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Results for “principal component analysis (PCA)”

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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Using Principal Component Analysis (PCA) to Speed up Radiative Transfer (RT) Computations

Multiple scattering RT calculations time-consuming. Need a speed improvement of about 1000 (for OCO)! Solution: Make use of redundancies in spectra. Correlated-k (Lacis and Wang, Lacis and Oinas, Goody et al, Fu and Liou) Problem: Assume that spectral variation of atmospheric optical properties spatially correlated at all points along optical path. High accuracy (HI) and 2-stream (2S) calculations have high correlation. Single scattering (SS) computations highly scenario-dependent, but not time consuming. Perform SS and 2S calculations at every wavelength. Perform small number of HI computations. Need to compute correction factor B at every wavelength.

data sets↗
NASA NTRS2019

Discerning the Impact of Powder Feedstock Variability on Structure, Property, and Performance of Selective Laser Melted Alloy 718: A Principal Component Analysis (PCA) of Feedstock Variability

Extensive mechanical, chemical and microstructural analyses were conducted on additively manufactured Alloy 718 to characterize powders from multiple vendors to determine the effects of variations observed in the powders had on the consolidated material. With over 190 variables examined, it was necessary to reduce the number of variables and identify the variables and classes of variables that had the greatest effect. Principle Component Analysis (PCA) was used to reduce the number of variable to effectively 12 while identifying several classes of variables as most important.

Ellis, David↗
NASA NTRS1989

Data structure characterization of miltispectral data using principal component and principal factor analysis

Both principal component analysis (PCA) and principal factor analysis (PFA) were used to analyze an experimental multispectral data structure in terms of common and unique variance. Only the common variance of the multispectral data was associated with the principal factor, while higher-order principal components were associated with both common and unique variance. The unique variance was found to represent small spectral variations within each cover type as well as noise vectors, and was most abundant in the lower-order principal components. The lower-order principal components can be useful in research designed to discriminate minor physical variations within features, and to highlight localized change when using multitemporal-multispectral data. Conversely, PFA of the multispectral data provided an insight into a great potential for discriminating basic land-cover types by excluding the unique variance which was related to the noise and minor spectral variations.

Lee, Jae K.↗
NASA NTRS2002

Continuum Fitting HST QSO Spectra

The Principal Component Analysis (PCA) method which we are using to fit and describe QSO spectra relies upon the fact that QSO continuum are generally very smooth and simple except for emission and absorption lines. To see this we need high signal-to-noise (S/N) spectra of QSOs at low redshift which have relatively few absorption lines in the Lyman-a forest. We need a large number of such spectra to use as the basis set for the PCA analysis which will find the set of principal component spectra which describe the QSO family as a whole. We have found that too few HST spectra have the required S/N and hence we need to supplement them with ground based spectra of QSOs at higher redshift. We have many such spectra and we have been working to make them suitable for this analysis. We have concentrated on this topic since 12/15/01.

Tytler, David↗
NASA NTRS2006

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

displacement↗
NASA NTRS2023

Portland Urban Development: Quantifying and Visualizing Urban Heat with Compounding Vulnerabilities to Support Community Depaving Initiatives

Urban heat is a pressing concern in Portland, Oregon as climate change induced heat waves increase. Cities experience higher temperatures due to the urban heat island effect (UHI), and environmental injustice and disenfranchisement in minority communities expose low-income and Black, Indigenous, and People of Color (BIPOC) residents to more extreme and debilitating heat events. Our team identified Portland’s communities on the frontlines of urban heat impacts by overlapping environmental and social vulnerabilities using NASA Earth observations. We partnered with Depave, a Portland-based nonprofit that works alongside communities to replace pavement with greenspace in historically disenfranchised areas. Using Landsat 8 Thermal Infrared Sensor (TIRS) imagery, we mapped Land Surface Temperature (LST) and developed a heat-specific Social Vulnerability Index (SVI) through a Principal Component Analysis (PCA) to identify Portland’s communities with the highest potential heat vulnerability. Then, we calculated the temperature change of depaving in six case studies to quantify Depave's efforts in heat mitigation and environmental justice. Our analysis demonstrated that, throughout Portland, there are frontline communities experiencing high potential social vulnerability to extreme temperatures due to environmental injustices and over-pavement. Finally, Depave’s impact on urban heat is observable and quantifiable using remote-sensing data and tools, with an average of 1ºF LST decrease across the six case studies. We illustrated the significance of local urban heat mitigation efforts and propose next steps for conducting inclusive and intentional research that highlights the lived experiences and resilience of frontline communities.

