has developed the technologies required (weather measurement, wake prediction, and wake measurement) to determine the aircraft spacing needed to prevent wake vortex encounters in various weather conditions. The system performs weather measurements, predicts bounds on wake vortex behavior in those conditions, derives safe wake spacing criteria, and validates the wake predictions with wake vortex measurements. System performance to date indicates that the potential runway arrival rate increase with Aircraft VOrtex Spacing System (AVOSS), considering common path effects and ATC delivery variance, is 5% to 12% depending on the ratio of large and heavy aircraft. The concept demonstration system, using early generation algorithms and minimal optimization, is performing the wake predictions with adequate robustness such that only 4 hard exceedances have been observed in 1235 wake validation cases. This performance demonstrates the feasibility of predicting wake behavior bounds with multiple uncertainties present, including the unknown aircraft weight and speed, weather persistence between the wake prediction and the observations, and the location of the weather sensors several kilometers from the approach location. A concept for the use of the AVOSS system for parallel runway operations has been suggested, and an initial study at the JFK International Airport suggests that a simplified AVOSS system can be successfully operated using only a single lidar as both the weather sensor and the wake validation instrument. Such a selfcontained AVOSS would be suitable for wake separation close to the airport, as is required for parallel approach concepts such as SOIA.

Calibration of PMIS pavement performance prediction models.

DOT National Transportation Integrated Search

2012-02-01

Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

Surface Management System Departure Event Data Analysis

NASA Technical Reports Server (NTRS)

Monroe, Gilena A.

2010-01-01

This paper presents a data analysis of the Surface Management System (SMS) performance of departure events, including push-back and runway departure events.The paper focuses on the detection performance, or the ability to detect departure events, as well as the prediction performance of SMS. The results detail a modest overall detection performance of push-back events and a significantly high overall detection performance of runway departure events. The overall detection performance of SMS for push-back events is approximately 55%.The overall detection performance of SMS for runway departure events nears 100%. This paper also presents the overall SMS prediction performance for runway departure events as well as the timeliness of the Aircraft Situation Display for Industry data source for SMS predictions.

Building a generalized distributed system model

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi; Foudriat, E. C.

1991-01-01

A modeling tool for both analysis and design of distributed systems is discussed. Since many research institutions have access to networks of workstations, the researchers decided to build a tool running on top of the workstations to function as a prototype as well as a distributed simulator for a computing system. The effects of system modeling on performance prediction in distributed systems and the effect of static locking and deadlocks on the performance predictions of distributed transactions are also discussed. While the probability of deadlock is considerably small, its effects on performance could be significant.

Small wind turbine performance evaluation using field test data and a coupled aero-electro-mechanical model

NASA Astrophysics Data System (ADS)

Wallace, Brian D.

A series of field tests and theoretical analyses were performed on various wind turbine rotor designs at two Penn State residential-scale wind-electric facilities. This work involved the prediction and experimental measurement of the electrical and aerodynamic performance of three wind turbines; a 3 kW rated Whisper 175, 2.4 kW rated Skystream 3.7, and the Penn State designed Carolus wind turbine. Both the Skystream and Whisper 175 wind turbines are OEM blades which were originally installed at the facilities. The Carolus rotor is a carbon-fiber composite 2-bladed machine, designed and assembled at Penn State, with the intent of replacing the Whisper 175 rotor at the off-grid system. Rotor aerodynamic performance is modeled using WT_Perf, a National Renewable Energy Laboratory developed Blade Element Momentum theory based performance prediction code. Steady-state power curves are predicted by coupling experimentally determined electrical characteristics with the aerodynamic performance of the rotor simulated with WT_Perf. A dynamometer test stand is used to establish the electromechanical efficiencies of the wind-electric system generator. Through the coupling of WT_Perf and dynamometer test results, an aero-electro-mechanical analysis procedure is developed and provides accurate predictions of wind system performance. The analysis of three different wind turbines gives a comprehensive assessment of the capability of the field test facilities and the accuracy of aero-electro-mechanical analysis procedures. Results from this study show that the Carolus and Whisper 175 rotors are running at higher tip-speed ratios than are optimum for power production. The aero-electro-mechanical analysis predicted the high operating tip-speed ratios of the rotors and was accurate at predicting output power for the systems. It is shown that the wind turbines operate at high tip-speeds because of a miss-match between the aerodynamic drive torque and the operating torque of the wind-system generator. Through the change of load impedance on the wind generator, the research facility has the ability to modify the rotational speed of the wind turbines, allowing the rotors to perform closer to their optimum tip-speed. Comparisons between field test data and performance predictions show that the aero-electro-mechanical analysis was able to predict differences in power production and rotational speed which result from changes in the system load impedance.

Validation of International Space Station Electrical Performance Model via On-orbit Telemetry

NASA Technical Reports Server (NTRS)

Jannette, Anthony G.; Hojnicki, Jeffrey S.; McKissock, David B.; Fincannon, James; Kerslake, Thomas W.; Rodriguez, Carlos D.

2002-01-01

The first U.S. power module on International Space Station (ISS) was activated in December 2000. Comprised of solar arrays, nickel-hydrogen (NiH2) batteries, and a direct current power management and distribution (PMAD) system, the electric power system (EPS) supplies power to housekeeping and user electrical loads. Modeling EPS performance is needed for several reasons, but primarily to assess near-term planned and off-nominal operations and because the EPS configuration changes over the life of the ISS. The System Power Analysis for Capability Evaluation (SPACE) computer code is used to assess the ISS EPS performance. This paper describes the process of validating the SPACE EPS model via ISS on-orbit telemetry. To accomplish this goal, telemetry was first used to correct assumptions and component models in SPACE. Then on-orbit data was directly input to SPACE to facilitate comparing model predictions to telemetry. It will be shown that SPACE accurately predicts on-orbit component and system performance. For example, battery state-of-charge was predicted to within 0.6 percentage points over a 0 to 100 percent scale and solar array current was predicted to within a root mean square (RMS) error of 5.1 Amps out of a typical maximum of 220 Amps. First, SPACE model predictions are compared to telemetry for the ISS EPS components: solar arrays, NiH2 batteries, and the PMAD system. Second, SPACE predictions for the overall performance of the ISS EPS are compared to telemetry and again demonstrate model accuracy.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Shuttle STS-2 mission communication systems RF coverage and performance predictions. Volume 1: Ascent

NASA Technical Reports Server (NTRS)

Porter, J. A.; Gibson, J. S.; Kroll, Q. D.; Loh, Y. C.

1981-01-01

The RF communications capabilities and nominally expected performance for the ascent phase of the second orbital flight of the shuttle are provided. Predicted performance is given mainly in the form of plots of signal strength versus elapsed mission time for the STDN (downlink) and shuttle orbiter (uplink) receivers for the S-band PM and FM, and UHF systems. Performance of the NAV and landing RF systems is treated for RTLS abort, since in this case the spacecraft will loop around and return to the launch site. NAV and landing RF systems include TACAN, MSBLS, and C-band altimeter. Signal strength plots were produced by a computer program which combines the spacecraft trajectory, antenna patterns, transmit and receive performance characteristics, and system mathematical models. When available, measured spacecraft parameters were used in the predictions; otherwise, specified values were used. Specified ground station parameter values were also used. Thresholds and other criteria on the graphs are explained.

A quantitative measure for degree of automation and its relation to system performance and mental load.

PubMed

Wei, Z G; Macwan, A P; Wieringa, P A

1998-06-01

In this paper we quantitatively model degree of automation (DofA) in supervisory control as a function of the number and nature of tasks to be performed by the operator and automation. This model uses a task weighting scheme in which weighting factors are obtained from task demand load, task mental load, and task effect on system performance. The computation of DofA is demonstrated using an experimental system. Based on controlled experiments using operators, analyses of the task effect on system performance, the prediction and assessment of task demand load, and the prediction of mental load were performed. Each experiment had a different DofA. The effect of a change in DofA on system performance and mental load was investigated. It was found that system performance became less sensitive to changes in DofA at higher levels of DofA. The experimental data showed that when the operator controlled a partly automated system, perceived mental load could be predicted from the task mental load for each task component, as calculated by analyzing a situation in which all tasks were manually controlled. Actual or potential applications of this research include a methodology to balance and optimize the automation of complex industrial systems.

Development and in-flight performance of the Mariner 9 spacecraft propulsion system

NASA Technical Reports Server (NTRS)

Evans, D. D.; Cannova, R. D.; Cork, M. J.

1972-01-01

On November 14, 1971, Mariner 9 was decelerated into orbit about Mars by a 1334-newton (300-lbf) liquid bipropellant propulsion system. The development and in-flight performance are described and summarized of this pressure-fed, nitrogen tetroxide/monomethyl hydrazine bipropellant system. The design of all Mariner propulsion subsystems has been predicated upon the premise that simplicity of approach, coupled with thorough qualification and margin-limits testing, is the key to cost-effective reliability. The qualification test program and analytical modeling of the Mariner 9 subsystem are discussed. Since the propulsion subsystem is modular in nature, it was completely checked, serviced, and tested independent of the spacecraft. Proper prediction of in-flight performance required the development of three significant modeling tools to predict and account for nitrogen saturation of the propellant during the six-month coast period and to predict and statistically analyze in-flight data. The flight performance of the subsystem was excellent, as were the performance prediction correlations. These correlations are presented.

Validation and Inter-comparison Against Observations of GODAE Ocean View Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xu, J.; Davidson, F. J. M.; Smith, G. C.; Lu, Y.; Hernandez, F.; Regnier, C.; Drevillon, M.; Ryan, A.; Martin, M.; Spindler, T. D.; Brassington, G. B.; Oke, P. R.

2016-02-01

For weather forecasts, validation of forecast performance is done at the end user level as well as by the meteorological forecast centers. In the development of Ocean Prediction Capacity, the same level of care for ocean forecast performance and validation is needed. Herein we present results from a validation against observations of 6 Global Ocean Forecast Systems under the GODAE OceanView International Collaboration Network. These systems include the Global Ocean Ice Forecast System (GIOPS) developed by the Government of Canada, two systems PSY3 and PSY4 from the French Mercator-Ocean Ocean Forecasting Group, the FOAM system from UK met office, HYCOM-RTOFS from NOAA/NCEP/NWA of USA, and the Australian Bluelink-OceanMAPS system from the CSIRO, the Australian Meteorological Bureau and the Australian Navy.The observation data used in the comparison are sea surface temperature, sub-surface temperature, sub-surface salinity, sea level anomaly, and sea ice total concentration data. Results of the inter-comparison demonstrate forecast performance limits, strengths and weaknesses of each of the six systems. This work establishes validation protocols and routines by which all new prediction systems developed under the CONCEPTS Collaborative Network will be benchmarked prior to approval for operations. This includes anticipated delivery of CONCEPTS regional prediction systems over the next two years including a pan Canadian 1/12th degree resolution ice ocean prediction system and limited area 1/36th degree resolution prediction systems. The validation approach of comparing forecasts to observations at the time and location of the observation is called Class 4 metrics. It has been adopted by major international ocean prediction centers, and will be recommended to JCOMM-WMO as routine validation approach for operational oceanography worldwide.

Results of SEI Independent Research and Development Projects

DTIC Science & Technology

2009-12-01

Achieving Predictable Performance in Multicore Embedded Real - Time Systems Dionisio de Niz, Jeffrey Hansen, Gabriel Moreno, Daniel Plakosh, Jorgen Hanson...Description Languages.‖ Fourth Congress on Embedded Real - Time Systems (ERTS), January 2008. [Hansson 2008b] J. Hansson, P. H. Feiler, & J. Morley...Predictable Performance in Multicore Embedded Real - Time Systems Dionisio de Niz, Jeffrey Hansen, Gabriel Moreno, Daniel Plakosh, Jorgen Hanson, Mark

Performance prediction evaluation of ceramic materials in point-focusing solar receivers

NASA Technical Reports Server (NTRS)

Ewing, J.; Zwissler, J.

1979-01-01

A performance prediction was adapted to evaluate the use of ceramic materials in solar receivers for point focusing distributed applications. System requirements were determined including the receiver operating environment and system operating parameters for various engine types. Preliminary receiver designs were evolved from these system requirements. Specific receiver designs were then evaluated to determine material functional requirements.

Numerical predictions of EML (electromagnetic launcher) system performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schnurr, N.M.; Kerrisk, J.F.; Davidson, R.F.

1987-01-01

The performance of an electromagnetic launcher (EML) depends on a large number of parameters, including the characteristics of the power supply, rail geometry, rail and insulator material properties, injection velocity, and projectile mass. EML system performance is frequently limited by structural or thermal effects in the launcher (railgun). A series of computer codes has been developed at the Los Alamos National Laboratory to predict EML system performance and to determine the structural and thermal constraints on barrel design. These codes include FLD, a two-dimensional electrostatic code used to calculate the high-frequency inductance gradient and surface current density distribution for themore » rails; TOPAZRG, a two-dimensional finite-element code that simultaneously analyzes thermal and electromagnetic diffusion in the rails; and LARGE, a code that predicts the performance of the entire EML system. Trhe NIKE2D code, developed at the Lawrence Livermore National Laboratory, is used to perform structural analyses of the rails. These codes have been instrumental in the design of the Lethality Test System (LTS) at Los Alamos, which has an ultimate goal of accelerating a 30-g projectile to a velocity of 15 km/s. The capabilities of the individual codes and the coupling of these codes to perform a comprehensive analysis is discussed in relation to the LTS design. Numerical predictions are compared with experimental data and presented for the LTS prototype tests.« less

Quantifying effectiveness of failure prediction and response in HPC systems : methodology and example.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayo, Jackson R.; Chen, Frank Xiaoxiao; Pebay, Philippe Pierre

2010-06-01

Effective failure prediction and mitigation strategies in high-performance computing systems could provide huge gains in resilience of tightly coupled large-scale scientific codes. These gains would come from prediction-directed process migration and resource servicing, intelligent resource allocation, and checkpointing driven by failure predictors rather than at regular intervals based on nominal mean time to failure. Given probabilistic associations of outlier behavior in hardware-related metrics with eventual failure in hardware, system software, and/or applications, this paper explores approaches for quantifying the effects of prediction and mitigation strategies and demonstrates these using actual production system data. We describe context-relevant methodologies for determining themore » accuracy and cost-benefit of predictors. While many research studies have quantified the expected impact of growing system size, and the associated shortened mean time to failure (MTTF), on application performance in large-scale high-performance computing (HPC) platforms, there has been little if any work to quantify the possible gains from predicting system resource failures with significant but imperfect accuracy. This possibly stems from HPC system complexity and the fact that, to date, no one has established any good predictors of failure in these systems. Our work in the OVIS project aims to discover these predictors via a variety of data collection techniques and statistical analysis methods that yield probabilistic predictions. The question then is, 'How good or useful are these predictions?' We investigate methods for answering this question in a general setting, and illustrate them using a specific failure predictor discovered on a production system at Sandia.« less

Children's biological responsivity to acute stress predicts concurrent cognitive performance.

PubMed

Roos, Leslie E; Beauchamp, Kathryn G; Giuliano, Ryan; Zalewski, Maureen; Kim, Hyoun K; Fisher, Philip A

2018-04-10

Although prior research has characterized stress system reactivity (i.e. hypothalamic-pituitary-adrenal axis, HPAA; autonomic nervous system, ANS) in children, it has yet to examine the extent to which biological reactivity predicts concurrent goal-directed behavior. Here, we employed a stressor paradigm that allowed concurrent assessment of both stress system reactivity and performance on a speeded-response task to investigate the links between biological reactivity and cognitive function under stress. We further investigated gender as a moderator given previous research suggesting that the ANS may be particularly predictive of behavior in males due to gender differences in socialization. In a sociodemographically diverse sample of young children (N = 58, M age = 5.38 yrs; 44% male), individual differences in sociodemographic covariates (age, household income), HPAA (i.e. cortisol), and ANS (i.e. respiratory sinus arrhythmia, RSA, indexing the parasympathetic branch; pre-ejection period, PEP, indexing the sympathetic branch) function were assessed as predictors of cognitive performance under stress. We hypothesized that higher income, older age, and greater cortisol reactivity would be associated with better performance overall, and flexible ANS responsivity (i.e. RSA withdrawal, PEP shortening) would be predictive of performance for males. Overall, females performed better than males. Two-group SEM analyses suggest that, for males, greater RSA withdrawal to the stressor was associated with better performance, while for females, older age, higher income, and greater cortisol reactivity were associated with better performance. Results highlight the relevance of stress system reactivity to cognitive performance under stress. Future research is needed to further elucidate for whom and in what situations biological reactivity predicts goal-directed behavior.

Small Engine Technology (SET) - Task 13 ANOPP Noise Prediction for Small Engines: Jet Noise Prediction Module, Wing Shielding Module, and System Studies Results

NASA Technical Reports Server (NTRS)

Lieber, Lysbeth; Golub, Robert (Technical Monitor)

2000-01-01

This Final Report has been prepared by AlliedSignal Engines and Systems, Phoenix, Arizona, documenting work performed during the period May 1997 through June 1999, under the Small Engines Technology Program, Contract No. NAS3-27483, Task Order 13, ANOPP Noise Prediction for Small Engines. The report specifically covers the work performed under Subtasks 4, 5 and 6. Subtask 4 describes the application of a semi-empirical procedure for jet noise prediction, subtask 5 describes the development of a procedure to predict the effects of wing shielding, and subtask 6 describes the results of system studies of the benefits of the new noise technology on business and regional aircraft.

Predictability of Brayton electric power system performance

NASA Technical Reports Server (NTRS)

Klann, J. L.; Hettel, H. J.

1972-01-01

Data from the first tests of the 2- to 15-kilowatt space power system in a vacuum chamber were compared with predictions of both a pretest analysis and a modified version of that analysis. The pretest analysis predicted test results with differences of no more than 9 percent of the largest measured value for each quantity. The modified analysis correlated measurements. Differences in conversion efficiency and power output were no greater than plus or minus 2.5 percent. This modified analysis was used to project space performance maps for the current test system.

Predicting the thermal/structural performance of the atmospheric trace molecules spectroscopy /ATMOS/ Fourier transform spectrometer

NASA Technical Reports Server (NTRS)

Miller, J. M.

1980-01-01

ATMOS is a Fourier transform spectrometer to measure atmospheric trace molecules over a spectral range of 2-16 microns. Assessment of the system performance of ATMOS includes evaluations of optical system errors induced by thermal and structural effects. In order to assess the optical system errors induced from thermal and structural effects, error budgets are assembled during system engineering tasks and line of sight and wavefront deformations predictions (using operational thermal and vibration environments and computer models) are subsequently compared to the error budgets. This paper discusses the thermal/structural error budgets, modelling and analysis methods used to predict thermal/structural induced errors and the comparisons that show that predictions are within the error budgets.

An improved predictive functional control method with application to PMSM systems

NASA Astrophysics Data System (ADS)

Li, Shihua; Liu, Huixian; Fu, Wenshu

2017-01-01

In common design of prediction model-based control method, usually disturbances are not considered in the prediction model as well as the control design. For the control systems with large amplitude or strong disturbances, it is difficult to precisely predict the future outputs according to the conventional prediction model, and thus the desired optimal closed-loop performance will be degraded to some extent. To this end, an improved predictive functional control (PFC) method is developed in this paper by embedding disturbance information into the system model. Here, a composite prediction model is thus obtained by embedding the estimated value of disturbances, where disturbance observer (DOB) is employed to estimate the lumped disturbances. So the influence of disturbances on system is taken into account in optimisation procedure. Finally, considering the speed control problem for permanent magnet synchronous motor (PMSM) servo system, a control scheme based on the improved PFC method is designed to ensure an optimal closed-loop performance even in the presence of disturbances. Simulation and experimental results based on a hardware platform are provided to confirm the effectiveness of the proposed algorithm.

Mass Properties for Space Systems Standards Development

NASA Technical Reports Server (NTRS)

Beech, Geoffrey

2013-01-01

Current Verbiage in S-120 Applies to Dry Mass. Mass Margin is difference between Required Mass and Predicted Mass. Performance Margin is difference between Predicted Performance and Required Performance. Performance estimates and corresponding margin should be based on Predicted Mass (and other inputs). Contractor Mass Margin reserved from Performance Margin. Remaining performance margin allocated according to mass partials. Compliance can be evaluated effectively by comparison of three areas (preferably on a single sheet). Basic and Predicted Mass (including historical trend). Aggregate potential changes (threats and opportunities) which gives Mass Forecast. Mass Maturity by category (Estimated/Calculated/Actual).

Genomic Prediction Accounting for Genotype by Environment Interaction Offers an Effective Framework for Breeding Simultaneously for Adaptation to an Abiotic Stress and Performance Under Normal Cropping Conditions in Rice.

PubMed

Ben Hassen, Manel; Bartholomé, Jérôme; Valè, Giampiero; Cao, Tuong-Vi; Ahmadi, Nourollah

2018-05-09

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6% to 4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed. Copyright © 2018, G3: Genes, Genomes, Genetics.

Image processing system performance prediction and product quality evaluation

NASA Technical Reports Server (NTRS)

Stein, E. K.; Hammill, H. B. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A new technique for image processing system performance prediction and product quality evaluation was developed. It was entirely objective, quantitative, and general, and should prove useful in system design and quality control. The technique and its application to determination of quality control procedures for the Earth Resources Technology Satellite NASA Data Processing Facility are described.

Performance Predictions for the Adaptive Optics System at LCRD's Ground Station 1

NASA Technical Reports Server (NTRS)

Roberts, Lewis C., Jr.; Burruss, Rick; Roberts, Jennifer E.; Piazzolla, Sabino; Dew, Sharon; Truong, Tuan; Fregoso, Santos; Page, Norm

2015-01-01

NASA's LCRD mission will lay the foundation for future laser communication systems. We show the design of the Table Mountain ground station's AO system and time series of predicted coupling efficiency.

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

PubMed Central

2014-01-01

Motivation Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. Results We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set. PMID:24646119

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

PubMed

Simha, Ramanuja; Shatkay, Hagit

2014-03-19

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.

Single-pass memory system evaluation for multiprogramming workloads

NASA Technical Reports Server (NTRS)

Conte, Thomas M.; Hwu, Wen-Mei W.

1990-01-01

Modern memory systems are composed of levels of cache memories, a virtual memory system, and a backing store. Varying more than a few design parameters and measuring the performance of such systems has traditionally be constrained by the high cost of simulation. Models of cache performance recently introduced reduce the cost simulation but at the expense of accuracy of performance prediction. Stack-based methods predict performance accurately using one pass over the trace for all cache sizes, but these techniques have been limited to fully-associative organizations. This paper presents a stack-based method of evaluating the performance of cache memories using a recurrence/conflict model for the miss ratio. Unlike previous work, the performance of realistic cache designs, such as direct-mapped caches, are predicted by the method. The method also includes a new approach to the problem of the effects of multiprogramming. This new technique separates the characteristics of the individual program from that of the workload. The recurrence/conflict method is shown to be practical, general, and powerful by comparing its performance to that of a popular traditional cache simulator. The authors expect that the availability of such a tool will have a large impact on future architectural studies of memory systems.

Proposed evaluation framework for assessing operator performance with multisensor displays

NASA Technical Reports Server (NTRS)

Foyle, David C.

1992-01-01

Despite aggressive work on the development of sensor fusion algorithms and techniques, no formal evaluation procedures have been proposed. Based on existing integration models in the literature, an evaluation framework is developed to assess an operator's ability to use multisensor, or sensor fusion, displays. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The operator's performance with the sensor fusion display can be compared to the models' predictions based on the operator's performance when viewing the original sensor displays prior to fusion. This allows for the determination as to when a sensor fusion system leads to: 1) poorer performance than one of the original sensor displays (clearly an undesirable system in which the fused sensor system causes some distortion or interference); 2) better performance than with either single sensor system alone, but at a sub-optimal (compared to the model predictions) level; 3) optimal performance (compared to model predictions); or, 4) super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays. An experiment demonstrating the usefulness of the proposed evaluation framework is discussed.

Evaluating Alerting and Guidance Performance of a UAS Detect-And-Avoid System

NASA Technical Reports Server (NTRS)

Lee, Seung Man; Park, Chunki; Thipphavong, David P.; Isaacson, Douglas R.; Santiago, Confesor

2016-01-01

A key challenge to the routine, safe operation of unmanned aircraft systems (UAS) is the development of detect-and-avoid (DAA) systems to aid the UAS pilot in remaining "well clear" of nearby aircraft. The goal of this study is to investigate the effect of alerting criteria and pilot response delay on the safety and performance of UAS DAA systems in the context of routine civil UAS operations in the National Airspace System (NAS). A NAS-wide fast-time simulation study was conducted to assess UAS DAA system performance with a large number of encounters and a broad set of DAA alerting and guidance system parameters. Three attributes of the DAA system were controlled as independent variables in the study to conduct trade-off analyses: UAS trajectory prediction method (dead-reckoning vs. intent-based), alerting time threshold (related to predicted time to LoWC), and alerting distance threshold (related to predicted Horizontal Miss Distance, or HMD). A set of metrics, such as the percentage of true positive, false positive, and missed alerts, based on signal detection theory and analysis methods utilizing the Receiver Operating Characteristic (ROC) curves were proposed to evaluate the safety and performance of DAA alerting and guidance systems and aid development of DAA system performance standards. The effect of pilot response delay on the performance of DAA systems was evaluated using a DAA alerting and guidance model and a pilot model developed to support this study. A total of 18 fast-time simulations were conducted with nine different DAA alerting threshold settings and two different trajectory prediction methods, using recorded radar traffic from current Visual Flight Rules (VFR) operations, and supplemented with DAA-equipped UAS traffic based on mission profiles modeling future UAS operations. Results indicate DAA alerting distance threshold has a greater effect on DAA system performance than DAA alerting time threshold or ownship trajectory prediction method. Further analysis on the alert lead time (time in advance of predicted loss of well clear at which a DAA alert is first issued) indicated a strong positive correlation between alert lead time and DAA system performance (i.e. the ability of the UAS pilot to maneuver the unmanned aircraft to remain well clear). While bigger distance thresholds had beneficial effects on alert lead time and missed alert rate, it also generated a higher rate of false alerts. In the design and development of DAA alerting and guidance systems, therefore, the positive and negative effects of false alerts and missed alerts should be carefully considered to achieve acceptable alerting system performance by balancing false and missed alerts. The results and methodology presented in this study are expected to help stakeholders, policymakers and standards committees define the appropriate setting of DAA system parameter thresholds for UAS that ensure safety while minimizing operational impacts to the NAS and equipage requirements for its users before DAA operational performance standards can be finalized.

Measuring and Predicting Tag Importance for Image Retrieval.

PubMed

Li, Shangwen; Purushotham, Sanjay; Chen, Chen; Ren, Yuzhuo; Kuo, C-C Jay

2017-12-01

Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.

PubMed

Chen, Fu; Sun, Huiyong; Wang, Junmei; Zhu, Feng; Liu, Hui; Wang, Zhe; Lei, Tailong; Li, Youyong; Hou, Tingjun

2018-06-21

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (ϵ in ). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GBGBn1 model with ϵ in = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GBGBn1 model distinguished the near-native binding structures within the top 10 decoys for 118 out of the 149 protein-RNA systems (79.2%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Performance of the SERI parallel-passage dehumidifer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schlepp, D.; Barlow, R.

1984-09-01

The key component in improving the performance of solar desiccant cooling systems is the dehumidifier. A parallel-passage geometry for the desiccant dehumidifier has been identified as meeting key criteria of low pressure drop, high mass transfer efficiency, and compact size. An experimental program to build and test a small-scale prototype of this design was undertaken in FY 1982, and the results are presented in this report. Computer models to predict the adsorption/desorption behavior of desiccant dehumidifiers were updated to take into account the geometry of the bed and predict potential system performance using the new component design. The parallel-passage designmore » proved to have high mass transfer effectiveness and low pressure drop over a wide range of test conditions typical of desiccant cooling system operation. The prototype dehumidifier averaged 93% effectiveness at pressure drops of less than 50 Pa at design point conditions. Predictions of system performance using models validated with the experimental data indicate that system thermal coefficients of performance (COPs) of 1.0 to 1.2 and electrical COPs above 8.5 are possible using this design.« less

Analyses of ACPL thermal/fluid conditioning system

NASA Technical Reports Server (NTRS)

Stephen, L. A.; Usher, L. H.

1976-01-01

Results of engineering analyses are reported. Initial computations were made using a modified control transfer function where the systems performance was characterized parametrically using an analytical model. The analytical model was revised to represent the latest expansion chamber fluid manifold design, and systems performance predictions were made. Parameters which were independently varied in these computations are listed. Systems predictions which were used to characterize performance are primarily transient computer plots comparing the deviation between average chamber temperature and the chamber temperature requirement. Additional computer plots were prepared. Results of parametric computations with the latest fluid manifold design are included.

Integrating machine learning to achieve an automatic parameter prediction for practical continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Liu, Weiqi; Huang, Peng; Peng, Jinye; Fan, Jianping; Zeng, Guihua

2018-02-01

For supporting practical quantum key distribution (QKD), it is critical to stabilize the physical parameters of signals, e.g., the intensity, phase, and polarization of the laser signals, so that such QKD systems can achieve better performance and practical security. In this paper, an approach is developed by integrating a support vector regression (SVR) model to optimize the performance and practical security of the QKD system. First, a SVR model is learned to precisely predict the time-along evolutions of the physical parameters of signals. Second, such predicted time-along evolutions are employed as feedback to control the QKD system for achieving the optimal performance and practical security. Finally, our proposed approach is exemplified by using the intensity evolution of laser light and a local oscillator pulse in the Gaussian modulated coherent state QKD system. Our experimental results have demonstrated three significant benefits of our SVR-based approach: (1) it can allow the QKD system to achieve optimal performance and practical security, (2) it does not require any additional resources and any real-time monitoring module to support automatic prediction of the time-along evolutions of the physical parameters of signals, and (3) it is applicable to any measurable physical parameter of signals in the practical QKD system.

Deadbeat Predictive Controllers

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1997-01-01

Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

A Method to Predict Compressor Stall in the TF34-100 Turbofan Engine Utilizing Real-Time Performance Data

DTIC Science & Technology

2015-06-01

A METHOD TO PREDICT COMPRESSOR STALL IN THE TF34-100 TURBOFAN ENGINE UTILIZING REAL-TIME PERFORMANCE...THE TF34-100 TURBOFAN ENGINE UTILIZING REAL-TIME PERFORMANCE DATA THESIS Presented to the Faculty Department of Systems Engineering and...036 A METHOD TO PREDICT COMPRESSOR STALL IN THE TF34-100 TURBOFAN ENGINE UTILIZING REAL-TIME PERFORMANCE DATA Shuxiang ‘Albert’ Li, BS

Design and Performance of the Terrestrial Planet Finder Coronagraph

NASA Technical Reports Server (NTRS)

White, Mary L.; Shaklan, Stuart; Lisman, P. Doulas; Ho, Timothy; Mouroulis, Pantazis; Basinger, Scott; Ledeboer, Bill; Kwack, Eug; Kissil, Andy; Mosier, Gary;

Development and in-flight performance of the Mariner 9 spacecraft propulsion system

NASA Technical Reports Server (NTRS)

Evans, D. D.; Cannova, R. D.; Cork, M. J.

1973-01-01

On November 14, 1971, Mariner 9 was decelerated into orbit about Mars by a 1334 N (300 lbf) liquid bipropellant propulsion system. This paper describes and summarizes the development and in-flight performance of this pressure-fed, nitrogen tetroxide/monomethyl hydrazine bipropellant system. The design of all Mariner propulsion subsystems has been predicted upon the premise that simplicity of approach, coupled with thorough qualification and margin-limits testing, is the key to cost-effective reliability. The qualification test program and analytical modeling are also discussed. Since the propulsion subsystem is modular in nature, it was completely checked, serviced, and tested independent of the spacecraft. Proper prediction of in-flight performance required the development of three significant modeling tools to predict and account for nitrogen saturation of the propellant during the six-month coast period and to predict and statistically analyze in-flight data.

A threshold-free summary index of prediction accuracy for censored time to event data.

PubMed

Yuan, Yan; Zhou, Qian M; Li, Bingying; Cai, Hengrui; Chow, Eric J; Armstrong, Gregory T

2018-05-10

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a prespecified future time t 0 . Accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cutoff threshold value that dichotomizes the risk scores. The need for a cutoff value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status and competing risks. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between 2 risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing 2 risk score systems for predicting heart failure in childhood cancer survivors. Copyright © 2018 John Wiley & Sons, Ltd.

Analysis of Aurora's Performance Simulation Engine for Three Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freeman, Janine; Simon, Joseph

2015-07-07

Aurora Solar Inc. is building a cloud-based optimization platform to automate the design, engineering, and permit generation process of solar photovoltaic (PV) installations. They requested that the National Renewable Energy Laboratory (NREL) validate the performance of the PV system performance simulation engine of Aurora Solar’s solar design platform, Aurora. In previous work, NREL performed a validation of multiple other PV modeling tools 1, so this study builds upon that work by examining all of the same fixed-tilt systems with available module datasheets that NREL selected and used in the aforementioned study. Aurora Solar set up these three operating PV systemsmore » in their modeling platform using NREL-provided system specifications and concurrent weather data. NREL then verified the setup of these systems, ran the simulations, and compared the Aurora-predicted performance data to measured performance data for those three systems, as well as to performance data predicted by other PV modeling tools.« less

A correlational approach to predicting operator status

NASA Technical Reports Server (NTRS)

Shingledecker, Clark A.

1988-01-01

This paper discusses a research approach for identifying and validating candidate physiological and behavioral parameters which can be used to predict the performance capabilities of aircrew and other system operators. In this methodology, concurrent and advance correlations are computed between predictor values and criterion performance measures. Continuous performance and sleep loss are used as stressors to promote performance variation. Preliminary data are presented which suggest dependence of prediction capability on the resource allocation policy of the operator.

Calculating Reuse Distance from Source Code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Narayanan, Sri Hari Krishna; Hovland, Paul

The efficient use of a system is of paramount importance in high-performance computing. Applications need to be engineered for future systems even before the architecture of such a system is clearly known. Static performance analysis that generates performance bounds is one way to approach the task of understanding application behavior. Performance bounds provide an upper limit on the performance of an application on a given architecture. Predicting cache hierarchy behavior and accesses to main memory is a requirement for accurate performance bounds. This work presents our static reuse distance algorithm to generate reuse distance histograms. We then use these histogramsmore » to predict cache miss rates. Experimental results for kernels studied show that the approach is accurate.« less

Predictive control and estimation algorithms for the NASA/JPL 70-meter antennas

NASA Technical Reports Server (NTRS)

Gawronski, W.

1991-01-01

A modified output prediction procedure and a new controller design is presented based on the predictive control law. Also, a new predictive estimator is developed to complement the controller and to enhance system performance. The predictive controller is designed and applied to the tracking control of the Deep Space Network 70 m antennas. Simulation results show significant improvement in tracking performance over the linear quadratic controller and estimator presently in use.

Testing a Multi-Stage Screening System: Predicting Performance on Australia's National Achievement Test Using Teachers' Ratings of Academic and Social Behaviors

ERIC Educational Resources Information Center

Kettler, Ryan J.; Elliott, Stephen N.; Davies, Michael; Griffin, Patrick

2012-01-01

This study addresses the predictive validity of results from a screening system of academic enablers, with a sample of Australian elementary school students, when the criterion variable is end-of-year achievement. The investigation included (a) comparing the predictive validity of a brief criterion-referenced nomination system with more…

ILS Localizer Performance Prediction of an Alford 1B Array Near a Limited Access Road at the New Orleans Airport.

DOT National Transportation Integrated Search

1974-08-01

The Transportation Systems Center (TSC) ILS Localizer Performance Prediction Model was used to predict the derogation to an Alford 1B Localizer caused by vehicular traffic traveling on a roadway to be located in front of the localizer. Several differ...

Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.

PubMed

Krepkovich, Eileen T; Perreault, Eric J

2008-01-01

This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.

An Examination of Pennsylvania's Classroom Diagnostic Testing as a Predictive Model of Pennsylvania System of School Assessment Performance

ERIC Educational Resources Information Center

Matsanka, Christopher

2017-01-01

The purpose of this non-experimental quantitative study was to investigate the relationship between Pennsylvania's Classroom Diagnostic Tools (CDT) interim assessments and the state-mandated Pennsylvania System of School Assessment (PSSA) and to create linear regression equations that could be used as models to predict student performance on the…

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

Projecting technology change to improve space technology planning and systems management

NASA Astrophysics Data System (ADS)

Walk, Steven Robert

2011-04-01

Projecting technology performance evolution has been improving over the years. Reliable quantitative forecasting methods have been developed that project the growth, diffusion, and performance of technology in time, including projecting technology substitutions, saturation levels, and performance improvements. These forecasts can be applied at the early stages of space technology planning to better predict available future technology performance, assure the successful selection of technology, and improve technology systems management strategy. Often what is published as a technology forecast is simply scenario planning, usually made by extrapolating current trends into the future, with perhaps some subjective insight added. Typically, the accuracy of such predictions falls rapidly with distance in time. Quantitative technology forecasting (QTF), on the other hand, includes the study of historic data to identify one of or a combination of several recognized universal technology diffusion or substitution patterns. In the same manner that quantitative models of physical phenomena provide excellent predictions of system behavior, so do QTF models provide reliable technological performance trajectories. In practice, a quantitative technology forecast is completed to ascertain with confidence when the projected performance of a technology or system of technologies will occur. Such projections provide reliable time-referenced information when considering cost and performance trade-offs in maintaining, replacing, or migrating a technology, component, or system. This paper introduces various quantitative technology forecasting techniques and illustrates their practical application in space technology and technology systems management.

Retreatment Predictions in Odontology by means of CBR Systems.

PubMed

Campo, Livia; Aliaga, Ignacio J; De Paz, Juan F; García, Alvaro Enrique; Bajo, Javier; Villarubia, Gabriel; Corchado, Juan M

2016-01-01

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising.

Retreatment Predictions in Odontology by means of CBR Systems

PubMed Central

Campo, Livia; Aliaga, Ignacio J.; García, Alvaro Enrique; Villarubia, Gabriel; Corchado, Juan M.

2016-01-01

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising. PMID:26884749

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Lee H. (Inventor)

1989-01-01

The invention is a real-time takeoff and landing performance monitoring system which provides the pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V sub R) within the safe zone of the runway or stopping the aircraft on the runway after landing or take off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. An important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in headwind occurring as the takeoff roll progresses. The system displays the position of the airplane on the runway, indicating runway used and runway available, summarizes the critical information into a situation advisory flag, flags engine failures and off-nominal acceleration performance, and indicates where on the runway particular events such as decision speed (V sub 1), rotation speed (V sub R) and expected stop points will occur based on actual or predicted performance. The display also indicates airspeed, wind vector, engine pressure ratios, second segment climb speed, and balanced field length (BFL). The system detects performance deficiencies by comparing the airplane's present performance with a predicted nominal performance based upon the given conditions.

Turbine blade forced response prediction using FREPS

NASA Technical Reports Server (NTRS)

Murthy, Durbha, V.; Morel, Michael R.

1993-01-01

This paper describes a software system called FREPS (Forced REsponse Prediction System) that integrates structural dynamic, steady and unsteady aerodynamic analyses to efficiently predict the forced response dynamic stresses in axial flow turbomachinery blades due to aerodynamic and mechanical excitations. A flutter analysis capability is also incorporated into the system. The FREPS system performs aeroelastic analysis by modeling the motion of the blade in terms of its normal modes. The structural dynamic analysis is performed by a finite element code such as MSC/NASTRAN. The steady aerodynamic analysis is based on nonlinear potential theory and the unsteady aerodynamic analyses is based on the linearization of the non-uniform potential flow mean. The program description and presentation of the capabilities are reported herein. The effectiveness of the FREPS package is demonstrated on the High Pressure Oxygen Turbopump turbine of the Space Shuttle Main Engine. Both flutter and forced response analyses are performed and typical results are illustrated.

A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

PubMed Central

Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Chen, Yiping; Zhuang, Zhaowen; Cheng, Yongqiang; Deng, Bin; Wang, Liandong; Zeng, Yonghu; Gao, Lei

2014-01-01

This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good. PMID:24967605

Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy

NASA Astrophysics Data System (ADS)

Kurtulus, Bedri; Razack, Moumtaz

2010-02-01

SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.

Predictive sufficiency and the use of stored internal state

NASA Technical Reports Server (NTRS)

Musliner, David J.; Durfee, Edmund H.; Shin, Kang G.

1994-01-01

In all embedded computing systems, some delay exists between sensing and acting. By choosing an action based on sensed data, a system is essentially predicting that there will be no significant changes in the world during this delay. However, the dynamic and uncertain nature of the real world can make these predictions incorrect, and thus, a system may execute inappropriate actions. Making systems more reactive by decreasing the gap between sensing and action leaves less time for predictions to err, but still provides no principled assurance that they will be correct. Using the concept of predictive sufficiency described in this paper, a system can prove that its predictions are valid, and that it will never execute inappropriate actions. In the context of our CIRCA system, we also show how predictive sufficiency allows a system to guarantee worst-case response times to changes in its environment. Using predictive sufficiency, CIRCA is able to build real-time reactive control plans which provide a sound basis for performance guarantees that are unavailable with other reactive systems.

An overview of aerospace gas turbine technology of relevance to the development of the automotive gas turbine engine

NASA Technical Reports Server (NTRS)

Evans, D. G.; Miller, T. J.

1978-01-01

Technology areas related to gas turbine propulsion systems with potential for application to the automotive gas turbine engine are discussed. Areas included are: system steady-state and transient performance prediction techniques, compressor and turbine design and performance prediction programs and effects of geometry, combustor technology and advanced concepts, and ceramic coatings and materials technology.

Performance estimates for the Space Station power system Brayton Cycle compressor and turbine

NASA Technical Reports Server (NTRS)

Cummings, Robert L.

1989-01-01

The methods which have been used by the NASA Lewis Research Center for predicting Brayton Cycle compressor and turbine performance for different gases and flow rates are described. These methods were developed by NASA Lewis during the early days of Brayton cycle component development and they can now be applied to the task of predicting the performance of the Closed Brayton Cycle (CBC) Space Station Freedom power system. Computer programs are given for performing these calculations and data from previous NASA Lewis Brayton Compressor and Turbine tests is used to make accurate estimates of the compressor and turbine performance for the CBC power system. Results of these calculations are also given. In general, calculations confirm that the CBC Brayton Cycle contractor has made realistic compressor and turbine performance estimates.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

PubMed

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Cockpit System Situational Awareness Modeling Tool

NASA Technical Reports Server (NTRS)

Keller, John; Lebiere, Christian; Shay, Rick; Latorella, Kara

2004-01-01

This project explored the possibility of predicting pilot situational awareness (SA) using human performance modeling techniques for the purpose of evaluating developing cockpit systems. The Improved Performance Research Integration Tool (IMPRINT) was combined with the Adaptive Control of Thought-Rational (ACT-R) cognitive modeling architecture to produce a tool that can model both the discrete tasks of pilots and the cognitive processes associated with SA. The techniques for using this tool to predict SA were demonstrated using the newly developed Aviation Weather Information (AWIN) system. By providing an SA prediction tool to cockpit system designers, cockpit concepts can be assessed early in the design process while providing a cost-effective complement to the traditional pilot-in-the-loop experiments and data collection techniques.

Hebbian learning and predictive mirror neurons for actions, sensations and emotions

PubMed Central

Keysers, Christian; Gazzola, Valeria

2014-01-01

Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system. PMID:24778372

Hebbian learning and predictive mirror neurons for actions, sensations and emotions.

PubMed

Keysers, Christian; Gazzola, Valeria

2014-01-01

Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

Development, calibration, and validation of performance prediction models for the Texas M-E flexible pavement design system.

DOT National Transportation Integrated Search

2010-08-01

This study was intended to recommend future directions for the development of TxDOTs Mechanistic-Empirical : (TexME) design system. For stress predictions, a multi-layer linear elastic system was evaluated and its validity was : verified by compar...

Application of propagation predictions to Earth/space telecommunications system design

NASA Technical Reports Server (NTRS)

1981-01-01

The corresponding between a given propagation phenomenon and system performance is considered. Propagation data are related to system performance parameters, allowing the systems engineer to perform the analyses determining how well requirements are met by a given system design, and enabling the systems engineer to modify that design if necessary. The various ways of specifying performance criteria for different kinds of systems are discussed, and a general procedure for system design is presented and demonstrated.

The SAMPL4 host-guest blind prediction challenge: an overview.

PubMed

Muddana, Hari S; Fenley, Andrew T; Mobley, David L; Gilson, Michael K

2014-04-01

Prospective validation of methods for computing binding affinities can help assess their predictive power and thus set reasonable expectations for their performance in drug design applications. Supramolecular host-guest systems are excellent model systems for testing such affinity prediction methods, because their small size and limited conformational flexibility, relative to proteins, allows higher throughput and better numerical convergence. The SAMPL4 prediction challenge therefore included a series of host-guest systems, based on two hosts, cucurbit[7]uril and octa-acid. Binding affinities in aqueous solution were measured experimentally for a total of 23 guest molecules. Participants submitted 35 sets of computational predictions for these host-guest systems, based on methods ranging from simple docking, to extensive free energy simulations, to quantum mechanical calculations. Over half of the predictions provided better correlations with experiment than two simple null models, but most methods underperformed the null models in terms of root mean squared error and linear regression slope. Interestingly, the overall performance across all SAMPL4 submissions was similar to that for the prior SAMPL3 host-guest challenge, although the experimentalists took steps to simplify the current challenge. While some methods performed fairly consistently across both hosts, no single approach emerged as consistent top performer, and the nonsystematic nature of the various submissions made it impossible to draw definitive conclusions regarding the best choices of energy models or sampling algorithms. Salt effects emerged as an issue in the calculation of absolute binding affinities of cucurbit[7]uril-guest systems, but were not expected to affect the relative affinities significantly. Useful directions for future rounds of the challenge might involve encouraging participants to carry out some calculations that replicate each others' studies, and to systematically explore parameter options.

