SEARCH · Search NASA

Results for “Regression model”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRS1975

Optimal selection of passes

Preliminary numerical results obtained from the application of a linear feature selection technique to the determination of combinations of passes which best discriminate between a given set of crops in a given area of interest, are reported. The results obtained are not purported to hold in a general situation, but only for the given set of crops and the given, but unknown, levels of several factors-such as soil type, and fertilizer practice, holding in the area of interest. However, by identifying the various factors affecting the spectral signatures, and by formulating a regression model one could use the feature selection technique to determine the regression coefficients for predicting optimal passes for a given set of crops. Another use of the feature selection technique as applied to multiple pass registered data is the generation of enhanced grey scale displays by using a single linear combination of all channels of all designated passes as opposed to a single channel within a single pass.

Guseman, L. F., Jr.↗

NASA NTRS2002

Extreme Weather Events and Their Relationship to Low Frequency Teleconnection Patterns

A new method for identifying the structure and other characteristics of extreme weather events is introduced and applied to both model simulations and observations. The approach is based on a linear regression model that links daily extreme precipitation amounts for a particular point on the globe to precipitation and related quantities at all other points. We present here some initial results of our analysis of extreme precipitation events over the United States, including how they are influenced by ENSO and various large-scale teleconnection patterns such as the PNA. The results are based on simulations made with the NASA/NCAR AGCM (Lin and Rood 1996). The quality of the simulated climate for the NASA/NCAR AGCM forced with observed SSTs is described in Chang et al. (2001). The runs analyzed here consist of three 20-year runs forced with idealized cold, neutral and warm ENSO SST anomalies (superimposed on the mean seasonal cycle of SST). The idealized warm or cold SST anomalies are fixed throughout each 20- year simulation and consist of the first EOF (+/- 3 standard deviations) of monthly SST data. Comparisons are made with the results obtained from a similar analysis that uses daily NOAA precipitation observations (Higgins et al. 1996) over the United States and NCEP/NCAR reanalysis data for the period 1949-1998.

Chang, Yehui↗

NASA NTRS1992

Estimation of Kalman filter gain from output residuals

This paper presents a procedure for estimating the Kalman filter gain from output residuals. The system state space model is assumed to be known, but the process and noise covariance are unknown. The proposed procedure consists of three basic steps. First, the output residuals are computed from the given model and a given set of input-output data. Second, a linear regression model for this part of the response is computed by a least squares solution. Third, the Kalman filter gain is then estimated from the coefficients of this model. Numerical results using experimental data are presented to illustrate the validity of the developed procedure.

Juang, Jer-Nan↗

NASA NTRS2005

Hypersonic Wind Tunnel Calibration Using the Modern Design of Experiments

A calibration of a hypersonic wind tunnel has been conducted using formal experiment design techniques and response surface modeling. Data from a compact, highly efficient experiment was used to create a regression model of the pitot pressure as a function of the facility operating conditions as well as the longitudinal location within the test section. The new calibration utilized far fewer design points than prior experiments, but covered a wider range of the facility s operating envelope while revealing interactions between factors not captured in previous calibrations. A series of points chosen randomly within the design space was used to verify the accuracy of the response model. The development of the experiment design is discussed along with tactics used in the execution of the experiment to defend against systematic variation in the results. Trends in the data are illustrated, and comparisons are made to earlier findings.

Rhode, Matthew N.↗

NASA NTRS2021

Argentina Food Security & Agriculture: Crop Monitoring and Forecasting for Argentina using NASA Satellite Observations

Early harvest information helps guide agricultural commodity assessments in Argentina, providing valuable planning information to identify potentially food-insecure regions, anticipate transportation and storage demands, predict price fluctuations, and project commodity trends. However, crop yield estimates are currently subjective, based on interviews with qualified informants (i.e., farmers, agribusiness actors). In partnership with the Buenos Aires Grain Exchange, we leveraged Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) NASA Earth observations to develop a Google Earth Engine (GEE) toolset to monitor vegetation growth. The first component of the toolset produces spatial and temporal maps of temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI), allowing users to visualize the influence of the region’s climate and weather. Next, we developed an autoregressive model to predict NDVI several months in advance. Lastly, we created a linear regression model of crop yield and NDVI for soybeans, corn, and wheat, and input the forecasted NDVI to generate a predicted crop yield output. The NDVI forecasting model produced accurate predictions at two, four, and six months when examining the most recent growing season. In the crop yield model, soybeans exhibited moderately strong correlation, wheat had consistent weak correlation, and corn varied from weak to strong correlation depending on zone. This information is vital for vegetation growth monitoring by identifying areas of high growth and allocating resources to areas of lower growth to efficiently maximize crop yields.