Environmental justice↗
NASA NTRSDate not supplied

Forward and Inverse Models for Satellite Remote Sensors using Principal Component Analysis

Satellite remote sensors such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C make millions of observations each day with thousands of spectral channels for each observation; this poses challenges for efficiently inversion of the inherently large dataset as needed to retrieve atmospheric and surface properties. This presentation will illustrate the use of Principal Component Analysis (PCA) to speed up radiative transfer forward model calculations and to stabilize the inversion algorithms. A Principal Component-based radiative transfer model (PCRTM) developed at NASA Langley Research Center can simulate top of atmosphere (TOA) radiance or reflectance spectra from 50 cm-1 to 50000 cm-1 (200 m to 0.20 m quickly and accurately. PCRTM demonstrated very high accuracy relative to reference line-by-line radiative transfer models and it saves orders of magnitude computational time. Examples of the PCRTM model developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, CPF, TEMPO, SBG, OMI, and SCIAMACHY will be presented. In addition to using the PCRTM as forward model, the NASA Langley developed inversion algorithm also uses PCA to compress the state vector into a compressed dimension to speed up and stabilize the inversion process. Examples of retrieved atmospheric temperature, water vapor, CO2, CO, CH4, N2O, and O3 profiles, cloud properties (optical depth, size, phase, and height), and surface properties (surface emissivity spectra and skin temperatures) will be presented. This algorithm is being transitioned to the NASA Sounder SIPS and NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC).

forward model↗
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗
NASA NTRS2004

Automated Analysis, Classification, and Display of Waveforms

A computer program partly automates the analysis, classification, and display of waveforms represented by digital samples. In the original application for which the program was developed, the raw waveform data to be analyzed by the program are acquired from space-shuttle auxiliary power units (APUs) at a sampling rate of 100 Hz. The program could also be modified for application to other waveforms -- for example, electrocardiograms. The program begins by performing principal-component analysis (PCA) of 50 normal-mode APU waveforms. Each waveform is segmented. A covariance matrix is formed by use of the segmented waveforms. Three eigenvectors corresponding to three principal components are calculated. To generate features, each waveform is then projected onto the eigenvectors. These features are displayed on a three-dimensional diagram, facilitating the visualization of the trend of APU operations.

Kwan, Chiman↗
NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Brooks, Colin↗
NASA NTRS2000

Principal Component Analysis of Arctic Solar Irradiance Spectra

During the FIRE (First ISCPP Regional Experiment) Arctic Cloud Experiment and coincident SHEBA (Surface Heat Budget of the Arctic Ocean) campaign, detailed moderate resolution solar spectral measurements were made to study the radiative energy budget of the coupled Arctic Ocean - Atmosphere system. The NASA Ames Solar Spectral Flux Radiometers (SSFRs) were deployed on the NASA ER-2 and at the SHEBA ice camp. Using the SSFRs we acquired continuous solar spectral irradiance (380-2200 nm) throughout the atmospheric column. Principal Component Analysis (PCA) was used to characterize the several tens of thousands of retrieved SSFR spectra and to determine the number of independent pieces of information that exist in the visible to near-infrared solar irradiance spectra. It was found in both the upwelling and downwelling cases that almost 100% of the spectral information (irradiance retrieved from 1820 wavelength channels) was contained in the first six extracted principal components. The majority of the variability in the Arctic downwelling solar irradiance spectra was explained by a few fundamental components including infrared absorption, scattering, water vapor and ozone. PCA analysis of the SSFR upwelling Arctic irradiance spectra successfully separated surface ice and snow reflection from overlying cloud into distinct components.