Measured and estimated performance of a fleet of shaded photovoltaic systems with string and module-level inverters

DOE PAGES

MacAlpine, Sara; Deline, Chris; Dobos, Aron

2017-03-16

Shade obstructions can significantly impact the performance of photovoltaic (PV) systems. Although there are many models for partially shaded PV arrays, there is a lack of information available regarding their accuracy and uncertainty when compared with actual field performance. This work assesses the recorded performance of 46 residential PV systems, equipped with either string-level or module-level inverters, under a variety of shading conditions. We compare their energy production data to annual PV performance predictions, with a focus on the practical models developed here for National Renewable Energy Laboratory's system advisor model software. This includes assessment of shade extent on eachmore » PV system by using traditional onsite surveys and newer 3D obstruction modelling. The electrical impact of shade is modelled by either a nonlinear performance model or assumption of linear impact with shade extent, depending on the inverter type. When applied to the fleet of residential PV systems, performance is predicted with median annual bias errors of 2.5% or less, for systems with up to 20% estimated shading loss. The partial shade models are not found to add appreciable uncertainty to annual predictions of energy production for this fleet of systems but do introduce a monthly root-mean-square error of approximately 4%-9% due to seasonal effects. Here the use of a detailed 3D model results in similar or improved accuracy over site survey methods, indicating that, with proper description of shade obstructions, modelling of partially shaded PV arrays can be done completely remotely, potentially saving time and cost.« less

Performance of chromatographic systems to model soil-water sorption.

PubMed

Hidalgo-Rodríguez, Marta; Fuguet, Elisabet; Ràfols, Clara; Rosés, Martí

2012-08-24

A systematic approach for evaluating the goodness of chromatographic systems to model the sorption of neutral organic compounds by soil from water is presented in this work. It is based on the examination of the three sources of error that determine the overall variance obtained when soil-water partition coefficients are correlated against chromatographic retention factors: the variance of the soil-water sorption data, the variance of the chromatographic data, and the variance attributed to the dissimilarity between the two systems. These contributions of variance are easily predicted through the characterization of the systems by the solvation parameter model. According to this method, several chromatographic systems besides the reference octanol-water partition system have been selected to test their performance in the emulation of soil-water sorption. The results from the experimental correlations agree with the predicted variances. The high-performance liquid chromatography system based on an immobilized artificial membrane and the micellar electrokinetic chromatography systems of sodium dodecylsulfate and sodium taurocholate provide the most precise correlation models. They have shown to predict well soil-water sorption coefficients of several tested herbicides. Octanol-water partitions and high-performance liquid chromatography measurements using C18 columns are less suited for the estimation of soil-water partition coefficients. Copyright © 2012 Elsevier B.V. All rights reserved.

Watching novice action degrades expert motor performance: Causation between action production and outcome prediction of observed actions by humans

PubMed Central

Ikegami, Tsuyoshi; Ganesh, Gowrishankar

2014-01-01

Our social skills are critically determined by our ability to understand and appropriately respond to actions performed by others. However despite its obvious importance, the mechanisms enabling action understanding in humans have remained largely unclear. A popular but controversial belief is that parts of the motor system contribute to our ability to understand observed actions. Here, using a novel behavioral paradigm, we investigated this belief by examining a causal relation between action production, and a component of action understanding - outcome prediction, the ability of a person to predict the outcome of observed actions. We asked dart experts to watch novice dart throwers and predict the outcome of their throws. We modulated the feedbacks provided to them, caused a specific improvement in the expert's ability to predict watched actions while controlling the other experimental factors, and exhibited that a change (improvement) in their outcome prediction ability results in a progressive and proportional deterioration in the expert's own darts performance. This causal relationship supports involvement of the motor system in outcome prediction by humans of actions observed in others. PMID:25384755

Satellite voice broadcast. Volume 2: System study

NASA Technical Reports Server (NTRS)

Bachtell, E. E.; Bettadapur, S. S.; Coyner, J. V.; Farrell, C. E.

1985-01-01

The Technical Volume of the Satellite Broadcast System Study is presented. Designs are synthesized for direct sound broadcast satellite systems for HF-, VHF-, L-, and Ku-bands. Methods are developed and used to predict satellite weight, volume, and RF performance for the various concepts considered. Cost and schedule risk assessments are performed to predict time and cost required to implement selected concepts. Technology assessments and tradeoffs are made to identify critical enabling technologies that require development to bring technical risk to acceptable levels for full scale development.

Aptitude and Trait Predictors of Manned and Unmanned Aircraft Pilot Job Performance

DTIC Science & Technology

2016-04-22

actually fly RPAs. To address this gap, the present study evaluated pre-accession trait (Big Five personality domains) and aptitude (spatial...knowledge, and personality traits that predict successful job performance for manned aircraft pilots also predict successful job performance for RPA...aptitude and personality traits , job performance, remotely-piloted aircraft, unmanned aircraft systems 16. SECURITY CLASSIFICATION OF: 17

ILS Glide Slope Performance Prediction Multipath Scattering

DOT National Transportation Integrated Search

1976-12-01

A mathematical model has been developed which predicts the performance of ILS glide slope systems subject to multipath scattering and the effects of irregular terrain contours. The model is discussed in detail and then applied to a test case for purp...

Sun Series program for the REEDA System. [predicting orbital lifetime using sunspot values

NASA Technical Reports Server (NTRS)

Shankle, R. W.

1980-01-01

Modifications made to data bases and to four programs in a series of computer programs (Sun Series) which run on the REEDA HP minicomputer system to aid NASA's solar activity predictions used in orbital life time predictions are described. These programs utilize various mathematical smoothing technique and perform statistical and graphical analysis of various solar activity data bases residing on the REEDA System.

Learning Management System with Prediction Model and Course-Content Recommendation Module

ERIC Educational Resources Information Center

Evale, Digna S.

2017-01-01

Aim/Purpose: This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background: It utilized five-year historical data to find patterns for predicting student performance in Java Programming to generate…

Validation of the Combined Comorbidity Index of Charlson and Elixhauser to Predict 30-Day Mortality Across ICD-9 and ICD-10.

PubMed

Simard, Marc; Sirois, Caroline; Candas, Bernard

2018-05-01

To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems. Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values. Combined index [c-statistics: 0.853 (95% confidence interval: CI, 0.848-0.856)] performed better than original Charlson [0.841 (95% CI, 0.835-0.844)] or Elixhauser [0.841 (95% CI, 0.837-0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven: 0.852, Schneeweiss: 0.851, Charlson: 0.849). Results were consistent across both coding systems. The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.

Modeling and performance assessment in QinetiQ of EO and IR airborne reconnaissance systems

NASA Astrophysics Data System (ADS)

Williams, John W.; Potter, Gary E.

2002-11-01

QinetiQ are the technical authority responsible for specifying the performance requirements for the procurement of airborne reconnaissance systems, on behalf of the UK MoD. They are also responsible for acceptance of delivered systems, overseeing and verifying the installed system performance as predicted and then assessed by the contractor. Measures of functional capability are central to these activities. The conduct of these activities utilises the broad technical insight and wide range of analysis tools and models available within QinetiQ. This paper focuses on the tools, methods and models that are applicable to systems based on EO and IR sensors. The tools, methods and models are described, and representative output for systems that QinetiQ has been responsible for is presented. The principle capability applicable to EO and IR airborne reconnaissance systems is the STAR (Simulation Tools for Airborne Reconnaissance) suite of models. STAR generates predictions of performance measures such as GRD (Ground Resolved Distance) and GIQE (General Image Quality) NIIRS (National Imagery Interpretation Rating Scales). It also generates images representing sensor output, using the scene generation software CAMEO-SIM and the imaging sensor model EMERALD. The simulated image 'quality' is fully correlated with the predicted non-imaging performance measures. STAR also generates image and table data that is compliant with STANAG 7023, which may be used to test ground station functionality.

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

PubMed

Wöhrle, Hendrik; Tabie, Marc; Kim, Su Kyoung; Kirchner, Frank; Kirchner, Elsa Andrea

2017-07-03

A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient's upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

PubMed Central

Wöhrle, Hendrik; Tabie, Marc; Kim, Su Kyoung; Kirchner, Frank; Kirchner, Elsa Andrea

2017-01-01

A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision. PMID:28671632

A Performance Prediction Model for a Fault-Tolerant Computer During Recovery and Restoration

NASA Technical Reports Server (NTRS)

Obando, Rodrigo A.; Stoughton, John W.

1995-01-01

The modeling and design of a fault-tolerant multiprocessor system is addressed. Of interest is the behavior of the system during recovery and restoration after a fault has occurred. The multiprocessor systems are based on the Algorithm to Architecture Mapping Model (ATAMM) and the fault considered is the death of a processor. The developed model is useful in the determination of performance bounds of the system during recovery and restoration. The performance bounds include time to recover from the fault, time to restore the system, and determination of any permanent delay in the input to output latency after the system has regained steady state. Implementation of an ATAMM based computer was developed for a four-processor generic VHSIC spaceborne computer (GVSC) as the target system. A simulation of the GVSC was also written on the code used in the ATAMM Multicomputer Operating System (AMOS). The simulation is used to verify the new model for tracking the propagation of the delay through the system and predicting the behavior of the transient state of recovery and restoration. The model is shown to accurately predict the transient behavior of an ATAMM based multicomputer during recovery and restoration.

Predicting the Overall Spatial Quality of Automotive Audio Systems

NASA Astrophysics Data System (ADS)

Koya, Daisuke

The spatial quality of automotive audio systems is often compromised due to their unideal listening environments. Automotive audio systems need to be developed quickly due to industry demands. A suitable perceptual model could evaluate the spatial quality of automotive audio systems with similar reliability to formal listening tests but take less time. Such a model is developed in this research project by adapting an existing model of spatial quality for automotive audio use. The requirements for the adaptation were investigated in a literature review. A perceptual model called QESTRAL was reviewed, which predicts the overall spatial quality of domestic multichannel audio systems. It was determined that automotive audio systems are likely to be impaired in terms of the spatial attributes that were not considered in developing the QESTRAL model, but metrics are available that might predict these attributes. To establish whether the QESTRAL model in its current form can accurately predict the overall spatial quality of automotive audio systems, MUSHRA listening tests using headphone auralisation with head tracking were conducted to collect results to be compared against predictions by the model. Based on guideline criteria, the model in its current form could not accurately predict the overall spatial quality of automotive audio systems. To improve prediction performance, the QESTRAL model was recalibrated and modified using existing metrics of the model, those that were proposed from the literature review, and newly developed metrics. The most important metrics for predicting the overall spatial quality of automotive audio systems included those that were interaural cross-correlation (IACC) based, relate to localisation of the frontal audio scene, and account for the perceived scene width in front of the listener. Modifying the model for automotive audio systems did not invalidate its use for domestic audio systems. The resulting model predicts the overall spatial quality of 2- and 5-channel automotive audio systems with a cross-validation performance of R. 2 = 0.85 and root-mean-squareerror (RMSE) = 11.03%.

EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART II--OZONE PREDICTIONS. (R825260)

EPA Science Inventory

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectra...

Deriving Points of Departure and Performance Baselines for Predictive Modeling of Systemic Toxicity using ToxRefDB (SOT)

EPA Science Inventory

A primary goal of computational toxicology is to generate predictive models of toxicity. An elusive target of alternative test methods and models has been the accurate prediction of systemic toxicity points of departure (PoD). We aim not only to provide a large and valuable resou...

Acoustic prediction methods for the NASA generalized advanced propeller analysis system (GAPAS)

NASA Technical Reports Server (NTRS)

Padula, S. L.; Block, P. J. W.

1984-01-01

Classical methods of propeller performance analysis are coupled with state-of-the-art Aircraft Noise Prediction Program (ANOPP:) techniques to yield a versatile design tool, the NASA Generalized Advanced Propeller Analysis System (GAPAS) for the novel quiet and efficient propellers. ANOPP is a collection of modular specialized programs. GAPAS as a whole addresses blade geometry and aerodynamics, rotor performance and loading, and subsonic propeller noise.

Tank System Integrated Model: A Cryogenic Tank Performance Prediction Program

NASA Technical Reports Server (NTRS)

Bolshinskiy, L. G.; Hedayat, A.; Hastings, L. J.; Sutherlin, S. G.; Schnell, A. R.; Moder, J. P.

2017-01-01

Accurate predictions of the thermodynamic state of the cryogenic propellants, pressurization rate, and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning for future space exploration missions. This Technical Memorandum (TM) presents the analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, mixing, and condensation on the tank wall. This TM also includes comparisons of TankSIM program predictions with the test data andexamples of multiphase mission calculations.

Performance analysis of the ascent propulsion system of the Apollo spacecraft

NASA Technical Reports Server (NTRS)

Hooper, J. C., III

1973-01-01

Activities involved in the performance analysis of the Apollo lunar module ascent propulsion system are discussed. A description of the ascent propulsion system, including hardware, instrumentation, and system characteristics, is included. The methods used to predict the inflight performance and to establish performance uncertainties of the ascent propulsion system are discussed. The techniques of processing the telemetered flight data and performing postflight performance reconstruction to determine actual inflight performance are discussed. Problems that have been encountered and results from the analysis of the ascent propulsion system performance during the Apollo 9, 10, and 11 missions are presented.

A comparison of SAR ATR performance with information theoretic predictions

NASA Astrophysics Data System (ADS)

Blacknell, David

2003-09-01

Performance assessment of automatic target detection and recognition algorithms for SAR systems (or indeed any other sensors) is essential if the military utility of the system / algorithm mix is to be quantified. This is a relatively straightforward task if extensive trials data from an existing system is used. However, a crucial requirement is to assess the potential performance of novel systems as a guide to procurement decisions. This task is no longer straightforward since a hypothetical system cannot provide experimental trials data. QinetiQ has previously developed a theoretical technique for classification algorithm performance assessment based on information theory. The purpose of the study presented here has been to validate this approach. To this end, experimental SAR imagery of targets has been collected using the QinetiQ Enhanced Surveillance Radar to allow algorithm performance assessments as a number of parameters are varied. In particular, performance comparisons can be made for (i) resolutions up to 0.1m, (ii) single channel versus polarimetric (iii) targets in the open versus targets in scrubland and (iv) use versus non-use of camouflage. The change in performance as these parameters are varied has been quantified from the experimental imagery whilst the information theoretic approach has been used to predict the expected variation of performance with parameter value. A comparison of these measured and predicted assessments has revealed the strengths and weaknesses of the theoretical technique as will be discussed in the paper.

Prototype solar heating and combined heating and cooling systems

NASA Technical Reports Server (NTRS)

1977-01-01

System analysis activities were directed toward refining the heating system parameters. Trade studies were performed to support hardware selections for all systems and for the heating only operational test sites in particular. The heating system qualification tests were supported by predicting qualification test component performance prior to conducting the test.

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Labacqz, J. Victor (Technical Monitor)

1997-01-01

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

Real-time implementation of an interactive jazz accompaniment system

NASA Astrophysics Data System (ADS)

Deshpande, Nikhil

Modern computational algorithms and digital signal processing (DSP) are able to combine with human performers without forced or predetermined structure in order to create dynamic and real-time accompaniment systems. With modern computing power and intelligent algorithm layout and design, it is possible to achieve more detailed auditory analysis of live music. Using this information, computer code can follow and predict how a human's musical performance evolves, and use this to react in a musical manner. This project builds a real-time accompaniment system to perform together with live musicians, with a focus on live jazz performance and improvisation. The system utilizes a new polyphonic pitch detector and embeds it in an Ableton Live system - combined with Max for Live - to perform elements of audio analysis, generation, and triggering. The system also relies on tension curves and information rate calculations from the Creative Artificially Intuitive and Reasoning Agent (CAIRA) system to help understand and predict human improvisation. These metrics are vital to the core system and allow for extrapolated audio analysis. The system is able to react dynamically to a human performer, and can successfully accompany the human as an entire rhythm section.

Model predictive control based on reduced order models applied to belt conveyor system.

PubMed

Chen, Wei; Li, Xin

2016-11-01

In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

NASA Technical Reports Server (NTRS)

Patterson-Hine, Ann; Deb, Somnath; Kulkarni, Deepak; Wang, Yao; Lau, Sonie (Technical Monitor)

1998-01-01

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

NASA Astrophysics Data System (ADS)

Raimalwala, K.; Faragalli, M.; Reid, E.

2018-04-01

The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

ERIC Educational Resources Information Center

Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

2011-01-01

In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

Numerical Modeling of STARx for Ex Situ Soil Remediation

NASA Astrophysics Data System (ADS)

Gerhard, J.; Solinger, R. L.; Grant, G.; Scholes, G.

2016-12-01

Growing stockpiles of contaminated soils contaminated with petroleum hydrocarbons are an outstanding problem worldwide. Self-sustaining Treatment for Active Remediation (STAR) is an emerging technology based on smouldering combustion that has been successfully deployed for in situ remediation. STAR has also been developed for ex situ applications (STARx). This work used a two-dimensional numerical model to systematically explore the sensitivity of ex situ remedial performance to key design and operational parameters. First the model was calibrated and validated against pilot scale experiments, providing confidence that the rate and extent of treatment were correctly predicted. Simulations then investigated sensitivity of remedial performance to injected air flux, contaminant saturation, system configuration, heterogeneity of intrinsic permeability, heterogeneity of contaminant saturation, and system scale. Remedial performance was predicted to be most sensitive to the injected air flux, with higher air fluxes achieving higher treatment rates and remediating larger fractions of the initial contaminant mass. The uniformity of the advancing smouldering front was predicted to be highly dependent on effective permeability contrasts between treated and untreated sections of the contaminant pack. As a result, increased heterogeneity (of intrinsic permeability in particular) is predicted to lower remedial performance. Full-scale systems were predicted to achieve treatment rates an order of magnitude higher than the pilot scale for similar contaminant saturation and injected air flux. This work contributed to the large scale STARx treatment system that is being tested at a field site in Fall 2016.

Performance of Subsurface Tube Drainage System in Saline Soils: A Case Study

NASA Astrophysics Data System (ADS)

Pali, A. K.

2015-06-01

In order to improve the saline and water logged soils caused due to groundwater table rise, installation of subsurface drainage system is considered as one of the best remedies. However, the design of the drainage system has to be accurate so that the field performance results conform to the designed results. In this investigation, the field performance of subsurface tube drainage system installed at the study area was evaluated. The performance was evaluated on the basis of comparison of the designed value of water table drop as 30 cm after 2 days of drainage and predicted and field measured hydraulic heads for a consecutive drainage period of 14 days. The investigation revealed that the actual drop of water table after 2 days of drainage was 25 cm, about 17 % less than the designed value of 30 cm after 2 days of drainage. The comparison of hydraulic heads predicted by Van Schilfgaarde equation of unsteady drainage with the field-measured hydraulic heads showed that the deviation of predicted hydraulic heads varied within a range of ±8 % indicating high acceptability of Van Schlifgaarde equation for designing subsurface drainage system in saline and water logged soils resembling to that of the study area.

Thermoelectrically cooled cloud physics expansion chamber. [systems engineering and performance prediction

NASA Technical Reports Server (NTRS)

Buist, R. J.

1977-01-01

The design and fabrication of a thermoelectric chiller for use in chilling a liquid reservoir is described. Acceptance test results establish the accuracy of the thermal model and predict the unit performance under various conditions required by the overall spacelab program.

Predicting long-term performance of engineered geologic carbon dioxide storage systems to inform decisions amidst uncertainty

NASA Astrophysics Data System (ADS)

Pawar, R.

2016-12-01

Risk assessment and risk management of engineered geologic CO2 storage systems is an area of active investigation. The potential geologic CO2 storage systems currently under consideration are inherently heterogeneous and have limited to no characterization data. Effective risk management decisions to ensure safe, long-term CO2 storage requires assessing and quantifying risks while taking into account the uncertainties in a storage site's characteristics. The key decisions are typically related to definition of area of review, effective monitoring strategy and monitoring duration, potential of leakage and associated impacts, etc. A quantitative methodology for predicting a sequestration site's long-term performance is critical for making key decisions necessary for successful deployment of commercial scale geologic storage projects where projects will require quantitative assessments of potential long-term liabilities. An integrated assessment modeling (IAM) paradigm which treats a geologic CO2 storage site as a system made up of various linked subsystems can be used to predict long-term performance. The subsystems include storage reservoir, seals, potential leakage pathways (such as wellbores, natural fractures/faults) and receptors (such as shallow groundwater aquifers). CO2 movement within each of the subsystems and resulting interactions are captured through reduced order models (ROMs). The ROMs capture the complex physical/chemical interactions resulting due to CO2 movement and interactions but are computationally extremely efficient. The computational efficiency allows for performing Monte Carlo simulations necessary for quantitative probabilistic risk assessment. We have used the IAM to predict long-term performance of geologic CO2 sequestration systems and to answer questions related to probability of leakage of CO2 through wellbores, impact of CO2/brine leakage into shallow aquifer, etc. Answers to such questions are critical in making key risk management decisions. A systematic uncertainty quantification approach can been used to understand how uncertain parameters associated with different subsystems (e.g., reservoir permeability, wellbore cement permeability, wellbore density, etc.) impact the overall site performance predictions.

Predictive Control of Networked Multiagent Systems via Cloud Computing.

PubMed

Liu, Guo-Ping

2017-01-18

This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

Hybrid and Electric Advanced Vehicle Systems Simulation

NASA Technical Reports Server (NTRS)

Beach, R. F.; Hammond, R. A.; Mcgehee, R. K.

1985-01-01

Predefined components connected to represent wide variety of propulsion systems. Hybrid and Electric Advanced Vehicle System (HEAVY) computer program is flexible tool for evaluating performance and cost of electric and hybrid vehicle propulsion systems. Allows designer to quickly, conveniently, and economically predict performance of proposed drive train.

An evaluation of selected (Q)SARs/expert systems for the Prediction of Skin Sensitization Potential (WC10)

EPA Science Inventory

One of the alternative approaches to assessing skin sensitization hazard is through the use of (Q)SARs/expert systems. Here we evaluate the predictive performance of two expert systems (TIMES-SS and VEGA) and two SAR rulebases (OASIS protein binding alerts and Toxtree’s reactivit...

Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.

PubMed

An, Yi; Wang, Jiawei; Li, Chen; Leier, André; Marquez-Lago, Tatiana; Wilksch, Jonathan; Zhang, Yang; Webb, Geoffrey I; Song, Jiangning; Lithgow, Trevor

2018-01-01

Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

System performance predictions for Space Station Freedom's electric power system

NASA Technical Reports Server (NTRS)

Kerslake, Thomas W.; Hojnicki, Jeffrey S.; Green, Robert D.; Follo, Jeffrey C.

1993-01-01

Space Station Freedom Electric Power System (EPS) capability to effectively deliver power to housekeeping and user loads continues to strongly influence Freedom's design and planned approaches for assembly and operations. The EPS design consists of silicon photovoltaic (PV) arrays, nickel-hydrogen batteries, and direct current power management and distribution hardware and cabling. To properly characterize the inherent EPS design capability, detailed system performance analyses must be performed for early stages as well as for the fully assembled station up to 15 years after beginning of life. Such analyses were repeatedly performed using the FORTRAN code SPACE (Station Power Analysis for Capability Evaluation) developed at the NASA Lewis Research Center over a 10-year period. SPACE combines orbital mechanics routines, station orientation/pointing routines, PV array and battery performance models, and a distribution system load-flow analysis to predict EPS performance. Time-dependent, performance degradation, low earth orbit environmental interactions, and EPS architecture build-up are incorporated in SPACE. Results from two typical SPACE analytical cases are presented: (1) an electric load driven case and (2) a maximum EPS capability case.

3D Reacting Flow Analysis of LANTR Nozzles

NASA Astrophysics Data System (ADS)

Stewart, Mark E. M.; Krivanek, Thomas M.; Hemminger, Joseph A.; Bulman, M. J.

2006-01-01

This paper presents performance predictions for LANTR nozzles and the system implications for their use in a manned Mars mission. The LANTR concept is rocket thrust augmentation by injecting Oxygen into the nozzle to combust the Hydrogen exhaust of a Nuclear Thermal Rocket. The performance predictions are based on three-dimensional reacting flow simulations using VULCAN. These simulations explore a range of O2/H2 mixture ratios, injector configurations, and concepts. These performance predictions are used for a trade analysis within a system study for a manned Mars mission. Results indicate that the greatest benefit of LANTR will occur with In-Situ Resource Utilization (ISRU). However, Hydrogen propellant volume reductions may allow greater margins for fitting tanks within the launch vehicle where packaging issues occur.

Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

PubMed

Truong, Lisa; Ouedraogo, Gladys; Pham, LyLy; Clouzeau, Jacques; Loisel-Joubert, Sophie; Blanchet, Delphine; Noçairi, Hicham; Setzer, Woodrow; Judson, Richard; Grulke, Chris; Mansouri, Kamel; Martin, Matthew

2018-02-01

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log 10 to 0.85 log 10  mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log 10  mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log 10  mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log 10  mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for over 30,000 chemicals.

Verification of an analytic modeler for capillary pump loop thermal control systems

NASA Technical Reports Server (NTRS)

Schweickart, R. B.; Neiswanger, L.; Ku, J.

1987-01-01

A number of computer programs have been written to model two-phase heat transfer systems for space use. These programs support the design of thermal control systems and provide a method of predicting their performance in the wide range of thermal environments of space. Predicting the performance of one such system known as the capillary pump loop (CPL) is the intent of the CPL Modeler. By modeling two developed CPL systems and comparing the results with actual test data, the CPL Modeler has proven useful in simulating CPL operation. Results of the modeling effort are discussed, together with plans for refinements to the modeler.

Laboratory evaluation of the pointing stability of the ASPS Vernier System

NASA Technical Reports Server (NTRS)

1980-01-01

The annular suspension and pointing system (ASPS) is an end-mount experiment pointing system designed for use in the space shuttle. The results of the ASPS Vernier System (AVS) pointing stability tests conducted in a laboratory environment are documented. A simulated zero-G suspension was used to support the test payload in the laboratory. The AVS and the suspension were modelled and incorporated into a simulation of the laboratory test. Error sources were identified and pointing stability sensitivities were determined via simulation. Statistical predictions of laboratory test performance were derived and compared to actual laboratory test results. The predicted mean pointing stability during simulated shuttle disturbances was 1.22 arc seconds; the actual mean laboratory test pointing stability was 1.36 arc seconds. The successful prediction of laboratory test results provides increased confidence in the analytical understanding of the AVS magnetic bearing technology and allows confident prediction of in-flight performance. Computer simulations of ASPS, operating in the shuttle disturbance environment, predict in-flight pointing stability errors less than 0.01 arc seconds.

Predicted performance benefits of an adaptive digital engine control system of an F-15 airplane

NASA Technical Reports Server (NTRS)

Burcham, F. W., Jr.; Myers, L. P.; Ray, R. J.

1985-01-01

The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrating engine-airframe control systems. Currently this is accomplished on the NASA Ames Research Center's F-15 airplane. The two control modes used to implement the systems are an integrated flightpath management mode and in integrated adaptive engine control system (ADECS) mode. The ADECS mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the available engine stall margin are continually computed. The excess stall margin is traded for thrust. The predicted increase in engine performance due to the ADECS mode is presented in this report.

The discriminatory capability of existing scores to predict advanced colorectal neoplasia: a prospective colonoscopy study of 5,899 screening participants.

PubMed

Wong, Martin C S; Ching, Jessica Y L; Ng, Simpson; Lam, Thomas Y T; Luk, Arthur K C; Wong, Sunny H; Ng, Siew C; Ng, Simon S M; Wu, Justin C Y; Chan, Francis K L; Sung, Joseph J Y

2016-02-03

We evaluated the performance of seven existing risk scoring systems in predicting advanced colorectal neoplasia in an asymptomatic Chinese cohort. We prospectively recruited 5,899 Chinese subjects aged 50-70 years in a colonoscopy screening programme(2008-2014). Scoring systems under evaluation included two scoring tools from the US; one each from Spain, Germany, and Poland; the Korean Colorectal Screening(KCS) scores; and the modified Asia Pacific Colorectal Screening(APCS) scores. The c-statistics, sensitivity, specificity, positive predictive values(PPVs), and negative predictive values(NPVs) of these systems were evaluated. The resources required were estimated based on the Number Needed to Screen(NNS) and the Number Needed to Refer for colonoscopy(NNR). Advanced neoplasia was detected in 364 (6.2%) subjects. The German system referred the least proportion of subjects (11.2%) for colonoscopy, whilst the KCS scoring system referred the highest (27.4%). The c-statistics of all systems ranged from 0.56-0.65, with sensitivities ranging from 0.04-0.44 and specificities from 0.74-0.99. The modified APCS scoring system had the highest c-statistics (0.65, 95% C.I. 0.58-0.72). The NNS (12-19) and NNR (5-10) were similar among the scoring systems. The existing scoring systems have variable capability to predict advanced neoplasia among asymptomatic Chinese subjects, and further external validation should be performed.

Sensor image prediction techniques

NASA Astrophysics Data System (ADS)

Stenger, A. J.; Stone, W. R.; Berry, L.; Murray, T. J.

1981-02-01

The preparation of prediction imagery is a complex, costly, and time consuming process. Image prediction systems which produce a detailed replica of the image area require the extensive Defense Mapping Agency data base. The purpose of this study was to analyze the use of image predictions in order to determine whether a reduced set of more compact image features contains enough information to produce acceptable navigator performance. A job analysis of the navigator's mission tasks was performed. It showed that the cognitive and perceptual tasks he performs during navigation are identical to those performed for the targeting mission function. In addition, the results of the analysis of his performance when using a particular sensor can be extended to the analysis of this mission tasks using any sensor. An experimental approach was used to determine the relationship between navigator performance and the type of amount of information in the prediction image. A number of subjects were given image predictions containing varying levels of scene detail and different image features, and then asked to identify the predicted targets in corresponding dynamic flight sequences over scenes of cultural, terrain, and mixed (both cultural and terrain) content.

Prediction of wastewater treatment plants performance based on artificial fish school neural network

NASA Astrophysics Data System (ADS)

Zhang, Ruicheng; Li, Chong

2011-10-01

A reliable model for wastewater treatment plant is essential in providing a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, an artificial fish school neural network prediction model is established standing on actual operation data in the wastewater treatment system. The model overcomes several disadvantages of the conventional BP neural network. The results of model calculation show that the predicted value can better match measured value, played an effect on simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provides a simple and practical way for the operation and management in wastewater treatment plant, and has good research and engineering practical value.

Aircraft noise prediction program theoretical manual: Rotorcraft System Noise Prediction System (ROTONET), part 4

NASA Technical Reports Server (NTRS)

Weir, Donald S.; Jumper, Stephen J.; Burley, Casey L.; Golub, Robert A.

1995-01-01

This document describes the theoretical methods used in the rotorcraft noise prediction system (ROTONET), which is a part of the NASA Aircraft Noise Prediction Program (ANOPP). The ANOPP code consists of an executive, database manager, and prediction modules for jet engine, propeller, and rotor noise. The ROTONET subsystem contains modules for the prediction of rotor airloads and performance with momentum theory and prescribed wake aerodynamics, rotor tone noise with compact chordwise and full-surface solutions to the Ffowcs-Williams-Hawkings equations, semiempirical airfoil broadband noise, and turbulence ingestion broadband noise. Flight dynamics, atmosphere propagation, and noise metric calculations are covered in NASA TM-83199, Parts 1, 2, and 3.

On-Line Fringe Tracking and Prediction at IOTA

NASA Technical Reports Server (NTRS)

Wilson, Edward; Mah, Robert; Lau, Sonie (Technical Monitor)

1999-01-01

The Infrared/Optical Telescope Array (IOTA) is a multi-aperture Michelson interferometer located on Mt. Hopkins near Tucson, Arizona. To enable viewing of fainter targets, an on-line fringe tracking system is presently under development at NASA Ames Research Center. The system has been developed off-line using actual data from IOTA, and is presently undergoing on-line implementation at IOTA. The system has two parts: (1) a fringe tracking system that identifies the center of a fringe packet by fitting a parametric model to the data; and (2) a fringe packet motion prediction system that uses characteristics of past fringe packets to predict fringe packet motion. Combined, this information will be used to optimize on-line the scanning trajectory, resulting in improved visibility of faint targets. Fringe packet identification is highly accurate and robust (99% of the 4000 fringe packets were identified correctly, the remaining 1% were either out of the scan range or too noisy to be seen) and is performed in 30-90 milliseconds on a Pentium II-based computer. Fringe packet prediction, currently performed using an adaptive linear predictor, delivers a 10% improvement over the baseline of predicting no motion.

A Bayes network approach to uncertainty quantification in hierarchically developed computational models

DOE PAGES

Urbina, Angel; Mahadevan, Sankaran; Paez, Thomas L.

2012-03-01

Here, performance assessment of complex systems is ideally accomplished through system-level testing, but because they are expensive, such tests are seldom performed. On the other hand, for economic reasons, data from tests on individual components that are parts of complex systems are more readily available. The lack of system-level data leads to a need to build computational models of systems and use them for performance prediction in lieu of experiments. Because their complexity, models are sometimes built in a hierarchical manner, starting with simple components, progressing to collections of components, and finally, to the full system. Quantification of uncertainty inmore » the predicted response of a system model is required in order to establish confidence in the representation of actual system behavior. This paper proposes a framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions. It is based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.). Because epistemic sources of uncertainty were shown to be secondary, in this application, aleatoric only uncertainty is included in the present uncertainty quantification. An example showing application of the techniques to uncertainty quantification of measures of response of a real, complex aerospace system is included.« less

A Performance Prediction Model for a Fault-Tolerant Computer During Recovery and Restoration. Ph.D. Thesis Report, 1 Jan. - 31 Dec. 1992

NASA Technical Reports Server (NTRS)

Stoughton, John W.; Obando, Rodrigo A.

1993-01-01

The modeling and design of a fault-tolerant multiprocessor system is addressed. In particular, the behavior of the system during recovery and restoration after a fault has occurred is investigated. Given that a multicomputer system is designed using the Algorithm to Architecture to Mapping Model (ATAMM), and that a fault (death of a computing resource) occurs during its normal steady-state operation, a model is presented as a viable research tool for predicting the performance bounds of the system during its recovery and restoration phases. Furthermore, the bounds of the performance behavior of the system during this transient mode can be assessed. These bounds include: time to recover from the fault (t(sub rec)), time to restore the system (t(sub rec)) and whether there is a permanent delay in the system's Time Between Input and Output (TBIO) after the system has reached a steady state. An implementation of an ATAMM based computer was developed with the Generic VHSIC Spaceborne Computer (GVSC) as the target system. A simulation of the GVSC was also written based on the code used in ATAMM Multicomputer Operating System (AMOS). The simulation is in turn used to validate the new model in the usefulness and accuracy in tracking the propagation of the delay through the system and predicting the behavior in the transient state of recovery and restoration. The model is validated as an accurate method to predict the transient behavior of an ATAMM based multicomputer during recovery and restoration.

Effect of time step size and turbulence model on the open water hydrodynamic performance prediction of contra-rotating propellers

NASA Astrophysics Data System (ADS)

Wang, Zhan-zhi; Xiong, Ying

2013-04-01

A growing interest has been devoted to the contra-rotating propellers (CRPs) due to their high propulsive efficiency, torque balance, low fuel consumption, low cavitations, low noise performance and low hull vibration. Compared with the single-screw system, it is more difficult for the open water performance prediction because forward and aft propellers interact with each other and generate a more complicated flow field around the CRPs system. The current work focuses on the open water performance prediction of contra-rotating propellers by RANS and sliding mesh method considering the effect of computational time step size and turbulence model. The validation study has been performed on two sets of contra-rotating propellers developed by David W Taylor Naval Ship R & D center. Compared with the experimental data, it shows that RANS with sliding mesh method and SST k-ω turbulence model has a good precision in the open water performance prediction of contra-rotating propellers, and small time step size can improve the level of accuracy for CRPs with the same blade number of forward and aft propellers, while a relatively large time step size is a better choice for CRPs with different blade numbers.

Research and development on performance models of thermal imaging systems

NASA Astrophysics Data System (ADS)

Wang, Ji-hui; Jin, Wei-qi; Wang, Xia; Cheng, Yi-nan

2009-07-01

Traditional ACQUIRE models perform the discrimination tasks of detection (target orientation, recognition and identification) for military target based upon minimum resolvable temperature difference (MRTD) and Johnson criteria for thermal imaging systems (TIS). Johnson criteria is generally pessimistic for performance predict of sampled imager with the development of focal plane array (FPA) detectors and digital image process technology. Triangle orientation discrimination threshold (TOD) model, minimum temperature difference perceived (MTDP)/ thermal range model (TRM3) Model and target task performance (TTP) metric have been developed to predict the performance of sampled imager, especially TTP metric can provides better accuracy than the Johnson criteria. In this paper, the performance models above are described; channel width metrics have been presented to describe the synthesis performance including modulate translate function (MTF) channel width for high signal noise to ration (SNR) optoelectronic imaging systems and MRTD channel width for low SNR TIS; the under resolvable questions for performance assessment of TIS are indicated; last, the development direction of performance models for TIS are discussed.

Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data.

PubMed

Mounce, S R; Shepherd, W; Sailor, G; Shucksmith, J; Saul, A J

2014-01-01

Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.

Design of the Next Generation Aircraft Noise Prediction Program: ANOPP2

NASA Technical Reports Server (NTRS)

Lopes, Leonard V., Dr.; Burley, Casey L.

2011-01-01

The requirements, constraints, and design of NASA's next generation Aircraft NOise Prediction Program (ANOPP2) are introduced. Similar to its predecessor (ANOPP), ANOPP2 provides the U.S. Government with an independent aircraft system noise prediction capability that can be used as a stand-alone program or within larger trade studies that include performance, emissions, and fuel burn. The ANOPP2 framework is designed to facilitate the combination of acoustic approaches of varying fidelity for the analysis of noise from conventional and unconventional aircraft. ANOPP2 integrates noise prediction and propagation methods, including those found in ANOPP, into a unified system that is compatible for use within general aircraft analysis software. The design of the system is described in terms of its functionality and capability to perform predictions accounting for distributed sources, installation effects, and propagation through a non-uniform atmosphere including refraction and the influence of terrain. The philosophy of mixed fidelity noise prediction through the use of nested Ffowcs Williams and Hawkings surfaces is presented and specific issues associated with its implementation are identified. Demonstrations for a conventional twin-aisle and an unconventional hybrid wing body aircraft configuration are presented to show the feasibility and capabilities of the system. Isolated model-scale jet noise predictions are also presented using high-fidelity and reduced order models, further demonstrating ANOPP2's ability to provide predictions for model-scale test configurations.

Performance of Earthquake Early Warning Systems during the Major Events of the 2016-2017 Central Italy Seismic Sequence.

NASA Astrophysics Data System (ADS)

Festa, G.; Picozzi, M.; Alessandro, C.; Colombelli, S.; Cattaneo, M.; Chiaraluce, L.; Elia, L.; Martino, C.; Marzorati, S.; Supino, M.; Zollo, A.

2017-12-01

Earthquake early warning systems (EEWS) are systems nowadays contributing to the seismic risk mitigation actions, both in terms of losses and societal resilience, by issuing an alert promptly after the earthquake origin and before the ground shaking impacts the targets to be protected. EEWS systems can be grouped in two main classes: network based and stand-alone systems. Network based EEWS make use of dense seismic networks surrounding the fault (e.g. Near Fault Observatory; NFO) generating the event. The rapid processing of the P-wave early portion allows for the location and magnitude estimation of the event then used to predict the shaking through ground motion prediction equations. Stand-alone systems instead analyze the early P-wave signal to predict the ground shaking carried by the late S or surface waves, through empirically calibrated scaling relationships, at the recording site itself. We compared the network-based (PRESTo, PRobabilistic and Evolutionary early warning SysTem, www.prestoews.org, Satriano et al., 2011) and the stand-alone (SAVE, on-Site-Alert-leVEl, Caruso et al., 2017) systems, by analyzing their performance during the 2016-2017 Central Italy sequence. We analyzed 9 earthquakes having magnitude 5.0 < M < 6.5 at about 200 stations located within 200 km from the epicentral area, including stations of The Altotiberina NFO (TABOO). Performances are evaluated in terms of rate of success of ground shaking intensity prediction and available lead-time, i.e. the time available for security actions. PRESTo also evaluated the accuracy of location and magnitude. Both systems well predict the ground shaking nearby the event source, with a success rate around 90% within the potential damage zone. The lead-time is significantly larger for the network based system, increasing to more than 10s at 40 km from the event epicentre. The stand-alone system better performs in the near-source region showing a positive albeit small lead-time (<3s). Far away from the source, the performances slightly degrade, mostly owing to uncertain calibration of attenuation relationships. This study opens to the possibility of making EEWS operational in Italy, based on the available acceleration networks, by improving the capability of reducing the lead-time related to data telemetry.

A review and update of the NASA aircraft noise prediction program propeller analysis system

NASA Technical Reports Server (NTRS)

Golub, Robert A.; Nguyen, L. Cathy

1989-01-01

The National Aeronautics and Space Administration (NASA) Aircraft Noise Prediction Program (ANOPP) Propeller Analysis System (PAS) is a set of computational modules for predicting the aerodynamics, performance, and noise of propellers. The ANOPP PAS has the capability to predict noise levels for propeller aircraft certification and produce parametric scaling laws for the adjustment of measured data to reference conditions. A technical overview of the prediction techniques incorporated into the system is presented. The prediction system has been applied to predict the noise signature of a variety of propeller configurations including the effects of propeller angle of attack. A summary of these validation studies is discussed with emphasis being placed on the wind tunnel and flight test programs sponsored by the Federal Aviation Administration (FAA) for the Piper Cherokee Lance aircraft. A number of modifications and improvements have been made to the system and both DEC VAX and IBM-PC versions of the system have been added to the original CDC NOS version.

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Light-Frame Wall Systems: Performance and Predictability.

Treesearch

David S. Gromala

1983-01-01

This paper compares results of all wall tests with analytical predictions of performance.Conventional wood-stud walls of one configuration failed at bending loads that were 4 to 6 times design load.The computer model overpredicted wall strength by and average of 10 percent and deflection by an average of 6 percent.

Performance predictions for a parabolic localizer antenna on Runway 28R - San Francisco Airport.

DOT National Transportation Integrated Search

1973-06-01

The TSC ILS localizer model is used to predict the performance of the Texas Instruments "wide aperture" parabolic antenna as a localizer system for runway 28R at San Francisco Airport. Course derogation caused by the new American Airlines hangar is c...

Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa

2016-10-01

In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

Evaluation of average daily gain predictions by the integrated farm system model for forage-finished beef steers

USDA-ARS?s Scientific Manuscript database

Representing the performance of cattle finished on an all forage diet in process-based whole farm system models has presented a challenge. To address this challenge, a study was done to evaluate average daily gain (ADG) predictions of the Integrated Farm System Model (IFSM) for steers consuming all-...

Analysis of Satellite Communications Antenna Patterns

NASA Technical Reports Server (NTRS)

Rahmat-Samii, Y.

1985-01-01

Computer program accurately and efficiently predicts far-field patterns of offset, or symmetric, parabolic reflector antennas. Antenna designer uses program to study effects of varying geometrical and electrical (RF) parameters of parabolic reflector and its feed system. Accurate predictions of far-field patterns help designer predict overall performance of antenna. These reflectors used extensively in modern communications satellites and in multiple-beam and low side-lobe antenna systems.

Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

NASA Astrophysics Data System (ADS)

Biondi, D.; De Luca, D. L.

2013-02-01

SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty Processor.

Population-centered Risk- and Evidence-based Dental Interprofessional Care Team (PREDICT): study protocol for a randomized controlled trial.

PubMed

Cunha-Cruz, Joana; Milgrom, Peter; Shirtcliff, R Michael; Bailit, Howard L; Huebner, Colleen E; Conrad, Douglas; Ludwig, Sharity; Mitchell, Melissa; Dysert, Jeanne; Allen, Gary; Scott, JoAnna; Mancl, Lloyd

2015-06-20

To improve the oral health of low-income children, innovations in dental delivery systems are needed, including community-based care, the use of expanded duty auxiliary dental personnel, capitation payments, and global budgets. This paper describes the protocol for PREDICT (Population-centered Risk- and Evidence-based Dental Interprofessional Care Team), an evaluation project to test the effectiveness of new delivery and payment systems for improving dental care and oral health. This is a parallel-group cluster randomized controlled trial. Fourteen rural Oregon counties with a publicly insured (Medicaid) population of 82,000 children (0 to 21 years old) and pregnant women served by a managed dental care organization are randomized into test and control counties. In the test intervention (PREDICT), allied dental personnel provide screening and preventive services in community settings and case managers serve as patient navigators to arrange referrals of children who need dentist services. The delivery system intervention is paired with a compensation system for high performance (pay-for-performance) with efficient performance monitoring. PREDICT focuses on the following: 1) identifying eligible children and gaining caregiver consent for services in community settings (for example, schools); 2) providing risk-based preventive and caries stabilization services efficiently at these settings; 3) providing curative care in dental clinics; and 4) incentivizing local delivery teams to meet performance benchmarks. In the control intervention, care is delivered in dental offices without performance incentives. The primary outcome is the prevalence of untreated dental caries. Other outcomes are related to process, structure and cost. Data are collected through patient and staff surveys, clinical examinations, and the review of health and administrative records. If effective, PREDICT is expected to substantially reduce disparities in dental care and oral health. PREDICT can be disseminated to other care organizations as publicly insured clients are increasingly served by large practice organizations. ClinicalTrials.gov NCT02312921 6 December 2014. The Robert Wood Johnson Foundation and Advantage Dental Services, LLC, are supporting the evaluation.

