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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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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Measurement of surface microtopography using helicopter-mounted stereo film cameras and two stereo matching techniques

A common problem in acquiring ground-truth data for use in microwave interaction modeling is the capture of surface roughness data that are both sampled at distances comparable to a fraction of the wavelength and extensive enough to represent the surface statistics in at least one resolution cell of the microwave remote sensor used. A technique has been developed for acquiring the necessary photogrammetric data using twin 70-mm film cameras mounted on a helicopter boom. The apparatus is described, and the accuracy with which ground surface roughness can be characterized using this device is estimated. Both standard and cross-correlation methods were used for data reduction. Stereogrammetry is compared with a completely automated image-matching technique. Dense disparity images were generated from the helicopter stereo pairs. Using interior orientation parameters supplied by the camera manufacturers, and assuming that exterior orientation parameters remain constant between control target and test field photography, an extremely dense DEM (digital elevation model) for a test field has been derived. Results are compared, and accuracy estimates are presented.

Wall, S. D.↗
NASA NTRS1974

Channel simulation for optical communication systems

A technique is reported for simulating the signal fading that will be experienced by typical optical communication systems. The desired irradiance or amplitude fading statistics can be simulated by incorporating a linearized optical modulator subsystem between the transmitter and receiver. This technique has been implemented in the design and construction of a laboratory channel simulator. The design of the processing electronics is discussed along with the results of tests performed for each mode of operation.

Tycz, M.↗
NASA NTRS1977

Techniques for land use change detection using Landsat imagery

A variety of procedures were developed for the delineation of areas of land use change using Landsat Multispectral Scanner data and the generation of statistics revealing the nature of the changes involved (i.e., number of acres changed from rural to urban). Techniques of the Image Based Information System were utilized in all stages of the procedure, from logging the Landsat data and registering two frames of imagery, to extracting the changed areas and printing tabulations of land use change in acres. Two alternative methods of delineating land use change are presented while enumerating the steps of the entire process. The Houston, Texas urban area, and the Orlando, Florida urban area, are used as illustrative examples of various procedures.

Angelici, G. L.↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗
DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗
NASA NTRS1978

The role of drop velocity in statistical spray description

The justification for describing a spray by treating drop velocity as a random variable on an equal statistical basis with drop size was studied experimentally. A double-exposure technique using fluorescent drop photography was used to make size and velocity measurements at selected locations in a steady ethanol spray formed by a swirl atomizer. The size-velocity data were categorized to construct bivariate spray density functions to describe the spray immediately after formation and during downstream propagation. It was found that a statistical treatment of drop velocity was supported by the data. Spray density function shapes and modal characteristics depended strongly on position and the amount of droplet-gas interaction that had occurred. Bimodal density functions were formed by environmental interaction during downstream propagation. Large differences were also found between spatial mass density and mass flux size distributions at the same location.

Groeneweg, J. F.↗
DOE OSTI2017

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

09 BIOMASS FUELS↗
DOE OSTI2015

Detecting rare, abnormally large grains by x-ray diffraction

Bimodal grain structures are common in many alloys, arising from a number of different causes including incomplete recrystallization and abnormal grain growth. These bimodal grain structures have important technological implications, such as the well-known Goss texture which is now a cornerstone for electrical steels. Yet our ability to detect bimodal grain distributions is largely confined to brute force cross-sectional metallography. The present study presents a new method for rapid detection of unusually large grains embedded in a sea of much finer grains. Traditional X-ray diffraction-based grain size measurement techniques such as Scherrer, Williamson–Hall, or Warren–Averbach rely on peak breadth and shape to extract information regarding the average crystallite size. However, these line broadening techniques are not well suited to identify a very small fraction of abnormally large grains. The present method utilizes statistically anomalous intensity spikes in the Bragg peak to identify regions where abnormally large grains are contributing to diffraction. This needle-in-a-haystack technique is demonstrated on a nanocrystalline Ni–Fe alloy which has undergone fatigue-induced abnormal grain growth. In this demonstration, the technique readily identifies a few large grains that occupy <0.00001 % of the interrogation volume. Finally, while the technique is demonstrated in the current study on nanocrystalline metal, it would likely apply to any bimodal polycrystal including ultrafine grained and fine microcrystalline materials with sufficiently distinct bimodal grain statistics.

36 MATERIALS SCIENCE↗
DOE OSTI2014

0.94-2.42 μm ground-based transmission spectra of the hot Jupiter HD-189733b

We present here new transmission spectra of the hot Jupiter HD-189733b using the SpeX instrument on the NASA Infrared Telescope Facility. We obtained two nights of observations where we recorded the primary transit of the planet in the J, H, and K bands simultaneously, covering a spectral range from 0.94 to 2.42 μm. We used Fourier analysis and other detrending techniques validated previously on other data sets to clean the data. We tested the statistical significance of our results by calculating the autocorrelation function, and we found that, after the detrending, autocorrelative noise is diminished at most frequencies. Additionally, we repeated our analysis on the out-of-transit data only, showing that the residual telluric contamination is well within the error bars. While these techniques are very efficient when multiple nights of observations are combined together, our results prove that even one good night of observations is enough to provide statistically meaningful data. Our observed spectra are consistent with space-based data recorded in the same wavelength interval by multiple instruments, indicating that ground-based facilities are becoming a viable and complementary option to spaceborne observatories. The best fit to the features in our data was obtained with water vapor. Our error bars are not small enough to address the presence of additional molecules; however, by combining the information contained in other data sets with our results, it is possible to explain all the available observations with a modeled atmospheric spectrum containing water vapor, methane, carbon monoxide, and hazes/clouds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Magnetic resonance elastography SE-EPI vs GRE sequences at 3T in a pediatric population with liver disease