Rabbette, Maura↗
NASA NTRS2013

Principal Components Analysis of a JWST NIRSpec Detector Subsystem

We present principal component analysis (PCA) of a flight-representative James Webb Space Telescope NearInfrared Spectrograph (NIRSpec) Detector Subsystem. Although our results are specific to NIRSpec and its T - 40 K SIDECAR ASICs and 5 m cutoff H2RG detector arrays, the underlying technical approach is more general. We describe how we measured the systems response to small environmental perturbations by modulating a set of bias voltages and temperature. We used this information to compute the systems principal noise components. Together with information from the astronomical scene, we show how the zeroth principal component can be used to calibrate out the effects of small thermal and electrical instabilities to produce cosmetically cleaner images with significantly less correlated noise. Alternatively, if one were designing a new instrument, one could use a similar PCA approach to inform a set of environmental requirements (temperature stability, electrical stability, etc.) that enabled the planned instrument to meet performance requirements

Analysis↗
NASA NTRS2013

Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

Roberts, Yolanda, L.↗
NASA NTRS2011

Jovian Chromophore Characteristics from Multispectral HST Images

The chromophores responsible for coloring the jovian atmosphere are embedded within Jupiter's vertical aerosol structure. Sunlight propagates through this vertical distribution of aerosol particles, whose colors are defined by omega-bar (sub 0)(lambda), and we remotely observe the culmination of the radiative transfer as I/F(lambda). In this study, we employed a radiative transfer code to retrieve omega-bar (sub 0)(lambda) for particles in Jupiter's tropospheric haze at seven wavelengths in the near-UV and visible regimes. The data consisted of images of the 2008 passage of Oval BA to the south of the Great Red Spot obtained by the Wide Field Planetary Camera 2 on-board the Hubble Space Telescope. We present derived particle colors for locations that were selected from 14 weather regions, which spanned a large range of observed colors. All omega-bar (sub 0)(lambda) curves were absorbing in the blue, and omega-bar (sub 0)(lambda) increased monotonically to approximately unity as wavelength increased. We found accurate fits to all omega-bar (sub 0)(lambda) curves using an empirically derived functional form: omega-bar (sub 0)(lambda) = 1 A exp(-B lambda). The best-fit parameters for the mean omega-bar (sub 0)(lambda) curve were A = 25.4 and B = 0.0149 for lambda in units of nm. We performed a principal component analysis (PCA) on our omega-bar (sub 0)(lambda) results and found that one or two independent chromophores were sufficient to produce the variations in omega-bar (sub 0)(lambda). A PCA of I/F(lambda) for the same jovian locations resulted in principal components (PCs) with roughly the same variances as the omega-bar (sub 0)(lambda) PCA, but they did not result in a one-to-one mapping of PC amplitudes between the omega-bar (sub 0)(lambda) PCA and I/F(lambda) PCA. We suggest that statistical analyses performed on I/ F(lambda) image cubes have limited applicability to the characterization of chromophores in the jovian atmosphere due to the sensitivity of 1/ F(lambda) to horizontal variations in the vertical aerosol distribution.