DEVELOP Tech Paper↗

NASA NTRS1977

An empirical model for ocean radar backscatter and its application in inversion routine to eliminate wind speed and direction effects

Several regression models were tested to explain the wind direction dependence of the 1975 JONSWAP (Joint North Sea Wave Project) scatterometer data. The models consider the radar backscatter as a harmonic function of wind direction. The constant term accounts for the major effect of wind speed and the sinusoidal terms for the effects of direction. The fundamental accounts for the difference in upwind and downwind returns, while the second harmonic explains the upwind-crosswind difference. It is shown that a second harmonic model appears to adequately explain the angular variation. A simple inversion technique, which uses two orthogonal scattering measurements, is also described which eliminates the effect of wind speed and direction. Vertical polarization was shown to be more effective in determining both wind speed and direction than horizontal polarization.

Dome, G. J.↗

NASA NTRS2012

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Gorinevsky, Dimitry↗

NASA NTRSDate not supplied

Modeling approaches to estimate community annoyance due to sonic booms using data from repeated surveys

In an on-going project for NASA, we are developing a modeling approach to analyze the relationship between the noise level of sonic booms from an experimental supersonic plane and the level of annoyance measured through community response surveys. The goal of the project is to obtain a quantitative relationship between noise level and annoyance that is representative for the affected population. Particular modeling challenges include multiple annoyance measurements per survey respondent and very few occurrences of annoyance overall. To address these challenges, we propose a two-stage model for the presence of high annoyance, with the first stage modeling the probability that a respondent is ever highly annoyed and the second stage a multilevel logistic regression model for high annoyance based on noise level and demographic characteristics. We use a variation of Multilevel Regression and Poststratification (Gelman and Little, 1997) to obtain an overall representative noise-annoyance curve for the population. The approach is applied to data from a NASA pilot study.

Robyn Ferg↗

NASA NTRSDate not supplied

Modeling Approaches to Estimate Community Annoyance Due to Sonic Booms Using Data from Repeated Surveys

Robyn Ferg↗

NASA NTRS2017

Heuristic Area Cost Estimation for Observational Coverage Schedulers

This paper presents a comparison of heuris- tics used to estimate the amount of time it would take for a spacecraft to image an area using Boustrophedon decomposition (Choset and Pignon 1998). Machine learning tech- niques are used to characterize algorithmic performance of coverage algorithms. It is shown that an ordinary least-squares linear model is among the most accurate in a set of constant and linear order regression models both in terms of memory consumption and schedule duration. These are demonstrated using the ASPEN planning system (Fukunaga et al. 1997) on the Eagle Eye domain.

Knight, Russell↗

NASA NTRS1979

MSFC solar activity predictions for satellite orbital lifetime estimation

The procedure to predict solar activity indexes for use in upper atmosphere density models is given together with an example of the performance. The prediction procedure employs a least square linear regression model to generate the predicted smoothed vinculum R sub 13 and geomagnetic vinculum A sub p(13) values. Linear regression equations are then employed to compute corresponding vinculum F sub 10.7(13) solar flux values from the predicted vinculum R sub 13 values. The output is issued principally for satellite orbital lifetime estimations.

Fuler, H. C.↗

NASA NTRS1984

Evaluation of spatial, radiometric and spectral Thematic Mapper performance for coastal studies

The effect different wetland plant canopies have upon observed reflectance in Thematic Mapper bands is studied. The three major vegetation canopy types (broadleaf, gramineous and leafless) produce unique spectral responses for a similar quantity of live biomass. The spectral biomass estimate of a broadleaf canopy is most similar to the harvest biomass estimate when a broadleaf canopy radiance model is used. All major wetland vegetation species can be identified through TM imagery. Simple regression models are developed equating the vegetation index and the infrared index with biomass. The spectral radiance index largely agreed with harvest biomass estimates.