Modeling the prediction of business intelligence system effectiveness.

PubMed

Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

2016-01-01

Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

Bayesian Scoring Systems for Military Pelvic and Perineal Blast Injuries: Is it Time to Take a New Approach?

PubMed

Mossadegh, Somayyeh; He, Shan; Parker, Paul

2016-05-01

Various injury severity scores exist for trauma; it is known that they do not correlate accurately to military injuries. A promising anatomical scoring system for blast pelvic and perineal injury led to the development of an improved scoring system using machine-learning techniques. An unbiased genetic algorithm selected optimal anatomical and physiological parameters from 118 military cases. A Naïve Bayesian model was built using the proposed parameters to predict the probability of survival. Ten-fold cross validation was employed to evaluate its performance. Our model significantly out-performed Injury Severity Score (ISS), Trauma ISS, New ISS, and the Revised Trauma Score in virtually all areas; positive predictive value 0.8941, specificity 0.9027, accuracy 0.9056, and area under curve 0.9059. A two-sample t test showed that the predictive performance of the proposed scoring system was significantly better than the other systems (p < 0.001). With limited resources and the simplest of Bayesian methodologies, we have demonstrated that the Naïve Bayesian model performed significantly better in virtually all areas assessed by current scoring systems used for trauma. This is encouraging and highlights that more can be done to improve trauma systems not only for our military injured, but also for civilian trauma victims. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

Weighted hybrid technique for recommender system

NASA Astrophysics Data System (ADS)

Suriati, S.; Dwiastuti, Meisyarah; Tulus, T.

2017-12-01

Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering. Content-based filtering does not involve opinions from human to make the prediction, while collaborative filtering does, so collaborative filtering can predict more accurately. However, collaborative filtering cannot give prediction to items which have never been rated by any user. In order to cover the drawbacks of each approach with the advantages of other approach, both approaches can be combined with an approach known as hybrid technique. Hybrid technique used in this work is weighted technique in which the prediction score is combination linear of scores gained by techniques that are combined.The purpose of this work is to show how an approach of weighted hybrid technique combining content-based filtering and item-based collaborative filtering can work in a movie recommender system and to show the performance comparison when both approachare combined and when each approach works alone. There are three experiments done in this work, combining both techniques with different parameters. The result shows that the weighted hybrid technique that is done in this work does not really boost the performance up, but it helps to give prediction score for unrated movies that are impossible to be recommended by only using collaborative filtering.

A general computer model for predicting the performance of gas sorption refrigerators

NASA Technical Reports Server (NTRS)

Sigurdson, K. B.

1983-01-01

Projected performance requirements for cryogenic spacecraft sensor cooling systems which demand higher reliability and longer lifetimes are outlined. The gas/solid sorption refrigerator is viewed as a potential solution to cryogenic cooling needs. A software model of an entire gas sorption refrigerator system was developed. The numerical model, evaluates almost any combination and order of refrigerator components and any sorbent-sorbate pair or which the sorption isotherm data are available. Parametric curves for predicting system performance were generated for two types of refrigerators, a LaNi5-H2 absorption cooler and a Charcoal-N2 adsorption cooler. It is found that precooling temperature and heat exchanger effectiveness affect the refrigerator performance. It is indicated that gas sorption refrigerators are feasible for a number of space applications.

The wind power prediction research based on mind evolutionary algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

2018-04-01

When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

Application of System Identification Methods for Decoding Imagined Single-Joint Movements in an Individual with High Tetraplegia

PubMed Central

Ajiboye, A. Bolu; Hochberg, Leigh R.; Donoghue, John P.; Kirsch, Robert F.

2013-01-01

This study investigated the decoding of imagined arm movements from M1 in an individual with high level tetraplegia. The participant was instructed to imagine herself performing a series of single-joint arm movements, aided by the visual cue of an animate character performing these movements. System identification was used offline to predict the trajectories of the imagined movements and compare these predictions to the trajectories of the actual movements. We report rates of 25 – 50% for predicting completely imagined arm movements in the absence of a priori movements to aid in decoder building. PMID:21096197

Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes.

PubMed

Brondeel, Ruben; Pannier, Bruno; Chaix, Basile

2015-12-01

Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests. The RECORD GPS study collected real-life trip data from 236 participants over 7 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of a limited number of participants/trips to predict transportation modes for a large number of trips. The full model had a correct prediction rate of 90%. A simpler model of global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants. This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.

VWPS: A Ventilator Weaning Prediction System with Artificial Intelligence

NASA Astrophysics Data System (ADS)

Chen, Austin H.; Chen, Guan-Ting

How to wean patients efficiently off mechanical ventilation continues to be a challenge for medical professionals. In this paper we have described a novel approach to the study of a ventilator weaning prediction system (VWPS). Firstly, we have developed and written three Artificial Neural Network (ANN) algorithms to predict a weaning successful rate based on the clinical data. Secondly, we have implemented two user-friendly weaning success rate prediction systems; the VWPS system and the BWAP system. Both systems could be used to help doctors objectively and effectively predict whether weaning is appropriate for patients based on the patients' clinical data. Our system utilizes the powerful processing abilities of MatLab. Thirdly, we have calculated the performance through measures such as sensitivity and accuracy for these three algorithms. The results show a very high sensitivity (around 80%) and accuracy (around 70%). To our knowledge, this is the first design approach of its kind to be used in the study of ventilator weaning success rate prediction.

Estimation of Separation Buffers for Wind-Prediction Error in an Airborne Separation Assistance System

NASA Technical Reports Server (NTRS)

Consiglio, Maria C.; Hoadley, Sherwood T.; Allen, B. Danette

2009-01-01

Wind prediction errors are known to affect the performance of automated air traffic management tools that rely on aircraft trajectory predictions. In particular, automated separation assurance tools, planned as part of the NextGen concept of operations, must be designed to account and compensate for the impact of wind prediction errors and other system uncertainties. In this paper we describe a high fidelity batch simulation study designed to estimate the separation distance required to compensate for the effects of wind-prediction errors throughout increasing traffic density on an airborne separation assistance system. These experimental runs are part of the Safety Performance of Airborne Separation experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assurance systems. In this experiment, wind-prediction errors were varied between zero and forty knots while traffic density was increased several times current traffic levels. In order to accurately measure the full unmitigated impact of wind-prediction errors, no uncertainty buffers were added to the separation minima. The goal of the study was to measure the impact of wind-prediction errors in order to estimate the additional separation buffers necessary to preserve separation and to provide a baseline for future analyses. Buffer estimations from this study will be used and verified in upcoming safety evaluation experiments under similar simulation conditions. Results suggest that the strategic airborne separation functions exercised in this experiment can sustain wind prediction errors up to 40kts at current day air traffic density with no additional separation distance buffer and at eight times the current day with no more than a 60% increase in separation distance buffer.

SWAT system performance predictions

NASA Astrophysics Data System (ADS)

Parenti, Ronald R.; Sasiela, Richard J.

1993-03-01

In the next phase of Lincoln Laboratory's SWAT (Short-Wavelength Adaptive Techniques) program, the performance of a 241-actuator adaptive-optics system will be measured using a variety of synthetic-beacon geometries. As an aid in this experimental investigation, a detailed set of theoretical predictions has also been assembled. The computational tools that have been applied in this study include a numerical approach in which Monte-Carlo ray-trace simulations of accumulated phase error are developed, and an analytical analysis of the expected system behavior. This report describes the basis of these two computational techniques and compares their estimates of overall system performance. Although their regions of applicability tend to be complementary rather than redundant, good agreement is usually obtained when both sets of results can be derived for the same engagement scenario.

Economic Evaluation of Observatory Solar-Energy System

NASA Technical Reports Server (NTRS)

1982-01-01

Long-term economic performance of a commercial solar-energy system was analyzed and used to predict economic performance at four additional sites. Analysis described in report was done to demonstrate viability of design over a broad range of environmental/economic conditions. Topics covered are system description, study approach, economic analysis and system optimization.

Accuracy of 'My Gut Feeling:' Comparing System 1 to System 2 Decision-Making for Acuity Prediction, Disposition and Diagnosis in an Academic Emergency Department.

PubMed

Cabrera, Daniel; Thomas, Jonathan F; Wiswell, Jeffrey L; Walston, James M; Anderson, Joel R; Hess, Erik P; Bellolio, M Fernanda

2015-09-01

Current cognitive sciences describe decision-making using the dual-process theory, where a System 1 is intuitive and a System 2 decision is hypothetico-deductive. We aim to compare the performance of these systems in determining patient acuity, disposition and diagnosis. Prospective observational study of emergency physicians assessing patients in the emergency department of an academic center. Physicians were provided the patient's chief complaint and vital signs and allowed to observe the patient briefly. They were then asked to predict acuity, final disposition (home, intensive care unit (ICU), non-ICU bed) and diagnosis. A patient was classified as sick by the investigators using previously published objective criteria. We obtained 662 observations from 289 patients. For acuity, the observers had a sensitivity of 73.9% (95% CI [67.7-79.5%]), specificity 83.3% (95% CI [79.5-86.7%]), positive predictive value 70.3% (95% CI [64.1-75.9%]) and negative predictive value 85.7% (95% CI [82.0-88.9%]). For final disposition, the observers made a correct prediction in 80.8% (95% CI [76.1-85.0%]) of the cases. For ICU admission, emergency physicians had a sensitivity of 33.9% (95% CI [22.1-47.4%]) and a specificity of 96.9% (95% CI [94.0-98.7%]). The correct diagnosis was made 54% of the time with the limited data available. System 1 decision-making based on limited information had a sensitivity close to 80% for acuity and disposition prediction, but the performance was lower for predicting ICU admission and diagnosis. System 1 decision-making appears insufficient for final decisions in these domains but likely provides a cognitive framework for System 2 decision-making.

Predicting healthcare associated infections using patients' experiences

NASA Astrophysics Data System (ADS)

Pratt, Michael A.; Chu, Henry

2016-05-01

Healthcare associated infections (HAI) are a major threat to patient safety and are costly to health systems. Our goal is to predict the HAI performance of a hospital using the patients' experience responses as input. We use four classifiers, viz. random forest, naive Bayes, artificial feedforward neural networks, and the support vector machine, to perform the prediction of six types of HAI. The six types include blood stream, urinary tract, surgical site, and intestinal infections. Experiments show that the random forest and support vector machine perform well across the six types of HAI.

Predicting Team Performance through Human Behavioral Sensing and Quantitative Workflow Instrumentation

DTIC Science & Technology

2016-07-27

make risk-informed decisions during serious games . Statistical models of intra- game performance were developed to determine whether behaviors in...specific facets of the gameplay workflow were predictive of analytical performance and games outcomes. A study of over seventy instrumented teams revealed...more accurate game decisions. 2 Keywords: Humatics · Serious Games · Human-System Interaction · Instrumentation · Teamwork · Communication Analysis

ILS Glide Slope Performance Prediction. Volume B

DTIC Science & Technology

1974-09-01

figures are identical in both volumes. 󈧔. Abottec A mathematical model for predicting the performance of ILS glide slope arrays in the presence of...irregularities on the performance of ILS Glide Slope antenna systems, a mathematical -electromagnetic scattering computer model has been developed. This work was...Antenna ........... 4-4 9. Test Case Results ..................................... r-3 ix PART I. IEO -j 1.INTRODUCTION IA mathematical model has been

An enhanced MMW and SMMW/THz imaging system performance prediction and analysis tool for concealed weapon detection and pilotage obstacle avoidance

NASA Astrophysics Data System (ADS)

Murrill, Steven R.; Jacobs, Eddie L.; Franck, Charmaine C.; Petkie, Douglas T.; De Lucia, Frank C.

2015-10-01

The U.S. Army Research Laboratory (ARL) has continued to develop and enhance a millimeter-wave (MMW) and submillimeter- wave (SMMW)/terahertz (THz)-band imaging system performance prediction and analysis tool for both the detection and identification of concealed weaponry, and for pilotage obstacle avoidance. The details of the MATLAB-based model which accounts for the effects of all critical sensor and display components, for the effects of atmospheric attenuation, concealment material attenuation, and active illumination, were reported on at the 2005 SPIE Europe Security and Defence Symposium (Brugge). An advanced version of the base model that accounts for both the dramatic impact that target and background orientation can have on target observability as related to specular and Lambertian reflections captured by an active-illumination-based imaging system, and for the impact of target and background thermal emission, was reported on at the 2007 SPIE Defense and Security Symposium (Orlando). Further development of this tool that includes a MODTRAN-based atmospheric attenuation calculator and advanced system architecture configuration inputs that allow for straightforward performance analysis of active or passive systems based on scanning (single- or line-array detector element(s)) or staring (focal-plane-array detector elements) imaging architectures was reported on at the 2011 SPIE Europe Security and Defence Symposium (Prague). This paper provides a comprehensive review of a newly enhanced MMW and SMMW/THz imaging system analysis and design tool that now includes an improved noise sub-model for more accurate and reliable performance predictions, the capability to account for postcapture image contrast enhancement, and the capability to account for concealment material backscatter with active-illumination- based systems. Present plans for additional expansion of the model's predictive capabilities are also outlined.

Flight Tests of a Remaining Flying Time Prediction System for Small Electric Aircraft in the Presence of Faults

NASA Technical Reports Server (NTRS)

Hogge, Edward F.; Kulkarni, Chetan S.; Vazquez, Sixto L.; Smalling, Kyle M.; Strom, Thomas H.; Hill, Boyd L.; Quach, Cuong C.

2017-01-01

This paper addresses the problem of building trust in the online prediction of a battery powered aircraft's remaining flying time. A series of flight tests is described that make use of a small electric powered unmanned aerial vehicle (eUAV) to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time is two minutes. This notifies the pilot to transition to the landing phase of the flight. A second alarm is activated when the battery charge falls below a specified limit threshold. This threshold is the point at which the battery energy reserve would no longer safely support two repeated aborted landing attempts. During the test series, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the prediction, half of the tests were performed with, and half were performed without, a simulated powertrain fault. The pilot remotely engages a resistor bank at a specified time during the test flight to simulate a partial powertrain fault. The flying time prediction system is agnostic of the pilot's activation of the fault and must adapt to the vehicle's state. The time at which the limit threshold on battery charge is reached is then used to measure the accuracy of the remaining flying time predictions. Accuracy requirements for the alarms are considered and the results discussed.

Examination of multi-model ensemble seasonal prediction methods using a simple climate system

NASA Astrophysics Data System (ADS)

Kang, In-Sik; Yoo, Jin Ho

2006-02-01

A simple climate model was designed as a proxy for the real climate system, and a number of prediction models were generated by slightly perturbing the physical parameters of the simple model. A set of long (240 years) historical hindcast predictions were performed with various prediction models, which are used to examine various issues of multi-model ensemble seasonal prediction, such as the best ways of blending multi-models and the selection of models. Based on these results, we suggest a feasible way of maximizing the benefit of using multi models in seasonal prediction. In particular, three types of multi-model ensemble prediction systems, i.e., the simple composite, superensemble, and the composite after statistically correcting individual predictions (corrected composite), are examined and compared to each other. The superensemble has more of an overfitting problem than the others, especially for the case of small training samples and/or weak external forcing, and the corrected composite produces the best prediction skill among the multi-model systems.

Body size, performance and fitness in galapagos marine iguanas.

PubMed

Wikelski, Martin; Romero, L Michael

2003-07-01

Complex organismal traits such as body size are influenced by innumerable selective pressures, making the prediction of evolutionary trajectories for those traits difficult. A potentially powerful way to predict fitness in natural systems is to study the composite response of individuals in terms of performance measures, such as foraging or reproductive performance. Once key performance measures are identified in this top-down approach, we can determine the underlying physiological mechanisms and gain predictive power over long-term evolutionary processes. Here we use marine iguanas as a model system where body size differs by more than one order of magnitude between island populations. We identified foraging efficiency as the main performance measure that constrains body size. Mechanistically, foraging performance is determined by food pasture height and the thermal environment, influencing intake and digestion. Stress hormones may be a flexible way of influencing an individual's response to low-food situations that may be caused by high population density, famines, or anthropogenic disturbances like oil spills. Reproductive performance, on the other hand, increases with body size and is mediated by higher survival of larger hatchlings from larger females and increased mating success of larger males. Reproductive performance of males may be adjusted via plastic hormonal feedback mechanisms that allow individuals to assess their social rank annually within the current population size structure. When integrated, these data suggest that reproductive performance favors increased body size (influenced by reproductive hormones), with an overall limit imposed by foraging performance (influenced by stress hormones). Based on our mechanistic understanding of individual performances we predicted an evolutionary increase in maximum body size caused by global warming trends. We support this prediction using specimens collected during 1905. We also show in a common-garden experiment that body size may have a genetic component in iguanids. This 'performance paradigm' allows predictions about adaptive evolution in natural populations.

The robust model predictive control based on mixed H2/H∞ approach with separated performance formulations and its ISpS analysis

NASA Astrophysics Data System (ADS)

Li, Dewei; Li, Jiwei; Xi, Yugeng; Gao, Furong

2017-12-01

In practical applications, systems are always influenced by parameter uncertainties and external disturbance. Both the H2 performance and the H∞ performance are important for the real applications. For a constrained system, the previous designs of mixed H2/H∞ robust model predictive control (RMPC) optimise one performance with the other performance requirement as a constraint. But the two performances cannot be optimised at the same time. In this paper, an improved design of mixed H2/H∞ RMPC for polytopic uncertain systems with external disturbances is proposed to optimise them simultaneously. In the proposed design, the original uncertain system is decomposed into two subsystems by the additive character of linear systems. Two different Lyapunov functions are used to separately formulate the two performance indices for the two subsystems. Then, the proposed RMPC is designed to optimise both the two performances by the weighting method with the satisfaction of the H∞ performance requirement. Meanwhile, to make the design more practical, a simplified design is also developed. The recursive feasible conditions of the proposed RMPC are discussed and the closed-loop input state practical stable is proven. The numerical examples reflect the enlarged feasible region and the improved performance of the proposed design.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane and engine performance deficiencies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a predicted nominal performance based upon given conditions, performance deficiencies are detected by the system.

An empirical analysis of thermal protective performance of fabrics used in protective clothing.

PubMed

Mandal, Sumit; Song, Guowen

2014-10-01

Fabric-based protective clothing is widely used for occupational safety of firefighters/industrial workers. The aim of this paper is to study thermal protective performance provided by fabric systems and to propose an effective model for predicting the thermal protective performance under various thermal exposures. Different fabric systems that are commonly used to manufacture thermal protective clothing were selected. Laboratory simulations of the various thermal exposures were created to evaluate the protective performance of the selected fabric systems in terms of time required to generate second-degree burns. Through the characterization of selected fabric systems in a particular thermal exposure, various factors affecting the performances were statistically analyzed. The key factors for a particular thermal exposure were recognized based on the t-test analysis. Using these key factors, the performance predictive multiple linear regression and artificial neural network (ANN) models were developed and compared. The identified best-fit ANN models provide a basic tool to study thermal protective performance of a fabric. © The Author 2014. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

On predicting monitoring system effectiveness

NASA Astrophysics Data System (ADS)

Cappello, Carlo; Sigurdardottir, Dorotea; Glisic, Branko; Zonta, Daniele; Pozzi, Matteo

2015-03-01

While the objective of structural design is to achieve stability with an appropriate level of reliability, the design of systems for structural health monitoring is performed to identify a configuration that enables acquisition of data with an appropriate level of accuracy in order to understand the performance of a structure or its condition state. However, a rational standardized approach for monitoring system design is not fully available. Hence, when engineers design a monitoring system, their approach is often heuristic with performance evaluation based on experience, rather than on quantitative analysis. In this contribution, we propose a probabilistic model for the estimation of monitoring system effectiveness based on information available in prior condition, i.e. before acquiring empirical data. The presented model is developed considering the analogy between structural design and monitoring system design. We assume that the effectiveness can be evaluated based on the prediction of the posterior variance or covariance matrix of the state parameters, which we assume to be defined in a continuous space. Since the empirical measurements are not available in prior condition, the estimation of the posterior variance or covariance matrix is performed considering the measurements as a stochastic variable. Moreover, the model takes into account the effects of nuisance parameters, which are stochastic parameters that affect the observations but cannot be estimated using monitoring data. Finally, we present an application of the proposed model to a real structure. The results show how the model enables engineers to predict whether a sensor configuration satisfies the required performance.

Synchrophasor-Assisted Prediction of Stability/Instability of a Power System

NASA Astrophysics Data System (ADS)

Saha Roy, Biman Kumar; Sinha, Avinash Kumar; Pradhan, Ashok Kumar

2013-05-01

This paper presents a technique for real-time prediction of stability/instability of a power system based on synchrophasor measurements obtained from phasor measurement units (PMUs) at generator buses. For stability assessment the technique makes use of system severity indices developed using bus voltage magnitude obtained from PMUs and generator electrical power. Generator power is computed using system information and PMU information like voltage and current phasors obtained from PMU. System stability/instability is predicted when the indices exceeds a threshold value. A case study is carried out on New England 10-generator, 39-bus system to validate the performance of the technique.

Neural network based automatic limit prediction and avoidance system and method

NASA Technical Reports Server (NTRS)

Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)

2001-01-01

A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

Thermal Protection System Cavity Heating for Simplified and Actual Geometries Using Computational Fluid Dynamics Simulations with Unstructured Grids

NASA Technical Reports Server (NTRS)

McCloud, Peter L.

2010-01-01

Thermal Protection System (TPS) Cavity Heating is predicted using Computational Fluid Dynamics (CFD) on unstructured grids for both simplified cavities and actual cavity geometries. Validation was performed using comparisons to wind tunnel experimental results and CFD predictions using structured grids. Full-scale predictions were made for simplified and actual geometry configurations on the Space Shuttle Orbiter in a mission support timeframe.

A method of predicting flow rates required to achieve anti-icing performance with a porous leading edge ice protection system

NASA Technical Reports Server (NTRS)

Kohlman, D. L.; Albright, A. E.

1983-01-01

An analytical method was developed for predicting minimum flow rates required to provide anti-ice protection with a porous leading edge fluid ice protection system. The predicted flow rates compare with an average error of less than 10 percent to six experimentally determined flow rates from tests in the NASA Icing Research Tunnel on a general aviation wing section.

A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

NASA Astrophysics Data System (ADS)

ÁLvarez, A.; Orfila, A.; Tintoré, J.

2004-03-01

Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.

System model development for nuclear thermal propulsion

NASA Technical Reports Server (NTRS)

Walton, James T.; Hannan, Nelson A.; Perkins, Ken R.; Buksa, John H.; Worley, Brian A.; Dobranich, Dean

1992-01-01

A critical enabling technology in the evolutionary development of nuclear thermal propulsion (NTP) is the ability to predict the system performance under a variety of operating conditions. This is crucial for mission analysis and for control subsystem testing as well as for the modeling of various failure modes. Performance must be accurately predicted during steady-state and transient operation, including startup, shutdown, and post operation cooling. The development and application of verified and validated system models has the potential to reduce the design, testing, and cost and time required for the technology to reach flight-ready status. Since Oct. 1991, the U.S. Department of Energy (DOE), Department of Defense (DOD), and NASA have initiated critical technology development efforts for NTP systems to be used on Space Exploration Initiative (SEI) missions to the Moon and Mars. This paper presents the strategy and progress of an interagency NASA/DOE/DOD team for NTP system modeling. It is the intent of the interagency team to develop several levels of computer programs to simulate various NTP systems. The first level will provide rapid, parameterized calculations of overall system performance. Succeeding computer programs will provide analysis of each component in sufficient detail to guide the design teams and experimental efforts. The computer programs will allow simulation of the entire system to allow prediction of the integrated performance. An interagency team was formed for this task to use the best capabilities available and to assure appropriate peer review.

Numerical simulation of the cavitation characteristics of a mixed-flow pump

NASA Astrophysics Data System (ADS)

Chen, T.; Li, S. R.; Li, W. Z.; Liu, Y. L.; Wu, D. Z.; Wang, L. Q.

2013-12-01

As a kind of general equipment for fluid transportation, pumps were widely used in industry which includes many applications of high pressure, temperature and toxic fluids transportations. Performances of pumps affect the safety and reliability of the whole special equipment system. Cavitation in pumps cause the loss of performance and erosion of the blade, which could affect the running stability and reliability of the pump system. In this paper, a kind of numerical method for cavitaion performance prediction was presented. In order to investigate the accuracy of the method, CFD flow analysis and cavitation performance predictions of a mixed-flow pump were carried out. The numerical results were compared with the test results.

Flight Evaluation of Center-TRACON Automation System Trajectory Prediction Process

NASA Technical Reports Server (NTRS)

Williams, David H.; Green, Steven M.

1998-01-01

Two flight experiments (Phase 1 in October 1992 and Phase 2 in September 1994) were conducted to evaluate the accuracy of the Center-TRACON Automation System (CTAS) trajectory prediction process. The Transport Systems Research Vehicle (TSRV) Boeing 737 based at Langley Research Center flew 57 arrival trajectories that included cruise and descent segments; at the same time, descent clearance advisories from CTAS were followed. Actual trajectories of the airplane were compared with the trajectories predicted by the CTAS trajectory synthesis algorithms and airplane Flight Management System (FMS). Trajectory prediction accuracy was evaluated over several levels of cockpit automation that ranged from a conventional cockpit to performance-based FMS vertical navigation (VNAV). Error sources and their magnitudes were identified and measured from the flight data. The major source of error during these tests was found to be the predicted winds aloft used by CTAS. The most significant effect related to flight guidance was the cross-track and turn-overshoot errors associated with conventional VOR guidance. FMS lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and airplane performance model errors.

An Investigation of the Factors which Affect the Career Selection Process of Air Force Systems Command Company Grade Officers.

DTIC Science & Technology

1979-12-01

faction, occupational preference, or the desirability of good performance . Proposition 2, as formulated by Vroom , predicts the force to act in a...Human Performance , 9: 482-503 (1973). Lewis, Logan M. "Expectancy Theory as a Predictive Model of Career Intent, Job Satisfaction , and Institution... Satisfaction , Effort, Performance , and Retention of Naval Aviation Officers," Organizational Behavior and Human Performance , 8: 1-20 (1972). 102 and Lee Roy

A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

PubMed

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

NASA Technical Reports Server (NTRS)

Orme, John S.; Gilyard, Glenn B.

1992-01-01

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

Predictive validity of pre-admission assessments on medical student performance.

PubMed

Dabaliz, Al-Awwab; Kaadan, Samy; Dabbagh, M Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef; Mohamed, Ayman

2017-11-24

To examine the predictive validity of pre-admission variables on students' performance in a medical school in Saudi Arabia. In this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis. In preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students' progress test performance (p<0.001 and B=19.02). Only the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years.

Predictive validity of pre-admission assessments on medical student performance

PubMed Central

Dabaliz, Al-Awwab; Kaadan, Samy; Dabbagh, M. Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef

2017-01-01

Objectives To examine the predictive validity of pre-admission variables on students’ performance in a medical school in Saudi Arabia.  Methods In this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis. Results In preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students’ progress test performance (p<0.001 and B=19.02). Conclusions Only the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years. PMID:29176032

Basic performance of a multilayer insulation system containing 20 to 160 layers. [thermal effectiveness of aluminized Mylar-silk net system

NASA Technical Reports Server (NTRS)

Stochl, R. J.

1974-01-01

An experimental investigation was conducted to determine the thermal effectiveness of an aluminized Mylar-silk net insulation system containing up to 160 layers. The experimentally measured heat flux was compared with results predicted by using (1) a previously developed semi-empirical equation and (2) an effective-thermal-conductivity value. All tests were conducted at a nominal hot-boundary temperature of 294 K (530 R) with liquid hydrogen as the heat sink. The experimental results show that the insulation performed as expected and that both the semi-empirical equation and effective thermal conductivity of a small number of layers were adequate in predicting the thermal performance of a large number of layers of insulation.

Robot trajectory tracking with self-tuning predicted control

NASA Technical Reports Server (NTRS)

Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

Multimodel predictive system for carbon dioxide solubility in saline formation waters.

PubMed

Wang, Zan; Small, Mitchell J; Karamalidis, Athanasios K

2013-02-05

The prediction of carbon dioxide solubility in brine at conditions relevant to carbon sequestration (i.e., high temperature, pressure, and salt concentration (T-P-X)) is crucial when this technology is applied. Eleven mathematical models for predicting CO(2) solubility in brine are compared and considered for inclusion in a multimodel predictive system. Model goodness of fit is evaluated over the temperature range 304-433 K, pressure range 74-500 bar, and salt concentration range 0-7 m (NaCl equivalent), using 173 published CO(2) solubility measurements, particularly selected for those conditions. The performance of each model is assessed using various statistical methods, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Different models emerge as best fits for different subranges of the input conditions. A classification tree is generated using machine learning methods to predict the best-performing model under different T-P-X subranges, allowing development of a multimodel predictive system (MMoPS) that selects and applies the model expected to yield the most accurate CO(2) solubility prediction. Statistical analysis of the MMoPS predictions, including a stratified 5-fold cross validation, shows that MMoPS outperforms each individual model and increases the overall accuracy of CO(2) solubility prediction across the range of T-P-X conditions likely to be encountered in carbon sequestration applications.

DEPEND - A design environment for prediction and evaluation of system dependability

NASA Technical Reports Server (NTRS)

Goswami, Kumar K.; Iyer, Ravishankar K.

1990-01-01

The development of DEPEND, an integrated simulation environment for the design and dependability analysis of fault-tolerant systems, is described. DEPEND models both hardware and software components at a functional level, and allows automatic failure injection to assess system performance and reliability. It relieves the user of the work needed to inject failures, maintain statistics, and output reports. The automatic failure injection scheme is geared toward evaluating a system under high stress (workload) conditions. The failures that are injected can affect both hardware and software components. To illustrate the capability of the simulator, a distributed system which employs a prediction-based, dynamic load-balancing heuristic is evaluated. Experiments were conducted to determine the impact of failures on system performance and to identify the failures to which the system is especially susceptible.

Automated Concurrent Blackboard System Generation in C++

NASA Technical Reports Server (NTRS)

Kaplan, J. A.; McManus, J. W.; Bynum, W. L.

1999-01-01

In his 1992 Ph.D. thesis, "Design and Analysis Techniques for Concurrent Blackboard Systems", John McManus defined several performance metrics for concurrent blackboard systems and developed a suite of tools for creating and analyzing such systems. These tools allow a user to analyze a concurrent blackboard system design and predict the performance of the system before any code is written. The design can be modified until simulated performance is satisfactory. Then, the code generator can be invoked to generate automatically all of the code required for the concurrent blackboard system except for the code implementing the functionality of each knowledge source. We have completed the port of the source code generator and a simulator for a concurrent blackboard system. The source code generator generates the necessary C++ source code to implement the concurrent blackboard system using Parallel Virtual Machine (PVM) running on a heterogeneous network of UNIX(trademark) workstations. The concurrent blackboard simulator uses the blackboard specification file to predict the performance of the concurrent blackboard design. The only part of the source code for the concurrent blackboard system that the user must supply is the code implementing the functionality of the knowledge sources.

A hierarchical anatomical classification schema for prediction of phenotypic side effects

PubMed Central

Kanji, Rakesh

2018-01-01

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a ‘hierarchical anatomical classification schema’ which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects. PMID:29494708

A hierarchical anatomical classification schema for prediction of phenotypic side effects.

PubMed

Wadhwa, Somin; Gupta, Aishwarya; Dokania, Shubham; Kanji, Rakesh; Bagler, Ganesh

2018-01-01

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a 'hierarchical anatomical classification schema' which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

Effect of transient liquid flow on retention characteristics of screen acquisition systems. [design of Space Shuttle feed system

NASA Technical Reports Server (NTRS)

Cady, E. C.

1977-01-01

A design analysis, is developed based on experimental data, to predict the effects of transient flow and pressure surges (caused either by valve or pump operation, or by boiling of liquids in warm lines) on the retention performance of screen acquisition systems. A survey of screen liquid acquisition system applications was performed to determine appropriate system environment and classification. A screen model was developed which assumed that the screen device was a uniformly distributed composite orthotropic structure, and which accounted for liquid inflow/outflow, gas ingestion quality, screen stress, and liquid spill. A series of 177 tests using 13 specimens (5 screen meshes, 4 screen device construction/backup methods, and 2 orientations) with three test fluids (isopropyl alcohol, Freon 114, and LH2) provided data which verified important features of the screen model and resulted in a design tool which could accurately predict the transient startup performance acquisition devices.

Performance prediction of a synchronization link for distributed aerospace wireless systems.

PubMed

Wang, Wen-Qin; Shao, Huaizong

2013-01-01

For reasons of stealth and other operational advantages, distributed aerospace wireless systems have received much attention in recent years. In a distributed aerospace wireless system, since the transmitter and receiver placed on separated platforms which use independent master oscillators, there is no cancellation of low-frequency phase noise as in the monostatic cases. Thus, high accurate time and frequency synchronization techniques are required for distributed wireless systems. The use of a dedicated synchronization link to quantify and compensate oscillator frequency instability is investigated in this paper. With the mathematical statistical models of phase noise, closed-form analytic expressions for the synchronization link performance are derived. The possible error contributions including oscillator, phase-locked loop, and receiver noise are quantified. The link synchronization performance is predicted by utilizing the knowledge of the statistical models, system error contributions, and sampling considerations. Simulation results show that effective synchronization error compensation can be achieved by using this dedicated synchronization link.

"Sick" or "not-sick": accuracy of System 1 diagnostic reasoning for the prediction of disposition and acuity in patients presenting to an academic ED.

PubMed

Wiswell, Jeffrey; Tsao, Kenyon; Bellolio, M Fernanda; Hess, Erik P; Cabrera, Daniel

2013-10-01

System 1 decision-making is fast, resource economic, and intuitive (eg, "your gut feeling") and System 2 is slow, resource intensive, and analytic (eg, "hypothetico-deductive"). We evaluated the performance of disposition and acuity prediction by emergency physicians (EPs) using a System 1 decision-making process. We conducted a prospective observational study of attending EPs and emergency medicine residents. Physicians were provided patient demographics, chief complaint, and vital sign data and made two assessments on initial presentation: (1) likely disposition (discharge vs admission) and (2) "sick" vs "not-sick". A patient was adjudicated as sick if he/she had a disease process that was potentially life or limb threatening based on pre-defined operational, financial, or educationally derived criteria. We obtained 266 observations in 178 different patients. Physicians predicted patient disposition with the following performance: sensitivity 87.7% (95% CI 81.4-92.1), specificity 65.0% (95% CI 56.1-72.9), LR+ 2.51 (95% CI 1.95-3.22), LR- 0.19 (95% CI 0.12-0.30). For the sick vs not-sick assessment, providers had the following performance: sensitivity 66.2% (95% CI 55.1-75.8), specificity 88.4% (95% CI 83.0-92.2), LR+ 5.69 (95% CI 3.72-8.69), LR- 0.38 (95% CI 0.28-0.53). EPs are able to accurately predict the disposition of ED patients using system 1 diagnostic reasoning based on minimal available information. However, the prognostic accuracy of acuity prediction was limited. © 2013.

Development of a Simulation Capability for the Space Station Active Rack Isolation System

NASA Technical Reports Server (NTRS)

Johnson, Terry L.; Tolson, Robert H.

1998-01-01

To realize quality microgravity science on the International Space Station, many microgravity facilities will utilize the Active Rack Isolation System (ARIS). Simulation capabilities for ARIS will be needed to predict the microgravity environment. This paper discusses the development of a simulation model for use in predicting the performance of the ARIS in attenuating disturbances with frequency content between 0.01 Hz and 10 Hz. The derivation of the model utilizes an energy-based approach. The complete simulation includes the dynamic model of the ISPR integrated with the model for the ARIS controller so that the entire closed-loop system is simulated. Preliminary performance predictions are made for the ARIS in attenuating both off-board disturbances as well as disturbances from hardware mounted onboard the microgravity facility. These predictions suggest that the ARIS does eliminate resonant behavior detrimental to microgravity experimentation. A limited comparison is made between the simulation predictions of ARIS attenuation of off-board disturbances and results from the ARIS flight test. These comparisons show promise, but further tuning of the simulation is needed.

Predicting low-temperature free energy landscapes with flat-histogram Monte Carlo methods

NASA Astrophysics Data System (ADS)

Mahynski, Nathan A.; Blanco, Marco A.; Errington, Jeffrey R.; Shen, Vincent K.

2017-02-01

We present a method for predicting the free energy landscape of fluids at low temperatures from flat-histogram grand canonical Monte Carlo simulations performed at higher ones. We illustrate our approach for both pure and multicomponent systems using two different sampling methods as a demonstration. This allows us to predict the thermodynamic behavior of systems which undergo both first order and continuous phase transitions upon cooling using simulations performed only at higher temperatures. After surveying a variety of different systems, we identify a range of temperature differences over which the extrapolation of high temperature simulations tends to quantitatively predict the thermodynamic properties of fluids at lower ones. Beyond this range, extrapolation still provides a reasonably well-informed estimate of the free energy landscape; this prediction then requires less computational effort to refine with an additional simulation at the desired temperature than reconstruction of the surface without any initial estimate. In either case, this method significantly increases the computational efficiency of these flat-histogram methods when investigating thermodynamic properties of fluids over a wide range of temperatures. For example, we demonstrate how a binary fluid phase diagram may be quantitatively predicted for many temperatures using only information obtained from a single supercritical state.

Trait impulsivity predicts D-KEFS tower test performance in university students.

PubMed

Lyvers, Michael; Basch, Vanessa; Duff, Helen; Edwards, Mark S

2015-01-01

The present study examined a widely used self-report index of trait impulsiveness in relation to performance on a well-known neuropsychological executive function test in 70 university undergraduate students (50 women, 20 men) aged 18 to 24 years old. Participants completed the Barratt Impulsiveness Scale (BIS-11) and the Frontal Systems Behavior Scale (FrSBe), after which they performed the Tower Test of the Delis-Kaplan Executive Function System. Hierarchical linear regression showed that after controlling for gender, current alcohol consumption, age at onset of weekly alcohol use, and FrSBe scores, BIS-11 significantly predicted Tower Test Achievement scores, β = -.44, p < .01. The results indicate that self-reported impulsiveness is associated with poorer executive cognitive performance even in a sample likely to be characterized by relatively high general cognitive functioning (i.e., university students). The results also support the role of inhibition as a key aspect of executive task performance. Elevated scores on the BIS-11 and FrSBe are known to be linked to risky drinking in young adults as confirmed in this sample; however, only BIS-11 predicted Tower Test performance.

Academic motivation, self-concept, engagement, and performance in high school: key processes from a longitudinal perspective.

PubMed

Green, Jasmine; Liem, Gregory Arief D; Martin, Andrew J; Colmar, Susan; Marsh, Herbert W; McInerney, Dennis

2012-10-01

The study tested three theoretically/conceptually hypothesized longitudinal models of academic processes leading to academic performance. Based on a longitudinal sample of 1866 high-school students across two consecutive years of high school (Time 1 and Time 2), the model with the most superior heuristic value demonstrated: (a) academic motivation and self-concept positively predicted attitudes toward school; (b) attitudes toward school positively predicted class participation and homework completion and negatively predicted absenteeism; and (c) class participation and homework completion positively predicted test performance whilst absenteeism negatively predicted test performance. Taken together, these findings provide support for the relevance of the self-system model and, particularly, the importance of examining the dynamic relationships amongst engagement factors of the model. The study highlights implications for educational and psychological theory, measurement, and intervention. Copyright © 2012 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Initial Progress Rates as Related to Performance in a Personalized System of Instruction

ERIC Educational Resources Information Center

Henneberry, John K.

1976-01-01

Discusses research which explored the hypothesis that students who are fast starters in a personalized system of instruction psychology course would perform better and maintain faster course progress rates than slow starters. Findings indicate that students' starting pace is predictive of course performance and subsequent progress rates.…

Estimation and optimization of thermal performance of evacuated tube solar collector system

NASA Astrophysics Data System (ADS)

Dikmen, Erkan; Ayaz, Mahir; Ezen, H. Hüseyin; Küçüksille, Ecir U.; Şahin, Arzu Şencan

2014-05-01

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy (ANFIS) in order to predict the thermal performance of evacuated tube solar collector system have been used. The experimental data for the training and testing of the networks were used. The results of ANN are compared with ANFIS in which the same data sets are used. The R2-value for the thermal performance values of collector is 0.811914 which can be considered as satisfactory. The results obtained when unknown data were presented to the networks are satisfactory and indicate that the proposed method can successfully be used for the prediction of the thermal performance of evacuated tube solar collectors. In addition, new formulations obtained from ANN are presented for the calculation of the thermal performance. The advantages of this approaches compared to the conventional methods are speed, simplicity, and the capacity of the network to learn from examples. In addition, genetic algorithm (GA) was used to maximize the thermal performance of the system. The optimum working conditions of the system were determined by the GA.

Enhanced pid vs model predictive control applied to bldc motor

NASA Astrophysics Data System (ADS)

Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

2018-01-01

BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

The artificial membrane insert system as predictive tool for formulation performance evaluation.

PubMed

Berben, Philippe; Brouwers, Joachim; Augustijns, Patrick

2018-02-15

In view of the increasing interest of pharmaceutical companies for cell- and tissue-free models to implement permeation into formulation testing, this study explored the capability of an artificial membrane insert system (AMI-system) as predictive tool to evaluate the performance of absorption-enabling formulations. Firstly, to explore the usefulness of the AMI-system in supersaturation assessment, permeation was monitored after induction of different degrees of loviride supersaturation. Secondly, to explore the usefulness of the AMI-system in formulation evaluation, a two-stage dissolution test was performed prior to permeation assessment. Different case examples were selected based on the availability of in vivo (intraluminal and systemic) data: (i) a suspension of posaconazole (Noxafil ® ), (ii) a cyclodextrin-based formulation of itraconazole (Sporanox ® ), and (iii) a micronized (Lipanthyl ® ) and nanosized (Lipanthylnano ® ) formulation of fenofibrate. The obtained results demonstrate that the AMI-system is able to capture the impact of loviride supersaturation on permeation. Furthermore, the AMI-system correctly predicted the effects of (i) formulation pH on posaconazole absorption, (ii) dilution on cyclodextrin-based itraconazole absorption, and (iii) food intake on fenofibrate absorption. Based on the applied in vivo/in vitro approach, the AMI-system combined with simple dissolution testing appears to be a time- and cost-effective tool for the early-stage evaluation of absorption-enabling formulations. Copyright © 2017 Elsevier B.V. All rights reserved.

A predictive pilot model for STOL aircraft landing

NASA Technical Reports Server (NTRS)

Kleinman, D. L.; Killingsworth, W. R.

1974-01-01

An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

PubMed

Miranian, A; Abdollahzade, M

2013-02-01

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

The use of algorithmic behavioural transfer functions in parametric EO system performance models

NASA Astrophysics Data System (ADS)

Hickman, Duncan L.; Smith, Moira I.

2015-10-01

The use of mathematical models to predict the overall performance of an electro-optic (EO) system is well-established as a methodology and is used widely to support requirements definition, system design, and produce performance predictions. Traditionally these models have been based upon cascades of transfer functions based on established physical theory, such as the calculation of signal levels from radiometry equations, as well as the use of statistical models. However, the performance of an EO system is increasing being dominated by the on-board processing of the image data and this automated interpretation of image content is complex in nature and presents significant modelling challenges. Models and simulations of EO systems tend to either involve processing of image data as part of a performance simulation (image-flow) or else a series of mathematical functions that attempt to define the overall system characteristics (parametric). The former approach is generally more accurate but statistically and theoretically weak in terms of specific operational scenarios, and is also time consuming. The latter approach is generally faster but is unable to provide accurate predictions of a system's performance under operational conditions. An alternative and novel architecture is presented in this paper which combines the processing speed attributes of parametric models with the accuracy of image-flow representations in a statistically valid framework. An additional dimension needed to create an effective simulation is a robust software design whose architecture reflects the structure of the EO System and its interfaces. As such, the design of the simulator can be viewed as a software prototype of a new EO System or an abstraction of an existing design. This new approach has been used successfully to model a number of complex military systems and has been shown to combine improved performance estimation with speed of computation. Within the paper details of the approach and architecture are described in detail, and example results based on a practical application are then given which illustrate the performance benefits. Finally, conclusions are drawn and comments given regarding the benefits and uses of the new approach.

Performance of an Automated System for Control of Traffic in Terminal Airspace

NASA Technical Reports Server (NTRS)

Nikoleris, Tasos; Erzberger, Heinz; Paielli, Russell A.; Chu, Yung-Cheng

2016-01-01

This paper examines the performance of a system that performs automated conflict resolution and arrival scheduling for aircraft in the terminal airspace around major airports. Such a system has the potential to perform separation assurance and arrival sequencing tasks that are currently handled manually by human controllers. The performance of the system is tested against several simulated traffic scenarios that are characterized by the rate at which air traffic is metered into the terminal airspace. For each traffic scenario, the levels of performance that are examined include: number of conflicts predicted to occur, types of resolution maneuver used to resolve predicted conflicts, and the amount of delay for all flights. The simulation results indicate that the percentage of arrivals that required a maneuver that changes the flight's horizontal route ranged between 11% and 15% in all traffic scenarios. That finding has certain implications if this automated system were to be implemented simply as a decision support tool. It is also found that arrival delay due to purely wake vortex separation requirements on final approach constituted only between 29% and 35% of total arrival delay, while the remaining major portion of it is mainly due to delay back propagation effects.