Strycker, Paul D.↗
NASA NTRS2005

Real-Time Principal-Component Analysis

A recently written computer program implements dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN), which was described in Method of Real-Time Principal-Component Analysis (NPO-40034) NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 59. To recapitulate: DOGEDYN is a method of sequential principal-component analysis (PCA) suitable for such applications as data compression and extraction of features from sets of data. In DOGEDYN, input data are represented as a sequence of vectors acquired at sampling times. The learning algorithm in DOGEDYN involves sequential extraction of principal vectors by means of a gradient descent in which only the dominant element is used at each iteration. Each iteration includes updating of elements of a weight matrix by amounts proportional to a dynamic initial learning rate chosen to increase the rate of convergence by compensating for the energy lost through the previous extraction of principal components. In comparison with a prior method of gradient-descent-based sequential PCA, DOGEDYN involves less computation and offers a greater rate of learning convergence. The sequential DOGEDYN computations require less memory than would parallel computations for the same purpose. The DOGEDYN software can be executed on a personal computer.

Duong, Vu↗
NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Principal component analysis (PCA) has been the center of many surrogate models used to characterize fluid flows in recent years. However, little work has been done to character- ize the uncertainty in the PCA transform itself and its effect on derived surrogate models. To explore the uncertainty, a typical interpolated surrogate model is constructed for a represen- tative aerodynamic body from computational data. The computational data is then resampled to explore the robustness of the PCA transformation. The variations of the model predictions during this resampling are analyzed to get a measure of confidence in the PCA transformation, which is then applied to the interpolated surrogate model to get uncertainty on integrated force predictions. An initial test case has been explored with promising results.

Principal Component Analysis↗
NASA NTRS2001

Principal Components Analysis of Triaxial Vibration Data From Helicopter Transmissions

Research on the nature of the vibration data collected from helicopter transmissions during flight experiments has led to several crucial observations believed to be responsible for the high rates of false alarms and missed detections in aircraft vibration monitoring systems. This work focuses on one such finding, namely, the need to consider additional sources of information about system vibrations. In this light, helicopter transmission vibration data, collected using triaxial accelerometers, were explored in three different directions, analyzed for content, and then combined using Principal Components Analysis (PCA) to analyze changes in directionality. In this paper, the PCA transformation is applied to 176 test conditions/data sets collected from an OH58C helicopter to derive the overall experiment-wide covariance matrix and its principal eigenvectors. The experiment-wide eigenvectors. are then projected onto the individual test conditions to evaluate changes and similarities in their directionality based on the various experimental factors. The paper will present the foundations of the proposed approach, addressing the question of whether experiment-wide eigenvectors accurately model the vibration modes in individual test conditions. The results will further determine the value of using directionality and triaxial accelerometers for vibration monitoring and anomaly detection.

Tumer, Irem Y.↗
NASA NTRS2019

Principal Component Analysis of Up-The-Ramp Sampled Infrared Array Data

We describe the results of principal component analysis (PCA) of up-the-ramp sampled infrared (IR) array data from the Hubble Space Telescope wide field camera 3 (WFC3 IR), James Webb Space Telescope NIRSpec, and prototype Wide Field Infrared Survey Telescope’s wide field instrument detectors. These systems use, respectively, Teledyne H1R, H2RG, and H4RG-10 near-IR detector arrays with a variety of IR array controllers. The PCA shows that the Legendre polynomials approximate the principal components of these systems (i.e., they roughly diagonalize the covariance matrix). In contrast to the monomial basis that is widely used for polynomial fitting and linearization today, the Legendre polynomials are an orthonormal basis. They provide a quantifiable, compact, and (nearly) linearly uncorrelated representation of the information content of the data. By fitting a few Legendre polynomials, nearly all of the meaningful information in representative WFC3 astronomical datacubes can be condensed from 15 up-the-ramp samples down to 6 compressible Legendre coefficients per pixel. The higher order coefficients contain time domain information that is lost when one projects up-the-ramp sampled datacubes onto two-dimensional images by fitting a straight line, even if the data are linearized before fitting the line. Going forward, we believe that this time domain information is potentially important for disentangling the various nonlinearities that can affect IR array observations, i.e., inherent pixel nonlinearity, persistence, burn in, brighter-fatter effect, (potentially) nonlinear interpixel capacitance, and perhaps others.

Rauscher, Bernard J.↗