Klemas, V.↗

NASA NTRS2004

CDF and PDF Comparison Between Humacao, Puerto Rico and Florida

The knowledge of the atmospherics phenomenon is an important part in the communication system. The principal factor that contributes to the attenuation in a Ka band communication system is the rain attenuation. We have four years of tropical region observations. The data in the tropical region was taken in Humacao, Puerto Rico. Previous data had been collected at various climate regions such as desserts, template area and sub-tropical regions. Figure 1 shows the ITU-R rain zone map for North America. Rain rates are important to the rain attenuation prediction models. The models that predict attenuation generally are of two different kinds. The first one is the regression models. By using a data set these models provide an idea of the observed attenuation and rain rates distribution in the present, past and future. The second kinds of models are physical models which use the probability density functions (PDF).

Gonzalez-Rodriguez, Rosana↗

NASA NTRS2022

Comparison of Likelihood Methods for Generalized Linear Mixed Models with Application to Quiet Supersonic Flights 2018 Data

Repeated measurement will be a feature of the survey data collected during the Quesst missionX-59 community response tests (CRT). Since each participant will report his or her categorical level of annoyance in response to multiple events, the responses from any single individual may be correlated with one another. Several models within the class of generalized linear mixed models (GLMM) are pertinent to the analysis of correlated categorical outcomes; the random intercept logistic regression model is one example. Both Bayesian and frequentist methods for fitting these models are available, with frequentist methods relying on some form of approximation (of either an integral or the integrand) that appears in the marginal likelihood function. Given several anticipated similarities of the X-59 CRT data to data collected during a past risk reduction, Quiet Supersonic Flights 2018 (QSF18), this short note is intended to create awareness. It documents an instance in which a reported population average dose-response relationship derived from QSF18 single event data was distorted by the integral approximation applied in likelihood-based methods. We review some of the available literature on the topic, compare the outputs of several different computational approaches implemented in available statistical software, and present simple corrective actions that may be useful during the Quesst mission.

dose-response model↗

NASA NTRS2011

Multi-Axis Identifiability Using Single-Surface Parameter Estimation Maneuvers on the X-48B Blended Wing Body

The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.

Ratnayake, Nalin A.↗

NASA NTRS2020

Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests

Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) data. The regression model is then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska.

Moghaddam, Mahta↗

NASA NTRS2000

Validity of VO(2 max) in predicting blood volume: implications for the effect of fitness on aging

A multiple regression model was constructed to investigate the premise that blood volume (BV) could be predicted using several anthropometric variables, age, and maximal oxygen uptake (VO(2 max)). To test this hypothesis, age, calculated body surface area (height/weight composite), percent body fat (hydrostatic weight), and VO(2 max) were regressed on to BV using data obtained from 66 normal healthy men. Results from the evaluation of the full model indicated that the most parsimonious result was obtained when age and VO(2 max) were regressed on BV expressed per kilogram body weight. The full model accounted for 52% of the total variance in BV per kilogram body weight. Both age and VO(2 max) were related to BV in the positive direction. Percent body fat contributed <1% to the explained variance in BV when expressed in absolute BV (ml) or as BV per kilogram body weight. When the model was cross validated on 41 new subjects and BV per kilogram body weight was reexpressed as raw BV, the results indicated that the statistical model would be stable under cross validation (e.g., predictive applications) with an accuracy of +/- 1,200 ml at 95% confidence. Our results support the hypothesis that BV is an increasing function of aerobic fitness and to a lesser extent the age of the subject. The results may have implication as to a mechanism by which aerobic fitness and activity may be protective against reduced BV associated with aging.

NASA Discipline Cardiopulmonary↗

NASA NTRSDate not supplied

Case Study: Analysis of Autonomous Center line Tracking Neural Networks

Deep neural networks have gained widespread usage in a number of applications. However, limitations such as lack of explainability and robustness inhibit building trust in their behavior, which is crucial in safety critical applications such as autonomous driving. Therefore, techniques which aid in understanding and providing guarantees for neural network behavior are the need of the hour. In this paper, we present a case study applying a recently proposed technique, Prophecy, to analyze the behavior of a neural network model, provided by our industry partner and used for autonomous guiding of airplanes on taxi runways. This regression model takes as input an image of the runway and produces two outputs, cross-track error and heading error, which represent the position of the plane relative to the center line. We use the Prophecy tool to extract neuron activation patterns for the correctness and safety properties of the model. We show the use of these patterns to identify features of the input that explain correct and incorrect behavior. We also use the patterns to provide guarantees of consistent behavior. We explore a novel idea of using sequences of images (instead of single images) to obtain good explanations and identify regions of consistent behavior.

Deep Neural Networks↗