Preschoolers' precision of the approximate number system predicts later school mathematics performance.

PubMed

Mazzocco, Michèle M M; Feigenson, Lisa; Halberda, Justin

2011-01-01

The Approximate Number System (ANS) is a primitive mental system of nonverbal representations that supports an intuitive sense of number in human adults, children, infants, and other animal species. The numerical approximations produced by the ANS are characteristically imprecise and, in humans, this precision gradually improves from infancy to adulthood. Throughout development, wide ranging individual differences in ANS precision are evident within age groups. These individual differences have been linked to formal mathematics outcomes, based on concurrent, retrospective, or short-term longitudinal correlations observed during the school age years. However, it remains unknown whether this approximate number sense actually serves as a foundation for these school mathematics abilities. Here we show that ANS precision measured at preschool, prior to formal instruction in mathematics, selectively predicts performance on school mathematics at 6 years of age. In contrast, ANS precision does not predict non-numerical cognitive abilities. To our knowledge, these results provide the first evidence for early ANS precision, measured before the onset of formal education, predicting later mathematical abilities.

Preschoolers' Precision of the Approximate Number System Predicts Later School Mathematics Performance

PubMed Central

Mazzocco, Michèle M. M.; Feigenson, Lisa; Halberda, Justin

2011-01-01

The Approximate Number System (ANS) is a primitive mental system of nonverbal representations that supports an intuitive sense of number in human adults, children, infants, and other animal species. The numerical approximations produced by the ANS are characteristically imprecise and, in humans, this precision gradually improves from infancy to adulthood. Throughout development, wide ranging individual differences in ANS precision are evident within age groups. These individual differences have been linked to formal mathematics outcomes, based on concurrent, retrospective, or short-term longitudinal correlations observed during the school age years. However, it remains unknown whether this approximate number sense actually serves as a foundation for these school mathematics abilities. Here we show that ANS precision measured at preschool, prior to formal instruction in mathematics, selectively predicts performance on school mathematics at 6 years of age. In contrast, ANS precision does not predict non-numerical cognitive abilities. To our knowledge, these results provide the first evidence for early ANS precision, measured before the onset of formal education, predicting later mathematical abilities. PMID:21935362

A Statistical Evaluation of the Diagnostic Performance of MEDAS-The Medical Emergency Decision Assistance System

PubMed Central

Georgakis, D. Christine; Trace, David A.; Naeymi-Rad, Frank; Evens, Martha

1990-01-01

Medical expert systems require comprehensive evaluation of their diagnostic accuracy. The usefulness of these systems is limited without established evaluation methods. We propose a new methodology for evaluating the diagnostic accuracy and the predictive capacity of a medical expert system. We have adapted to the medical domain measures that have been used in the social sciences to examine the performance of human experts in the decision making process. Thus, in addition to the standard summary measures, we use measures of agreement and disagreement, and Goodman and Kruskal's λ and τ measures of predictive association. This methodology is illustrated by a detailed retrospective evaluation of the diagnostic accuracy of the MEDAS system. In a study using 270 patients admitted to the North Chicago Veterans Administration Hospital, diagnoses produced by MEDAS are compared with the discharge diagnoses of the attending physicians. The results of the analysis confirm the high diagnostic accuracy and predictive capacity of the MEDAS system. Overall, the agreement of the MEDAS system with the “gold standard” diagnosis of the attending physician has reached a 90% level.

Low NO(x) Combustor Development

NASA Technical Reports Server (NTRS)

Kastl, J. A.; Herberling, P. V.; Matulaitis, J. M.

2005-01-01

The goal of these efforts was the development of an ultra-low emissions, lean-burn combustor for the High Speed Civil Transport. The HSCT Mach 2.4 FLADE C1 Cycle was selected as the baseline engine cycle. A preliminary compilation of performance requirements for the HSCT combustor system was developed. The emissions goals of the program, baseline engine cycle, and standard combustor performance requirements were considered in developing the compilation of performance requirements. Seven combustor system designs were developed. The development of these system designs was facilitated by the use of spreadsheet-type models which predicted performance of the combustor systems over the entire flight envelope of the HSCT. A chemical kinetic model was developed for an LPP combustor and employed to study NO(x) formation kinetics, and CO burnout. These predictions helped to define the combustor residence time. Five fuel-air mixer concepts were analyzed for use in the combustor system designs. One of the seven system designs, one using the Swirl-Jet and Cyclone Swirler fuel-air mixers, was selected for a preliminary mechanical design study.

A modified TNM staging system for non-metastatic colorectal cancer based on nomogram analysis of SEER database.

PubMed

Kong, Xiangxing; Li, Jun; Cai, Yibo; Tian, Yu; Chi, Shengqiang; Tong, Danyang; Hu, Yeting; Yang, Qi; Li, Jingsong; Poston, Graeme; Yuan, Ying; Ding, Kefeng

2018-01-08

To revise the American Joint Committee on Cancer TNM staging system for colorectal cancer (CRC) based on a nomogram analysis of Surveillance, Epidemiology, and End Results (SEER) database, and to prove the rationality of enhancing T stage's weighting in our previously proposed T-plus staging system. Total 115,377 non-metastatic CRC patients from SEER were randomly grouped as training and testing set by ratio 1:1. The Nomo-staging system was established via three nomograms based on 1-year, 2-year and 3-year disease specific survival (DSS) Logistic regression analysis of the training set. The predictive value of Nomo-staging system for the testing set was evaluated by concordance index (c-index), likelihood ratio (L.R.) and Akaike information criteria (AIC) for 1-year, 2-year, 3-year overall survival (OS) and DSS. Kaplan-Meier survival curve was used to valuate discrimination and gradient monotonicity. And an external validation was performed on database from the Second Affiliated Hospital of Zhejiang University (SAHZU). Patients with T1-2 N1 and T1N2a were classified into stage II while T4 N0 patients were classified into stage III in Nomo-staging system. Kaplan-Meier survival curves of OS and DSS in testing set showed Nomo-staging system performed better in discrimination and gradient monotonicity, and the external validation in SAHZU database also showed distinctly better discrimination. The Nomo-staging system showed higher value in L.R. and c-index, and lower value in AIC when predicting OS and DSS in testing set. The Nomo-staging system showed better performance in prognosis prediction and the weight of lymph nodes status in prognosis prediction should be cautiously reconsidered.

Modeling and Evaluating Pilot Performance in NextGen: Review of and Recommendations Regarding Pilot Modeling Efforts, Architectures, and Validation Studies

NASA Technical Reports Server (NTRS)

Wickens, Christopher; Sebok, Angelia; Keller, John; Peters, Steve; Small, Ronald; Hutchins, Shaun; Algarin, Liana; Gore, Brian Francis; Hooey, Becky Lee; Foyle, David C.

2013-01-01

NextGen operations are associated with a variety of changes to the national airspace system (NAS) including changes to the allocation of roles and responsibilities among operators and automation, the use of new technologies and automation, additional information presented on the flight deck, and the entire concept of operations (ConOps). In the transition to NextGen airspace, aviation and air operations designers need to consider the implications of design or system changes on human performance and the potential for error. To ensure continued safety of the NAS, it will be necessary for researchers to evaluate design concepts and potential NextGen scenarios well before implementation. One approach for such evaluations is through human performance modeling. Human performance models (HPMs) provide effective tools for predicting and evaluating operator performance in systems. HPMs offer significant advantages over empirical, human-in-the-loop testing in that (1) they allow detailed analyses of systems that have not yet been built, (2) they offer great flexibility for extensive data collection, (3) they do not require experimental participants, and thus can offer cost and time savings. HPMs differ in their ability to predict performance and safety with NextGen procedures, equipment and ConOps. Models also vary in terms of how they approach human performance (e.g., some focus on cognitive processing, others focus on discrete tasks performed by a human, while others consider perceptual processes), and in terms of their associated validation efforts. The objectives of this research effort were to support the Federal Aviation Administration (FAA) in identifying HPMs that are appropriate for predicting pilot performance in NextGen operations, to provide guidance on how to evaluate the quality of different models, and to identify gaps in pilot performance modeling research, that could guide future research opportunities. This research effort is intended to help the FAA evaluate pilot modeling efforts and select the appropriate tools for future modeling efforts to predict pilot performance in NextGen operations.

Model Verification and Validation Concepts for a Probabilistic Fracture Assessment Model to Predict Cracking of Knife Edge Seals in the Space Shuttle Main Engine High Pressure Oxidizer

NASA Technical Reports Server (NTRS)

Pai, Shantaram S.; Riha, David S.

2013-01-01

Physics-based models are routinely used to predict the performance of engineered systems to make decisions such as when to retire system components, how to extend the life of an aging system, or if a new design will be safe or available. Model verification and validation (V&V) is a process to establish credibility in model predictions. Ideally, carefully controlled validation experiments will be designed and performed to validate models or submodels. In reality, time and cost constraints limit experiments and even model development. This paper describes elements of model V&V during the development and application of a probabilistic fracture assessment model to predict cracking in space shuttle main engine high-pressure oxidizer turbopump knife-edge seals. The objective of this effort was to assess the probability of initiating and growing a crack to a specified failure length in specific flight units for different usage and inspection scenarios. The probabilistic fracture assessment model developed in this investigation combined a series of submodels describing the usage, temperature history, flutter tendencies, tooth stresses and numbers of cycles, fatigue cracking, nondestructive inspection, and finally the probability of failure. The analysis accounted for unit-to-unit variations in temperature, flutter limit state, flutter stress magnitude, and fatigue life properties. The investigation focused on the calculation of relative risk rather than absolute risk between the usage scenarios. Verification predictions were first performed for three units with known usage and cracking histories to establish credibility in the model predictions. Then, numerous predictions were performed for an assortment of operating units that had flown recently or that were projected for future flights. Calculations were performed using two NASA-developed software tools: NESSUS(Registered Trademark) for the probabilistic analysis, and NASGRO(Registered Trademark) for the fracture mechanics analysis. The goal of these predictions was to provide additional information to guide decisions on the potential of reusing existing and installed units prior to the new design certification.

The value of the injury severity score in pediatric trauma: Time for a new definition of severe injury?

PubMed

Brown, Joshua B; Gestring, Mark L; Leeper, Christine M; Sperry, Jason L; Peitzman, Andrew B; Billiar, Timothy R; Gaines, Barbara A

2017-06-01

The Injury Severity Score (ISS) is the most commonly used injury scoring system in trauma research and benchmarking. An ISS greater than 15 conventionally defines severe injury; however, no studies evaluate whether ISS performs similarly between adults and children. Our objective was to evaluate ISS and Abbreviated Injury Scale (AIS) to predict mortality and define optimal thresholds of severe injury in pediatric trauma. Patients from the Pennsylvania trauma registry 2000-2013 were included. Children were defined as younger than 16 years. Logistic regression predicted mortality from ISS for children and adults. The optimal ISS cutoff for mortality that maximized diagnostic characteristics was determined in children. Regression also evaluated the association between mortality and maximum AIS in each body region, controlling for age, mechanism, and nonaccidental trauma. Analysis was performed in single and multisystem injuries. Sensitivity analyses with alternative outcomes were performed. Included were 352,127 adults and 50,579 children. Children had similar predicted mortality at ISS of 25 as adults at ISS of 15 (5%). The optimal ISS cutoff in children was ISS greater than 25 and had a positive predictive value of 19% and negative predictive value of 99% compared to a positive predictive value of 7% and negative predictive value of 99% for ISS greater than 15 to predict mortality. In single-system-injured children, mortality was associated with head (odds ratio, 4.80; 95% confidence interval, 2.61-8.84; p < 0.01) and chest AIS (odds ratio, 3.55; 95% confidence interval, 1.81-6.97; p < 0.01), but not abdomen, face, neck, spine, or extremity AIS (p > 0.05). For multisystem injury, all body region AIS scores were associated with mortality except extremities. Sensitivity analysis demonstrated ISS greater than 23 to predict need for full trauma activation, and ISS greater than 26 to predict impaired functional independence were optimal thresholds. An ISS greater than 25 may be a more appropriate definition of severe injury in children. Pattern of injury is important, as only head and chest injury drive mortality in single-system-injured children. These findings should be considered in benchmarking and performance improvement efforts. Epidemiologic study, level III.

Actual versus predicted performance of an active solar heating system - A comparison using FCHART 4.0

NASA Astrophysics Data System (ADS)

Wetzel, P. E.

1981-11-01

The performance of an active solar heating system added to a house in Denver, CO was compared with predictions made by the FCHART 4.0 computer program. The house featured 43.23 sq m of collectors with an ethylene-glycol/water heat transfer fluid, and a 3.23 cu m storage tank. The house hot water was preheated in the storage tank, and home space heat was furnished whenever the storage water was above 32 C. Actual meteorological and heating demand data were used for the comparison, rather than long-term averages. Although monthly predictions by the FCHART program were found to diverge from measured data, the annual demand and supply predictions provided a good fit, i.e. within 9%, and were within 1% of the measured solar energy contributed to storage.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Lee H., Jr. (Inventor)

1994-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (VR) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Jr., Lee H. (Inventor)

1996-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Physics-of-Failure Approach to Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Assessment of the Hong Kong Liver Cancer Staging System in Europe.

PubMed

Kolly, Philippe; Reeves, Helen; Sangro, Bruno; Knöpfli, Marina; Candinas, Daniel; Dufour, Jean-François

2016-06-01

European and American guidelines have endorsed the Barcelona Clinic Liver Cancer (BCLC) staging system. The aim of this study was to assess the performance of the recently developed Hong Kong Liver Cancer (HKLC) classification as a staging system for hepatocellular carcinoma (HCC) in Europe. We used a pooled set of 1693 HCC patients combining three prospective European cohorts. Discrimination ability between the nine substages and five stages of the HKLC classification system was assessed. To evaluate the predictive power of the HKLC and BCLC staging systems on overall survival, Nagelkerke pseudo R2, Bayesian Information Criterion and Harrell's concordance index were calculated. The number of patients who would benefit from a curative therapy was assessed for both staging systems. The HKLC classification in nine substages shows suboptimal discrimination between the staging groups. The classification in five stages shows better discrimination between groups. However, the BCLC classification performs better than the HKLC classification in the ability to predict overall survival (OS). The HKLC treatment algorithm tags significantly more patients to curative therapy than the BCLC. The BCLC staging system performs better for European patients than the HKLC staging system in predicting OS. Twice more patients are eligible for a curative therapy with the HKLC algorithm; whether this translates in survival benefit remains to be investigated. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

NASA Technical Reports Server (NTRS)

Figueroa, Jorge F.; Walker, Mark G.; Kapadia, Ravi; Morris, Jonathan

2012-01-01

Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

Apollo 16, LM-11 descent propulsion system final flight evaluation

NASA Technical Reports Server (NTRS)

Avvenire, A. T.

1974-01-01

The performance of the LM-11 descent propulsion system during the Apollo 16 missions was evaluated and found satisfactory. The average engine effective specific impulse was 0.1 second higher than predicted, but well within the predicted one sigma uncertainty of 0.2 seconds. Several flight measurement discrepancies existed during the flight as follows: (1) the chamber pressure transducer had a noticeable drift, exhibiting a maximum error of about 1.5 psi at approximately 130 seconds after engine ignition, (2) the fuel and oxidizer interface pressure measurements appeared to be low during the entire flight, and (3) the fuel propellant quantity gaging system did not perform within expected accuracies.

MSFC Skylab attitude and pointing control system mission evaluation

NASA Technical Reports Server (NTRS)

Chubb, W. B.

1974-01-01

The results of detailed performance analyses of the attitude and pointing control system in-orbit hardware and software on Skylab are reported. Performance is compared with requirements, test results, and prelaunch predictions. A brief history of the altitude and pointing control system evolution leading to the launch configuration is presented. The report states that the attitude and pointing system satisfied all requirements.

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

NASA Astrophysics Data System (ADS)

Ibrahim, Wael Refaat Anis

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

75 FR 53277 - Notice of Workshop on Polymers for Photovoltaic Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-31

... photovoltaic systems; testing, performance, and reliability of polymers in photovoltaic systems; impact of... mentioned topics; presentation of a NIST- developed accelerated aging and service life prediction...

Emotional intelligence predicts success in medical school.

PubMed

Libbrecht, Nele; Lievens, Filip; Carette, Bernd; Côté, Stéphane

2014-02-01

Accumulating evidence suggests that effective communication and interpersonal sensitivity during interactions between doctors and patients impact therapeutic outcomes. There is an important need to identify predictors of these behaviors, because traditional tests used in medical admissions offer limited predictions of "bedside manners" in medical practice. This study examined whether emotional intelligence would predict the performance of 367 medical students in medical school courses on communication and interpersonal sensitivity. One of the dimensions of emotional intelligence, the ability to regulate emotions, predicted performance in courses on communication and interpersonal sensitivity over the next 3 years of medical school, over and above cognitive ability and conscientiousness. Emotional intelligence did not predict performance on courses on medical subject domains. The results suggest that medical schools may better predict who will communicate effectively and show interpersonal sensitivity if they include measures of emotional intelligence in their admission systems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Effectiveness and acceptance of the intelligent speeding prediction system (ISPS).

PubMed

Zhao, Guozhen; Wu, Changxu

2013-03-01

The intelligent speeding prediction system (ISPS) is an in-vehicle speed assistance system developed to provide quantitative predictions of speeding. Although the ISPS's prediction of speeding has been validated, whether the ISPS can regulate a driver's speed behavior or whether a driver accepts the ISPS needs further investigation. Additionally, compared to the existing intelligent speed adaptation (ISA) system, whether the ISPS performs better in terms of reducing excessive speeds and improving driving safety needs more direct evidence. An experiment was conducted to assess and compare the effectiveness and acceptance of the ISPS and the ISA. We conducted a driving simulator study with 40 participants. System type served as a between-subjects variable with four levels: no speed assistance system, pre-warning system developed based on the ISPS, post-warning system ISA, and combined pre-warning and ISA system. Speeding criterion served as a within-subjects variable with two levels: lower (posted speed limit plus 1 mph) and higher (posted speed limit plus 5 mph) speed threshold. Several aspects of the participants' driving speed, speeding measures, lead vehicle response, and subjective measures were collected. Both pre-warning and combined systems led to greater minimum time-to-collision. The combined system resulted in slower driving speed, fewer speeding exceedances, shorter speeding duration, and smaller speeding magnitude. The results indicate that both pre-warning and combined systems have the potential to improve driving safety and performance. Copyright © 2012 Elsevier Ltd. All rights reserved.

An Enlisted Performance Prediction Model for Aviation Structural Mechanics.

DTIC Science & Technology

1983-09-01

D7- R136 784 RN ENLISTED PERFORMANCE PREDICTION MODEL FOR AVIATION 112 STRUCTURAL MECHANICS(U) NAVAL POSTGRADUATE SCHOOL MONTEREY CA R DWWHITMIRE ET...Selection 28 AESTRACT (C10000O 09OW1 @ewo o It 000041 .eewe 111Id f OF blook iubee0) ’The purpose of this thesis is to determine if the Navy’s system of...K ’Z-4 Dean of Info Policy Sciences 3 ABSTRACT The purpose of this thesis is to determine if the Navy’s system of assigning personnel to the

Predictive Caching Using the TDAG Algorithm

NASA Technical Reports Server (NTRS)

Laird, Philip; Saul, Ronald

1992-01-01

We describe how the TDAG algorithm for learning to predict symbol sequences can be used to design a predictive cache store. A model of a two-level mass storage system is developed and used to calculate the performance of the cache under various conditions. Experimental simulations provide good confirmation of the model.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Effect of Uncertainty on Deterministic Runway Scheduling

NASA Technical Reports Server (NTRS)

Gupta, Gautam; Malik, Waqar; Jung, Yoon C.

2012-01-01

Active runway scheduling involves scheduling departures for takeoffs and arrivals for runway crossing subject to numerous constraints. This paper evaluates the effect of uncertainty on a deterministic runway scheduler. The evaluation is done against a first-come- first-serve scheme. In particular, the sequence from a deterministic scheduler is frozen and the times adjusted to satisfy all separation criteria; this approach is tested against FCFS. The comparison is done for both system performance (throughput and system delay) and predictability, and varying levels of congestion are considered. The modeling of uncertainty is done in two ways: as equal uncertainty in availability at the runway as for all aircraft, and as increasing uncertainty for later aircraft. Results indicate that the deterministic approach consistently performs better than first-come-first-serve in both system performance and predictability.

Microinverter Thermal Performance in the Real-World: Measurements and Modeling

PubMed Central

Hossain, Mohammad Akram; Xu, Yifan; Peshek, Timothy J.; Ji, Liang; Abramson, Alexis R.; French, Roger H.

2015-01-01

Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation. PMID:26147339

Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.

PubMed

Lesnik, Keaton Larson; Liu, Hong

2017-09-19

The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including eight separate substrates and three different wastewaters. Artificial Neural Networks (ANNs) were used to establish mathematical relationships between wastewater/solution characteristics, biofilm communities, and reactor performance. ANN models that incorporated biotic interactions predicted reactor performance outcomes more accurately than those that did not. The average percent error of power density predictions was 16.01 ± 4.35%, while the average percent error of Coulombic efficiency and COD removal rate predictions were 1.77 ± 0.57% and 4.07 ± 1.06%, respectively. Predictions of power density improved to within 5.76 ± 3.16% percent error through classifying taxonomic data at the family versus class level. Results suggest that the microbial communities and performance of bioelectrochemical systems can be accurately predicted using data-mining, machine-learning techniques.

Geoscience Laser Ranging System design and performance predictions

NASA Technical Reports Server (NTRS)

Anderson, Kent L.

1991-01-01

The Geoscience Laser System (GLRS) will be a high-precision distance-measuring instrument planned for deployment on the EOS-B platform. Its primary objectives are to perform ranging measurements to ground targets to monitor crustal deformation and tectonic plate motions, and nadir-looking altimetry to determine ice sheet thicknesses, surface topography, and vertical profiles of clouds and aerosols. The system uses a mode-locked, 3-color Nd:YAG laser source, a Microchannel Plate-PMT for absolute time-of-flight (TOF) measurement (at 532 nm), a streak camera for TOF 2-color dispersion measurement (532 nm and 355 nm), and a Si avalanche photodiode for altimeter waveform detection (1064 nm). The performance goals are to make ranging measurements to ground targets with about 1 cm accuracy, and altimetry height measurements over ice with 10 cm accuracy. This paper presents an overview of the design concept developed during a phase B study. System engineering issues and trade studies are discussed, with particular attention to error budgets and performance predictions.

Implicit and Explicit Knowledge Both Improve Dual Task Performance in a Continuous Pursuit Tracking Task.

PubMed

Ewolds, Harald E; Bröker, Laura; de Oliveira, Rita F; Raab, Markus; Künzell, Stefan

2017-01-01

The goal of this study was to investigate the effect of predictability on dual-task performance in a continuous tracking task. Participants practiced either informed (explicit group) or uninformed (implicit group) about a repeated segment in the curves they had to track. In Experiment 1 participants practices the tracking task only, dual-task performance was assessed after by combining the tracking task with an auditory reaction time task. Results showed both groups learned equally well and tracking performance on a predictable segment in the dual-task condition was better than on random segments. However, reaction times did not benefit from a predictable tracking segment. To investigate the effect of learning under dual-task situation participants in Experiment 2 practiced the tracking task while simultaneously performing the auditory reaction time task. No learning of the repeated segment could be demonstrated for either group during the training blocks, in contrast to the test-block and retention test, where participants performed better on the repeated segment in both dual-task and single-task conditions. Only the explicit group improved from test-block to retention test. As in Experiment 1, reaction times while tracking a predictable segment were no better than reaction times while tracking a random segment. We concluded that predictability has a positive effect only on the predictable task itself possibly because of a task-shielding mechanism. For dual-task training there seems to be an initial negative effect of explicit instructions, possibly because of fatigue, but the advantage of explicit instructions was demonstrated in a retention test. This might be due to the explicit memory system informing or aiding the implicit memory system.

Implicit and Explicit Knowledge Both Improve Dual Task Performance in a Continuous Pursuit Tracking Task

PubMed Central

Ewolds, Harald E.; Bröker, Laura; de Oliveira, Rita F.; Raab, Markus; Künzell, Stefan

2017-01-01

The goal of this study was to investigate the effect of predictability on dual-task performance in a continuous tracking task. Participants practiced either informed (explicit group) or uninformed (implicit group) about a repeated segment in the curves they had to track. In Experiment 1 participants practices the tracking task only, dual-task performance was assessed after by combining the tracking task with an auditory reaction time task. Results showed both groups learned equally well and tracking performance on a predictable segment in the dual-task condition was better than on random segments. However, reaction times did not benefit from a predictable tracking segment. To investigate the effect of learning under dual-task situation participants in Experiment 2 practiced the tracking task while simultaneously performing the auditory reaction time task. No learning of the repeated segment could be demonstrated for either group during the training blocks, in contrast to the test-block and retention test, where participants performed better on the repeated segment in both dual-task and single-task conditions. Only the explicit group improved from test-block to retention test. As in Experiment 1, reaction times while tracking a predictable segment were no better than reaction times while tracking a random segment. We concluded that predictability has a positive effect only on the predictable task itself possibly because of a task-shielding mechanism. For dual-task training there seems to be an initial negative effect of explicit instructions, possibly because of fatigue, but the advantage of explicit instructions was demonstrated in a retention test. This might be due to the explicit memory system informing or aiding the implicit memory system. PMID:29312083

B-52 control configured vehicles: Flight test results

NASA Technical Reports Server (NTRS)

Arnold, J. I.; Murphy, F. B.

1976-01-01

Recently completed B-52 Control Configured Vehicles (CCV) flight testing is summarized, and results are compared to analytical predictions. Results are presented for five CCV system concepts: ride control, maneuver load control, flutter mode control, augmented stability, and fatigue reduction. Test results confirm analytical predictions and show that CCV system concepts achieve performance goals when operated individually or collectively.

Predictive Effects of Online Peer Feedback Types on Performance Quality

ERIC Educational Resources Information Center

Yu, Fu-Yun; Wu, Chun-Ping

2013-01-01

This study examined the individual and combined predictive effects of two types of feedback (i.e., quantitative ratings and descriptive comments) in online peer-assessment learning systems on the quality of produced work. A total of 233 students participated in the study for six weeks. An online learning system that allows students to contribute…

Prediction of missing links and reconstruction of complex networks

NASA Astrophysics Data System (ADS)

Zhang, Cheng-Jun; Zeng, An

2016-04-01

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

Development of standardized air-blown coal gasifier/gas turbine concepts for future electric power systems, Volume 4. Appendix C: Design and performance of standardized fixed bed air-blown gasifier IGCC systems for future electric power generation: Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1991-02-01

This appendix is a compilation of work done to predict overall cycle performance from gasifier to generator terminals. A spreadsheet has been generated for each case to show flows within a cycle. The spreadsheet shows gaseous or solid composition of flow, temperature of flow, quantity of flow, and heat heat content of flow. Prediction of steam and gas turbine performance was obtained by the computer program GTPro. Outputs of all runs for each combined cycle reviewed has been added to this appendix. A process schematic displaying all flows predicted through GTPro and the spreadsheet is also added to this appendix.more » The numbered bubbles on the schematic correspond to columns on the top headings of the spreadsheet.« less

Concepts Within Reach: Action Performance Predicts Action Language Processing in Stroke

PubMed Central

Desai, Rutvik H.; Herter, Troy; Riccardi, Nicholas; Rorden, Chris; Fridriksson, Julius

2015-01-01

The relationship between the brain’s conceptual or semantic and sensory-motor systems remains controversial. Here, we tested manual and conceptual abilities of 41 chronic stroke patients in order to examine their relationship. Manual abilities were assed through a reaching task using an exoskeleton robot. Semantic abilities were assessed with implicit as well as explicit semantic tasks, for both verbs and nouns. The results show that that the degree of selective impairment for action word processing was predicted by the degree of impairment in reaching performance. Moreover, the implicit semantic measures showed a correlation with a global reaching parameter, while the explicit semantic similarity judgment task predicted performance in action initiation. These results indicate that action concepts are dynamically grounded through motoric simulations, and that more details are simulated for more explicit semantic tasks. This is evidence for a close and causal relationship between sensory-motor and conceptual systems of the brain. PMID:25858602

Predicting Operator Execution Times Using CogTool

NASA Technical Reports Server (NTRS)

Santiago-Espada, Yamira; Latorella, Kara A.

2013-01-01

Researchers and developers of NextGen systems can use predictive human performance modeling tools as an initial approach to obtain skilled user performance times analytically, before system testing with users. This paper describes the CogTool models for a two pilot crew executing two different types of a datalink clearance acceptance tasks, and on two different simulation platforms. The CogTool time estimates for accepting and executing Required Time of Arrival and Interval Management clearances were compared to empirical data observed in video tapes and registered in simulation files. Results indicate no statistically significant difference between empirical data and the CogTool predictions. A population comparison test found no significant differences between the CogTool estimates and the empirical execution times for any of the four test conditions. We discuss modeling caveats and considerations for applying CogTool to crew performance modeling in advanced cockpit environments.

Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miltiadis Alamaniotis; Vivek Agarwal

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are thenmore » inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.« less

Advances and Computational Tools towards Predictable Design in Biological Engineering

PubMed Central

2014-01-01

The design process of complex systems in all the fields of engineering requires a set of quantitatively characterized components and a method to predict the output of systems composed by such elements. This strategy relies on the modularity of the used components or the prediction of their context-dependent behaviour, when parts functioning depends on the specific context. Mathematical models usually support the whole process by guiding the selection of parts and by predicting the output of interconnected systems. Such bottom-up design process cannot be trivially adopted for biological systems engineering, since parts function is hard to predict when components are reused in different contexts. This issue and the intrinsic complexity of living systems limit the capability of synthetic biologists to predict the quantitative behaviour of biological systems. The high potential of synthetic biology strongly depends on the capability of mastering this issue. This review discusses the predictability issues of basic biological parts (promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids) when used to engineer simple and complex gene expression systems in Escherichia coli. A comparison between bottom-up and trial-and-error approaches is performed for all the discussed elements and mathematical models supporting the prediction of parts behaviour are illustrated. PMID:25161694

Progress of Aircraft System Noise Assessment with Uncertainty Quantification for the Environmentally Responsible Aviation Project

NASA Technical Reports Server (NTRS)

Thomas, Russell H.; Burley, Casey L.; Guo, Yueping

2016-01-01

Aircraft system noise predictions have been performed for NASA modeled hybrid wing body aircraft advanced concepts with 2025 entry-into-service technology assumptions. The system noise predictions developed over a period from 2009 to 2016 as a result of improved modeling of the aircraft concepts, design changes, technology development, flight path modeling, and the use of extensive integrated system level experimental data. In addition, the system noise prediction models and process have been improved in many ways. An additional process is developed here for quantifying the uncertainty with a 95% confidence level. This uncertainty applies only to the aircraft system noise prediction process. For three points in time during this period, the vehicle designs, technologies, and noise prediction process are documented. For each of the three predictions, and with the information available at each of those points in time, the uncertainty is quantified using the direct Monte Carlo method with 10,000 simulations. For the prediction of cumulative noise of an advanced aircraft at the conceptual level of design, the total uncertainty band has been reduced from 12.2 to 9.6 EPNL dB. A value of 3.6 EPNL dB is proposed as the lower limit of uncertainty possible for the cumulative system noise prediction of an advanced aircraft concept.

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1) for operational seasonal forecasting

NASA Astrophysics Data System (ADS)

Takaya, Yuhei; Yasuda, Tamaki; Fujii, Yosuke; Matsumoto, Satoshi; Soga, Taizo; Mori, Hirotoshi; Hirai, Masayuki; Ishikawa, Ichiro; Sato, Hitoshi; Shimpo, Akihiko; Kamachi, Masafumi; Ose, Tomoaki

2017-01-01

This paper describes the operational seasonal prediction system of the Japan Meteorological Agency (JMA), the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1), which was in operation at JMA during the period between February 2010 and May 2015. The predictive skill of the system was assessed with a set of retrospective seasonal predictions (reforecasts) covering 30 years (1981-2010). JMA/MRI-CPS1 showed reasonable predictive skill for the El Niño-Southern Oscillation, comparable to the skills of other state-of-the-art systems. The one-tiered approach adopted in JMA/MRI-CPS1 improved its overall predictive skills for atmospheric predictions over those of the two-tiered approach of the previous uncoupled system. For 3-month predictions with a 1-month lead, JMA/MRI-CPS1 showed statistically significant skills in predicting 500-hPa geopotential height and 2-m temperature in East Asia in most seasons; thus, it is capable of providing skillful seasonal predictions for that region. Furthermore, JMA/MRI-CPS1 was superior overall to the previous system for atmospheric predictions with longer (4-month) lead times. In particular, JMA/MRI-CPS1 was much better able to predict the Asian Summer Monsoon than the previous two-tiered system. This enhanced performance was attributed to the system's ability to represent atmosphere-ocean coupled variability over the Indian Ocean and the western North Pacific from boreal winter to summer following winter El Niño events, which in turn influences the East Asian summer climate through the Pacific-Japan teleconnection pattern. These substantial improvements obtained by using an atmosphere-ocean coupled general circulation model underpin its success in providing more skillful seasonal forecasts on an operational basis.

New PDS will predict performance of pallets made with used parts

Treesearch

John W. Clarke; Marshall S. White; Philip A. Araman

2001-01-01

The Pallet Design System (PDS) is a computer design program developed by Virginia Tech, the National Wooden Pallet & Container Association, and the U.S. Forest Service to quickly and accurately predict the performance of new wood pallets. PDS has been upgraded annually since its original version in 1984. All of the previous upgrades, however, have continued to...

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran.

PubMed

Ghaemi, Z; Alimohammadi, A; Farnaghi, M

2018-04-20

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

2013-01-07

Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive controlmore » (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.« less

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

2013-04-03

Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive controlmore » (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.« less

A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

PubMed Central

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications.

PubMed

Lei, Guoqing; Dou, Yong; Wan, Wen; Xia, Fei; Li, Rongchun; Ma, Meng; Zou, Dan

2012-01-01

Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.

Calibration and combination of monthly near-surface temperature and precipitation predictions over Europe

NASA Astrophysics Data System (ADS)

Rodrigues, Luis R. L.; Doblas-Reyes, Francisco J.; Coelho, Caio A. S.

2018-02-01

A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often.

Evaluating the complementary roles of an SJT and academic assessment for entry into clinical practice.

PubMed

Cousans, Fran; Patterson, Fiona; Edwards, Helena; Walker, Kim; McLachlan, John C; Good, David

2017-05-01

Although there is extensive evidence confirming the predictive validity of situational judgement tests (SJTs) in medical education, there remains a shortage of evidence for their predictive validity for performance of postgraduate trainees in their first role in clinical practice. Moreover, to date few researchers have empirically examined the complementary roles of academic and non-academic selection methods in predicting in-role performance. This is an important area of enquiry as despite it being common practice to use both types of methods within a selection system, there is currently no evidence that this approach translates into increased predictive validity of the selection system as a whole, over that achieved by the use of a single selection method. In this preliminary study, the majority of the range of scores achieved by successful applicants to the UK Foundation Programme provided a unique opportunity to address both of these areas of enquiry. Sampling targeted high (>80th percentile) and low (<20th percentile) scorers on the SJT. Supervisors rated 391 trainees' in-role performance, and incidence of remedial action was collected. SJT and academic performance scores correlated with supervisor ratings (r = .31 and .28, respectively). The relationship was stronger between the SJT and in-role performance for the low scoring group (r = .33, high scoring group r = .11), and between academic performance and in-role performance for the high scoring group (r = .29, low scoring group r = .11). Trainees with low SJT scores were almost five times more likely to receive remedial action. Results indicate that an SJT for entry into trainee physicians' first role in clinical practice has good predictive validity of supervisor-rated performance and incidence of remedial action. In addition, an SJT and a measure of academic performance appeared to be complementary to each other. These initial findings suggest that SJTs may be more predictive at the lower end of a scoring distribution, and academic attainment more predictive at the higher end.

A Performance Weighted Collaborative Filtering algorithm for personalized radiology education.

PubMed

Lin, Hongli; Yang, Xuedong; Wang, Weisheng; Luo, Jiawei

2014-10-01

Devising an accurate prediction algorithm that can predict the difficulty level of cases for individuals and then selects suitable cases for them is essential to the development of a personalized training system. In this paper, we propose a novel approach, called Performance Weighted Collaborative Filtering (PWCF), to predict the difficulty level of each case for individuals. The main idea of PWCF is to assign an optimal weight to each rating used for predicting the difficulty level of a target case for a trainee, rather than using an equal weight for all ratings as in traditional collaborative filtering methods. The assigned weight is a function of the performance level of the trainee at which the rating was made. The PWCF method and the traditional method are compared using two datasets. The experimental data are then evaluated by means of the MAE metric. Our experimental results show that PWCF outperforms the traditional methods by 8.12% and 17.05%, respectively, over the two datasets, in terms of prediction precision. This suggests that PWCF is a viable method for the development of personalized training systems in radiology education. Copyright © 2014. Published by Elsevier Inc.

User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

PubMed

Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung C

2018-01-01

Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation ( r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

Effective grouping for energy and performance: Construction of adaptive, sustainable, and maintainable data storage

NASA Astrophysics Data System (ADS)

Essary, David S.

The performance gap between processors and storage systems has been increasingly critical over the years. Yet the performance disparity remains, and further, storage energy consumption is rapidly becoming a new critical problem. While smarter caching and predictive techniques do much to alleviate this disparity, the problem persists, and data storage remains a growing contributor to latency and energy consumption. Attempts have been made at data layout maintenance, or intelligent physical placement of data, yet in practice, basic heuristics remain predominant. Problems that early studies sought to solve via layout strategies were proven to be NP-Hard, and data layout maintenance today remains more art than science. With unknown potential and a domain inherently full of uncertainty, layout maintenance persists as an area largely untapped by modern systems. But uncertainty in workloads does not imply randomness; access patterns have exhibited repeatable, stable behavior. Predictive information can be gathered, analyzed, and exploited to improve data layouts. Our goal is a dynamic, robust, sustainable predictive engine, aimed at improving existing layouts by replicating data at the storage device level. We present a comprehensive discussion of the design and construction of such a predictive engine, including workload evaluation, where we present and evaluate classical workloads as well as our own highly detailed traces collected over an extended period. We demonstrate significant gains through an initial static grouping mechanism, and compare against an optimal grouping method of our own construction, and further show significant improvement over competing techniques. We also explore and illustrate the challenges faced when moving from static to dynamic (i.e. online) grouping, and provide motivation and solutions for addressing these challenges. These challenges include metadata storage, appropriate predictive collocation, online performance, and physical placement. We reduced the metadata needed by several orders of magnitude, reducing the required volume from more than 14% of total storage down to less than 1/2%. We also demonstrate how our collocation strategies outperform competing techniques. Finally, we present our complete model and evaluate a prototype implementation against real hardware. This model was demonstrated to be capable of reducing device-level accesses by up to 65%. Keywords: computer systems, collocation, data management, file systems, grouping, metadata, modeling and prediction, operating systems, performance, power, secondary storage.

Propagation effects handbook for satellite systems design. A summary of propagation impairments on 10 to 100 GHz satellite links with techniques for system design

NASA Technical Reports Server (NTRS)

Ippolito, Louis J.

1989-01-01

The NASA Propagation Effects Handbook for Satellite Systems Design provides a systematic compilation of the major propagation effects experienced on space-Earth paths in the 10 to 100 GHz frequency band region. It provides both a detailed description of the propagation phenomenon and a summary of the impact of the effect on the communications system design and performance. Chapter 2 through 5 describe the propagation effects, prediction models, and available experimental data bases. In Chapter 6, design techniques and prediction methods available for evaluating propagation effects on space-Earth communication systems are presented. Chapter 7 addresses the system design process and how the effects of propagation on system design and performance should be considered and how that can be mitigated. Examples of operational and planned Ku, Ka, and EHF satellite communications systems are given.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deline, C.

Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order to give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses with and without distributed power electronics, along with experimental validation results. Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order tomore » give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses.« less

Control and Optimization of Electric Ship Propulsion Systems with Hybrid Energy Storage

NASA Astrophysics Data System (ADS)

Hou, Jun

Electric ships experience large propulsion-load fluctuations on their drive shaft due to encountered waves and the rotational motion of the propeller, affecting the reliability of the shipboard power network and causing wear and tear. This dissertation explores new solutions to address these fluctuations by integrating a hybrid energy storage system (HESS) and developing energy management strategies (EMS). Advanced electric propulsion drive concepts are developed to improve energy efficiency, performance and system reliability by integrating HESS, developing advanced control solutions and system integration strategies, and creating tools (including models and testbed) for design and optimization of hybrid electric drive systems. A ship dynamics model which captures the underlying physical behavior of the electric ship propulsion system is developed to support control development and system optimization. To evaluate the effectiveness of the proposed control approaches, a state-of-the-art testbed has been constructed which includes a system controller, Li-Ion battery and ultra-capacitor (UC) modules, a high-speed flywheel, electric motors with their power electronic drives, DC/DC converters, and rectifiers. The feasibility and effectiveness of HESS are investigated and analyzed. Two different HESS configurations, namely battery/UC (B/UC) and battery/flywheel (B/FW), are studied and analyzed to provide insights into the advantages and limitations of each configuration. Battery usage, loss analysis, and sensitivity to battery aging are also analyzed for each configuration. In order to enable real-time application and achieve desired performance, a model predictive control (MPC) approach is developed, where a state of charge (SOC) reference of flywheel for B/FW or UC for B/UC is used to address the limitations imposed by short predictive horizons, because the benefits of flywheel and UC working around high-efficiency range are ignored by short predictive horizons. Given the multi-frequency characteristics of load fluctuations, a filter-based control strategy is developed to illustrate the importance of the coordination within the HESS. Without proper control strategies, the HESS solution could be worse than a single energy storage system solution. The proposed HESS, when introduced into an existing shipboard electrical propulsion system, will interact with the power generation systems. A model-based analysis is performed to evaluate the interactions of the multiple power sources when a hybrid energy storage system is introduced. The study has revealed undesirable interactions when the controls are not coordinated properly, and leads to the conclusion that a proper EMS is needed. Knowledge of the propulsion-load torque is essential for the proposed system-level EMS, but this load torque is immeasurable in most marine applications. To address this issue, a model-based approach is developed so that load torque estimation and prediction can be incorporated into the MPC. In order to evaluate the effectiveness of the proposed approach, an input observer with linear prediction is developed as an alternative approach to obtain the load estimation and prediction. Comparative studies are performed to illustrate the importance of load torque estimation and prediction, and demonstrate the effectiveness of the proposed approach in terms of improved efficiency, enhanced reliability, and reduced wear and tear. Finally, the real-time MPC algorithm has been implemented on a physical testbed. Three different efforts have been made to enable real-time implementation: a specially tailored problem formulation, an efficient optimization algorithm and a multi-core hardware implementation. Compared to the filter-based strategy, the proposed real-time MPC achieves superior performance, in terms of the enhanced system reliability, improved HESS efficiency, and extended battery life.

Prognostics for Microgrid Components

NASA Technical Reports Server (NTRS)

Saxena, Abhinav

2012-01-01

Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

Space Station Freedom electrical performance model

NASA Technical Reports Server (NTRS)

Hojnicki, Jeffrey S.; Green, Robert D.; Kerslake, Thomas W.; Mckissock, David B.; Trudell, Jeffrey J.

1993-01-01

The baseline Space Station Freedom electric power system (EPS) employs photovoltaic (PV) arrays and nickel hydrogen (NiH2) batteries to supply power to housekeeping and user electrical loads via a direct current (dc) distribution system. The EPS was originally designed for an operating life of 30 years through orbital replacement of components. As the design and development of the EPS continues, accurate EPS performance predictions are needed to assess design options, operating scenarios, and resource allocations. To meet these needs, NASA Lewis Research Center (LeRC) has, over a 10 year period, developed SPACE (Station Power Analysis for Capability Evaluation), a computer code designed to predict EPS performance. This paper describes SPACE, its functionality, and its capabilities.

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Texas cracking performance prediction, simulation, and binder recommendation.

DOT National Transportation Integrated Search

2014-10-01

Recent studies show some mixes with softer binders used outside of Texas (e.g., Minnesotas Cold Weather Road Research Facility mixes) have both good rutting and cracking performance. However, the current binder performance grading (PG) system fail...

Rail-highway crossing accident prediction research results - FY80

DOT National Transportation Integrated Search

1981-01-01

This report presents the results of research performed at the : Transportation Systems Center (TSC) dealing with mathematical : methods of predicting accidents at rail-highway crossings. The : work consists of three parts : Part I - Revised DOT Accid...

Evaluation of the synoptic and mesoscale predictive capabilities of a mesoscale atmospheric simulation system

NASA Technical Reports Server (NTRS)

Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K.; Keyser, D. A.; Mccumber, M. C.

1983-01-01

The overall performance characteristics of a limited area, hydrostatic, fine (52 km) mesh, primitive equation, numerical weather prediction model are determined in anticipation of satellite data assimilations with the model. The synoptic and mesoscale predictive capabilities of version 2.0 of this model, the Mesoscale Atmospheric Simulation System (MASS 2.0), were evaluated. The two part study is based on a sample of approximately thirty 12h and 24h forecasts of atmospheric flow patterns during spring and early summer. The synoptic scale evaluation results benchmark the performance of MASS 2.0 against that of an operational, synoptic scale weather prediction model, the Limited area Fine Mesh (LFM). The large sample allows for the calculation of statistically significant measures of forecast accuracy and the determination of systematic model errors. The synoptic scale benchmark is required before unsmoothed mesoscale forecast fields can be seriously considered.

Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems

NASA Technical Reports Server (NTRS)

Fusaro, Robert L.

1998-01-01

Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.

Aircraft noise prediction program propeller analysis system IBM-PC version user's manual version 2.0

NASA Technical Reports Server (NTRS)

Nolan, Sandra K.

1988-01-01

The IBM-PC version of the Aircraft Noise Prediction Program (ANOPP) Propeller Analysis System (PAS) is a set of computational programs for predicting the aerodynamics, performance, and noise of propellers. The ANOPP-PAS is a subset of a larger version of ANOPP which can be executed on CDC or VAX computers. This manual provides a description of the IBM-PC version of the ANOPP-PAS and its prediction capabilities, and instructions on how to use the system on an IBM-XT or IBM-AT personal computer. Sections within the manual document installation, system design, ANOPP-PAS usage, data entry preprocessors, and ANOPP-PAS functional modules and procedures. Appendices to the manual include a glossary of ANOPP terms and information on error diagnostics and recovery techniques.

[Predictive value of breast imaging report and database system (BIRADS) to detect cancer in a reference regional hospital].

PubMed

Bellolio, Enrique; Pineda, Viviana; Burgos, María Eugenia; Iriarte, M José; Becker, Renato; Araya, Juan Carlos; Villaseca, Miguel; Mardones, Noldy

2015-12-01

To validate the BIRADS in mammography, the calculation of its predictive value in each center is required, as recommended by the American College of Radiology. To determine the predictive value of the BIRADS system in our center. All ultrasound guided needle percutaneous biopsies, performed at our center between 2006 and 2010 were reviewed. Predictive value, sensitivity, specificity and diagnostic accuracy of BIRADS were calculated, with a confidence interval of 95%. Of 1,313 biopsies available, 1,058 met the inclusion criteria. Fifty eight percent of biopsies were performed to women with mammographies classified as BIRADS 4 or 5. The presence of cancer in mammographies classified as BIRADS 0 was 4%. The prevalence of cancer for mammographies BIRADS 1, 2, 3, 4 and 5 were 0, 3, 2.7, 17.7 and 72.4% respectively. The positive and negative predictive values of BIRADS classification were 55 and 92 % respectively. In our institution BIRADS classification 4 and 5 has a high positive predictive value for detecting cancer as in developed countries.

Space and ground segment performance of the FORMOSAT-3/COSMIC mission: four years in orbit

NASA Astrophysics Data System (ADS)

Fong, C.-J.; Whiteley, D.; Yang, E.; Cook, K.; Chu, V.; Schreiner, B.; Ector, D.; Wilczynski, P.; Liu, T.-Y.; Yen, N.

2011-01-01

The FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) mission consisting of six Low-Earth-Orbit (LEO) satellites is the world's first demonstration constellation using radio occultation signals from Global Positioning System (GPS) satellites. The radio occultation signals are retrieved in near real-time for global weather/climate monitoring, numerical weather prediction, and space weather research. The mission has processed on average 1400 to 1800 high-quality atmospheric sounding profiles per day. The atmospheric radio occultation soundings data are assimilated into operational numerical weather prediction models for global weather prediction, including typhoon/hurricane/cyclone forecasts. The radio occultation data has shown a positive impact on weather predictions at many national weather forecast centers. A proposed follow-on mission transitions the program from the current experimental research system to a significantly improved real-time operational system, which will reliably provide 8000 radio occultation soundings per day. The follow-on mission as planned will consist of 12 satellites with a data latency of 45 min, which will provide greatly enhanced opportunities for operational forecasts and scientific research. This paper will address the FORMOSAT-3/COSMIC system and mission overview, the spacecraft and ground system performance after four years in orbit, the lessons learned from the encountered technical challenges and observations, and the expected design improvements for the new spacecraft and ground system.

Reducing usage of the computational resources by event driven approach to model predictive control

NASA Astrophysics Data System (ADS)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

Multiple Sensitivity Testing for Regional Air Quality Model in summer 2014

NASA Astrophysics Data System (ADS)

Tang, Y.; Lee, P.; Pan, L.; Tong, D.; Kim, H. C.; Huang, M.; Wang, J.; McQueen, J.; Lu, C. H.; Artz, R. S.

2015-12-01

The NOAA Air Resources laboratory leads to improve the performance of the U.S. Air Quality Forecasting Capability (NAQFC). It is operational in NOAA National Centers for Environmental Prediction (NCEP) which focuses on predicting surface ozone and PM2.5. In order to improve its performance, we tested several approaches, including NOAA Environmental Modeling System Global Aerosol Component (NGAC) simulation derived ozone and aerosol lateral boundary conditions (LBC), bi-direction NH3 emission and HMS(Hazard Mapping System)-BlueSky emission with the latest U.S. EPA Community Multi-scale Air Quality model (CMAQ) version and the U.S EPA National Emission Inventory (NEI)-2011 anthropogenic emissions. The operational NAQFC uses static profiles for its lateral boundary condition (LBC), which does not impose severe issue for near-surface air quality prediction. However, its degraded performance for the upper layer (e.g. above 3km) is evident when comparing with aircraft measured ozone. NCEP's Global Forecast System (GFS) has tracer O3 prediction treated as 3-D prognostic variable (Moorthi and Iredell, 1998) after being initialized with Solar Backscatter Ultra Violet-2 (SBUV-2) satellite data. We applied that ozone LBC to the CMAQ's upper layers and yield more reasonable O3 prediction than that with static LBC comparing with the aircraft data in Discover-AQ Colorado campaign. NGAC's aerosol LBC also improved the PM2.5 prediction with more realistic background aerosols. The bi-direction NH3 emission used in CMAQ also help reduce the NH3 and nitrate under-prediction issue. During summer 2014, strong wildfires occurred in northwestern USA, and we used the US Forest Service's BlueSky fire emission with HMS fire counts to drive CMAQ and tested the difference of day-1 and day-2 fire emission estimation. Other related issues were also discussed.

Estimations of Atmospheric Conditions for Input to the Radar Performance Surface

DTIC Science & Technology

2007-12-01

timely atmospheric and ocean surface descriptions on features that impact radar and electro-optical sensor systems . The first part of this study is an...Navy’s Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS®) are compared to in-situ data to assess the sensitivities of air-sea...temperature measurements to make direct comparisons to the Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS®) as a prime source of input to the

Predicting Cost/Performance Trade-Offs for Whitney: A Commodity Computing Cluster

NASA Technical Reports Server (NTRS)

Becker, Jeffrey C.; Nitzberg, Bill; VanderWijngaart, Rob F.; Kutler, Paul (Technical Monitor)

1997-01-01

Recent advances in low-end processor and network technology have made it possible to build a "supercomputer" out of commodity components. We develop simple models of the NAS Parallel Benchmarks version 2 (NPB 2) to explore the cost/performance trade-offs involved in building a balanced parallel computer supporting a scientific workload. We develop closed form expressions detailing the number and size of messages sent by each benchmark. Coupling these with measured single processor performance, network latency, and network bandwidth, our models predict benchmark performance to within 30%. A comparison based on total system cost reveals that current commodity technology (200 MHz Pentium Pros with 100baseT Ethernet) is well balanced for the NPBs up to a total system cost of around $1,000,000.

Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Lee H., Jr. (Inventor)

1988-01-01

The invention is believed a major improvement that will have a broad application in governmental and commercial aviation. It provides a dynamic method and system for monitoring and simultaneously displaying in easily scanned form the available, predicted, and actual thrust of a jet aircraft engine under actual operating conditions. The available and predicted thrusts are based on the performance of a functional model of the aircraft engine under the same operating conditions. Other critical performance parameters of the aircraft engine and functional model are generated and compared, the differences in value being simultaneously displayed in conjunction with the displayed thrust values. Thus, the displayed information permits the pilot to make power adjustments directly while keeping him aware of total performance at a glance of a single display panel.

Performance Prediction of a Synchronization Link for Distributed Aerospace Wireless Systems

PubMed Central

Shao, Huaizong

2013-01-01

For reasons of stealth and other operational advantages, distributed aerospace wireless systems have received much attention in recent years. In a distributed aerospace wireless system, since the transmitter and receiver placed on separated platforms which use independent master oscillators, there is no cancellation of low-frequency phase noise as in the monostatic cases. Thus, high accurate time and frequency synchronization techniques are required for distributed wireless systems. The use of a dedicated synchronization link to quantify and compensate oscillator frequency instability is investigated in this paper. With the mathematical statistical models of phase noise, closed-form analytic expressions for the synchronization link performance are derived. The possible error contributions including oscillator, phase-locked loop, and receiver noise are quantified. The link synchronization performance is predicted by utilizing the knowledge of the statistical models, system error contributions, and sampling considerations. Simulation results show that effective synchronization error compensation can be achieved by using this dedicated synchronization link. PMID:23970828

Solar space- and water-heating system at Stanford University. Central Food Services Building

NASA Astrophysics Data System (ADS)

1980-05-01

The closed-loop drain-back system is described as offering dependability of gravity drain-back freeze protection, low maintenance, minimal costs, and simplicity. The system features an 840 square-foot collector and storage capacity of 1550 gallons. The acceptance testing and the predicted system performance data are briefly described. Solar performance calculations were performed using a computer design program (FCHART). Bidding, costs, and economics of the system are reviewed. Problems are discussed and solutions and recommendations given. An operation and maintenance manual is given.

A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation.

PubMed

Tseng, Yi-Ju; Wu, Jung-Hsuan; Lin, Hui-Chi; Chen, Ming-Yuan; Ping, Xiao-Ou; Sun, Chun-Chuan; Shang, Rung-Ji; Sheng, Wang-Huei; Chen, Yee-Chun; Lai, Feipei; Chang, Shan-Chwen

2015-09-21

Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.

Predictive modeling and reducing cyclic variability in autoignition engines

DOEpatents

Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

2016-08-30

Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

The computational challenges of Earth-system science.

PubMed

O'Neill, Alan; Steenman-Clark, Lois

2002-06-15

The Earth system--comprising atmosphere, ocean, land, cryosphere and biosphere--is an immensely complex system, involving processes and interactions on a wide range of space- and time-scales. To understand and predict the evolution of the Earth system is one of the greatest challenges of modern science, with success likely to bring enormous societal benefits. High-performance computing, along with the wealth of new observational data, is revolutionizing our ability to simulate the Earth system with computer models that link the different components of the system together. There are, however, considerable scientific and technical challenges to be overcome. This paper will consider four of them: complexity, spatial resolution, inherent uncertainty and time-scales. Meeting these challenges requires a significant increase in the power of high-performance computers. The benefits of being able to make reliable predictions about the evolution of the Earth system should, on their own, amply repay this investment.

Applying Neural Networks in Optical Communication Systems: Possible Pitfalls

NASA Astrophysics Data System (ADS)

Eriksson, Tobias A.; Bulow, Henning; Leven, Andreas

2017-12-01

We investigate the risk of overestimating the performance gain when applying neural network based receivers in systems with pseudo random bit sequences or with limited memory depths, resulting in repeated short patterns. We show that with such sequences, a large artificial gain can be obtained which comes from pattern prediction rather than predicting or compensating the studied channel/phenomena.

Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

ERIC Educational Resources Information Center

Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

2001-01-01

Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

An overview of aerospace gas turbine technology of relevance to the development of the automotive gas turbine engine

NASA Technical Reports Server (NTRS)

Evans, D. G.; Miller, T. J.

1978-01-01

The NASA-Lewis Research Center (LeRC) has conducted, and has sponsored with industry and universities, extensive research into many of the technology areas related to gas turbine propulsion systems. This aerospace-related technology has been developed at both the component and systems level, and may have significant potential for application to the automotive gas turbine engine. This paper summarizes this technology and lists the associated references. The technology areas are system steady-state and transient performance prediction techniques, compressor and turbine design and performance prediction programs and effects of geometry, combustor technology and advanced concepts, and ceramic coatings and materials technology.

Improved neutron activation prediction code system development

NASA Technical Reports Server (NTRS)

Saqui, R. M.

1971-01-01

Two integrated neutron activation prediction code systems have been developed by modifying and integrating existing computer programs to perform the necessary computations to determine neutron induced activation gamma ray doses and dose rates in complex geometries. Each of the two systems is comprised of three computational modules. The first program module computes the spatial and energy distribution of the neutron flux from an input source and prepares input data for the second program which performs the reaction rate, decay chain and activation gamma source calculations. A third module then accepts input prepared by the second program to compute the cumulative gamma doses and/or dose rates at specified detector locations in complex, three-dimensional geometries.

To branch out or stay focused? Affective shifts differentially predict organizational citizenship behavior and task performance.

PubMed

Yang, Liu-Qin; Simon, Lauren S; Wang, Lei; Zheng, Xiaoming

2016-06-01

We draw from personality systems interaction (PSI) theory (Kuhl, 2000) and regulatory focus theory (Higgins, 1997) to examine how dynamic positive and negative affective processes interact to predict both task and contextual performance. Using a twice-daily diary design over the course of a 3-week period, results from multilevel regression analysis revealed that distinct patterns of change in positive and negative affect optimally predicted contextual and task performance among a sample of 71 employees at a medium-sized technology company. Specifically, within persons, increases (upshifts) in positive affect over the course of a workday better predicted the subsequent day's organizational citizenship behavior (OCB) when such increases were coupled with decreases (downshifts) in negative affect. The optimal pattern of change in positive and negative affect differed, however, in predicting task performance. That is, upshifts in positive affect over the course of the workday better predicted the subsequent day's task performance when such upshifts were accompanied by upshifts in negative affect. The contribution of our findings to PSI theory and the broader affective and motivation regulation literatures, along with practical implications, are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Thermo-physical performance prediction of the KSC Ground Operation Demonstration Unit for liquid hydrogen

NASA Astrophysics Data System (ADS)

Baik, J. H.; Notardonato, W. U.; Karng, S. W.; Oh, I.

2015-12-01

NASA Kennedy Space Center (KSC) researchers have been working on enhanced and modernized cryogenic liquid propellant handling techniques to reduce life cycle costs of propellant management system for the unique KSC application. The KSC Ground Operation Demonstration Unit (GODU) for liquid hydrogen (LH2) plans to demonstrate integrated refrigeration, zero-loss flexible term storage of LH2, and densified hydrogen handling techniques. The Florida Solar Energy Center (FSEC) has partnered with the KSC researchers to develop thermal performance prediction model of the GODU for LH2. The model includes integrated refrigeration cooling performance, thermal losses in the tank and distribution lines, transient system characteristics during chilling and loading, and long term steady-state propellant storage. This paper will discuss recent experimental data of the GODU for LH2 system and modeling results.

Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

PubMed

Lee, Joon; Maslove, David M; Dubin, Joel A

2015-01-01

Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to meaningful use of EMR data.

Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric

PubMed Central

Lee, Joon; Maslove, David M.; Dubin, Joel A.

2015-01-01

Background Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. Methods and Findings We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. Conclusions The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to meaningful use of EMR data. PMID:25978419

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

Viking orbiter system primary mission

NASA Technical Reports Server (NTRS)

Goudy, J. R.

1977-01-01

An overview of Viking Orbiter (VO) system and subsystem performances during the primary mission (the time period from VO-1 launch on August 20, 1975, through November 15, 1976) is presented. Brief descriptions, key design requirements, pertinent historical information, unique applications or situations, and predicted versus actual performances are included for all VO-1 and VO-2 subsystems, both individually and as an integrated system.

Sensitivity of Space Station alpha joint robust controller to structural modal parameter variations

NASA Technical Reports Server (NTRS)

Kumar, Renjith R.; Cooper, Paul A.; Lim, Tae W.

1991-01-01

The photovoltaic array sun tracking control system of Space Station Freedom is described. A synthesis procedure for determining optimized values of the design variables of the control system is developed using a constrained optimization technique. The synthesis is performed to provide a given level of stability margin, to achieve the most responsive tracking performance, and to meet other design requirements. Performance of the baseline design, which is synthesized using predicted structural characteristics, is discussed and the sensitivity of the stability margin is examined for variations of the frequencies, mode shapes and damping ratios of dominant structural modes. The design provides enough robustness to tolerate a sizeable error in the predicted modal parameters. A study was made of the sensitivity of performance indicators as the modal parameters of the dominant modes vary. The design variables are resynthesized for varying modal parameters in order to achieve the most responsive tracking performance while satisfying the design requirements. This procedure of reoptimization design parameters would be useful in improving the control system performance if accurate model data are provided.

Integrating Cache Performance Modeling and Tuning Support in Parallelization Tools

NASA Technical Reports Server (NTRS)

Waheed, Abdul; Yan, Jerry; Saini, Subhash (Technical Monitor)

1998-01-01

With the resurgence of distributed shared memory (DSM) systems based on cache-coherent Non Uniform Memory Access (ccNUMA) architectures and increasing disparity between memory and processors speeds, data locality overheads are becoming the greatest bottlenecks in the way of realizing potential high performance of these systems. While parallelization tools and compilers facilitate the users in porting their sequential applications to a DSM system, a lot of time and effort is needed to tune the memory performance of these applications to achieve reasonable speedup. In this paper, we show that integrating cache performance modeling and tuning support within a parallelization environment can alleviate this problem. The Cache Performance Modeling and Prediction Tool (CPMP), employs trace-driven simulation techniques without the overhead of generating and managing detailed address traces. CPMP predicts the cache performance impact of source code level "what-if" modifications in a program to assist a user in the tuning process. CPMP is built on top of a customized version of the Computer Aided Parallelization Tools (CAPTools) environment. Finally, we demonstrate how CPMP can be applied to tune a real Computational Fluid Dynamics (CFD) application.

Pressure control and analysis report: Hydrogen Thermal Test Article (HTTA)

NASA Technical Reports Server (NTRS)

1971-01-01

Tasks accomplished during the HTTA Program study period included: (1) performance of a literature review to provide system guidelines; (2) development of analytical procedures needed to predict system performance; (3) design and analysis of the HTTA pressurization system considering (a) future utilization of results in the design of a spacecraft maneuvering system propellant package, (b) ease of control and operation, (c) system safety, and (d) hardware cost; and (4) making conclusions and recommendations for systems design.

Fourier transform wavefront control with adaptive prediction of the atmosphere.

PubMed

Poyneer, Lisa A; Macintosh, Bruce A; Véran, Jean-Pierre

2007-09-01

Predictive Fourier control is a temporal power spectral density-based adaptive method for adaptive optics that predicts the atmosphere under the assumption of frozen flow. The predictive controller is based on Kalman filtering and a Fourier decomposition of atmospheric turbulence using the Fourier transform reconstructor. It provides a stable way to compensate for arbitrary numbers of atmospheric layers. For each Fourier mode, efficient and accurate algorithms estimate the necessary atmospheric parameters from closed-loop telemetry and determine the predictive filter, adjusting as conditions change. This prediction improves atmospheric rejection, leading to significant improvements in system performance. For a 48x48 actuator system operating at 2 kHz, five-layer prediction for all modes is achievable in under 2x10(9) floating-point operations/s.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

PubMed

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Prediction and characterization of application power use in a high-performance computing environment

DOE PAGES

Bugbee, Bruce; Phillips, Caleb; Egan, Hilary; ...

2017-02-27

Power use in data centers and high-performance computing (HPC) facilities has grown in tandem with increases in the size and number of these facilities. Substantial innovation is needed to enable meaningful reduction in energy footprints in leadership-class HPC systems. In this paper, we focus on characterizing and investigating application-level power usage. We demonstrate potential methods for predicting power usage based on a priori and in situ characteristics. Lastly, we highlight a potential use case of this method through a simulated power-aware scheduler using historical jobs from a real scientific HPC system.

Coupled lagged ensemble weather- and river runoff prediction in complex Alpine terrain

NASA Astrophysics Data System (ADS)

Smiatek, Gerhard; Kunstmann, Harald; Werhahn, Johannes

2013-04-01

It is still a challenge to predict fast reacting streamflow precipitation response in Alpine terrain. Civil protection measures require flood prediction in 24 - 48 lead time. This holds particularly true for the Ammer River region which was affected by century floods in 1999, 2003 and 2005. Since 2005 a coupled NWP/Hydrology model system is operated in simulating and predicting the Ammer River discharges. The Ammer River catchment is located in the Bavarian Ammergau Alps and alpine forelands, Germany. With elevations reaching 2185 m and annual mean precipitation between 1100 and 2000 mm it represents very demanding test ground for a river runoff prediction system. The one way coupled system utilizes a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. The major components of the system are the MM5 NWP model run at 3.5 km resolution and initialized twice a day, the hydrology model WaSiM-ETH run at 100 m resolution and Perl object environment (POE) implementing the networking and the system operation. Results obtained in the years 2005-2012 reveal that river runoff simulations depict already high correlation (NSC in range 0.53 and 0.95) with observed runoff in retrospective runs with monitored meteorology data, but suffer from errors in quantitative precipitation forecast (QPF) from the employed numerical weather prediction model. We evaluate the NWP model accuracy, especially the precipitation intensity, frequency and location and put a focus on the performance gain of bias adjustment procedures. We show how this enhanced QFP data help to reduce the uncertainty in the discharge prediction. In addition to the HND (Hochwassernachrichtendienst, Bayern) observations TERENO Longterm Observatory hydrometeorological observation data are available since 2011. They are used to evaluate the NWP performance and setup of a bias correction procedure based on ensemble postprocessing applying Bayesian (BMA) model averaging. We first present briefly the technical setup of the operational coupled lagged NWP/Hydrology model system and then focus on the evaluation of the NWP model, the BMA enhanced QPF and its application within the Ammer simulation system in the period 2011 - 2012

AEETES - A solar reflux receiver thermal performance numerical model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogan, R.E. Jr.

1994-02-01

Reflux solar receivers for dish-Stirling electric power generation systems are currently being investigated by several companies and laboratories. In support of these efforts, the AEETES thermal performance numerical model has been developed to predict thermal performance of pool-boiler and heat-pipe reflux receivers. The formulation of the AEETES numerical model, which is applicable to axisymmetric geometries with asymmetric incident fluxes, is presented in detail. Thermal efficiency predictions agree to within 4.1% with test data from on-sun tests of a pool-boiler reflux receiver. Predicted absorber and sidewall temperatures agree with thermocouple data to within 3.3 and 7.3%, respectively. The importance of accountingmore » for the asymmetric incident fluxes is demonstrated in comparisons with predictions using azimuthally averaged variables. The predicted receiver heat losses are characterized in terms of convective, solar radiative, and infrared radiative, and conductive heat transfer mechanisms.« less

CSI feedback-based CS for underwater acoustic adaptive modulation OFDM system with channel prediction

NASA Astrophysics Data System (ADS)

Kuai, Xiao-yan; Sun, Hai-xin; Qi, Jie; Cheng, En; Xu, Xiao-ka; Guo, Yu-hui; Chen, You-gan

2014-06-01

In this paper, we investigate the performance of adaptive modulation (AM) orthogonal frequency division multiplexing (OFDM) system in underwater acoustic (UWA) communications. The aim is to solve the problem of large feedback overhead for channel state information (CSI) in every subcarrier. A novel CSI feedback scheme is proposed based on the theory of compressed sensing (CS). We propose a feedback from the receiver that only feedback the sparse channel parameters. Additionally, prediction of the channel state is proposed every several symbols to realize the AM in practice. We describe a linear channel prediction algorithm which is used in adaptive transmission. This system has been tested in the real underwater acoustic channel. The linear channel prediction makes the AM transmission techniques more feasible for acoustic channel communications. The simulation and experiment show that significant improvements can be obtained both in bit error rate (BER) and throughput in the AM scheme compared with the fixed Quadrature Phase Shift Keying (QPSK) modulation scheme. Moreover, the performance with standard CS outperforms the Discrete Cosine Transform (DCT) method.

PVWatts Version 1 Technical Reference

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dobos, A. P.

2013-10-01

The NREL PVWatts(TM) calculator is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected photovoltaic system based on a few simple inputs. PVWatts combines a number of sub-models to predict overall system performance, and makes several hidden assumptions about performance parameters. This technical reference details the individual sub-models, documents assumptions and hidden parameters, and explains the sequence of calculations that yield the final system performance estimation.

Acoustic Characterization and Prediction of Representative, Small-Scale Rotary-Wing Unmanned Aircraft System Components

NASA Technical Reports Server (NTRS)

Zawodny, Nikolas S.; Boyd, D. Douglas, Jr.; Burley, Casey L.

2016-01-01

In this study, hover performance and acoustic measurements are taken on two different isolated rotors representative of small-scale rotary-wing unmanned aircraft systems (UAS) for a range of rotation rates. Each rotor system consists of two fixed-pitch blades powered by a brushless motor. For nearly the same thrust condition, significant differences in overall sound pressure level (OASPL), up to 8 dB, and directivity were observed between the two rotor systems. Differences are shown to be in part attributed to different rotor tip speeds, along with increased broadband and motor noise levels. In addition to acoustic measurements, aeroacoustic predictions were implemented in order to better understand the noise content of the rotor systems. Numerical aerodynamic predictions were computed using the unsteady Reynoldsaveraged Navier Stokes code OVERFLOW2 on one of the isolated rotors, while analytical predictions were computed using the Propeller Analysis System of the Aircraft NOise Prediction Program (ANOPP-PAS) on the two rotor configurations. Preliminary semi-empirical frequency domain broadband noise predictions were also carried out based on airfoil self-noise theory in a rotational reference frame. The prediction techniques further supported trends identified in the experimental data analysis. The brushless motors were observed to be important noise contributors and warrant further investigation. It is believed that UAS acoustic prediction capabilities must consider both rotor and motor components as part of a combined noise-generating system.

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

DOE Data Explorer

Sheppy, Michael; Beach, A.; Pless, Shanti

2016-08-09

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error.

PubMed

Bosman, Lisa B; Darling, Seth B

2018-06-01

The advent of modern solar energy technologies can improve the costs of energy consumption on a global, national, and regional level, ultimately spanning stakeholders from governmental entities to utility companies, corporations, and residential homeowners. For those stakeholders experiencing the four seasons, accurately accounting for snow-related energy losses is important for effectively predicting photovoltaic performance energy generation and valuation. This paper provides an examination of a new, simplified approach to decrease snow-related forecasting error, in comparison to current solar energy performance models. A new method is proposed to allow model designers, and ultimately users, the opportunity to better understand the return on investment for solar energy systems located in snowy environments. The new method is validated using two different sets of solar energy systems located near Green Bay, WI, USA: a 3.0-kW micro inverter system and a 13.2-kW central inverter system. Both systems were unobstructed, facing south, and set at a tilt of 26.56°. Data were collected beginning in May 2014 (micro inverter system) and October 2014 (central inverter system), through January 2018. In comparison to reference industry standard solar energy prediction applications (PVWatts and PVsyst), the new method results in lower mean absolute percent errors per kilowatt hour of 0.039 and 0.055%, respectively, for the micro inverter system and central inverter system. The statistical analysis provides support for incorporating this new method into freely available, online, up-to-date prediction applications, such as PVWatts and PVsyst.

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

NASA Astrophysics Data System (ADS)

Liu, Wenyang; Sawant, Amit; Ruan, Dan

2016-07-01

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

CO2 removal by solid amine sorbents. 1: Experimental studies of amine resin IR-45 with regard to spacecraft applications. 2: Computer program for predicting the transient performance of solid amine sorbent systems

NASA Technical Reports Server (NTRS)

Wright, R. M.; Hwang, K. C.

1973-01-01

The sorbent behavior of solid amine resin IR-45 with regard to potential use in regenerative CO2-removal systems for manned spacecraft is considered. Measurements of equilibrium sorption capacity of IR-45 for water and for CO2 are reported, and the dynamic mass transfer behavior of IR-45 beds is studied under conditions representative of those expected in a manned spacecraft. A digital computer program was written for the transient performance prediction of CO2 removal systems comprised of solid amine beds. Also evaluated are systems employing inorganic molecular-sieve sorbents. Tests show that there is definitely an effect of water loading on the absorption rate.

Cooperative airframe/propulsion control for supersonic cruise aircraft

NASA Technical Reports Server (NTRS)

Schweikhard, W. G.; Berry, D. T.

1974-01-01

Interactions between propulsion systems and flight controls have emerged as a major control problem on supersonic cruise aircraft. This paper describes the nature and causes of these interactions and the approaches to predicting and solving the problem. Integration of propulsion and flight control systems appears to be the most promising solution if the interaction effects can be adequately predicted early in the vehicle design. Significant performance, stability, and control improvements may be realized from a cooperative control system.

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Comparison of Different Scoring Systems Based on Both Donor and Recipient Characteristics for Predicting Outcome after Living Donor Liver Transplantation.

PubMed

Ma, Yucheng; Wang, Qing; Yang, Jiayin; Yan, Lunan

2015-01-01

In order to provide a good match between donor and recipient in liver transplantation, four scoring systems [the product of donor age and Model for End-stage Liver Disease score (D-MELD), the score to predict survival outcomes following liver transplantation (SOFT), the balance of risk score (BAR), and the transplant risk index (TRI)] based on both donor and recipient parameters were designed. This study was conducted to evaluate the performance of the four scores in living donor liver transplantation (LDLT) and compare them with the MELD score. The clinical data of 249 adult patients undergoing LDLT in our center were retrospectively evaluated. The area under the receiver operating characteristic curves (AUCs) of each score were calculated and compared at 1-, 3-, 6-month and 1-year after LDLT. The BAR at 1-, 3-, 6-month and 1-year after LDLT and the D-MELD and TRI at 1-, 3- and 6-month after LDLT showed acceptable performances in the prediction of survival (AUC>0.6), while the SOFT showed poor discrimination at 6-month after LDLT (AUC = 0.569). In addition, the D-MELD and BAR displayed positive correlations with the length of ICU stay (D-MELD, p = 0.025; BAR, p = 0.022). The SOFT was correlated with the time of mechanical ventilation (p = 0.022). The D-MELD, BAR and TRI provided acceptable performance in predicting survival after LDLT. However, even though these scoring systems were based on both donor and recipient parameters, only the BAR provided better performance than the MELD in predicting 1-year survival after LDLT.

Comparison of Different Scoring Systems Based on Both Donor and Recipient Characteristics for Predicting Outcome after Living Donor Liver Transplantation

PubMed Central

2015-01-01

Background and Objectives In order to provide a good match between donor and recipient in liver transplantation, four scoring systems [the product of donor age and Model for End-stage Liver Disease score (D-MELD), the score to predict survival outcomes following liver transplantation (SOFT), the balance of risk score (BAR), and the transplant risk index (TRI)] based on both donor and recipient parameters were designed. This study was conducted to evaluate the performance of the four scores in living donor liver transplantation (LDLT) and compare them with the MELD score. Patients and Methods The clinical data of 249 adult patients undergoing LDLT in our center were retrospectively evaluated. The area under the receiver operating characteristic curves (AUCs) of each score were calculated and compared at 1-, 3-, 6-month and 1-year after LDLT. Results The BAR at 1-, 3-, 6-month and 1-year after LDLT and the D-MELD and TRI at 1-, 3- and 6-month after LDLT showed acceptable performances in the prediction of survival (AUC>0.6), while the SOFT showed poor discrimination at 6-month after LDLT (AUC = 0.569). In addition, the D-MELD and BAR displayed positive correlations with the length of ICU stay (D-MELD, p = 0.025; BAR, p = 0.022). The SOFT was correlated with the time of mechanical ventilation (p = 0.022). Conclusion The D-MELD, BAR and TRI provided acceptable performance in predicting survival after LDLT. However, even though these scoring systems were based on both donor and recipient parameters, only the BAR provided better performance than the MELD in predicting 1-year survival after LDLT. PMID:26378786

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications

PubMed Central

2012-01-01

Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

1997 NASA High-Speed Research Program Aerodynamic Performance Workshop. Volume 2; High Lift

NASA Technical Reports Server (NTRS)

Baize, Daniel G. (Editor)

1999-01-01

The High-Speed Research Program and NASA Langley Research Center sponsored the NASA High-Speed Research Program Aerodynamic Performance Workshop on February 25-28, 1997. The workshop was designed to bring together NASA and industry High-Speed Civil Transport (HSCT) Aerodynamic Performance technology development participants in areas of Configuration Aerodynamics (transonic and supersonic cruise drag, prediction and minimization), High-Lift, Flight Controls, Supersonic Laminar Flow Control, and Sonic Boom Prediction. The workshop objectives were to (1) report the progress and status of HSCT aerodynamic performance technology development; (2) disseminate this technology within the appropriate technical communities; and (3) promote synergy among the scientist and engineers working HSCT aerodynamics. In particular, single- and multi-point optimized HSCT configurations, HSCT high-lift system performance predictions, and HSCT Motion Simulator results were presented along with executives summaries for all the Aerodynamic Performance technology areas.

1997 NASA High-Speed Research Program Aerodynamic Performance Workshop. Volume 1; Configuration Aerodynamics

NASA Technical Reports Server (NTRS)

Baize, Daniel G. (Editor)

1999-01-01

The High-Speed Research Program and NASA Langley Research Center sponsored the NASA High-Speed Research Program Aerodynamic Performance Workshop on February 25-28, 1997. The workshop was designed to bring together NASA and industry High-Speed Civil Transport (HSCT) Aerodynamic Performance technology development participants in area of Configuration Aerodynamics (transonic and supersonic cruise drag prediction and minimization), High-Lift, Flight Controls, Supersonic Laminar Flow Control, and Sonic Boom Prediction. The workshop objectives were to (1) report the progress and status of HSCT aerodyamic performance technology development; (2) disseminate this technology within the appropriate technical communities; and (3) promote synergy among the scientist and engineers working HSCT aerodynamics. In particular, single- and multi-point optimized HSCT configurations, HSCT high-lift system performance predictions, and HSCT Motion Simulator results were presented along with executive summaries for all the Aerodynamic Performance technology areas.

1997 NASA High-Speed Research Program Aerodynamic Performance Workshop. Volume 1; Configuration Aerodynamics

NASA Technical Reports Server (NTRS)

Baize, Daniel G. (Editor)

1999-01-01

The High-Speed Research Program and NASA Langley Research Center sponsored the NASA High-Speed Research Program Aerodynamic Performance Workshop on February 25-28, 1997. The workshop was designed to bring together NASA and industry High-Speed Civil Transport (HSCT) Aerodynamic Performance technology development participants in areas of Configuration Aerodynamics (transonic and supersonic cruise drag prediction and minimization), High-Lift, Flight Controls, Supersonic Laminar Flow Control, and Sonic Boom Prediction. The workshop objectives were to (1) report the progress and status of HSCT aerodynamic performance technology development; (2) disseminate this technology within the appropriate technical communities; and (3) promote synergy among the scientist and engineers working HSCT aerodynamics. In particular, single- and multi-point optimized HSCT configurations, HSCT high-lift system performance predictions, and HSCT Motion Simulator results were presented along with executive summaries for all the Aerodynamic Performance technology areas.

1997 NASA High-Speed Research Program Aerodynamic Performance Workshop. Volume 1; Configuration Aerodynamics

NASA Technical Reports Server (NTRS)

Baize, Daniel G. (Editor)

1999-01-01

The High-Speed Research Program and NASA Langley Research Center sponsored the NASA High-Speed Research Program Aerodynamic Performance Workshop on February 25-28, 1997. The workshop was designed to bring together NASA and industry High-Speed Civil Transport (HSCT) Aerodynamic Performance technology development participants in area of Configuration Aerodynamics (transonic and supersonic cruise drag prediction and minimization), High-Lift, Flight Controls, Supersonic Laminar Flow Control, and Sonic Boom Prediction. The workshop objectives were to (1) report the progress and status of HSCT aerodynamic performance technology development; (2) disseminate this technology within the appropriate technical communities; and (3) promote synergy among the scientist and engineers working HSCT aerodynamics. In particular, single- and multi-point optimized HSCT configurations, HSCT high-lift system performance predictions, and HSCT Motion Simulator results were presented along with executive summaries for all the Aerodynamic Performance technology areas.

Visual search for object categories is predicted by the representational architecture of high-level visual cortex

PubMed Central

Alvarez, George A.; Nakayama, Ken; Konkle, Talia

2016-01-01

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing. PMID:27832600

Compression of stereoscopic video using MPEG-2

NASA Astrophysics Data System (ADS)

Puri, A.; Kollarits, Richard V.; Haskell, Barry G.

1995-10-01

Many current as well as emerging applications in areas of entertainment, remote operations, manufacturing industry and medicine can benefit from the depth perception offered by stereoscopic video systems which employ two views of a scene imaged under the constraints imposed by human visual system. Among the many challenges to be overcome for practical realization and widespread use of 3D/stereoscopic systems are good 3D displays and efficient techniques for digital compression of enormous amounts of data while maintaining compatibility with normal video decoding and display systems. After a brief introduction to the basics of 3D/stereo including issues of depth perception, stereoscopic 3D displays and terminology in stereoscopic imaging and display, we present an overview of tools in the MPEG-2 video standard that are relevant to our discussion on compression of stereoscopic video, which is the main topic of this paper. Next, we outilne the various approaches for compression of stereoscopic video and then focus on compatible stereoscopic video coding using MPEG-2 Temporal scalability concepts. Compatible coding employing two different types of prediction structures become potentially possible, disparity compensated prediction and combined disparity and motion compensated predictions. To further improve coding performance and display quality, preprocessing for reducing mismatch between the two views forming stereoscopic video is considered. Results of simulations performed on stereoscopic video of normal TV resolution are then reported comparing the performance of two prediction structures with the simulcast solution. It is found that combined disparity and motion compensated prediction offers the best performance. Results indicate that compression of both views of stereoscopic video of normal TV resolution appears feasible in a total of 6 to 8 Mbit/s. We then discuss regarding multi-viewpoint video, a generalization of stereoscopic video. Finally, we describe ongoing efforts within MPEG-2 to define a profile for stereoscopic video coding, as well as, the promise of MPEG-4 in addressing coding of multi-viewpoint video.

Compression of stereoscopic video using MPEG-2

NASA Astrophysics Data System (ADS)

Puri, Atul; Kollarits, Richard V.; Haskell, Barry G.

1995-12-01

Many current as well as emerging applications in areas of entertainment, remote operations, manufacturing industry and medicine can benefit from the depth perception offered by stereoscopic video systems which employ two views of a scene imaged under the constraints imposed by human visual system. Among the many challenges to be overcome for practical realization and widespread use of 3D/stereoscopic systems are good 3D displays and efficient techniques for digital compression of enormous amounts of data while maintaining compatibility with normal video decoding and display systems. After a brief introduction to the basics of 3D/stereo including issues of depth perception, stereoscopic 3D displays and terminology in stereoscopic imaging and display, we present an overview of tools in the MPEG-2 video standard that are relevant to our discussion on compression of stereoscopic video, which is the main topic of this paper. Next, we outline the various approaches for compression of stereoscopic video and then focus on compatible stereoscopic video coding using MPEG-2 Temporal scalability concepts. Compatible coding employing two different types of prediction structures become potentially possible, disparity compensated prediction and combined disparity and motion compensated predictions. To further improve coding performance and display quality, preprocessing for reducing mismatch between the two views forming stereoscopic video is considered. Results of simulations performed on stereoscopic video of normal TV resolution are then reported comparing the performance of two prediction structures with the simulcast solution. It is found that combined disparity and motion compensated prediction offers the best performance. Results indicate that compression of both views of stereoscopic video of normal TV resolution appears feasible in a total of 6 to 8 Mbit/s. We then discuss regarding multi-viewpoint video, a generalization of stereoscopic video. Finally, we describe ongoing efforts within MPEG-2 to define a profile for stereoscopic video coding, as well as, the promise of MPEG-4 in addressing coding of multi-viewpoint video.

A Comparison of Two Scoring Methods for an Automated Speech Scoring System

ERIC Educational Resources Information Center

Xi, Xiaoming; Higgins, Derrick; Zechner, Klaus; Williamson, David

2012-01-01

This paper compares two alternative scoring methods--multiple regression and classification trees--for an automated speech scoring system used in a practice environment. The two methods were evaluated on two criteria: construct representation and empirical performance in predicting human scores. The empirical performance of the two scoring models…

Preliminary engineering report for design of a subscale ejector/diffuser system for high expansion ratio space engine testing

NASA Technical Reports Server (NTRS)

Wojciechowski, C. J.; Kurzius, S. C.; Doktor, M. F.

1984-01-01

The design of a subscale jet engine driven ejector/diffuser system is examined. Analytical results and preliminary design drawings and plans are included. Previously developed performance prediction techniques are verified. A safety analysis is performed to determine the mechanism for detonation suppression.

Prediction Model for Predicting Powdery Mildew using ANN for Medicinal Plant— Picrorhiza kurrooa

NASA Astrophysics Data System (ADS)

Shivling, V. D.; Ghanshyam, C.; Kumar, Rakesh; Kumar, Sanjay; Sharma, Radhika; Kumar, Dinesh; Sharma, Atul; Sharma, Sudhir Kumar

2017-02-01

Plant disease fore casting system is an important system as it can be used for prediction of disease, further it can be used as an alert system to warn the farmers in advance so as to protect their crop from being getting infected. Fore casting system will predict the risk of infection for crop by using the environmental factors that favor in germination of disease. In this study an artificial neural network based system for predicting the risk of powdery mildew in Picrorhiza kurrooa was developed. For development, Levenberg-Marquardt backpropagation algorithm was used having a single hidden layer of ten nodes. Temperature and duration of wetness are the major environmental factors that favor infection. Experimental data was used as a training set and some percentage of data was used for testing and validation. The performance of the system was measured in the form of the coefficient of correlation (R), coefficient of determination (R2), mean square error and root mean square error. For simulating the network an inter face was developed. Using this interface the network was simulated by putting temperature and wetness duration so as to predict the level of risk at that particular value of the input data.

Performances and reliability predictions of optical data transmission links using a system simulator for aerospace applications

NASA Astrophysics Data System (ADS)

Bechou, L.; Deshayes, Y.; Aupetit-Berthelemot, C.; Guerin, A.; Tronche, C.

Space missions for Earth Observation are called upon to carry a growing number of instruments in their payload, whose performances are increasing. Future space systems are therefore intended to generate huge amounts of data and a key challenge in coming years will therefore lie in the ability to transmit that significant quantity of data to ground. Thus very high data rate Payload Telemetry (PLTM) systems will be required to face the demand of the future Earth Exploration Satellite Systems and reliability is one of the major concern of such systems. An attractive approach associated with the concept of predictive modeling consists in analyzing the impact of components malfunctioning on the optical link performances taking into account the network requirements and experimental degradation laws. Reliability estimation is traditionally based on life-testing and a basic approach is to use Telcordia requirements (468GR) for optical telecommunication applications. However, due to the various interactions between components, operating lifetime of a system cannot be taken as the lifetime of the less reliable component. In this paper, an original methodology is proposed to estimate reliability of an optical communication system by using a dedicated system simulator for predictive modeling and design for reliability. At first, we present frameworks of point-to-point optical communication systems for space applications where high data rate (or frequency bandwidth), lower cost or mass saving are needed. Optoelectronics devices used in these systems can be similar to those found in terrestrial optical network. Particularly we report simulation results of transmission performances after introduction of DFB Laser diode parameters variations versus time extrapolated from accelerated tests based on terrestrial or submarine telecommunications qualification standards. Simulations are performed to investigate and predict the consequence of degradations of the Laser diode (acting as a - requency carrier) on system performances (eye diagram, quality factor and BER). The studied link consists in 4× 2.5 Gbits/s WDM channels with direct modulation and equally spaced (0,8 nm) around the 1550 nm central wavelength. Results clearly show that variation of fundamental parameters such as bias current or central wavelength induces a penalization of dynamic performances of the complete WDM link. In addition different degradation kinetics of aged Laser diodes from a same batch have been implemented to build the final distribution of Q-factor and BER values after 25 years. When considering long optical distance, fiber attenuation, EDFA noise, dispersion, PMD, ... penalize network performances that can be compensated using Forward Error Correction (FEC) coding. Three methods have been investigated in the case of On-Off Keying (OOK) transmission over an unipolar optical channel corrupted by Gaussian noise. Such system simulations highlight the impact of component parameter degradations on the whole network performances allowing to optimize various time and cost consuming sensitivity analyses at the early stage of the system development. Thus the validity of failure criteria in relation with mission profiles can be evaluated representing a significant part of the general PDfR effort in particular for aerospace applications.

Laser line scan performance prediction

NASA Astrophysics Data System (ADS)

Mahoney, Kevin L.; Schofield, Oscar; Kerfoot, John; Giddings, Tom; Shirron, Joe; Twardowski, Mike

2007-09-01

The effectiveness of sensors that use optical measurements for the laser detection and identification of subsurface mines is directly related to water clarity. The primary objective of the work presented here was to use the optical data collected by UUV (Slocum Glider) surveys of an operational areas to estimate the performance of an electro-optical identification (EOID) Laser Line Scan (LLS) system during RIMPAC 06, an international naval exercise off the coast of Hawaii. Measurements of optical backscattering and beam attenuation were made with a Wet Labs, Inc. Scattering Absorption Meter (SAM), mounted on a Rutgers University/Webb Research Slocum glider. The optical data universally indicated extremely clear water in the operational area, except very close to shore. The beam-c values from the SAM sensor were integrated to three attenuation lengths to provide an estimate of how well the LLS would perform in detecting and identifying mines in the operational areas. Additionally, the processed in situ optical data served as near-real-time input to the Electro-Optic Detection Simulator, ver. 3 (EODES-3; Metron, Inc.) model for EOID performance prediction. Both methods of predicting LLS performance suggested a high probability of detection and probability of identification. These predictions were validated by the actual performance of the LLS as the EOID system yielded imagery from which reliable mine identification could be made. Future plans include repeating this work in more optically challenging water types to demonstrate the utility of pre-mission UUV surveys of operational areas as a tactical decision aid for planning EOID missions.

Initial Design and Construction of a Mobil Regenerative Fuel Cell System

NASA Technical Reports Server (NTRS)

Colozza, Anthony J.; Maloney, Thomas; Hoberecht, Mark (Technical Monitor)

2003-01-01

The design and initial construction of a mobile regenerative power system is described. The main components of the power system consists of a photovoltaic array, regenerative fuel cell and electrolyzer. The system is mounted on a modified landscape trailer and is completely self contained. An operational analysis is also presented that shows predicted performance for the system at various times of the year. The operational analysis consists of performing an energy balance on the system based on array output and total desired operational time.

Ceramic Matrix Composites (CMC) Life Prediction Development

NASA Technical Reports Server (NTRS)

Levine, Stanley R.; Verrilli, Michael J.; Thomas, David J.; Halbig, Michael C.; Calomino, Anthony M.; Ellis, John R.; Opila, Elizabeth J.

1990-01-01

Advanced launch systems will very likely incorporate fiber reinforced ceramic matrix composites (CMC) in critical propulsion and airframe components. The use of CMC will save weight, increase operating margin, safety and performance, and improve reuse capability. For reusable and single mission use, accurate life prediction is critical to success. The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation.

Performance evaluation of infrared imaging system in field test

NASA Astrophysics Data System (ADS)

Wang, Chensheng; Guo, Xiaodong; Ren, Tingting; Zhang, Zhi-jie

2014-11-01

Infrared imaging system has been applied widely in both military and civilian fields. Since the infrared imager has various types and different parameters, for system manufacturers and customers, there is great demand for evaluating the performance of IR imaging systems with a standard tool or platform. Since the first generation IR imager was developed, the standard method to assess the performance has been the MRTD or related improved methods which are not perfect adaptable for current linear scanning imager or 2D staring imager based on FPA detector. For this problem, this paper describes an evaluation method based on the triangular orientation discrimination metric which is considered as the effective and emerging method to evaluate the synthesis performance of EO system. To realize the evaluation in field test, an experiment instrument is developed. And considering the importance of operational environment, the field test is carried in practical atmospheric environment. The test imagers include panoramic imaging system and staring imaging systems with different optics and detectors parameters (both cooled and uncooled). After showing the instrument and experiment setup, the experiment results are shown. The target range performance is analyzed and discussed. In data analysis part, the article gives the range prediction values obtained from TOD method, MRTD method and practical experiment, and shows the analysis and results discussion. The experimental results prove the effectiveness of this evaluation tool, and it can be taken as a platform to give the uniform performance prediction reference.

Turbine Performance Optimization Task Status

NASA Technical Reports Server (NTRS)

Griffin, Lisa W.; Turner, James E. (Technical Monitor)

2001-01-01

Capability to optimize for turbine performance and accurately predict unsteady loads will allow for increased reliability, Isp, and thrust-to-weight. The development of a fast, accurate aerodynamic design, analysis, and optimization system is required.

Pilot Performance With Predictive System Status Information

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.

1997-01-01

Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, the benefits of predictive information have not been quantitatively demonstrated. The study described here attempted to identify and quantify these benefits if they existed. In this simulator experiment, three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and four initial times to an alert (1 minute, 5 minutes, 15 minutes, and ETA+ 45 minutes) were found to affect when subjects accomplished certain actions, such as accessing pertinent checklists, declaring emergencies, diverting, and calling the flight attendant and dispatch.

Performance Measures for Adaptive Decisioning Systems

DTIC Science & Technology

1991-09-11

set to hypothesis space mapping best approximates the known map. Two assumptions, a sufficiently representative training set and the ability of the...successful prediction of LINEXT performance. The LINEXT algorithm above performs the decision space mapping on the training-set ele- ments exactly. For a

Skylab extravehicular mobility unit thermal simulator

NASA Technical Reports Server (NTRS)

Hixon, C. W.; Phillips, M. A.

1974-01-01

The analytical methods, thermal model, and user's instructions for the Skylab Extravehicular Mobility Unit (SEMU) routine are presented. This digital computer program was developed for detailed thermal performance predictions of the SEMU on the NASA-JSC Univac 1108 computer system. It accounts for conductive, convective, and radiant heat transfer as well as fluid flow and special component characterization. The program provides thermal performance predictions for a 967 node thermal model in one thirty-sixth (1/36) of mission time when operated at a calculating interval of three minutes (mission time). The program has the operational flexibility to: (1) accept card or magnetic tape data input for the thermal model describing the SEMU structure, fluid systems, crewman and component performance, (2) accept card and/or magnetic tape input of internally generated heat and heat influx from the space environment, and (3) output tabular or plotted histories of temperature, flow rates, and other parameters describing system operating modes.

Heave-pitch-roll analysis and testing of air cushion landing systems

NASA Technical Reports Server (NTRS)

Boghani, A. B.; Captain, K. M.; Wormley, D. N.

1978-01-01

The analytical tools (analysis and computer simulation) needed to explain and predict the dynamic operation of air cushion landing systems (ACLS) is described. The following tasks were performed: the development of improved analytical models for the fan and the trunk; formulation of a heave pitch roll analysis for the complete ACLS; development of a general purpose computer simulation to evaluate landing and taxi performance of an ACLS equipped aircraft; and the verification and refinement of the analysis by comparison with test data obtained through lab testing of a prototype cushion. Demonstration of simulation capabilities through typical landing and taxi simulation of an ACLS aircraft are given. Initial results show that fan dynamics have a major effect on system performance. Comparison with lab test data (zero forward speed) indicates that the analysis can predict most of the key static and dynamic parameters (pressure, deflection, acceleration, etc.) within a margin of a 10 to 25 percent.

Materials integrity in microsystems: a framework for a petascale predictive-science-based multiscale modeling and simulation system

NASA Astrophysics Data System (ADS)

To, Albert C.; Liu, Wing Kam; Olson, Gregory B.; Belytschko, Ted; Chen, Wei; Shephard, Mark S.; Chung, Yip-Wah; Ghanem, Roger; Voorhees, Peter W.; Seidman, David N.; Wolverton, Chris; Chen, J. S.; Moran, Brian; Freeman, Arthur J.; Tian, Rong; Luo, Xiaojuan; Lautenschlager, Eric; Challoner, A. Dorian

2008-09-01

Microsystems have become an integral part of our lives and can be found in homeland security, medical science, aerospace applications and beyond. Many critical microsystem applications are in harsh environments, in which long-term reliability needs to be guaranteed and repair is not feasible. For example, gyroscope microsystems on satellites need to function for over 20 years under severe radiation, thermal cycling, and shock loading. Hence a predictive-science-based, verified and validated computational models and algorithms to predict the performance and materials integrity of microsystems in these situations is needed. Confidence in these predictions is improved by quantifying uncertainties and approximation errors. With no full system testing and limited sub-system testings, petascale computing is certainly necessary to span both time and space scales and to reduce the uncertainty in the prediction of long-term reliability. This paper presents the necessary steps to develop predictive-science-based multiscale modeling and simulation system. The development of this system will be focused on the prediction of the long-term performance of a gyroscope microsystem. The environmental effects to be considered include radiation, thermo-mechanical cycling and shock. Since there will be many material performance issues, attention is restricted to creep resulting from thermal aging and radiation-enhanced mass diffusion, material instability due to radiation and thermo-mechanical cycling and damage and fracture due to shock. To meet these challenges, we aim to develop an integrated multiscale software analysis system that spans the length scales from the atomistic scale to the scale of the device. The proposed software system will include molecular mechanics, phase field evolution, micromechanics and continuum mechanics software, and the state-of-the-art model identification strategies where atomistic properties are calibrated by quantum calculations. We aim to predict the long-term (in excess of 20 years) integrity of the resonator, electrode base, multilayer metallic bonding pads, and vacuum seals in a prescribed mission. Although multiscale simulations are efficient in the sense that they focus the most computationally intensive models and methods on only the portions of the space time domain needed, the execution of the multiscale simulations associated with evaluating materials and device integrity for aerospace microsystems will require the application of petascale computing. A component-based software strategy will be used in the development of our massively parallel multiscale simulation system. This approach will allow us to take full advantage of existing single scale modeling components. An extensive, pervasive thrust in the software system development is verification, validation, and uncertainty quantification (UQ). Each component and the integrated software system need to be carefully verified. An UQ methodology that determines the quality of predictive information available from experimental measurements and packages the information in a form suitable for UQ at various scales needs to be developed. Experiments to validate the model at the nanoscale, microscale, and macroscale are proposed. The development of a petascale predictive-science-based multiscale modeling and simulation system will advance the field of predictive multiscale science so that it can be used to reliably analyze problems of unprecedented complexity, where limited testing resources can be adequately replaced by petascale computational power, advanced verification, validation, and UQ methodologies.

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

Development of speed models for improving travel forecasting and highway performance evaluation : [technical summary].

DOT National Transportation Integrated Search

2013-12-01

Travel forecasting models predict travel demand based on the present transportation system and its use. Transportation modelers must develop, validate, and calibrate models to ensure that predicted travel demand is as close to reality as possible. Mo...

Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a method and system for monitoring and directly displaying the actual thrust produced by a jet aircraft engine under determined operating conditions and the available thrust and predicted (commanded) thrust of a functional model of an ideal engine under the same determined operating conditions. A first set of actual value output signals representative of a plurality of actual performance parameters of the engine under the determined operating conditions is generated and compared with a second set of predicted value output signals representative of the predicted value of corresponding performance parameters of a functional model of the engine under the determined operating conditions to produce a third set of difference value output signals within a range of normal, caution, or warning limit values. A thrust indicator displays when any one of the actual value output signals is in the warning range while shaping function means shape each of the respective difference output signals as each approaches the limit of the respective normal, caution, and warning range limits.

Predictability of the geospace variations and measuring the capability to model the state of the system

NASA Astrophysics Data System (ADS)

Pulkkinen, A.

2012-12-01

Empirical modeling has been the workhorse of the past decades in predicting the state of the geospace. For example, numerous empirical studies have shown that global geoeffectiveness indices such as Kp and Dst are generally well predictable from the solar wind input. These successes have been facilitated partly by the strongly externally driven nature of the system. Although characterizing the general state of the system is valuable and empirical modeling will continue playing an important role, refined physics-based quantification of the state of the system has been the obvious next step in moving toward more mature science. Importantly, more refined and localized products are needed also for space weather purposes. Predictions of local physical quantities are necessary to make physics-based links to the impacts on specific systems. As we have introduced more localized predictions of the geospace state one central question is how predictable these local quantities are? This complex question can be addressed by rigorously measuring the model performance against the observed data. Space sciences community has made great advanced on this topic over the past few years and there are ongoing efforts in SHINE, CEDAR and GEM to carry out community-wide evaluations of the state-of-the-art solar and heliospheric, ionosphere-thermosphere and geospace models, respectively. These efforts will help establish benchmarks and thus provide means to measure the progress in the field analogous to monitoring of the improvement in lower atmospheric weather predictions carried out rigorously since 1980s. In this paper we will discuss some of the latest advancements in predicting the local geospace parameters and give an overview of some of the community efforts to rigorously measure the model performances. We will also briefly discuss some of the future opportunities for advancing the geospace modeling capability. These will include further development in data assimilation and ensemble modeling (e.g. taking into account uncertainty in the inflow boundary conditions).

Thrust Augmentation with Mixer/Ejector Systems

NASA Technical Reports Server (NTRS)

Presz, Walter M., Jr.; Reynolds, Gary; Hunter, Craig

2002-01-01

Older commercial aircraft often exceed FAA (Federal Aviation Administration) sideline noise regulations. The major problem is the jet noise associated with the high exhaust velocities of the low bypass ratio engines on such aircraft. Mixer/ejector exhaust systems can provide a simple means of reducing the jet noise on these aircraft by mixing cool ambient air with the high velocity engine gases before they are exhausted to ambient. This paper presents new information on thrust performance predictions, and thrust augmentation capabilities of mixer/ejectors. Results are presented from the recent development program of the patented Alternating Lobe Mixer Ejector Concept (ALMEC) suppressor system for the Gulfstream GII, GIIB and GIII aircraft. Mixer/ejector performance procedures are presented which include classical control volume analyses, compound compressible flow theory, lobed nozzle loss correlations and state of the art computational fluid dynamic predictions. The mixer/ejector thrust predictions are compared to subscale wind tunnel test model data and actual aircraft flight test measurements. The results demonstrate that a properly designed mixer/ejector noise suppressor can increase effective engine bypass ratio and generate large thrust gains at takeoff conditions with little or no thrust loss at cruise conditions. The cruise performance obtained for such noise suppressor systems is shown to be a strong function of installation effects on the aircraft.

Automatic Speech Recognition Predicts Speech Intelligibility and Comprehension for Listeners With Simulated Age-Related Hearing Loss.

PubMed

Fontan, Lionel; Ferrané, Isabelle; Farinas, Jérôme; Pinquier, Julien; Tardieu, Julien; Magnen, Cynthia; Gaillard, Pascal; Aumont, Xavier; Füllgrabe, Christian

2017-09-18

The purpose of this article is to assess speech processing for listeners with simulated age-related hearing loss (ARHL) and to investigate whether the observed performance can be replicated using an automatic speech recognition (ASR) system. The long-term goal of this research is to develop a system that will assist audiologists/hearing-aid dispensers in the fine-tuning of hearing aids. Sixty young participants with normal hearing listened to speech materials mimicking the perceptual consequences of ARHL at different levels of severity. Two intelligibility tests (repetition of words and sentences) and 1 comprehension test (responding to oral commands by moving virtual objects) were administered. Several language models were developed and used by the ASR system in order to fit human performances. Strong significant positive correlations were observed between human and ASR scores, with coefficients up to .99. However, the spectral smearing used to simulate losses in frequency selectivity caused larger declines in ASR performance than in human performance. Both intelligibility and comprehension scores for listeners with simulated ARHL are highly correlated with the performances of an ASR-based system. In the future, it needs to be determined if the ASR system is similarly successful in predicting speech processing in noise and by older people with ARHL.

A predictive control framework for torque-based steering assistance to improve safety in highway driving

NASA Astrophysics Data System (ADS)

Ercan, Ziya; Carvalho, Ashwin; Tseng, H. Eric; Gökaşan, Metin; Borrelli, Francesco

2018-05-01

Haptic shared control framework opens up new perspectives on the design and implementation of the driver steering assistance systems which provide torque feedback to the driver in order to improve safety. While designing such a system, it is important to account for the human-machine interactions since the driver feels the feedback torque through the hand wheel. The controller should consider the driver's impact on the steering dynamics to achieve a better performance in terms of driver's acceptance and comfort. In this paper we present a predictive control framework which uses a model of driver-in-the-loop steering dynamics to optimise the torque intervention with respect to the driver's neuromuscular response. We first validate the system in simulations to compare the performance of the controller in nominal and model mismatch cases. Then we implement the controller in a test vehicle and perform experiments with a human driver. The results show the effectiveness of the proposed system in avoiding hazardous situations under different driver behaviours.

Advanced variable speed air source integrated heat pump (AS-IHP) development - CRADA final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baxter, Van D.; Rice, C. Keith; Munk, Jeffrey D.

2015-09-30

Between August 2011 and September 2015, Oak Ridge National Laboratory (ORNL) and Nordyne, LLC (now Nortek Global HVAC LLC, NGHVAC) engaged in a Cooperative Research and Development Agreement (CRADA) to develop an air-source integrated heat pump (AS-IHP) system for the US residential market. Two generations of laboratory prototype systems were designed, fabricated, and lab-tested during 2011-2013. Performance maps for the system were developed using the latest research version of the DOE/ORNL Heat Pump Design Model, or HPDM, (Rice 1991; Rice and Jackson 2005; Shen et al 2012) as calibrated against the lab test data. These maps were the input tomore » the TRNSYS (SOLAR Energy Laboratory, et al, 2010) system to predict annual performance relative to a baseline suite of equipment meeting minimum efficiency standards in effect in 2006 (combination of 13 SEER air-source heat pump (ASHP) and resistance water heater with Energy Factor (EF) of 0.9). Predicted total annual energy savings, while providing space conditioning and water heating for a tight, well insulated 2600 ft2 (242 m2) house at 5 U.S. locations, ranged from 46 to 61%, averaging 52%, relative to the baseline system (lowest savings at the cold-climate Chicago location). Predicted energy use for water heating was reduced 62 to 76% relative to resistance WH. Based on these lab prototype test and analyses results a field test prototype was designed and fabricated by NGHVAC. The unit was installed in a 2400 ft2 (223 m2) research house in Knoxville, TN and field tested from May 2014 to April 2015. Based on the demonstrated field performance of the AS-IHP prototype and estimated performance of a baseline system operating under the same loads and weather conditions, it was estimated that the prototype would achieve ~40% energy savings relative to the minimum efficiency suite. The estimated WH savings were >60% and SC mode savings were >50%. But estimated SH savings were only about 20%. It is believed that had the test house been better insulated (more like the house used for the savings predictions noted above) and the IHP system nominal capacity been a bit lower that the energy savings estimate would have been closer to 45% or more (similar to the analytical prediction for the cold climate location of Chicago).« less

Viking 75 project: Viking lander system primary mission performance report

NASA Technical Reports Server (NTRS)

Cooley, C. G.

1977-01-01

Viking Lander hardware performance during launch, interplanetary cruise, Mars orbit insertion, preseparation, separation through landing, and the primary landed mission, with primary emphasis on Lander engineering and science hardware operations, the as-flown mission are described with respect to Lander system performance and anomalies during the various mission phases. The extended mission and predicted Lander performance is discussed along with a summary of Viking goals, mission plans, and description of the Lander, and its subsystem definitions.

Design of Supersonic Transport Flap Systems for Thrust Recovery at Subsonic Speeds

NASA Technical Reports Server (NTRS)

Mann, Michael J.; Carlson, Harry W.; Domack, Christopher S.

1999-01-01

A study of the subsonic aerodynamics of hinged flap systems for supersonic cruise commercial aircraft has been conducted using linear attached-flow theory that has been modified to include an estimate of attainable leading edge thrust and an approximate representation of vortex forces. Comparisons of theoretical predictions with experimental results show that the theory gives a reasonably good and generally conservative estimate of the performance of an efficient flap system and provides a good estimate of the leading and trailing-edge deflection angles necessary for optimum performance. A substantial reduction in the area of the inboard region of the leading edge flap has only a minor effect on the performance and the optimum deflection angles. Changes in the size of the outboard leading-edge flap show that performance is greatest when this flap has a chord equal to approximately 30 percent of the wing chord. A study was also made of the performance of various combinations of individual leading and trailing-edge flaps, and the results show that aerodynamic efficiencies as high as 85 percent of full suction are predicted.

Solar Field Optical Characterization at Stillwater Geothermal/Solar Hybrid Plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Guangdong; Turchi, Craig

Concentrating solar power (CSP) can provide additional thermal energy to boost geothermal plant power generation. For a newly constructed solar field at a geothermal power plant site, it is critical to properly characterize its performance so that the prediction of thermal power generation can be derived to develop an optimum operating strategy for a hybrid system. In the past, laboratory characterization of a solar collector has often extended into the solar field performance model and has been used to predict the actual solar field performance, disregarding realistic impacting factors. In this work, an extensive measurement on mirror slope error andmore » receiver position error has been performed in the field by using the optical characterization tool called Distant Observer (DO). Combining a solar reflectance sampling procedure, a newly developed solar characterization program called FirstOPTIC and public software for annual performance modeling called System Advisor Model (SAM), a comprehensive solar field optical characterization has been conducted, thus allowing for an informed prediction of solar field annual performance. The paper illustrates this detailed solar field optical characterization procedure and demonstrates how the results help to quantify an appropriate tracking-correction strategy to improve solar field performance. In particular, it is found that an appropriate tracking-offset algorithm can improve the solar field performance by about 15%. The work here provides a valuable reference for the growing CSP industry.« less

Solar Field Optical Characterization at Stillwater Geothermal/Solar Hybrid Plant

DOE PAGES

Zhu, Guangdong; Turchi, Craig

2017-01-27

Concentrating solar power (CSP) can provide additional thermal energy to boost geothermal plant power generation. For a newly constructed solar field at a geothermal power plant site, it is critical to properly characterize its performance so that the prediction of thermal power generation can be derived to develop an optimum operating strategy for a hybrid system. In the past, laboratory characterization of a solar collector has often extended into the solar field performance model and has been used to predict the actual solar field performance, disregarding realistic impacting factors. In this work, an extensive measurement on mirror slope error andmore » receiver position error has been performed in the field by using the optical characterization tool called Distant Observer (DO). Combining a solar reflectance sampling procedure, a newly developed solar characterization program called FirstOPTIC and public software for annual performance modeling called System Advisor Model (SAM), a comprehensive solar field optical characterization has been conducted, thus allowing for an informed prediction of solar field annual performance. The paper illustrates this detailed solar field optical characterization procedure and demonstrates how the results help to quantify an appropriate tracking-correction strategy to improve solar field performance. In particular, it is found that an appropriate tracking-offset algorithm can improve the solar field performance by about 15%. The work here provides a valuable reference for the growing CSP industry.« less

Comparison of scoring systems for nonvariceal upper gastrointestinal bleeding: a multicenter prospective cohort study.

PubMed

Yang, Hae Min; Jeon, Seong Woo; Jung, Jin Tae; Lee, Dong Wook; Ha, Chang Yoon; Park, Kyung Sik; Lee, Si Hyung; Yang, Chang Heon; Park, Jun Hyung; Park, Youn Sun

2016-01-01

The Glasgow-Blatchford score (GBS) and Rockall score (RS) are widely used to assess risk in patients with upper gastrointestinal bleeding (UGIB). We compared both scoring systems and evaluated their clinical usefulness. Between February 2011 and December 2013, 1584 patients with nonvariceal UGIB were included in the study. A prospective study was conducted to compare the performance of the GBS, pre-RS, and full RS. We compared the performance of these scores using receiver operating characteristic curves. For prediction of the need for hospital-based intervention, the GBS was similar to the full RS (area under the receiver operating characteristic curves [AUROC] 0.705 vs 0.727; P = 0.282) and superior to the pre-RS (AUROC 0.705 vs 0.601; P < 0.0001). In predicting death, the full RS was superior to the GBS (AUROC 0.758 vs 0.644; P = 0.0006) and similar to the pre-RS (AUROC 0.758 vs 0.754; P = 0.869). In predicting rebleeding, the full RS was superior to both GBS (AUROC 0.642 vs 0.585; P = 0.031) and pre-RS (AUROC 0.642 vs 0.593; P = 0.0003). Of 1584 patients, 13 (0.8%) scored 0 on the GBS. Therapeutic intervention was not performed in any of these patients. The GBS is more useful than the pre-RS for predicting the need for hospital-based intervention. A cutoff value of 0 for low-risk patients who might be suitable for outpatient management is useful. The full RS is helpful in predicting death. None of the systems accurately predict rebleeding with a low AUROC. ( cris.nih.go.kr/KCT0000514). © 2015 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

Study of cavitating inducer instabilities

NASA Technical Reports Server (NTRS)

Young, W. E.; Murphy, R.; Reddecliff, J. M.

1972-01-01

An analytic and experimental investigation into the causes and mechanisms of cavitating inducer instabilities was conducted. Hydrofoil cascade tests were performed, during which cavity sizes were measured. The measured data were used, along with inducer data and potential flow predictions, to refine an analysis for the prediction of inducer blade suction surface cavitation cavity volume. Cavity volume predictions were incorporated into a linearized system model, and instability predictions for an inducer water test loop were generated. Inducer tests were conducted and instability predictions correlated favorably with measured instability data.

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

NASA Astrophysics Data System (ADS)

Bazzazi, Abbas Aghajani; Esmaeili, Mohammad

2012-12-01

Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

PubMed Central

Safa Sadiq, Ali; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches. PMID:25574490

An adaptive handover prediction scheme for seamless mobility based wireless networks.

PubMed

Sadiq, Ali Safa; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Model predictive control design for polytopic uncertain systems by synthesising multi-step prediction scenarios

NASA Astrophysics Data System (ADS)

Lu, Jianbo; Xi, Yugeng; Li, Dewei; Xu, Yuli; Gan, Zhongxue

2018-01-01

A common objective of model predictive control (MPC) design is the large initial feasible region, low online computational burden as well as satisfactory control performance of the resulting algorithm. It is well known that interpolation-based MPC can achieve a favourable trade-off among these different aspects. However, the existing results are usually based on fixed prediction scenarios, which inevitably limits the performance of the obtained algorithms. So by replacing the fixed prediction scenarios with the time-varying multi-step prediction scenarios, this paper provides a new insight into improvement of the existing MPC designs. The adopted control law is a combination of predetermined multi-step feedback control laws, based on which two MPC algorithms with guaranteed recursive feasibility and asymptotic stability are presented. The efficacy of the proposed algorithms is illustrated by a numerical example.

High-speed prediction of crystal structures for organic molecules

NASA Astrophysics Data System (ADS)

Obata, Shigeaki; Goto, Hitoshi

2015-02-01

We developed a master-worker type parallel algorithm for allocating tasks of crystal structure optimizations to distributed compute nodes, in order to improve a performance of simulations for crystal structure predictions. The performance experiments were demonstrated on TUT-ADSIM supercomputer system (HITACHI HA8000-tc/HT210). The experimental results show that our parallel algorithm could achieve speed-ups of 214 and 179 times using 256 processor cores on crystal structure optimizations in predictions of crystal structures for 3-aza-bicyclo(3.3.1)nonane-2,4-dione and 2-diazo-3,5-cyclohexadiene-1-one, respectively. We expect that this parallel algorithm is always possible to reduce computational costs of any crystal structure predictions.

Handling Trajectory Uncertainties for Airborne Conflict Management

NASA Technical Reports Server (NTRS)

Barhydt, Richard; Doble, Nathan A.; Karr, David; Palmer, Michael T.

2005-01-01

Airborne conflict management is an enabling capability for NASA's Distributed Air-Ground Traffic Management (DAG-TM) concept. DAGTM has the goal of significantly increasing capacity within the National Airspace System, while maintaining or improving safety. Under DAG-TM, autonomous aircraft maintain separation from each other and from managed aircraft unequipped for autonomous flight. NASA Langley Research Center has developed the Autonomous Operations Planner (AOP), an onboard decision support system that provides airborne conflict management (ACM) and strategic flight planning support for autonomous aircraft pilots. The AOP performs conflict detection, prevention, and resolution from nearby traffic aircraft and area hazards. Traffic trajectory information is assumed to be provided by Automatic Dependent Surveillance Broadcast (ADS-B). Reliable trajectory prediction is a key capability for providing effective ACM functions. Trajectory uncertainties due to environmental effects, differences in aircraft systems and performance, and unknown intent information lead to prediction errors that can adversely affect AOP performance. To accommodate these uncertainties, the AOP has been enhanced to create cross-track, vertical, and along-track buffers along the predicted trajectories of both ownship and traffic aircraft. These buffers will be structured based on prediction errors noted from previous simulations such as a recent Joint Experiment between NASA Ames and Langley Research Centers and from other outside studies. Currently defined ADS-B parameters related to navigation capability, trajectory type, and path conformance will be used to support the algorithms that generate the buffers.

Mars Science Laboratory Rover System Thermal Test

NASA Technical Reports Server (NTRS)

Novak, Keith S.; Kempenaar, Joshua E.; Liu, Yuanming; Bhandari, Pradeep; Dudik, Brenda A.

2012-01-01

On November 26, 2011, NASA launched a large (900 kg) rover as part of the Mars Science Laboratory (MSL) mission to Mars. The MSL rover is scheduled to land on Mars on August 5, 2012. Prior to launch, the Rover was successfully operated in simulated mission extreme environments during a 16-day long Rover System Thermal Test (STT). This paper describes the MSL Rover STT, test planning, test execution, test results, thermal model correlation and flight predictions. The rover was tested in the JPL 25-Foot Diameter Space Simulator Facility at the Jet Propulsion Laboratory (JPL). The Rover operated in simulated Cruise (vacuum) and Mars Surface environments (8 Torr nitrogen gas) with mission extreme hot and cold boundary conditions. A Xenon lamp solar simulator was used to impose simulated solar loads on the rover during a bounding hot case and during a simulated Mars diurnal test case. All thermal hardware was exercised and performed nominally. The Rover Heat Rejection System, a liquid-phase fluid loop used to transport heat in and out of the electronics boxes inside the rover chassis, performed better than predicted. Steady state and transient data were collected to allow correlation of analytical thermal models. These thermal models were subsequently used to predict rover thermal performance for the MSL Gale Crater landing site. Models predict that critical hardware temperatures will be maintained within allowable flight limits over the entire 669 Sol surface mission.

Using Vision and Speech Features for Automated Prediction of Performance Metrics in Multimodal Dialogs. Research Report. ETS RR-17-20

ERIC Educational Resources Information Center

Ramanarayanan, Vikram; Lange, Patrick; Evanini, Keelan; Molloy, Hillary; Tsuprun, Eugene; Qian, Yao; Suendermann-Oeft, David

2017-01-01

Predicting and analyzing multimodal dialog user experience (UX) metrics, such as overall call experience, caller engagement, and latency, among other metrics, in an ongoing manner is important for evaluating such systems. We investigate automated prediction of multiple such metrics collected from crowdsourced interactions with an open-source,…

Prediction of shipboard electromagnetic interference (EMI) problems using artificial intelligence (AI) technology

NASA Technical Reports Server (NTRS)

Swanson, David J.

1990-01-01

The electromagnetic interference prediction problem is characteristically ill-defined and complicated. Severe EMI problems are prevalent throughout the U.S. Navy, causing both expected and unexpected impacts on the operational performance of electronic combat systems onboard ships. This paper focuses on applying artificial intelligence (AI) technology to the prediction of ship related electromagnetic interference (EMI) problems.

Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming

ERIC Educational Resources Information Center

Zafra, Amelia; Ventura, Sebastian

2009-01-01

The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rottmann, Joerg; Berbeco, Ross

Purpose: Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. Methods: The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable tomore » overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal–external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Results: Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz). Conclusions: A linear prediction model can reduce latency induced tracking errors by an average of about 50% in real-time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.« less

Using an external surrogate for predictor model training in real-time motion management of lung tumors.

PubMed

Rottmann, Joerg; Berbeco, Ross

2014-12-01

Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable to overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal-external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz). A linear prediction model can reduce latency induced tracking errors by an average of about 50% in real-time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.

Development of a Pavement Maintenance Management System. Volume 9. Development of Airfield Pavement Performance Prediction Models.

DTIC Science & Technology

1984-05-01

materials, traffic, and climate, were used to develop PCI and key distress prediction models for both asphalt-concrete- and jointed-concrete- surfaced...Predicted PCI for PCC and AC/PCC Pavements Using Model Presented in Section III ...... 35 31 Effect of PCC Thickness on the PCI as a Function of Age...of Corner Breaking Observed vs Predicted Percent of Corner Breaking Using Model Presented in Section III

Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains

PubMed Central

Kang, Yong-Shin; Park, Il-Ha; Youm, Sekyoung

2016-01-01

In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase. PMID:27983654

Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains.

PubMed

Kang, Yong-Shin; Park, Il-Ha; Youm, Sekyoung

2016-12-14

In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase.

Solar-energy-system performance evaluation, General Electric - Milwaukee operational test site, Milwaukee, Wisconsin, September 1980 - March 1981

NASA Astrophysics Data System (ADS)

Howard, R. G.

The active solar energy system for a recreation hall for senior citizens in Wisconsin, is equipped with 1290 square feet of evacuated tube collectors, 3000 gallons of water in a tank, and a natural gas fired furnace for auxiliary space heating and a natural gas fired domestic water heater. The solar fraction, solar savings ratio, conventional fuel savings, system performance factor, and solar system coefficient of performance are given as well as performance data for the collector, storage, domestic hot water, and space heating subsystems, operating energy, energy savings, and weather conditions. Predicted performance data are also given for comparison with the measured data.

Railroad Performance Model

DOT National Transportation Integrated Search

1977-10-01

This report describes an operational, though preliminary, version of the Railroad Performance Model, which is a computer simulation model of the nation's railroad system. The ultimate purpose of this model is to predict the effect of changes in gover...

Thick thermal barrier coatings for diesel engines

NASA Technical Reports Server (NTRS)

Beardsley, M. Brad

1995-01-01

Caterpillar's approach to applying thick thermal barrier coatings (TTBC's) to diesel engine combustion chambers has been to use advanced modeling techniques to predict engine conditions and combine this information with fundamental property evaluation of TTBC systems to predict engine performance and TTBC stress states. Engine testing has been used to verify the predicted performance of the TTBC systems and provide information on failure mechanisms. The objective Caterpillar's program to date has been to advance the fundamental understanding of thick thermal barrier coating systems. Previous reviews of thermal barrier coating technology concluded that the current level of understanding of coating system behavior is inadequate and the lack of fundamental understanding may impeded the application of TTBC's to diesel engines. Areas of TTBC technology being examined in this program include powder characteristics and chemistry; bond coat composition; coating design, microstructure, and thickness as they affect properties, durability, and reliability; and TTBC 'aging' effects (microstructural and property changes) under diesel engine operating conditions. Methods to evaluate the reliability and durability of TTBC's have been developed that attempt to understand the fundamental strength of TTBC's for particular stress states.

Thick thermal barrier coatings for diesel engines

NASA Technical Reports Server (NTRS)

Beardsley, M. B.

1995-01-01

Caterpillar's approach to applying Thick Thermal Barrier Coatings (TTBC's) to diesel engine combustion chambers has been to use advanced modeling techniques to predict engine conditions and combine this information with fundamental property evaluation of TTBC systems to predict engine performance and TTBC stress states. Engine testing has been used to verify the predicted performance of the TTBC systems and provide information on failure mechanisms. The objective of Caterpillar's subcontract with ORNL is to advance the fundamental understanding of thick thermal barrier coating systems. Previous reviews of thermal barrier coating technology concluded that the current level of understanding of coating system behavior is inadequate and the lack of fundamental understanding may impede the application of TTBC's to diesel engines. Areas of TTBC technology being examined in this program include powder characteristics and chemistry; bond coat composition; coating design, microstructure, and thickness as they affect properties, durability, and reliability; and TTBC 'aging' effects (microstructural and property changes) under diesel engine operating conditions. Methods to evaluate the reliability and durability of TTBC's have been developed that attempt to understand the fundamental strength of TTBC's for particular stress states.

Model Predictions and Observed Performance of JWST's Cryogenic Position Metrology System

NASA Technical Reports Server (NTRS)

Lunt, Sharon R.; Rhodes, David; DiAntonio, Andrew; Boland, John; Wells, Conrad; Gigliotti, Trevis; Johanning, Gary

2016-01-01

The James Webb Space Telescope cryogenic testing requires measurement systems that both obtain a very high degree of accuracy and can function in that environment. Close-range photogrammetry was identified as meeting those criteria. Testing the capability of a close-range photogrammetric system prior to its existence is a challenging problem. Computer simulation was chosen over building a scaled mock-up to allow for increased flexibility in testing various configurations. Extensive validation work was done to ensure that the actual as-built system meet accuracy and repeatability requirements. The simulated image data predicted the uncertainty in measurement to be within specification and this prediction was borne out experimentally. Uncertainty at all levels was verified experimentally to be less than 0.1 millimeters.

Apollo 14 mission report. Supplement 5: Descent propulsion system final flight evaluation

NASA Technical Reports Server (NTRS)

Avvenire, A. T.; Wood, S. C.

1972-01-01

The performance of the LM-8 descent propulsion system during the Apollo 14 mission was evaluated and found to be satisfactory. The average engine effective specific impulse was 0.1 second higher than predicted, but well within the predicted l sigma uncertainty. The engine performance corrected to standard inlet conditions for the FTP portion of the burn at 43 seconds after ignition was as follows: thrust, 9802, lbf; specific impulse, 304.1 sec; and propellant mixture ratio, 1603. These values are + or - 0.8, -0.06, and + or - 0.3 percent different respectively, from the values reported from engine acceptance tests and were within specification limits.

System identification of an unmanned quadcopter system using MRAN neural

NASA Astrophysics Data System (ADS)

Pairan, M. F.; Shamsudin, S. S.

2017-12-01

This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

Application of thrusting ejectors to tactical aircraft having vertical lift and short-field capability

NASA Technical Reports Server (NTRS)

Koenig, D. G.; Stoll, F.; Aoyagi, K.

1981-01-01

The status of ejector development in terms of application to V/STOL aircraft is reported in three categories: aircraft systems and ejector concepts; ejector performance including prediction techniques and experimental data base available; and, integration of the ejector with complete aircraft configurations. Available prediction techniques are reviewed and performance of three ejector designs with vertical lift capability is summarized. Applications of the 'fuselage' and 'short diffuser' ejectors to fighter aircraft are related to current and planned research programs. Recommendations are listed for effort needed to evaluate installed performance.

Improving Resource Selection and Scheduling Using Predictions. Chapter 1

NASA Technical Reports Server (NTRS)

Smith, Warren

2003-01-01

The introduction of computational grids has resulted in several new problems in the area of scheduling that can be addressed using predictions. The first problem is selecting where to run an application on the many resources available in a grid. Our approach to help address this problem is to provide predictions of when an application would start to execute if submitted to specific scheduled computer systems. The second problem is gaining simultaneous access to multiple computer systems so that distributed applications can be executed. We help address this problem by investigating how to support advance reservations in local scheduling systems. Our approaches to both of these problems are based on predictions for the execution time of applications on space- shared parallel computers. As a side effect of this work, we also discuss how predictions of application run times can be used to improve scheduling performance.

Optimal control model predictions of system performance and attention allocation and their experimental validation in a display design study

NASA Technical Reports Server (NTRS)

Johannsen, G.; Govindaraj, T.

1980-01-01

The influence of different types of predictor displays in a longitudinal vertical takeoff and landing (VTOL) hover task is analyzed in a theoretical study. Several cases with differing amounts of predictive and rate information are compared. The optimal control model of the human operator is used to estimate human and system performance in terms of root-mean-square (rms) values and to compute optimized attention allocation. The only part of the model which is varied to predict these data is the observation matrix. Typical cases are selected for a subsequent experimental validation. The rms values as well as eye-movement data are recorded. The results agree favorably with those of the theoretical study in terms of relative differences. Better matching is achieved by revised model input data.

Two-Speed Gearbox Dynamic Simulation Predictions and Test Validation

NASA Technical Reports Server (NTRS)

Lewicki, David G.; DeSmidt, Hans; Smith, Edward C.; Bauman, Steven W.

2010-01-01

Dynamic simulations and experimental validation tests were performed on a two-stage, two-speed gearbox as part of the drive system research activities of the NASA Fundamental Aeronautics Subsonics Rotary Wing Project. The gearbox was driven by two electromagnetic motors and had two electromagnetic, multi-disk clutches to control output speed. A dynamic model of the system was created which included a direct current electric motor with proportional-integral-derivative (PID) speed control, a two-speed gearbox with dual electromagnetically actuated clutches, and an eddy current dynamometer. A six degree-of-freedom model of the gearbox accounted for the system torsional dynamics and included gear, clutch, shaft, and load inertias as well as shaft flexibilities and a dry clutch stick-slip friction model. Experimental validation tests were performed on the gearbox in the NASA Glenn gear noise test facility. Gearbox output speed and torque as well as drive motor speed and current were compared to those from the analytical predictions. The experiments correlate very well with the predictions, thus validating the dynamic simulation methodologies.

Advanced Earth-to-orbit propulsion technology program overview: Impact of civil space technology initiative

NASA Technical Reports Server (NTRS)

Stephenson, Frank W., Jr.

1988-01-01

The NASA Earth-to-Orbit (ETO) Propulsion Technology Program is dedicated to advancing rocket engine technologies for the development of fully reusable engine systems that will enable space transportation systems to achieve low cost, routine access to space. The program addresses technology advancements in the areas of engine life extension/prediction, performance enhancements, reduced ground operations costs, and in-flight fault tolerant engine operations. The primary objective is to acquire increased knowledge and understanding of rocket engine chemical and physical processes in order to evolve more realistic analytical simulations of engine internal environments, to derive more accurate predictions of steady and unsteady loads, and using improved structural analyses, to more accurately predict component life and performance, and finally to identify and verify more durable advanced design concepts. In addition, efforts were focused on engine diagnostic needs and advances that would allow integrated health monitoring systems to be developed for enhanced maintainability, automated servicing, inspection, and checkout, and ultimately, in-flight fault tolerant engine operations.

Deriving the polarization behavior of many-layer mirror coatings

NASA Astrophysics Data System (ADS)

White, Amanda J.; Harrington, David M.; Sueoka, Stacey R.

2018-06-01

End-to-end models of astronomical instrument performance are becoming commonplace to demonstrate feasibility and guarantee performance at large observatories. Astronomical techniques like adaptive optics and high contrast imaging have made great strides towards making detailed performance predictions, however, for polarimetric techniques, fundamental tools for predicting performance do not exist. One big missing piece is predicting the wavelength and field of view dependence of a many-mirror articulated optical system particularly with complex protected metal coatings. Predicting polarization performance of instruments requires combining metrology of mirror coatings, tools to create mirror coating models, and optical modeling software for polarized beam propagation. The inability to predict instrument induced polarization or to define polarization performance expectations has far reaching implications for up and coming major observatories, such as the Daniel K. Inouye Solar Telescope (DKIST), that aim to take polarization measurements at unprecedented sensitivity and resolution.Here we present a method for modelling the wavelength dependent refractive index of an optic using Berreman calculus - a mathematical formalism that describes how an electromagnetic field propagates through a birefringent medium. From Berreman calculus, we can better predict the Mueller matrix, diattenuation, and retardance of an arbitrary thicknesses of amorphous many-layer coatings as well as stacks of birefringent crystals from laboratory measurements. This will allow for the wavelength dependent refractive index to be accurately determined and the polarization behavior to be derived for a given optic.

Effects of regionalization decisions on an O/E index for the US national assessment

EPA Science Inventory

We examined the effects of different regionalization schemes on the performance of River Invertebrate Prediction and Classification System (RIVPACS)-type predictive models in assessing the biological conditions of streams of the US for the National Wadeable Streams Assessment (WS...

EOID System Model Validation, Metrics, and Synthetic Clutter Generation

DTIC Science & Technology

2003-09-30

Our long-term goal is to accurately predict the capability of the current generation of laser-based underwater imaging sensors to perform Electro ... Optic Identification (EOID) against relevant targets in a variety of realistic environmental conditions. The models will predict the impact of

Absolute fracture risk assessment using lumbar spine and femoral neck bone density measurements: derivation and validation of a hybrid system.

PubMed

Leslie, William D; Lix, Lisa M

2011-03-01

The World Health Organization (WHO) Fracture Risk Assessment Tool (FRAX) computes 10-year probability of major osteoporotic fracture from multiple risk factors, including femoral neck (FN) T-scores. Lumbar spine (LS) measurements are not currently part of the FRAX formulation but are used widely in clinical practice, and this creates confusion when there is spine-hip discordance. Our objective was to develop a hybrid 10-year absolute fracture risk assessment system in which nonvertebral (NV) fracture risk was assessed from the FN and clinical vertebral (V) fracture risk was assessed from the LS. We identified 37,032 women age 45 years and older undergoing baseline FN and LS dual-energy X-ray absorptiometry (DXA; 1990-2005) from a population database that contains all clinical DXA results for the Province of Manitoba, Canada. Results were linked to longitudinal health service records for physician billings and hospitalizations to identify nontrauma vertebral and nonvertebral fracture codes after bone mineral density (BMD) testing. The population was randomly divided into equal-sized derivation and validation cohorts. Using the derivation cohort, three fracture risk prediction systems were created from Cox proportional hazards models (adjusted for age and multiple FRAX risk factors): FN to predict combined all fractures, FN to predict nonvertebral fractures, and LS to predict vertebral (without nonvertebral) fractures. The hybrid system was the sum of nonvertebral risk from the FN model and vertebral risk from the LS model. The FN and hybrid systems were both strongly predictive of overall fracture risk (p < .001). In the validation cohort, ROC analysis showed marginally better performance of the hybrid system versus the FN system for overall fracture prediction (p = .24) and significantly better performance for vertebral fracture prediction (p < .001). In a discordance subgroup with FN and LS T-score differences greater than 1 SD, there was a significant improvement in overall fracture prediction with the hybrid method (p = .025). Risk reclassification under the hybrid system showed better alignment with observed fracture risk, with 6.4% of the women reclassified to a different risk category. In conclusion, a hybrid 10-year absolute fracture risk assessment system based on combining FN and LS information is feasible. The improvement in fracture risk prediction is small but supports clinical interest in a system that integrates LS in fracture risk assessment. Copyright © 2011 American Society for Bone and Mineral Research.

Discriminability of Prediction Artifacts in a Time Delayed Virtual Environment

NASA Technical Reports Server (NTRS)

Adelstein, Bernard D.; Jung, Jae Y.; Ellis, Stephen R.

2001-01-01

Overall latency remains an impediment to perceived image stability and consequently to human performance in virtual environment (VE) systems. Predictive compensators have been proposed as a means to mitigate these shortcomings, but they introduce rendering errors because of induced motion overshoot and heightened noise. Discriminability of these compensator artifacts was investigated by a protocol in which head tracked image stability for 35 ms baseline VE system latency was compared against artificially added (16.7 to 100 ms) latency compensated by a previously studied Kalman Filter (K-F) predictor. A control study in which uncompensated 16.7 to 100 ms latencies were compared against the baseline was also performed. Results from 10 subjects in the main study and 8 in the control group indicate that predictive compensation artifacts are less discernible than the disruptions of uncompensated time delay for the shorter but not the longer added latencies. We propose that noise magnification and overshoot are contributory cues to the presence of predictive compensation.

Photonic single nonlinear-delay dynamical node for information processing

NASA Astrophysics Data System (ADS)

Ortín, Silvia; San-Martín, Daniel; Pesquera, Luis; Gutiérrez, José Manuel

2012-06-01

An electro-optical system with a delay loop based on semiconductor lasers is investigated for information processing by performing numerical simulations. This system can replace a complex network of many nonlinear elements for the implementation of Reservoir Computing. We show that a single nonlinear-delay dynamical system has the basic properties to perform as reservoir: short-term memory and separation property. The computing performance of this system is evaluated for two prediction tasks: Lorenz chaotic time series and nonlinear auto-regressive moving average (NARMA) model. We sweep the parameters of the system to find the best performance. The results achieved for the Lorenz and the NARMA-10 tasks are comparable to those obtained by other machine learning methods.

A physiologically informed virtual reality based social communication system for individuals with autism.

PubMed

Lahiri, Uttama; Bekele, Esubalew; Dohrmann, Elizabeth; Warren, Zachary; Sarkar, Nilanjan

2015-04-01

Clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorders (ASD). This project evaluated the application of a novel physiologically responsive virtual reality based technological system for conversation skills in a group of adolescents with ASD. The system altered components of conversation based on (1) performance alone or (2) the composite effect of performance and physiological metrics of predicted engagement (e.g., gaze pattern, pupil dilation, blink rate). Participants showed improved performance and looking pattern within the physiologically sensitive system as compared to the performance based system. This suggests that physiologically informed technologies may have the potential of being an effective tool in the hands of interventionists.

Asynchronous decision making in a memorized paddle pressing task

NASA Astrophysics Data System (ADS)

Dankert, James R.; Olson, Byron; Si, Jennie

2008-12-01

This paper presents a method for asynchronous decision making using recorded neural data in a binary decision task. This is a demonstration of a technique for developing motor cortical neural prosthetics that do not rely on external cued timing information. The system presented in this paper uses support vector machines and leaky integrate-and-fire elements to predict directional paddle presses. In addition to the traditional metrics of accuracy, asynchronous systems must also optimize the time needed to make a decision. The system presented is able to predict paddle presses with a median accuracy of 88% and all decisions are made before the time of the actual paddle press. An alternative bit rate measure of performance is defined to show that the system proposed here is able to perform the task with the same efficiency as the rats.

Development of dry coal feeders

NASA Technical Reports Server (NTRS)

Bonin, J. H.; Cantey, D. E.; Daniel, A. D., Jr.; Meyer, J. W.

1977-01-01

Design and fabrication of equipment of feed coal into pressurized environments were investigated. Concepts were selected based on feeder system performance and economic projections. These systems include: two approaches using rotating components, a gas or steam driven ejector, and a modified standpipe feeder concept. Results of development testing of critical components, design procedures, and performance prediction techniques are reviewed.

The Five Key Questions of Human Performance Modeling.

PubMed

Wu, Changxu

2018-01-01

Via building computational (typically mathematical and computer simulation) models, human performance modeling (HPM) quantifies, predicts, and maximizes human performance, human-machine system productivity and safety. This paper describes and summarizes the five key questions of human performance modeling: 1) Why we build models of human performance; 2) What the expectations of a good human performance model are; 3) What the procedures and requirements in building and verifying a human performance model are; 4) How we integrate a human performance model with system design; and 5) What the possible future directions of human performance modeling research are. Recent and classic HPM findings are addressed in the five questions to provide new thinking in HPM's motivations, expectations, procedures, system integration and future directions.

Probabilistic Seeking Prediction in P2P VoD Systems

NASA Astrophysics Data System (ADS)

Wang, Weiwei; Xu, Tianyin; Gao, Yang; Lu, Sanglu

In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking prediction which is to predict the user's next seeking position so that the system can proactively make response. We present a novel method for solving this problem. In our method, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training dataset to state transitions according to the abstract states, we use a simpel probabilistic contingency table to build the prediction model. We design an experiment on the synthetic P2P VoD dataset. The results demonstrate the effectiveness of our method.

Photovoltaic performance models: an evaluation with actual field data

NASA Astrophysics Data System (ADS)

TamizhMani, Govindasamy; Ishioye, John-Paul; Voropayev, Arseniy; Kang, Yi

2008-08-01

Prediction of energy production is crucial to the design and installation of the building integrated photovoltaic systems. This prediction should be attainable based on the commonly available parameters such as system size, orientation and tilt angle. Several commercially available as well as free downloadable software tools exist to predict energy production. Six software models have been evaluated in this study and they are: PV Watts, PVsyst, MAUI, Clean Power Estimator, Solar Advisor Model (SAM) and RETScreen. This evaluation has been done by comparing the monthly, seasonaly and annually predicted data with the actual, field data obtained over a year period on a large number of residential PV systems ranging between 2 and 3 kWdc. All the systems are located in Arizona, within the Phoenix metropolitan area which lies at latitude 33° North, and longitude 112 West, and are all connected to the electrical grid.

Comparison of in silico models for prediction of mutagenicity.

PubMed

Bakhtyari, Nazanin G; Raitano, Giuseppa; Benfenati, Emilio; Martin, Todd; Young, Douglas

2013-01-01

Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

Performance simulation of a grid connected photovoltaic power system using TRNSYS 17

NASA Astrophysics Data System (ADS)

Raja Sekhar, Y.; Ganesh, D.; Kumar, A. Suresh; Abraham, Raju; Padmanathan, P.

2017-11-01

Energy plays an important role in a country’s economic growth in the current energy scenario, the major problem is depletion of energy sources (non-renewable) are more than being formed. One of the prominent solutions is minimizing the use of fossil fuels by utilization of renewable energy resources. A photovoltaic system is an efficient option in terms of utilizing the solar energy resource. The electricity output produced by the photovoltaic systems depends upon the incident solar radiation. This paper examines the performance simulation of 200KW photovoltaic power system at VIT University, Vellore. The main objective of this paper is to correlate the results between the predicted simulation data and the experimental data. The simulation tool used here is TRNSYS. Using TRNSYS modelling prediction of electricity produced throughout the year can be calculated with the help of TRNSYS weather station. The deviation of the simulated results with the experimented results varies due to the choice of weather station. Results from the field test and simulation results are to be correlated to attain the maximum performance of the system.

An empirical study of the predictive validity of number grades in medical school using 3 decades of longitudinal data: implications for a grading system.

PubMed

Gonnella, Joseph S; Erdmann, James B; Hojat, Mohammadreza

2004-04-01

Context It is important to establish the predictive validity of medical school grades. The strength of predictive validity and the ability to identify at-risk students in medical schools depends upon assessment systems such as number grades, pass/fail (P/F) or honours/pass/fail (H/P/F) systems. Objective This study was designed to examine the predictive validity of number grades in medical school, and to determine whether any important information is lost in a shift from number to P/F and H/P/F grading systems. Subjects The participants in this prospective, longitudinal study were 6656 medical students who studied at Jefferson Medical College over 3 decades. They were grouped into 10 deciles based on their number grades in Year 1 of medical school. Methods Participants were compared on academic accomplishments in Years 2 and 3 of medical school, medical school class rank, delayed graduation and attrition, performance on medical licensing examinations and clinical competence ratings in the first postgraduate year. Results Results supported the short- and longterm predictive validity of the number grades. Ratings of clinical competence beyond medical school were predicted by number grades in medical school. We demonstrated that small differences in number grades are statistically meaningful, and that important information for identifying students in need of remedial education is lost when students who narrowly meet faculty's expectations are included with the rest of the class in a broad 'pass' category. Conclusions The findings refute the argument that knowledge of sciences basic to medicine is not critical to subsequent performance in medical school and beyond if an appropriate evaluation system is used. Furthermore, the results of this study raise questions about abandoning number grades in favour of a pass/fail system. Consideration of these findings in policy decisions regarding assessment systems of medical students is recommended.

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Effects of regional groundwater flow on the performance of an aquifer thermal energy storage system under continuous operation

NASA Astrophysics Data System (ADS)

Lee, Kun Sang

2014-01-01

Numerical investigations and a thermohydraulic evaluation are presented for two-well models of an aquifer thermal energy storage (ATES) system operating under a continuous flow regime. A three-dimensional numerical model for groundwater flow and heat transport is used to analyze the thermal energy storage in the aquifer. This study emphasizes the influence of regional groundwater flow on the heat transfer and storage of the system under various operation scenarios. For different parameters of the system, performances were compared in terms of the temperature of recovered water and the temperature field in the aquifer. The calculated temperature at the producing well varies within a certain range throughout the year, reflecting the seasonal (quarterly) temperature variation of the injected water. The pressure gradient across the system, which determines the direction and velocity of regional groundwater flow, has a substantial influence on the convective heat transport and performance of aquifer thermal storage. Injection/production rate and geometrical size of the aquifer used in the model also impact the predicted temperature distribution at each stage and the recovery water temperature. The hydrogeological-thermal simulation is shown to play an integral part in the prediction of performance of processes as complicated as those in ATES systems.

Performance and Reliability Optimization for Aerospace Systems subject to Uncertainty and Degradation

NASA Technical Reports Server (NTRS)

Miller, David W.; Uebelhart, Scott A.; Blaurock, Carl

2004-01-01

This report summarizes work performed by the Space Systems Laboratory (SSL) for NASA Langley Research Center in the field of performance optimization for systems subject to uncertainty. The objective of the research is to develop design methods and tools to the aerospace vehicle design process which take into account lifecycle uncertainties. It recognizes that uncertainty between the predictions of integrated models and data collected from the system in its operational environment is unavoidable. Given the presence of uncertainty, the goal of this work is to develop means of identifying critical sources of uncertainty, and to combine these with the analytical tools used with integrated modeling. In this manner, system uncertainty analysis becomes part of the design process, and can motivate redesign. The specific program objectives were: 1. To incorporate uncertainty modeling, propagation and analysis into the integrated (controls, structures, payloads, disturbances, etc.) design process to derive the error bars associated with performance predictions. 2. To apply modern optimization tools to guide in the expenditure of funds in a way that most cost-effectively improves the lifecycle productivity of the system by enhancing the subsystem reliability and redundancy. The results from the second program objective are described. This report describes the work and results for the first objective: uncertainty modeling, propagation, and synthesis with integrated modeling.

Bayesian decision support for coding occupational injury data.

PubMed

Nanda, Gaurav; Grattan, Kathleen M; Chu, MyDzung T; Davis, Letitia K; Lehto, Mark R

2016-06-01

Studies on autocoding injury data have found that machine learning algorithms perform well for categories that occur frequently but often struggle with rare categories. Therefore, manual coding, although resource-intensive, cannot be eliminated. We propose a Bayesian decision support system to autocode a large portion of the data, filter cases for manual review, and assist human coders by presenting them top k prediction choices and a confusion matrix of predictions from Bayesian models. We studied the prediction performance of Single-Word (SW) and Two-Word-Sequence (TW) Naïve Bayes models on a sample of data from the 2011 Survey of Occupational Injury and Illness (SOII). We used the agreement in prediction results of SW and TW models, and various prediction strength thresholds for autocoding and filtering cases for manual review. We also studied the sensitivity of the top k predictions of the SW model, TW model, and SW-TW combination, and then compared the accuracy of the manually assigned codes to SOII data with that of the proposed system. The accuracy of the proposed system, assuming well-trained coders reviewing a subset of only 26% of cases flagged for review, was estimated to be comparable (86.5%) to the accuracy of the original coding of the data set (range: 73%-86.8%). Overall, the TW model had higher sensitivity than the SW model, and the accuracy of the prediction results increased when the two models agreed, and for higher prediction strength thresholds. The sensitivity of the top five predictions was 93%. The proposed system seems promising for coding injury data as it offers comparable accuracy and less manual coding. Accurate and timely coded occupational injury data is useful for surveillance as well as prevention activities that aim to make workplaces safer. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

SEASAT economic assessment. Volume 2: The SEASAT system description and performance. [performance prediction and systems analysis for seasat satellites

NASA Technical Reports Server (NTRS)

1975-01-01

Results are presented of preliminary trade-off studies of operational SEASAT systems. The trade-off studies were used as the basis for the estimation of costs and net benefits of the operational SEASAT system. Also presented are the preliminary results of simulation studies that were designed to lead to a measure of the impact of SEASAT data through the use of numerical weather forecast models.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

PubMed Central

Yoo, Tae Keun; Kim, Deok Won; Choi, Soo Beom; Oh, Ein; Park, Jee Soo

2016-01-01

Background Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. Methods The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. Results The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). Conclusions The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk. PMID:26859664

The financial performance of hospitals belonging to health networks and systems.

PubMed

Bazzoli, G J; Chan, B; Shortell, S M; D'Aunno, T

2000-01-01

The U.S. health industry is experiencing substantial restructuring through ownership consolidation and development of new forms of interorganizational relationships. Using an established taxonomy of health networks and systems, this paper develops and tests four hypotheses related to hospital financial performance. Consistent with our predictions, we find that hospitals in health systems that had unified ownership generally had better financial performance than hospitals in contractually based health networks. Among health network hospitals, those belonging to highly centralized networks had better financial performance than those belonging to more decentralized networks. However, health system hospitals in moderately centralized systems performed better than those in highly centralized systems. Finally, hospitals in networks or systems with little differentiation or centralization experienced the poorest financial performance. These results are consistent with resource dependence, transaction cost economics, and institutional theories of organizational behavior, and provide a conceptual and empirical baseline for future research.

Implementation and Test of the Automatic Flight Dynamics Operations for Geostationary Satellite Mission

NASA Astrophysics Data System (ADS)

Park, Sangwook; Lee, Young-Ran; Hwang, Yoola; Javier Santiago Noguero Galilea

2009-12-01

This paper describes the Flight Dynamics Automation (FDA) system for COMS Flight Dynamics System (FDS) and its test result in terms of the performance of the automation jobs. FDA controls the flight dynamics functions such as orbit determination, orbit prediction, event prediction, and fuel accounting. The designed FDA is independent from the specific characteristics which are defined by spacecraft manufacturer or specific satellite missions. Therefore, FDA could easily links its autonomous job control functions to any satellite mission control system with some interface modification. By adding autonomous system along with flight dynamics system, it decreases the operator’s tedious and repeated jobs but increase the usability and reliability of the system. Therefore, FDA is used to improve the completeness of whole mission control system’s quality. The FDA is applied to the real flight dynamics system of a geostationary satellite, COMS and the experimental test is performed. The experimental result shows the stability and reliability of the mission control operations through the automatic job control.

Parts and Components Reliability Assessment: A Cost Effective Approach

NASA Technical Reports Server (NTRS)

Lee, Lydia

2009-01-01

System reliability assessment is a methodology which incorporates reliability analyses performed at parts and components level such as Reliability Prediction, Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) to assess risks, perform design tradeoffs, and therefore, to ensure effective productivity and/or mission success. The system reliability is used to optimize the product design to accommodate today?s mandated budget, manpower, and schedule constraints. Stand ard based reliability assessment is an effective approach consisting of reliability predictions together with other reliability analyses for electronic, electrical, and electro-mechanical (EEE) complex parts and components of large systems based on failure rate estimates published by the United States (U.S.) military or commercial standards and handbooks. Many of these standards are globally accepted and recognized. The reliability assessment is especially useful during the initial stages when the system design is still in the development and hard failure data is not yet available or manufacturers are not contractually obliged by their customers to publish the reliability estimates/predictions for their parts and components. This paper presents a methodology to assess system reliability using parts and components reliability estimates to ensure effective productivity and/or mission success in an efficient manner, low cost, and tight schedule.

Fan Noise Prediction with Applications to Aircraft System Noise Assessment

NASA Technical Reports Server (NTRS)

Nark, Douglas M.; Envia, Edmane; Burley, Casey L.

2009-01-01

This paper describes an assessment of current fan noise prediction tools by comparing measured and predicted sideline acoustic levels from a benchmark fan noise wind tunnel test. Specifically, an empirical method and newly developed coupled computational approach are utilized to predict aft fan noise for a benchmark test configuration. Comparisons with sideline noise measurements are performed to assess the relative merits of the two approaches. The study identifies issues entailed in coupling the source and propagation codes, as well as provides insight into the capabilities of the tools in predicting the fan noise source and subsequent propagation and radiation. In contrast to the empirical method, the new coupled computational approach provides the ability to investigate acoustic near-field effects. The potential benefits/costs of these new methods are also compared with the existing capabilities in a current aircraft noise system prediction tool. The knowledge gained in this work provides a basis for improved fan source specification in overall aircraft system noise studies.

Prediction of the partitioning behaviour of proteins in aqueous two-phase systems using only their amino acid composition.

PubMed

Salgado, J Cristian; Andrews, Barbara A; Ortuzar, Maria Fernanda; Asenjo, Juan A

2008-01-18

The prediction of the partition behaviour of proteins in aqueous two-phase systems (ATPS) using mathematical models based on their amino acid composition was investigated. The predictive models are based on the average surface hydrophobicity (ASH). The ASH was estimated by means of models that use the three-dimensional structure of proteins and by models that use only the amino acid composition of proteins. These models were evaluated for a set of 11 proteins with known experimental partition coefficient in four-phase systems: polyethylene glycol (PEG) 4000/phosphate, sulfate, citrate and dextran and considering three levels of NaCl concentration (0.0% w/w, 0.6% w/w and 8.8% w/w). The results indicate that such prediction is feasible even though the quality of the prediction depends strongly on the ATPS and its operational conditions such as the NaCl concentration. The ATPS 0 model which use the three-dimensional structure obtains similar results to those given by previous models based on variables measured in the laboratory. In addition it maintains the main characteristics of the hydrophobic resolution and intrinsic hydrophobicity reported before. Three mathematical models, ATPS I-III, based only on the amino acid composition were evaluated. The best results were obtained by the ATPS I model which assumes that all of the amino acids are completely exposed. The performance of the ATPS I model follows the behaviour reported previously, i.e. its correlation coefficients improve as the NaCl concentration increases in the system and, therefore, the effect of the protein hydrophobicity prevails over other effects such as charge or size. Its best predictive performance was obtained for the PEG/dextran system at high NaCl concentration. An increase in the predictive capacity of at least 54.4% with respect to the models which use the three-dimensional structure of the protein was obtained for that system. In addition, the ATPS I model exhibits high correlation coefficients in that system being higher than 0.88 on average. The ATPS I model exhibited correlation coefficients higher than 0.67 for the rest of the ATPS at high NaCl concentration. Finally, we tested our best model, the ATPS I model, on the prediction of the partition coefficient of the protein invertase. We found that the predictive capacities of the ATPS I model are better in PEG/dextran systems, where the relative error of the prediction with respect to the experimental value is 15.6%.

MSFC Skylab structures and mechanical systems mission evaluation

NASA Technical Reports Server (NTRS)

1974-01-01

A performance analysis for structural and mechanical major hardware systems and components is presented. Development background testing, modifications, and requirement adjustments are included. Functional narratives are provided for comparison purposes as are predicted design performance criterion. Each item is evaluated on an individual basis: that is, (1) history (requirements, design, manufacture, and test); (2) in-orbit performance (description and analysis); and (3) conclusions and recommendations regarding future space hardware application. Overall, the structural and mechanical performance of the Skylab hardware was outstanding.

Development of a wideband pulse quaternary modulation system. [for an operational 400 Mbps baseband laser communication system

NASA Technical Reports Server (NTRS)

Federhofer, J. A.

1974-01-01

Laboratory data verifying the pulse quaternary modulation (PQM) theoretical predictions is presented. The first laboratory PQM laser communication system was successfully fabricated, integrated, tested and demonstrated. System bit error rate tests were performed and, in general, indicated approximately a 2 db degradation from the theoretically predicted results. These tests indicated that no gross errors were made in the initial theoretical analysis of PQM. The relative ease with which the entire PQM laboratory system was integrated and tested indicates that PQM is a viable candidate modulation scheme for an operational 400 Mbps baseband laser communication system.

A Multiple Agent Model of Human Performance in Automated Air Traffic Control and Flight Management Operations

NASA Technical Reports Server (NTRS)

Corker, Kevin; Pisanich, Gregory; Condon, Gregory W. (Technical Monitor)

1995-01-01

A predictive model of human operator performance (flight crew and air traffic control (ATC)) has been developed and applied in order to evaluate the impact of automation developments in flight management and air traffic control. The model is used to predict the performance of a two person flight crew and the ATC operators generating and responding to clearances aided by the Center TRACON Automation System (CTAS). The purpose of the modeling is to support evaluation and design of automated aids for flight management and airspace management and to predict required changes in procedure both air and ground in response to advancing automation in both domains. Additional information is contained in the original extended abstract.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Burst muscle performance predicts the speed, acceleration, and turning performance of Anna’s hummingbirds

PubMed Central

Segre, Paolo S; Dakin, Roslyn; Zordan, Victor B; Dickinson, Michael H; Straw, Andrew D; Altshuler, Douglas L

2015-01-01

Despite recent advances in the study of animal flight, the biomechanical determinants of maneuverability are poorly understood. It is thought that maneuverability may be influenced by intrinsic body mass and wing morphology, and by physiological muscle capacity, but this hypothesis has not yet been evaluated because it requires tracking a large number of free flight maneuvers from known individuals. We used an automated tracking system to record flight sequences from 20 Anna's hummingbirds flying solo and in competition in a large chamber. We found that burst muscle capacity predicted most performance metrics. Hummingbirds with higher burst capacity flew with faster velocities, accelerations, and rotations, and they used more demanding complex turns. In contrast, body mass did not predict variation in maneuvering performance, and wing morphology predicted only the use of arcing turns and high centripetal accelerations. Collectively, our results indicate that burst muscle capacity is a key predictor of maneuverability. DOI: http://dx.doi.org/10.7554/eLife.11159.001 PMID:26583753

Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

2014-01-01

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

EXCLUSION OF RARE TAXA AFFECTS PERFORMANCE OF THE O/E INDEX IN BIOASSESSMENTS

EPA Science Inventory

The contribution of rare taxa to bioassessments based on multispecies assemblages is the subject of continued debate. As a result, users of predictive models such as River InVertebrate Prediction and Classification System (RIVPACS) disagree on whether to exclude locally rare taxa...

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

Program Predicts Time Courses of Human/Computer Interactions

NASA Technical Reports Server (NTRS)

Vera, Alonso; Howes, Andrew

2005-01-01

CPM X is a computer program that predicts sequences of, and amounts of time taken by, routine actions performed by a skilled person performing a task. Unlike programs that simulate the interaction of the person with the task environment, CPM X predicts the time course of events as consequences of encoded constraints on human behavior. The constraints determine which cognitive and environmental processes can occur simultaneously and which have sequential dependencies. The input to CPM X comprises (1) a description of a task and strategy in a hierarchical description language and (2) a description of architectural constraints in the form of rules governing interactions of fundamental cognitive, perceptual, and motor operations. The output of CPM X is a Program Evaluation Review Technique (PERT) chart that presents a schedule of predicted cognitive, motor, and perceptual operators interacting with a task environment. The CPM X program allows direct, a priori prediction of skilled user performance on complex human-machine systems, providing a way to assess critical interfaces before they are deployed in mission contexts.

Predictive accuracy of a model of volatile anesthetic uptake.

PubMed

Kennedy, R Ross; French, Richard A; Spencer, Christopher

2002-12-01

A computer program that models anesthetic uptake and distribution has been in use in our department for 20 yr as a teaching tool. New anesthesia machines that electronically measure fresh gas flow rates and vaporizer settings allowed us to assess the performance of this model during clinical anesthesia. Gas flow, vaporizer settings, and end-tidal concentrations were collected from the anesthesia machine (Datex S/5 ADU) at 10-s intervals during 30 elective anesthetics. These were entered into the uptake model. Expired anesthetic vapor concentrations were calculated and compared with actual values as measured by the patient monitor (Datex AS/3). Sevoflurane was used in 16 patients and isoflurane in 14 patients. For all patients, the median performance error was -0.24%, the median absolute performance error was 13.7%, divergence was 2.3%/h, and wobble was 3.1%. There was no significant difference between sevoflurane and isoflurane. This model predicted expired concentrations well in these patients. These results are similar to those seen when comparing calculated and actual propofol concentrations in propofol infusion systems and meet published guidelines for the accuracy of models used in target-controlled anesthesia systems. This model may be useful for predicting responses to changes in fresh gas and vapor settings. We compared measured inhaled anesthetic concentrations with those predicted by a model. The method used for comparison has been used to study models of propofol administration. Our model predicts expired isoflurane and sevoflurane concentrations at least as well as common propofol models predict arterial propofol concentrations.

Analysis of Complex Valve and Feed Systems

NASA Technical Reports Server (NTRS)

Ahuja, Vineet; Hosangadi, Ashvin; Shipman, Jeremy; Cavallo, Peter; Dash, Sanford

2007-01-01

A numerical framework for analysis of complex valve systems supports testing of propulsive systems by simulating key valve and control system components in the test loop. In particular, it is designed to enhance the analysis capability in terms of identifying system transients and quantifying the valve response to these transients. This system has analysis capability for simulating valve motion in complex systems operating in diverse flow regimes ranging from compressible gases to cryogenic liquids. A key feature is the hybrid, unstructured framework with sub-models for grid movement and phase change including cryogenic cavitations. The multi-element unstructured framework offers improved predictions of valve performance characteristics under steady conditions for structurally complex valves such as pressure regulator valve. Unsteady simulations of valve motion using this computational approach have been carried out for various valves in operation at Stennis Space Center such as the split-body valve and the 10-in. (approx.25.4-cm) LOX (liquid oxygen) valve and the 4-in. (approx.10 cm) Y-pattern valve (liquid nitrogen). Such simulations make use of variable grid topologies, thereby permitting solution accuracy and resolving important flow physics in the seat region of the moving valve. An advantage to this software includes possible reduction in testing costs incurred due to disruptions relating to unexpected flow transients or functioning of valve/flow control systems. Prediction of the flow anomalies leading to system vibrations, flow resonance, and valve stall can help in valve scheduling and significantly reduce the need for activation tests. This framework has been evaluated for its ability to predict performance metrics like flow coefficient for cavitating venturis and valve coefficient curves, and could be a valuable tool in predicting and understanding anomalous behavior of system components at rocket propulsion testing and design sites.

Predicting human activities in sequences of actions in RGB-D videos

NASA Astrophysics Data System (ADS)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

An integrated physiology model to study regional lung damage effects and the physiologic response

PubMed Central

2014-01-01

Background This work expands upon a previously developed exercise dynamic physiology model (DPM) with the addition of an anatomic pulmonary system in order to quantify the impact of lung damage on oxygen transport and physical performance decrement. Methods A pulmonary model is derived with an anatomic structure based on morphometric measurements, accounting for heterogeneous ventilation and perfusion observed experimentally. The model is incorporated into an existing exercise physiology model; the combined system is validated using human exercise data. Pulmonary damage from blast, blunt trauma, and chemical injury is quantified in the model based on lung fluid infiltration (edema) which reduces oxygen delivery to the blood. The pulmonary damage component is derived and calibrated based on published animal experiments; scaling laws are used to predict the human response to lung injury in terms of physical performance decrement. Results The augmented dynamic physiology model (DPM) accurately predicted the human response to hypoxia, altitude, and exercise observed experimentally. The pulmonary damage parameters (shunt and diffusing capacity reduction) were fit to experimental animal data obtained in blast, blunt trauma, and chemical damage studies which link lung damage to lung weight change; the model is able to predict the reduced oxygen delivery in damage conditions. The model accurately estimates physical performance reduction with pulmonary damage. Conclusions We have developed a physiologically-based mathematical model to predict performance decrement endpoints in the presence of thoracic damage; simulations can be extended to estimate human performance and escape in extreme situations. PMID:25044032

A novel toolpath force prediction algorithm using CAM volumetric data for optimizing robotic arthroplasty.

PubMed

Kianmajd, Babak; Carter, David; Soshi, Masakazu

2016-10-01

Robotic total hip arthroplasty is a procedure in which milling operations are performed on the femur to remove material for the insertion of a prosthetic implant. The robot performs the milling operation by following a sequential list of tool motions, also known as a toolpath, generated by a computer-aided manufacturing (CAM) software. The purpose of this paper is to explain a new toolpath force prediction algorithm that predicts cutting forces, which results in improving the quality and safety of surgical systems. With a custom macro developed in the CAM system's native application programming interface, cutting contact patch volume was extracted from CAM simulations. A time domain cutting force model was then developed through the use of a cutting force prediction algorithm. The second portion validated the algorithm by machining a hip canal in simulated bone using a CNC machine. Average cutting forces were measured during machining using a dynamometer and compared to the values predicted from CAM simulation data using the proposed method. The results showed the predicted forces matched the measured forces in both magnitude and overall pattern shape. However, due to inconsistent motion control, the time duration of the forces was slightly distorted. Nevertheless, the algorithm effectively predicted the forces throughout an entire hip canal procedure. This method provides a fast and easy technique for predicting cutting forces during orthopedic milling by utilizing data within a CAM software.

NASA Lewis Stirling engine computer code evaluation

NASA Technical Reports Server (NTRS)

Sullivan, Timothy J.

1989-01-01

In support of the U.S. Department of Energy's Stirling Engine Highway Vehicle Systems program, the NASA Lewis Stirling engine performance code was evaluated by comparing code predictions without engine-specific calibration factors to GPU-3, P-40, and RE-1000 Stirling engine test data. The error in predicting power output was -11 percent for the P-40 and 12 percent for the Re-1000 at design conditions and 16 percent for the GPU-3 at near-design conditions (2000 rpm engine speed versus 3000 rpm at design). The efficiency and heat input predictions showed better agreement with engine test data than did the power predictions. Concerning all data points, the error in predicting the GPU-3 brake power was significantly larger than for the other engines and was mainly a result of inaccuracy in predicting the pressure phase angle. Analysis into this pressure phase angle prediction error suggested that improvements to the cylinder hysteresis loss model could have a significant effect on overall Stirling engine performance predictions.

Nonlinear analysis and performance evaluation of the Annular Suspension and Pointing System (ASPS)

NASA Technical Reports Server (NTRS)

Joshi, S. M.

1978-01-01

The Annular Suspension and Pointing System (ASPS) can provide high accurate fine pointing for a variety of solar-, stellar-, and Earth-viewing scientific instruments during space shuttle orbital missions. In this report, a detailed nonlinear mathematical model is developed for the ASPS/Space Shuttle system. The equations are augmented with nonlinear models of components such as magnetic actuators and gimbal torquers. Control systems and payload attitude state estimators are designed in order to obtain satisfactory pointing performance, and statistical pointing performance is predicted in the presence of measurement noise and disturbances.

Bayesian performance metrics and small system integration in recent homeland security and defense applications

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Kostrzewski, Andrew; Patton, Edward; Pradhan, Ranjit; Shih, Min-Yi; Walter, Kevin; Savant, Gajendra; Shie, Rick; Forrester, Thomas

2010-04-01

In this paper, Bayesian inference is applied to performance metrics definition of the important class of recent Homeland Security and defense systems called binary sensors, including both (internal) system performance and (external) CONOPS. The medical analogy is used to define the PPV (Positive Predictive Value), the basic Bayesian metrics parameter of the binary sensors. Also, Small System Integration (SSI) is discussed in the context of recent Homeland Security and defense applications, emphasizing a highly multi-technological approach, within the broad range of clusters ("nexus") of electronics, optics, X-ray physics, γ-ray physics, and other disciplines.

Integrated modeling tool for performance engineering of complex computer systems

NASA Technical Reports Server (NTRS)

Wright, Gary; Ball, Duane; Hoyt, Susan; Steele, Oscar

1989-01-01

This report summarizes Advanced System Technologies' accomplishments on the Phase 2 SBIR contract NAS7-995. The technical objectives of the report are: (1) to develop an evaluation version of a graphical, integrated modeling language according to the specification resulting from the Phase 2 research; and (2) to determine the degree to which the language meets its objectives by evaluating ease of use, utility of two sets of performance predictions, and the power of the language constructs. The technical approach followed to meet these objectives was to design, develop, and test an evaluation prototype of a graphical, performance prediction tool. The utility of the prototype was then evaluated by applying it to a variety of test cases found in the literature and in AST case histories. Numerous models were constructed and successfully tested. The major conclusion of this Phase 2 SBIR research and development effort is that complex, real-time computer systems can be specified in a non-procedural manner using combinations of icons, windows, menus, and dialogs. Such a specification technique provides an interface that system designers and architects find natural and easy to use. In addition, PEDESTAL's multiview approach provides system engineers with the capability to perform the trade-offs necessary to produce a design that meets timing performance requirements. Sample system designs analyzed during the development effort showed that models could be constructed in a fraction of the time required by non-visual system design capture tools.

Simple Scoring System to Predict In-Hospital Mortality After Surgery for Infective Endocarditis.

PubMed

Gatti, Giuseppe; Perrotti, Andrea; Obadia, Jean-François; Duval, Xavier; Iung, Bernard; Alla, François; Chirouze, Catherine; Selton-Suty, Christine; Hoen, Bruno; Sinagra, Gianfranco; Delahaye, François; Tattevin, Pierre; Le Moing, Vincent; Pappalardo, Aniello; Chocron, Sidney

2017-07-20

Aspecific scoring systems are used to predict the risk of death postsurgery in patients with infective endocarditis (IE). The purpose of the present study was both to analyze the risk factors for in-hospital death, which complicates surgery for IE, and to create a mortality risk score based on the results of this analysis. Outcomes of 361 consecutive patients (mean age, 59.1±15.4 years) who had undergone surgery for IE in 8 European centers of cardiac surgery were recorded prospectively, and a risk factor analysis (multivariable logistic regression) for in-hospital death was performed. The discriminatory power of a new predictive scoring system was assessed with the receiver operating characteristic curve analysis. Score validation procedures were carried out. Fifty-six (15.5%) patients died postsurgery. BMI >27 kg/m 2 (odds ratio [OR], 1.79; P =0.049), estimated glomerular filtration rate <50 mL/min (OR, 3.52; P <0.0001), New York Heart Association class IV (OR, 2.11; P =0.024), systolic pulmonary artery pressure >55 mm Hg (OR, 1.78; P =0.032), and critical state (OR, 2.37; P =0.017) were independent predictors of in-hospital death. A scoring system was devised to predict in-hospital death postsurgery for IE (area under the receiver operating characteristic curve, 0.780; 95% CI, 0.734-0.822). The score performed better than 5 of 6 scoring systems for in-hospital death after cardiac surgery that were considered. A simple scoring system based on risk factors for in-hospital death was specifically created to predict mortality risk postsurgery in patients with IE. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

The Predictive Validity of Interim Assessment Scores Based on the Full-Information Bifactor Model for the Prediction of End-of-Grade Test Performance

ERIC Educational Resources Information Center

Immekus, Jason C.; Atitya, Ben

2016-01-01

Interim tests are a central component of district-wide assessment systems, yet their technical quality to guide decisions (e.g., instructional) has been repeatedly questioned. In response, the study purpose was to investigate the validity of a series of English Language Arts (ELA) interim assessments in terms of dimensionality and prediction of…

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

NASA Astrophysics Data System (ADS)

Hong, S.-M.; Jung, B.-H.; Ruan, D.

2011-03-01

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space.

PubMed

Hong, S-M; Jung, B-H; Ruan, D

2011-03-21

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Positive lubrication system

NASA Technical Reports Server (NTRS)

Smith, Dennis W.; Hooper, Fred L.

1990-01-01

As part of the development of an autonomous lubrication system for spin bearings, a system was developed to deliver oil to grease-lubricated bearings upon demand. This positive oil delivery system (PLUS) consists of a pressurized reservoir with a built-in solenoid valve that delivers a predictable quantity of oil to the spin bearing through a system of stainless steel tubes. Considerable testing was performed on the PLUS to characterize its performance and verify its effectiveness, along with qualifying it for flight. Additional development is underway that will lead to the fully autonomous active lubrication system.

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.

PubMed

Najah, A; El-Shafie, A; Karim, O A; El-Shafie, Amr H

2014-02-01

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

Progress towards an Optimization Methodology for Combustion-Driven Portable Thermoelectric Power Generation Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnan, Shankar; Karri, Naveen K.; Gogna, Pawan K.

2012-03-13

Enormous military and commercial interests exist in developing quiet, lightweight, and compact thermoelectric (TE) power generation systems. This paper investigates design integration and analysis of an advanced TE power generation system implementing JP-8 fueled combustion and thermal recuperation. Design and development of a portable TE power system using a JP-8 combustor as a high temperature heat source and optimal process flows depend on efficient heat generation, transfer, and recovery within the system are explored. Design optimization of the system required considering the combustion system efficiency and TE conversion efficiency simultaneously. The combustor performance and TE sub-system performance were coupled directlymore » through exhaust temperatures, fuel and air mass flow rates, heat exchanger performance, subsequent hot-side temperatures, and cold-side cooling techniques and temperatures. Systematic investigation of this system relied on accurate thermodynamic modeling of complex, high-temperature combustion processes concomitantly with detailed thermoelectric converter thermal/mechanical modeling. To this end, this work reports on design integration of systemlevel process flow simulations using commercial software CHEMCADTM with in-house thermoelectric converter and module optimization, and heat exchanger analyses using COMSOLTM software. High-performance, high-temperature TE materials and segmented TE element designs are incorporated in coupled design analyses to achieve predicted TE subsystem level conversion efficiencies exceeding 10%. These TE advances are integrated with a high performance microtechnology combustion reactor based on recent advances at the Pacific Northwest National Laboratory (PNNL). Predictions from this coupled simulation established a basis for optimal selection of fuel and air flow rates, thermoelectric module design and operating conditions, and microtechnology heat-exchanger design criteria. This paper will discuss this simulation process that leads directly to system efficiency power maps defining potentially available optimal system operating conditions and regimes. This coupled simulation approach enables pathways for integrated use of high-performance combustor components, high performance TE devices, and microtechnologies to produce a compact, lightweight, combustion driven TE power system prototype that operates on common fuels.« less

Validation of the Hong Kong Liver Cancer Staging System in Determining Prognosis of the North American Patients Following Intra-arterial Therapy

PubMed Central

Sohn, Jae Ho; Duran, Rafael; Zhao, Yan; Fleckenstein, Florian; Chapiro, Julius; Sahu, Sonia P.; Schernthaner, Rüdiger E.; Qian, Tianchen; Lee, Howard; Zhao, Li; Hamilton, James; Frangakis, Constantine; Lin, MingDe; Salem, Riad; Geschwind, Jean-Francois

2018-01-01

Background & Aims There is debate over the best way to stage hepatocellular carcinoma (HCC). We attempted to validate the prognostic and clinical utility of the recently developed Hong Kong Liver Cancer (HKLC) staging system, a hepatitis B-based model, and compared data with that from the Barcelona Clinic Liver Cancer (BCLC) staging system in a North American population who underwent intra-arterial therapy (IAT). Methods We performed a retrospective analysis of data from 1009 patients with HCC who underwent intra-arterial therapy from 2000 through 2014. Most patients had hepatitis C or unresectable tumors; all patients underwent IAT, with or without resection, transplantation, and/or systemic chemotherapy. We calculated HCC stage for each patient using 5-stage HKLC (HKLC-5) and 9-stage HKLC (HKLC-9) system classifications, as well as the BCLC system. Survival information was collected up until end of 2014 at which point living or unconfirmed patients were censored. We compared performance of the BCLC, HKLC-5, and HKLC-9 systems in predicting patient outcomes using Kaplan-Meier estimates, calibration plots, c-statistic, Akaike information criterion, and the likelihood ratio test. Results Median overall survival time, calculated from first IAT until date of death or censorship, for the entire cohort (all stages) was 9.8 months. The BCLC and HKLC staging systems predicted patient survival times with significance (P<.001). HKLC-5 and HKLC-9 each demonstrated good calibration. The HKLC-5 system outperformed the BCLC system in predicting patient survival times (HKLC c=0.71, Akaike information criterion=6242; BCLC c=0.64, Akaike information criterion=6320), reducing error in predicting survival time (HKLC reduced error by 14%, BCLC reduced error by 12%), and homogeneity (HKLC χ2=201; P<.001; BCLC χ2=119; P<.001) and monotonicity (HKLC linear trend χ2=193; P<.001; BCLC linear trend χ2=111; P<.001). Small proportions of patients with HCC of stages IV or V, according to the HKLC system, survived for 6 months and 4 months, respectively. Conclusion In a retrospective analysis of patients who underwent IAT for unresectable HCC, we found the HKLC-5 staging system to have the best combination of performances in survival separation, calibration, and discrimination; it consistently outperformed the BCLC system in predicting survival times of patients. The HKLC system identified patients with HCC of stages IV and V who are unlikely to benefit from IAT. PMID:27847278

Validation of the Hong Kong Liver Cancer Staging System in Determining Prognosis of the North American Patients Following Intra-arterial Therapy.

PubMed

Sohn, Jae Ho; Duran, Rafael; Zhao, Yan; Fleckenstein, Florian; Chapiro, Julius; Sahu, Sonia; Schernthaner, Rüdiger E; Qian, Tianchen; Lee, Howard; Zhao, Li; Hamilton, James; Frangakis, Constantine; Lin, MingDe; Salem, Riad; Geschwind, Jean-Francois

2017-05-01

There is debate over the best way to stage hepatocellular carcinoma (HCC). We attempted to validate the prognostic and clinical utility of the recently developed Hong Kong Liver Cancer (HKLC) staging system, a hepatitis B-based model, and compared data with that from the Barcelona Clinic Liver Cancer (BCLC) staging system in a North American population that underwent intra-arterial therapy (IAT). We performed a retrospective analysis of data from 1009 patients with HCC who underwent IAT from 2000 through 2014. Most patients had hepatitis C or unresectable tumors; all patients underwent IAT, with or without resection, transplantation, and/or systemic chemotherapy. We calculated HCC stage for each patient using 5-stage HKLC (HKLC-5) and 9-stage HKLC (HKLC-9) system classifications, and the BCLC system. Survival information was collected up until the end of 2014 at which point living or unconfirmed patients were censored. We compared performance of the BCLC, HKLC-5, and HKLC-9 systems in predicting patient outcomes using Kaplan-Meier estimates, calibration plots, C statistic, Akaike information criterion, and the likelihood ratio test. Median overall survival time, calculated from first IAT until date of death or censorship, for the entire cohort (all stages) was 9.8 months. The BCLC and HKLC staging systems predicted patient survival times with significance (P < .001). HKLC-5 and HKLC-9 each demonstrated good calibration. The HKLC-5 system outperformed the BCLC system in predicting patient survival times (HKLC C = 0.71, Akaike information criterion = 6242; BCLC C = 0.64, Akaike information criterion = 6320), reducing error in predicting survival time (HKLC reduced error by 14%, BCLC reduced error by 12%), and homogeneity (HKLC chi-square = 201, P < .001; BCLC chi-square = 119, P < .001) and monotonicity (HKLC linear trend chi-square = 193, P < .001; BCLC linear trend chi-square = 111, P < .001). Small proportions of patients with HCC of stages IV or V, according to the HKLC system, survived for 6 months and 4 months, respectively. In a retrospective analysis of patients who underwent IAT for unresectable HCC, we found the HKLC-5 staging system to have the best combination of performances in survival separation, calibration, and discrimination; it consistently outperformed the BCLC system in predicting survival times of patients. The HKLC system identified patients with HCC of stages IV and V who are unlikely to benefit from IAT. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Improving personalized link prediction by hybrid diffusion

NASA Astrophysics Data System (ADS)

Liu, Jin-Hu; Zhu, Yu-Xiao; Zhou, Tao

2016-04-01

Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which each individual will get equal number of diversiform predictions. While the performances of many classical algorithms are not satisfactory under this framework, thus new algorithms are in urgent need. Motivated by previous researches in other fields, we generalize heat conduction process to the framework of personalized link prediction and find that this method outperforms many classical similarity-based algorithms, especially in the performance of diversity. In addition, we demonstrate that adding one ground node that is supposed to connect all the nodes in the system will greatly benefit the performance of heat conduction. Finally, better hybrid algorithms composed of local random walk and heat conduction have been proposed. Numerical results show that the hybrid algorithms can outperform other algorithms simultaneously in all four adopted metrics: AUC, precision, recall and hamming distance. In a word, this work may shed some light on the in-depth understanding of the effect of physical processes in personalized link prediction.

Optimal strategy analysis based on robust predictive control for inventory system with random demand

NASA Astrophysics Data System (ADS)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

Structural, Thermal, and Optical Performance (STOP) Modeling and Results for the James Webb Space Telescope Integrated Science Instrument Module

NASA Technical Reports Server (NTRS)

Gracey, Renee; Bartoszyk, Andrew; Cofie, Emmanuel; Comber, Brian; Hartig, George; Howard, Joseph; Sabatke, Derek; Wenzel, Greg; Ohl, Raymond

2016-01-01

The James Webb Space Telescope includes the Integrated Science Instrument Module (ISIM) element that contains four science instruments (SI) including a Guider. We performed extensive structural, thermal, and optical performance(STOP) modeling in support of all phases of ISIM development. In this paper, we focus on modeling and results associated with test and verification. ISIMs test program is bound by ground environments, mostly notably the 1g and test chamber thermal environments. This paper describes STOP modeling used to predict ISIM system performance in 0g and at various on-orbit temperature environments. The predictions are used to project results obtained during testing to on-orbit performance.

Zero-G Thermodynamic Venting System (TVS) Performance Prediction Program

NASA Technical Reports Server (NTRS)

Nguyen, Han

1994-01-01

This report documents the Zero-g Thermodynamic Venting System (TVS) performance prediction computer program. The zero-g TVS is a device that destratifies and rejects environmentally induced zero-g thermal gradients in the LH2 storage transfer system. A recirculation pump and spray injection manifold recirculates liquid throughout the length of the tank thereby destratifying both the ullage gas and liquid bulk. Heat rejection is accomplished by the opening of the TVS control valve which allows a small flow rate to expand to a low pressure thereby producing a low temperature heat sink which is used to absorb heat from the recirculating liquid flow. The program was written in FORTRAN 77 language on the HP-9000 and IBM PC computers. It can be run on various platforms with a FORTRAN compiler.

Solar Power System Options for the Radiation and Technology Demonstration Spacecraft

NASA Technical Reports Server (NTRS)

Kerslake, Thomas W.; Haraburda, Francis M.; Riehl, John P.

2000-01-01

The Radiation and Technology Demonstration (RTD) Mission has the primary objective of demonstrating high-power (10 kilowatts) electric thruster technologies in Earth orbit. This paper discusses the conceptual design of the RTD spacecraft photovoltaic (PV) power system and mission performance analyses. These power system studies assessed multiple options for PV arrays, battery technologies and bus voltage levels. To quantify performance attributes of these power system options, a dedicated Fortran code was developed to predict power system performance and estimate system mass. The low-thrust mission trajectory was analyzed and important Earth orbital environments were modeled. Baseline power system design options are recommended on the basis of performance, mass and risk/complexity. Important findings from parametric studies are discussed and the resulting impacts to the spacecraft design and cost.

A side-by-side comparison of CPV module and system performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muller, Matthew; Marion, Bill; Kurtz, Sarah

A side-by-side comparison is made between concentrator photovoltaic module and system direct current aperture efficiency data with a focus on quantifying system performance losses. The individual losses measured/calculated, when combined, are in good agreement with the total loss seen between the module and the system. Results indicate that for the given test period, the largest individual loss of 3.7% relative is due to the baseline performance difference between the individual module and the average for the 200 modules in the system. A basic empirical model is derived based on module spectral performance data and the tabulated losses between the modulemore » and the system. The model predicts instantaneous system direct current aperture efficiency with a root mean square error of 2.3% relative.« less

External Validation of European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) for Risk Prioritization in an Iranian Population

PubMed Central

Atashi, Alireza; Amini, Shahram; Tashnizi, Mohammad Abbasi; Moeinipour, Ali Asghar; Aazami, Mathias Hossain; Tohidnezhad, Fariba; Ghasemi, Erfan; Eslami, Saeid

2018-01-01

Introduction The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. Objective The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). Conclusion Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran. PMID:29617500

Yield performance and stability of CMS-based triticale hybrids.

PubMed

Mühleisen, Jonathan; Piepho, Hans-Peter; Maurer, Hans Peter; Reif, Jochen Christoph

2015-02-01

CMS-based triticale hybrids showed only marginal midparent heterosis for grain yield and lower dynamic yield stability compared to inbred lines. Hybrids of triticale (×Triticosecale Wittmack) are expected to possess outstanding yield performance and increased dynamic yield stability. The objectives of the present study were to (1) examine the optimum choice of the biometrical model to compare yield stability of hybrids versus lines, (2) investigate whether hybrids exhibit a more pronounced grain yield performance and yield stability, and (3) study optimal strategies to predict yield stability of hybrids. Thirteen female and seven male parental lines and their 91 factorial hybrids as well as 30 commercial lines were evaluated for grain yield in up to 20 environments. Hybrids were produced using a cytoplasmic male sterility (CMS)-inducing cytoplasm that originated from Triticumtimopheevii Zhuk. We found that the choice of the biometrical model can cause contrasting results and concluded that a group-by-environment interaction term should be added to the model when estimating stability variance of hybrids and lines. midparent heterosis for grain yield was on average 3 % with a range from -15.0 to 11.5 %. No hybrid outperformed the best inbred line. Hybrids had, on average, lower dynamic yield stability compared to the inbred lines. Grain yield performance of hybrids could be predicted based on midparent values and general combining ability (GCA)-predicted values. In contrast, stability variance of hybrids could be predicted only based on GCA-predicted values. We speculated that negative effects of the used CMS cytoplasm might be the reason for the low performance and yield stability of the hybrids. For this purpose a detailed study on the reasons for the drawback of the currently existing CMS system in triticale is urgently required comprising also the search of potentially alternative hybridization systems.

Comparison of three scoring systems in predicting clinical outcomes in patients with acute upper gastrointestinal bleeding: a prospective observational study.

PubMed

Zhong, Min; Chen, Wan Jun; Lu, Xiao Ye; Qian, Jie; Zhu, Chang Qing

2016-12-01

To compare the performances of the Glasgow-Blatchford score (GBS), modified GBS (mGBS) and AIMS65 in predicting clinical outcomes in patients with acute upper gastrointestinal bleeding (AUGIB). This study enrolled 320 consecutive patients with AUGIB. Patients at high and low risks of developing adverse clinical outcomes (rebleeding, the need of clinical intervention and death) were categorized according to the GBS, mGBS and AIMS65 scoring systems. The outcome of the patients were the occurrences of adverse clinical outcomes. The areas under the receiver operating characteristics curve (AUROC) of three scoring systems were compared. Irrespective of the systems used, the high-risk groups showed higher rates of rebleeding, intervention and death compared with the low-risk groups (P < 0.05). For the prediction of rebleeding, AIMS65 (AUROC 0.735, 95% CI 0.667-0.802) performed significantly better than GBS (AUROC 0.672, 95% CI 0.597-0.747; P < 0.01) and mGBS (AUROC 0.677, 95% CI 0.602-0.753; P < 0.01). For the prediction of interventions, there was no significant difference among the three systems (GBS: AUROC 0.769, 95% CI 0.668-0.870; mGBS: AUROC 0.745, 95% CI 0.643-0.847; AIMS65: AUROC 0.746, 95% CI 0.640-0.851). For the prediction of in-hospital mortality, there was no significant difference among the three systems (GBS: AUROC 0.796, 95% CI 0.694-0.898; mGBS: AUROC 0.803, 95% CI 0.703-0.904; AIMS65: AUROC 0.786, 95% CI 0.670-0.903). The three scoring systems are reliable and accurate in predicting the rates of rebleeding, surgery and mortality in AUGIB. However, AIMS65 outperforms GBS and mGBS in predicting rebleeding. © 2016 Chinese Medical Association Shanghai Branch, Chinese Society of Gastroenterology, Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.

Performance Prediction Relationships for AM2 Airfield Matting Developed from Full-Scale Accelerated Testing and Laboratory Experimentation

DTIC Science & Technology

2018-01-01

work, the prevailing methods used to predict the performance of AM2 were based on the CBR design procedure for flexible pavements using a small number...suitable for design and evaluation frameworks currently used for airfield pavements and matting systems. DISCLAIMER: The contents of this report...methods used to develop the equivalency curves equated the mat-surfaced area to an equivalent thickness of flexible pavement using the CBR design

Clinical time series prediction: towards a hierarchical dynamical system framework

PubMed Central

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. PMID:25534671

Analysis and correlation of the test data from an advanced technology rotor system

NASA Technical Reports Server (NTRS)

Jepson, D.; Moffitt, R.; Hilzinger, K.; Bissell, J.

1983-01-01

Comparisons were made of the performance and blade vibratory loads characteristics for an advanced rotor system as predicted by analysis and as measured in a 1/5 scale model wind tunnel test, a full scale model wind tunnel test and flight test. The accuracy with which the various tools available at the various stages in the design/development process (analysis, model test etc.) could predict final characteristics as measured on the aircraft was determined. The accuracy of the analyses in predicting the effects of systematic tip planform variations investigated in the full scale wind tunnel test was evaluated.

Hysteresis prediction inside magnetic shields and application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morić, Igor; CNES, Edouard Belin 18, 31400 Toulouse; De Graeve, Charles-Marie

2014-07-15

We have developed a simple model that is able to describe and predict hysteresis behavior inside Mumetal magnetic shields, when the shields are submitted to ultra-low frequency (<0.01 Hz) magnetic perturbations with amplitudes lower than 60 μT. This predictive model has been implemented in a software to perform an active compensation system. With this compensation the attenuation of longitudinal magnetic fields is increased by two orders of magnitude. The system is now integrated in the cold atom space clock called PHARAO. The clock will fly onboard the International Space Station in the frame of the ACES space mission.

Man-Machine Integration Design and Analysis System (MIDAS) v5: Augmentations, Motivations, and Directions for Aeronautics Applications

NASA Technical Reports Server (NTRS)

Gore, Brian F.

2011-01-01

As automation and advanced technologies are introduced into transport systems ranging from the Next Generation Air Transportation System termed NextGen, to the advanced surface transportation systems as exemplified by the Intelligent Transportations Systems, to future systems designed for space exploration, there is an increased need to validly predict how the future systems will be vulnerable to error given the demands imposed by the assistive technologies. One formalized approach to study the impact of assistive technologies on the human operator in a safe and non-obtrusive manner is through the use of human performance models (HPMs). HPMs play an integral role when complex human-system designs are proposed, developed, and tested. One HPM tool termed the Man-machine Integration Design and Analysis System (MIDAS) is a NASA Ames Research Center HPM software tool that has been applied to predict human-system performance in various domains since 1986. MIDAS is a dynamic, integrated HPM and simulation environment that facilitates the design, visualization, and computational evaluation of complex man-machine system concepts in simulated operational environments. The paper will discuss a range of aviation specific applications including an approach used to model human error for NASA s Aviation Safety Program, and what-if analyses to evaluate flight deck technologies for NextGen operations. This chapter will culminate by raising two challenges for the field of predictive HPMs for complex human-system designs that evaluate assistive technologies: that of (1) model transparency and (2) model validation.

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

PubMed Central

Prill, Robert J.; Marbach, Daniel; Saez-Rodriguez, Julio; Sorger, Peter K.; Alexopoulos, Leonidas G.; Xue, Xiaowei; Clarke, Neil D.; Altan-Bonnet, Gregoire; Stolovitzky, Gustavo

2010-01-01

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature. PMID:20186320

A method for estimating the performance of photovoltaic systems

NASA Astrophysics Data System (ADS)

Clark, D. R.; Klein, S. A.; Beckman, W. A.

A method is presented for predicting the long-term average performance of photovoltaic systems having storage batteries and subject to any diurnal load profile. The monthly-average fraction of the load met by the system is estimated from array parameters and monthly-average meteorological data. The method is based on radiation statistics, and utilizability, and can account for variability in the electrical demand as well as for the variability in solar radiation.

Method for controlling start-up and steady state performance of a closed split flow recompression brayton cycle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pasch, James Jay

A method of resolving a balanced condition that generates control parameters for start-up and steady state operating points and various component and cycle performances for a closed split flow recompression cycle system. The method provides for improved control of a Brayton cycle thermal to electrical power conversion system. The method may also be used for system design, operational simulation and/or parameter prediction.

Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa; Jackson, Tom

2017-09-01

This paper investigates the polymer electrolyte membrane (PEM) fuel cell internal behaviour variation at different operating condition, with characterization test data taken at predefined inspection times, and uses the determined internal behaviour evolution to predict the future PEM fuel cell performance. For this purpose, a PEM fuel cell behaviour model is used, which can be related to various fuel cell losses. By matching the model to the collected polarization curves from the PEM fuel cell system, the variation of fuel cell internal behaviour can be obtained through the determined model parameters. From the results, the source of PEM fuel cell degradation during its lifetime at different conditions can be better understood. Moreover, with determined fuel cell internal behaviour, the future fuel cell performance can be obtained by predicting the future model parameters. By comparing with prognostic results using adaptive neuro fuzzy inference system (ANFIS), the proposed prognostic analysis can provide better predictions for PEM fuel cell performance at dynamic condition, and with the understanding of variation in PEM fuel cell internal behaviour, mitigation strategies can be designed to extend the fuel cell performance.

Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

NASA Technical Reports Server (NTRS)

Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

2014-01-01

This presentation describes the capabilities of three-dimensional thermal power model of advanced stirling radioisotope generator (ASRG). The performance of the ASRG is presented for different scenario, such as Venus flyby with or without the auxiliary cooling system.

Lay out, test verification and in orbit performance of HELIOS a temperature control system

NASA Technical Reports Server (NTRS)

Brungs, W.

1975-01-01

HELIOS temperature control system is described. The main design features and the impact of interactions between experiment, spacecraft system, and temperature control system requirements on the design are discussed. The major limitations of the thermal design regarding a closer sun approach are given and related to test experience and performance data obtained in orbit. Finally the validity of the test results achieved with prototype and flight spacecraft is evaluated by comparison between test data, orbit temperature predictions and flight data.

GPS-based system for satellite tracking and geodesy

NASA Technical Reports Server (NTRS)

Bertiger, Willy I.; Thornton, Catherine L.

1989-01-01

High-performance receivers and data processing systems developed for GPS are reviewed. The GPS Inferred Positioning System (GIPSY) and the Orbiter Analysis and Simulation Software (OASIS) are described. The OASIS software is used to assess GPS system performance using GIPSY for data processing. Consideration is given to parameter estimation for multiday arcs, orbit repeatability, orbit prediction, daily baseline repeatability, agreement with VLBI, and ambiguity resolution. Also, the dual-frequency Rogue receiver, which can track up to eight GPS satellites simultaneously, is discussed.

Next-Term Student Performance Prediction: A Recommender Systems Approach

ERIC Educational Resources Information Center

Sweeney, Mack; Rangwala, Huzefa; Lester, Jaime; Johri, Aditya

2016-01-01

An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success,…

Predicting Student Performance in a Collaborative Learning Environment

ERIC Educational Resources Information Center

Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan.

PubMed

Chang, Hsien-Yen; Weiner, Jonathan P

2010-01-18

Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme. A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level. The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall. Given the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.

Speech Perception With Combined Electric-Acoustic Stimulation: A Simulation and Model Comparison.

PubMed

Rader, Tobias; Adel, Youssef; Fastl, Hugo; Baumann, Uwe

2015-01-01

The aim of this study is to simulate speech perception with combined electric-acoustic stimulation (EAS), verify the advantage of combined stimulation in normal-hearing (NH) subjects, and then compare it with cochlear implant (CI) and EAS user results from the authors' previous study. Furthermore, an automatic speech recognition (ASR) system was built to examine the impact of low-frequency information and is proposed as an applied model to study different hypotheses of the combined-stimulation advantage. Signal-detection-theory (SDT) models were applied to assess predictions of subject performance without the need to assume any synergistic effects. Speech perception was tested using a closed-set matrix test (Oldenburg sentence test), and its speech material was processed to simulate CI and EAS hearing. A total of 43 NH subjects and a customized ASR system were tested. CI hearing was simulated by an aurally adequate signal spectrum analysis and representation, the part-tone-time-pattern, which was vocoded at 12 center frequencies according to the MED-EL DUET speech processor. Residual acoustic hearing was simulated by low-pass (LP)-filtered speech with cutoff frequencies 200 and 500 Hz for NH subjects and in the range from 100 to 500 Hz for the ASR system. Speech reception thresholds were determined in amplitude-modulated noise and in pseudocontinuous noise. Previously proposed SDT models were lastly applied to predict NH subject performance with EAS simulations. NH subjects tested with EAS simulations demonstrated the combined-stimulation advantage. Increasing the LP cutoff frequency from 200 to 500 Hz significantly improved speech reception thresholds in both noise conditions. In continuous noise, CI and EAS users showed generally better performance than NH subjects tested with simulations. In modulated noise, performance was comparable except for the EAS at cutoff frequency 500 Hz where NH subject performance was superior. The ASR system showed similar behavior to NH subjects despite a positive signal-to-noise ratio shift for both noise conditions, while demonstrating the synergistic effect for cutoff frequencies ≥300 Hz. One SDT model largely predicted the combined-stimulation results in continuous noise, while falling short of predicting performance observed in modulated noise. The presented simulation was able to demonstrate the combined-stimulation advantage for NH subjects as observed in EAS users. Only NH subjects tested with EAS simulations were able to take advantage of the gap listening effect, while CI and EAS user performance was consistently degraded in modulated noise compared with performance in continuous noise. The application of ASR systems seems feasible to assess the impact of different signal processing strategies on speech perception with CI and EAS simulations. In continuous noise, SDT models were largely able to predict the performance gain without assuming any synergistic effects, but model amendments are required to explain the gap listening effect in modulated noise.

"Sturdy as a house with four windows," the star tracker of the future

NASA Astrophysics Data System (ADS)

Duivenvoorde, Tom; Leijtens, Johan; van der Heide, Erik J.

2017-11-01

Ongoing miniaturization of spacecraft demands the reduction in size of Attitude and Orbit Control Systems (AOCS). Therefore TNO has created a new design of a multi aperture, high performance, and miniaturized star tracker. The innovative design incorporates the latest developments in camera technology, attitude calculation and mechanical design into a system with 5 arc seconds accuracy, making the system usable for many applications. In this paper the results are presented of the system design and analysis, as well as the performance predictions for the Multi Aperture Baffled Star Tracker (MABS). The highly integrated system consists of multiple apertures without the need for external baffles, resulting in major advantages in mass, volume, alignment with the spacecraft and relative aperture stability. In the analysis part of this paper, the thermal and mechanical stability are discussed. In the final part the simulation results will be described that have lead to the predicted accuracy of the star tracker system and a peek into the future of attitude sensors is given.

Brayton Power Conversion System Parametric Design Modelling for Nuclear Electric Propulsion

NASA Technical Reports Server (NTRS)

Ashe, Thomas L.; Otting, William D.

1993-01-01

The parametrically based closed Brayton cycle (CBC) computer design model was developed for inclusion into the NASA LeRC overall Nuclear Electric Propulsion (NEP) end-to-end systems model. The code is intended to provide greater depth to the NEP system modeling which is required to more accurately predict the impact of specific technology on system performance. The CBC model is parametrically based to allow for conducting detailed optimization studies and to provide for easy integration into an overall optimizer driver routine. The power conversion model includes the modeling of the turbines, alternators, compressors, ducting, and heat exchangers (hot-side heat exchanger and recuperator). The code predicts performance to significant detail. The system characteristics determined include estimates of mass, efficiency, and the characteristic dimensions of the major power conversion system components. These characteristics are parametrically modeled as a function of input parameters such as the aerodynamic configuration (axial or radial), turbine inlet temperature, cycle temperature ratio, power level, lifetime, materials, and redundancy.

Evaluation of the suitability of chromatographic systems to predict human skin permeation of neutral compounds.

PubMed

Hidalgo-Rodríguez, Marta; Soriano-Meseguer, Sara; Fuguet, Elisabet; Ràfols, Clara; Rosés, Martí

2013-12-18

Several chromatographic systems (three systems of high-performance liquid chromatography and two micellar electrokinetic chromatography systems) besides the reference octanol-water partition system are evaluated by a systematic procedure previously proposed in order to know their ability to model human skin permeation. The precision achieved when skin-water permeability coefficients are correlated against chromatographic retention factors is predicted within the framework of the solvation parameter model. It consists in estimating the contribution of error due to the biological and chromatographic data, as well as the error coming from the dissimilarity between the human skin permeation and the chromatographic systems. Both predictions and experimental tests show that all correlations are greatly affected by the considerable uncertainty of the skin permeability data and the error associated to the dissimilarity between the systems. Correlations with much better predictive abilities are achieved when the volume of the solute is used as additional variable, which illustrates the main roles of both lipophilicity and size of the solute to penetrate through the skin. In this way, the considered systems are able to give precise estimations of human skin permeability coefficients. In particular, the HPLC systems with common C18 columns provide the best performances in emulating the permeation of neutral compounds from aqueous solution through the human skin. As a result, a methodology based on easy, fast, and economical HPLC measurements in a common C18 column has been developed. After a validation based on training and test sets, the method has been applied with good results to the estimation of skin permeation of several hormones and pesticides. Copyright © 2013 Elsevier B.V. All rights reserved.

Prediction-based sampled-data H∞ controller design for attitude stabilisation of a rigid spacecraft with disturbances

NASA Astrophysics Data System (ADS)

Zhu, Baolong; Zhang, Zhiping; Zhou, Ding; Ma, Jie; Li, Shunli

2017-08-01

This paper investigates the H∞ control problem of the attitude stabilisation of a rigid spacecraft with external disturbances using prediction-based sampled-data control strategy. Aiming to achieve a 'virtual' closed-loop system, a type of parameterised sampled-data controller is designed by introducing a prediction mechanism. The resultant closed-loop system is equivalent to a hybrid system featured by a continuous-time and an impulsive differential system. By using a time-varying Lyapunov functional, a generalised bounded real lemma (GBRL) is first established for a kind of impulsive differential system. Based on this GBRL and Lyapunov functional approach, a sufficient condition is derived to guarantee the closed-loop system to be asymptotically stable and to achieve a prescribed H∞ performance. In addition, the controller parameter tuning is cast into a convex optimisation problem. Simulation and comparative results are provided to illustrate the effectiveness of the developed control scheme.

The effects of instructional sets on reactions to and performance on an intelligent tutoring system

NASA Technical Reports Server (NTRS)

Johnson, Debra Steele

1993-01-01

The effects of a contextual factor, i.e., task instructions, on performance on and reactions to an Intellegent Tutoring System (ITS) training Remote Manipulator System (RMS) tasks were examined. The results supported the first prediction that task instructions could be used to successfully induce a mastery versus an achievement orientation. Previous research suggests that a mastery orientation can result in beneficial effects on learning and performance of complex tasks. Furthermore, the results supported the second prediction that a mastery orientation would have beneficial effects on learning and performance as well as affective and cognitive reactions to the ITS tasks. Moreover, the results indicated that a mastery orientation was especially beneficial for the more complex ITS tasks and later in task practice, i.e., when a task was performed for the second time. A mastery orientation is posited to have its beneficial effects by focusing more effort and attention on task performance. Conclusions are drawn with some caution due to the small number of subjects, although the results for these subjects were consistent across multiple trials and multiple measures of performance. ITS designers are urged to consider contextual factors such as task instructions and feedback in terms of their potential to induce a mastery versus an achievement orientation.

An analytical technique for predicting the characteristics of a flexible wing equipped with an active flutter-suppression system and comparison with wind-tunnel data

NASA Technical Reports Server (NTRS)

Abel, I.

1979-01-01

An analytical technique for predicting the performance of an active flutter-suppression system is presented. This technique is based on the use of an interpolating function to approximate the unsteady aerodynamics. The resulting equations are formulated in terms of linear, ordinary differential equations with constant coefficients. This technique is then applied to an aeroelastic model wing equipped with an active flutter-suppression system. Comparisons between wind-tunnel data and analysis are presented for the wing both with and without active flutter suppression. Results indicate that the wing flutter characteristics without flutter suppression can be predicted very well but that a more adequate model of wind-tunnel turbulence is required when the active flutter-suppression system is used.

Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

PubMed

Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

2014-01-01

Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set B are found in Figure 1. Therapy-success prediction of first-line therapies with DEnoIAS performed better than DEonlyIAS (p<10-16). Therapy success prediction benefits from the consideration of all available mutations. The increase in performance was largest in first-line therapies with transmitted drug-resistance mutations.

Performance prediction of a ducted rocket combustor

NASA Astrophysics Data System (ADS)

Stowe, Robert

2001-07-01

The ducted rocket is a supersonic flight propulsion system that takes the exhaust from a solid fuel gas generator, mixes it with air, and burns it to produce thrust. To develop such systems, the use of numerical models based on Computational Fluid Dynamics (CFD) is increasingly popular, but their application to reacting flow requires specific attention and validation. Through a careful examination of the governing equations and experimental measurements, a CFD-based method was developed to predict the performance of a ducted rocket combustor. It uses an equilibrium-chemistry Probability Density Function (PDF) combustion model, with a gaseous and a separate stream of 75 nm diameter carbon spheres to represent the fuel. After extensive validation with water tunnel and direct-connect combustion experiments over a wide range of geometries and test conditions, this CFD-based method was able to predict, within a good degree of accuracy, the combustion efficiency of a ducted rocket combustor.

Predicting Positive Education Outcomes for Emerging Adults in Mental Health Systems of Care.

PubMed

Brennan, Eileen M; Nygren, Peggy; Stephens, Robert L; Croskey, Adrienne

2016-10-01

Emerging adults who receive services based on positive youth development models have shown an ability to shape their own life course to achieve positive goals. This paper reports secondary data analysis from the Longitudinal Child and Family Outcome Study including 248 culturally diverse youth ages 17 through 22 receiving mental health services in systems of care. After 12 months of services, school performance was positively related to youth ratings of school functioning and service participation and satisfaction. Regression analysis revealed ratings of young peoples' perceptions of school functioning, and their experience in services added to the significant prediction of satisfactory school performance, even controlling for sex and attendance. Finally, in addition to expected predictors, participation in planning their own services significantly predicted enrollment in higher education for those who finished high school. Findings suggest that programs and practices based on positive youth development approaches can improve educational outcomes for emerging adults.

Ignition of a granular propellant bed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wildegger-Gaissmaier, A.E.; Johnston, I.R.

1996-08-01

An experimental and theoretical study is reported on the ignition process of a low vulnerability ammunition (LOVA) propellant bed in a 127-mm (5-in) bore gun charge. The theoretical investigation was with a two-phase flow interior ballistics code and the model predictions showed the marked influence the igniter system can have on pressure wave development, flame spreading, and the overall interior ballistics performance. A number of different igniter systems were investigated in an empty and propellant-filled gun simulator. Pressure, flame spreading, and high-speed film records were used to analyze the ignition/combustion event. The model predictions for flame spreading were confirmed qualitativelymore » by the experimental data. Full-scale instrumented gun firings were conducted with the optimized igniter design. Pressure waves were not detected in the charge during the firings. Model predictions on overall interior ballistics performance agreed well with the firing data.« less

Analysis prediction of Indonesian banks (BCA, BNI, MANDIRI) using adaptive neuro-fuzzy inference system (ANFIS) and investment strategies

NASA Astrophysics Data System (ADS)

Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep

2015-09-01

Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Assessing the predictive value of the American Board of Family Practice In-training Examination.

PubMed

Replogle, William H; Johnson, William D

2004-03-01

The American Board of Family Practice In-training Examination (ABFP ITE) is a cognitive examination similar in content to the ABFP Certification Examination (CE). The ABFP ITE is widely used in family medicine residency programs. It was originally developed and intended to be used for assessment of groups of residents. Despite lack of empirical support, however, some residency programs are using ABFP ITE scores as individual resident performance indicators. This study's objective was to estimate the positive predictive value of the ABFP ITE for identifying residents at risk for poor performance on the ABFP CE or a subsequent ABFP ITE. We used a normal distribution model for correlated test scores and Monte Carlo simulation to investigate the effect of test reliability (measurement errors) on the positive predictive value of the ABFP ITE. The positive predictive value of the composite score was .72. The positive predictive value of the eight specialty subscales ranged from .26 to .57. Only the composite score of the ABFP ITE has acceptable positive predictive value to be used as part of a comprehension resident evaluation system. The ABFP ITE specialty subscales do not have sufficient positive predictive value or reliability to warrant use as performance indicators.

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

Evaluation of a conceptual framework for predicting navigation performance in virtual reality.

PubMed

Grübel, Jascha; Thrash, Tyler; Hölscher, Christoph; Schinazi, Victor R

2017-01-01

Previous research in spatial cognition has often relied on simple spatial tasks in static environments in order to draw inferences regarding navigation performance. These tasks are typically divided into categories (e.g., egocentric or allocentric) that reflect different two-systems theories. Unfortunately, this two-systems approach has been insufficient for reliably predicting navigation performance in virtual reality (VR). In the present experiment, participants were asked to learn and navigate towards goal locations in a virtual city and then perform eight simple spatial tasks in a separate environment. These eight tasks were organised along four orthogonal dimensions (static/dynamic, perceived/remembered, egocentric/allocentric, and distance/direction). We employed confirmatory and exploratory analyses in order to assess the relationship between navigation performance and performances on these simple tasks. We provide evidence that a dynamic task (i.e., intercepting a moving object) is capable of predicting navigation performance in a familiar virtual environment better than several categories of static tasks. These results have important implications for studies on navigation in VR that tend to over-emphasise the role of spatial memory. Given that our dynamic tasks required efficient interaction with the human interface device (HID), they were more closely aligned with the perceptuomotor processes associated with locomotion than wayfinding. In the future, researchers should consider training participants on HIDs using a dynamic task prior to conducting a navigation experiment. Performances on dynamic tasks should also be assessed in order to avoid confounding skill with an HID and spatial knowledge acquisition.

Evaluation of a conceptual framework for predicting navigation performance in virtual reality

PubMed Central

Thrash, Tyler; Hölscher, Christoph; Schinazi, Victor R.

2017-01-01

Previous research in spatial cognition has often relied on simple spatial tasks in static environments in order to draw inferences regarding navigation performance. These tasks are typically divided into categories (e.g., egocentric or allocentric) that reflect different two-systems theories. Unfortunately, this two-systems approach has been insufficient for reliably predicting navigation performance in virtual reality (VR). In the present experiment, participants were asked to learn and navigate towards goal locations in a virtual city and then perform eight simple spatial tasks in a separate environment. These eight tasks were organised along four orthogonal dimensions (static/dynamic, perceived/remembered, egocentric/allocentric, and distance/direction). We employed confirmatory and exploratory analyses in order to assess the relationship between navigation performance and performances on these simple tasks. We provide evidence that a dynamic task (i.e., intercepting a moving object) is capable of predicting navigation performance in a familiar virtual environment better than several categories of static tasks. These results have important implications for studies on navigation in VR that tend to over-emphasise the role of spatial memory. Given that our dynamic tasks required efficient interaction with the human interface device (HID), they were more closely aligned with the perceptuomotor processes associated with locomotion than wayfinding. In the future, researchers should consider training participants on HIDs using a dynamic task prior to conducting a navigation experiment. Performances on dynamic tasks should also be assessed in order to avoid confounding skill with an HID and spatial knowledge acquisition. PMID:28915266

Thermal barrier coating life prediction model development

NASA Technical Reports Server (NTRS)

Hillery, R. V.; Pilsner, B. H.; Mcknight, R. L.; Cook, T. S.; Hartle, M. S.

1988-01-01

This report describes work performed to determine the predominat modes of degradation of a plasma sprayed thermal barrier coating system and to develop and verify life prediction models accounting for these degradation modes. The primary TBC system consisted of a low pressure plasma sprayed NiCrAlY bond coat, an air plasma sprayed ZrO2-Y2O3 top coat, and a Rene' 80 substrate. The work was divided into 3 technical tasks. The primary failure mode to be addressed was loss of the zirconia layer through spalling. Experiments showed that oxidation of the bond coat is a significant contributor to coating failure. It was evident from the test results that the species of oxide scale initially formed on the bond coat plays a role in coating degradation and failure. It was also shown that elevated temperature creep of the bond coat plays a role in coating failure. An empirical model was developed for predicting the test life of specimens with selected coating, specimen, and test condition variations. In the second task, a coating life prediction model was developed based on the data from Task 1 experiments, results from thermomechanical experiments performed as part of Task 2, and finite element analyses of the TBC system during thermal cycles. The third and final task attempted to verify the validity of the model developed in Task 2. This was done by using the model to predict the test lives of several coating variations and specimen geometries, then comparing these predicted lives to experimentally determined test lives. It was found that the model correctly predicts trends, but that additional refinement is needed to accurately predict coating life.

A Physiologically Informed Virtual Reality Based Social Communication System for Individuals with Autism

PubMed Central

Bekele, Esubalew; Dohrmann, Elizabeth; Warren, Zachary; Sarkar, Nilanjan

2014-01-01

Clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorders (ASD). This project evaluated the application of a novel physiologically responsive virtual reality based technological system for conversation skills in a group of adolescents with ASD. The system altered components of conversation based on (1) performance alone or (2) the composite effect of performance and physiological metrics of predicted engagement (e.g., gaze pattern, pupil dilation, blink rate). Participants showed improved performance and looking pattern within the physiologically sensitive system as compared to the performance based system. This suggests that physiologically informed technologies may have the potential of being an effective tool in the hands of interventionists. PMID:25261247

? filtering for stochastic systems driven by Poisson processes

NASA Astrophysics Data System (ADS)

Song, Bo; Wu, Zheng-Guang; Park, Ju H.; Shi, Guodong; Zhang, Ya

2015-01-01

This paper investigates the ? filtering problem for stochastic systems driven by Poisson processes. By utilising the martingale theory such as the predictable projection operator and the dual predictable projection operator, this paper transforms the expectation of stochastic integral with respect to the Poisson process into the expectation of Lebesgue integral. Then, based on this, this paper designs an ? filter such that the filtering error system is mean-square asymptotically stable and satisfies a prescribed ? performance level. Finally, a simulation example is given to illustrate the effectiveness of the proposed filtering scheme.

EEG potentials predict upcoming emergency brakings during simulated driving

NASA Astrophysics Data System (ADS)

Haufe, Stefan; Treder, Matthias S.; Gugler, Manfred F.; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin

2011-10-01

Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h-1 driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.

EEG potentials predict upcoming emergency brakings during simulated driving.

PubMed

Haufe, Stefan; Treder, Matthias S; Gugler, Manfred F; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin

2011-10-01

Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h(-1) driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis.

PubMed

Wang, QuanQiu; Xu, Rong

2017-07-01

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. edu/MetabolitePredict. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrated Modeling Activities for the James Webb Space Telescope (JWST): Structural-Thermal-Optical Analysis

NASA Technical Reports Server (NTRS)

Johnston, John D.; Parrish, Keith; Howard, Joseph M.; Mosier, Gary E.; McGinnis, Mark; Bluth, Marcel; Kim, Kevin; Ha, Hong Q.

2004-01-01

This is a continuation of a series of papers on modeling activities for JWST. The structural-thermal- optical, often referred to as "STOP", analysis process is used to predict the effect of thermal distortion on optical performance. The benchmark STOP analysis for JWST assesses the effect of an observatory slew on wavefront error. The paper begins an overview of multi-disciplinary engineering analysis, or integrated modeling, which is a critical element of the JWST mission. The STOP analysis process is then described. This process consists of the following steps: thermal analysis, structural analysis, and optical analysis. Temperatures predicted using geometric and thermal math models are mapped to the structural finite element model in order to predict thermally-induced deformations. Motions and deformations at optical surfaces are input to optical models and optical performance is predicted using either an optical ray trace or WFE estimation techniques based on prior ray traces or first order optics. Following the discussion of the analysis process, results based on models representing the design at the time of the System Requirements Review. In addition to baseline performance predictions, sensitivity studies are performed to assess modeling uncertainties. Of particular interest is the sensitivity of optical performance to uncertainties in temperature predictions and variations in metal properties. The paper concludes with a discussion of modeling uncertainty as it pertains to STOP analysis.

An analysis of the relationship of seven selected variables to State Board Test Pool Examination performance of the University of Tennessee, Knoxville, College of Nursing.

PubMed

Sharp, T G

1984-02-01

The study was designed to determine whether any one of seven selected variables or a combination of the variables is predictive of performance on the State Board Test Pool Examination. The selected variables studied were: high school grade point average (HSGPA), The University of Tennessee, Knoxville, College of Nursing grade point average (GPA), and American College Test Assessment (ACT) standard scores (English, ENG; mathematics, MA; social studies, SS; natural sciences, NSC; composite, COMP). Data utilized were from graduates of the baccalaureate program of The University of Tennessee, Knoxville, College of Nursing from 1974 through 1979. The sample of 322 was selected from a total population of 572. The Statistical Analysis System (SAS) was designed to accomplish analysis of the predictive relationship of each of the seven selected variables to State Board Test Pool Examination performance (result of pass or fail), a stepwise discriminant analysis was designed for determining the predictive relationship of the strongest combination of the independent variables to overall State Board Test Pool Examination performance (result of pass or fail), and stepwise multiple regression analysis was designed to determine the strongest predictive combination of selected variables for each of the five subexams of the State Board Test Pool Examination. The selected variables were each found to be predictive of SBTPE performance (result of pass or fail). The strongest combination for predicting SBTPE performance (result of pass or fail) was found to be GPA, MA, and NSC.

Prediction of Scour below Flip Bucket using Soft Computing Techniques

NASA Astrophysics Data System (ADS)

Azamathulla, H. Md.; Ab Ghani, Aminuddin; Azazi Zakaria, Nor

2010-05-01

The accurate prediction of the depth of scour around hydraulic structure (trajectory spillways) has been based on the experimental studies and the equations developed are mainly empirical in nature. This paper evaluates the performance of the soft computing (intelligence) techiques, Adaptive Neuro-Fuzzy System (ANFIS) and Genetic expression Programming (GEP) approach, in prediction of scour below a flip bucket spillway. The results are very promising, which support the use of these intelligent techniques in prediction of highly non-linear scour parameters.

Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

PubMed

Che Azemin, M Z; Kumar, Dinesh K; Wong, T Y; Wang, J J; Kawasaki, R; Mitchell, P; Arjunan, Sridhar P

2010-01-01

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.

Predictive Information: Status or Alert Information?

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Bruneau, Daniel; Press, Hayes N.

2008-01-01

Previous research investigating the efficacy of predictive information for detecting and diagnosing aircraft system failures found that subjects like to have predictive information concerning when a parameter would reach an alert range. This research focused on where the predictive information should be located, whether the information should be more closely associated with the parameter information or with the alert information. Each subject saw 3 forms of predictive information: (1) none, (2) a predictive alert message, and (3) predictive information on the status display. Generally, subjects performed better and preferred to have predictive information available although the difference between status and alert predictive information was minimal. Overall, for detection and recalling what happened, status predictive information is best; however for diagnosis, alert predictive information holds a slight edge.

Wave Rotor Research and Technology Development

NASA Technical Reports Server (NTRS)

Welch, Gerard E.

1998-01-01

Wave rotor technology offers the potential to increase the performance of gas turbine engines significantly, within the constraints imposed by current material temperature limits. The wave rotor research at the NASA Lewis Research Center is a three-element effort: 1) Development of design and analysis tools to accurately predict the performance of wave rotor components; 2) Experiments to characterize component performance; 3) System integration studies to evaluate the effect of wave rotor topping on the gas turbine engine system.

SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

PubMed Central

Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

2017-01-01

Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

PubMed

Hung, Andrew J; Chen, Jian; Che, Zhengping; Nilanon, Tanachat; Jarc, Anthony; Titus, Micha; Oh, Paul J; Gill, Inderbir S; Liu, Yan

2018-05-01

Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP). We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting. The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p < 0.001, r = 0.73), LOS (p = 0.05, r = 0.52), and Foley duration (p < 0.001, r = 0.45). The five most relevant APMs, adopted by the RF-50 algorithm in predicting, were largely related to camera manipulation. To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.

Validity of Treadmill-Derived Critical Speed on Predicting 5000-Meter Track-Running Performance.

PubMed

Nimmerichter, Alfred; Novak, Nina; Triska, Christoph; Prinz, Bernhard; Breese, Brynmor C

2017-03-01

Nimmerichter, A, Novak, N, Triska, C, Prinz, B, and Breese, BC. Validity of treadmill-derived critical speed on predicting 5,000-meter track-running performance. J Strength Cond Res 31(3): 706-714, 2017-To evaluate 3 models of critical speed (CS) for the prediction of 5,000-m running performance, 16 trained athletes completed an incremental test on a treadmill to determine maximal aerobic speed (MAS) and 3 randomly ordered runs to exhaustion at the [INCREMENT]70% intensity, at 110% and 98% of MAS. Critical speed and the distance covered above CS (D') were calculated using the hyperbolic speed-time (HYP), the linear distance-time (LIN), and the linear speed inverse-time model (INV). Five thousand meter performance was determined on a 400-m running track. Individual predictions of 5,000-m running time (t = [5,000-D']/CS) and speed (s = D'/t + CS) were calculated across the 3 models in addition to multiple regression analyses. Prediction accuracy was assessed with the standard error of estimate (SEE) from linear regression analysis and the mean difference expressed in units of measurement and coefficient of variation (%). Five thousand meter running performance (speed: 4.29 ± 0.39 m·s; time: 1,176 ± 117 seconds) was significantly better than the predictions from all 3 models (p < 0.0001). The mean difference was 65-105 seconds (5.7-9.4%) for time and -0.22 to -0.34 m·s (-5.0 to -7.5%) for speed. Predictions from multiple regression analyses with CS and D' as predictor variables were not significantly different from actual running performance (-1.0 to 1.1%). The SEE across all models and predictions was approximately 65 seconds or 0.20 m·s and is therefore considered as moderate. The results of this study have shown the importance of aerobic and anaerobic energy system contribution to predict 5,000-m running performance. Using estimates of CS and D' is valuable for predicting performance over race distances of 5,000 m.

Can We Predict Technical Aptitude?: A Systematic Review.

PubMed

Louridas, Marisa; Szasz, Peter; de Montbrun, Sandra; Harris, Kenneth A; Grantcharov, Teodor P

2016-04-01

To identify background characteristics and cognitive tests that may predict surgical trainees' future technical performance, and therefore be used to supplement existing surgical residency selection criteria. Assessment of technical skills is not commonly incorporated as part of the selection process for surgical trainees in North America. Emerging evidence, however, suggests that not all trainees are capable of reaching technical competence. Therefore, incorporating technical aptitude into selection processes may prove useful. A systematic search was carried out of the MEDLINE, PsycINFO, and Embase online databases to identify all studies that assessed associations between surrogate markers of innate technical abilities in surgical trainees, and whether these abilities correlate with technical performance. The quality of each study was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation system. A total of 8035 records were identified. After screening by title, abstract, and full text, 52 studies were included. Very few surrogate markers were found to predict technical performance. Significant associations with technical performance were seen for 1 of 23 participant-reported surrogate markers, 2 of 25 visual spatial tests, and 2 of 19 dexterity tests. The assessment of trainee Basic Performance Resources predicted technical performance in 62% and 75% of participants. To date, no single test has been shown to reliably predict the technical performance of surgical trainees. Strategies that rely on assessing multiple innate abilities, their interaction, and their relationship with technical skill may ultimately be more likely to serve as reliable predictors of future surgical performance.

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.

Characterization and Design of Digital Pointing Subsystem for Optical Communication Demonstrator

NASA Technical Reports Server (NTRS)

Racho, C.; Portillo, A.

1998-01-01

The Optical Communications Demonstrator (OCD) is a laboratory-based lasercom demonstration terminal designed to validate several key technologies, including beacon acquisition, high bandwidth tracking, precision bearn pointing, and point-ahead compensation functions. It has been under active development over the past few years. The instrument uses a CCD array detector for both spatial acquisition and high-bandwidth tracking, and a fiber coupled laser transmitter. The array detector tracking concept provides wide field-of-view acquisition and permits effective platform jitter compensation and point-ahead control using only one steering mirror. This paper describes the detailed design and characterization of the digital control loop system which includes the Fast Steering Mirror (FSM), the CCD image tracker, and the associated electronics. The objective is to improve the overall system performance using laboratory measured data. The. design of the digital control loop is based on a linear time invariant open loop model. The closed loop performance is predicted using the theoretical model. With the digital filter programmed into the OCD control software, data is collected to verify the predictions. This paper presents the results of the, system modeling and performance analysis. It has been shown that measurement data closely matches theoretical predictions. An important part of the laser communication experiment is the ability of FSM to track the laser beacon within the. required tolerances. The pointing must be maintained to an accuracy that is much smaller than the transmit signal beamwidth. For an earth orbit distance, the system must be able to track the receiving station to within a few microradians. The failure. to do so will result in a severely degraded system performance.

Predicting Student Success using Analytics in Course Learning Management Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olama, Mohammed M; Thakur, Gautam; McNair, Wade

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems,more » called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.« less

Control and prediction components of movement planning in stuttering vs. nonstuttering adults

PubMed Central

Daliri, Ayoub; Prokopenko, Roman A.; Flanagan, J. Randall; Max, Ludo

2014-01-01

Purpose Stuttering individuals show speech and nonspeech sensorimotor deficiencies. To perform accurate movements, the sensorimotor system needs to generate appropriate control signals and correctly predict their sensory consequences. Using a reaching task, we examined the integrity of these control and prediction components, separately, for movements unrelated to the speech motor system. Method Nine stuttering and nine nonstuttering adults made fast reaching movements to visual targets while sliding an object under the index finger. To quantify control, we determined initial direction error and end-point error. To quantify prediction, we calculated the correlation between vertical and horizontal forces applied to the object—an index of how well vertical force (preventing slip) anticipated direction-dependent variations in horizontal force (moving the object). Results Directional and end-point error were significantly larger for the stuttering group. Both groups performed similarly in scaling vertical force with horizontal force. Conclusions The stuttering group's reduced reaching accuracy suggests limitations in generating control signals for voluntary movements, even for non-orofacial effectors. Typical scaling of vertical force with horizontal force suggests an intact ability to predict the consequences of planned control signals. Stuttering may be associated with generalized deficiencies in planning control signals rather than predicting the consequences of those signals. PMID:25203459

Predicting student success using analytics in course learning management systems

NASA Astrophysics Data System (ADS)

Olama, Mohammed M.; Thakur, Gautam; McNair, Allen W.; Sukumar, Sreenivas R.

2014-05-01

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students' scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Economic optimization of operations for hybrid energy systems under variable markets

DOE PAGES

Chen, Jen; Garcia, Humberto E.

2016-05-21

We prosed a hybrid energy systems (HES) which is an important element to enable increasing penetration of clean energy. Our paper investigates the operations flexibility of HES, and develops a methodology for operations optimization for maximizing economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. In order to compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulationmore » results of two specific HES configurations are included to illustrate the proposed methodology and computational capability. These results demonstrate the economic viability of HES under proposed operations optimizer, suggesting the diversion of energy for alternative energy output while participating in the ancillary service market. Economic advantages of such operations optimizer and associated flexible operations are illustrated by comparing the economic performance of flexible operations against that of constant operations. Sensitivity analysis with respect to market variability and prediction error, are also performed.« less