Purpose: The goal of our study is to compare hepatic stiffness measures using gradient-recalled echo (GRE) versus spin-echo echo planar imaging (SE-EPI)-based MR Elastography (MRE) at 3T used to measure hepatic stiffness in a patients with suspected liver diseases. Materials and methods: This retrospective study included 52 patients with liver disease who underwent a 3T MRE exam including both an investigational SE-EPI-based technique and a product GRE-based technique. Regions of interest (ROI) were placed on the elastograms to measure elastography-derived liver stiffness as well as the area included within the ROIs. The mean liver stiffness values and area of ROIs were compared. Results: The mean liver stiffness was 3.72 kilopascal (kPa) ± 1.29 using GRE MRE and 3.78 kPa ± 1.13 using SE-EPI MRE. Measurement of liver stiffness showed excellent agreement between the two pulse sequences with a mean bias of − 0.1 kPa (range − 1.8 to 1.7 kPa) between sequences. The mean measurable ROI area was higher with SE-EPI (313.8 cm{sup 2} ± 213.8) than with the GRE technique (208.6 cm{sup 2} ± 114.8), and the difference was statistically significant (P < 0.05). Conclusions: Our data shows excellent agreement of measured liver stiffness between GRE and SE-EPI-based sequences at 3T. Our results show the advantage of a SE-EPI MRE sequence in terms of image quality, ROI size and acquisition time with equivalent liver stiffness measurements as compared to GRE-MRE sequence.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Study of aluminum particle combustion in solid propellant plumes using digital in-line holography and imaging pyrometry

The combustion of molten metals is an important area of study with applications ranging from solid aluminized rocket propellants to fireworks displays. Our work uses digital in-line holography (DIH) to experimentally quantify the three-dimensional position, size, and velocity of aluminum particles during combustion of ammonium perchlorate (AP) based solid-rocket propellants. Additionally, spatially resolved particle temperatures are simultaneously measured using two-color imaging pyrometry. To allow for fast characterization of the properties of tens of thousands of particles, automated data processing routines are proposed. In using these methods, statistics from aluminum particles with diameters ranging from 15 to 900 µm are collected at an ambient pressure of 83 kPa. In the first set of DIH experiments, increasing initial propellant temperature is shown to enhance the agglomeration of nascent aluminum at the burning surface, resulting in ejection of large molten aluminum particles into the exhaust plume. The resulting particle number and volume distributions are quantified. In the second set of simultaneous DIH and pyrometry experiments, particle size and velocity relationships as well as temperature statistics are explored. The average measured temperatures are found to be 2640 ± 282 K, which compares well with previous estimates of the range of particle and gas-phase temperatures. The novel methods proposed here represent new capabilities for simultaneous quantification of the joint size, velocity, and temperature statistics during the combustion of molten metal particles. The proposed techniques are expected to be useful for detailed performance assessment of metalized solid-rocket propellants.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding

The paper considers testing and signal identification for covariance matrices from two populations of marginally sub-Gaussian distributed. A multi-level thresholding procedure is proposed for testing the equality of two high-dimensional covariance matrices, which is designed to detect sparse and faint differences between the covariances. A novel -statistic composition is developed to establish the asymptotic distribution of the thresholding statistics in conjunction with the matrix blocking and the coupling techniques. It is shown that the proposed test is more powerful than the existing tests in detecting sparse and weak signals in covariances. Multiple testing procedures are constructed to discover different covariances and the sub-groups of variables with different covariance structures between the two populations. The proposed procedures are based on the multi-level thresholding test, which are able to control the false discovery proportion () with high power. Simulation experiments and a case study on the returns of the S&P 500 stocks before and after the COVID-19 pandemic are conducted to demonstrate and compare the utilities of the proposed methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Utilization of MCNP ® 6 implicit-capture simulations for quantification of systematic uncertainties from experimental environments

In this study, the high statistical precision that can be achieved using modern data collection techniques implies that systematic uncertainties that were ignored or not considered for past experiments must now be explored. The folding of Monte Carlo simulations into experimental data analysis, which is becoming common practice, requires an estimate of the systematic uncertainty present in the experimental result from uncertainties in the library of cross sections used during the simulation, but this source of uncertainty is typically not quantified. Here we describe a method to estimate this systematic uncertainty by varying the cross sections used during simulation runtime. As opposed carrying out hundreds of sequential analog Monte Carlo simulations, this task was accomplished through a post-processing calculation of output from a single simulation that was run using the implicit-capture formalism available with MCNP ® 6.2 and the PTRAC output format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

A non-destructive method for crack quantification in photovoltaic backsheets under accelerated and real-world exposures

The long-term durability of photovoltaic modules is paramount for the continued growth of the industry. Polymer backsheets are of particular concern since they provide electrical insulation and an environmental barrier. In this paper, 23 freestanding, multilayer backsheets with nine unique material combinations underwent four different weathering exposures under accelerated and real-world conditions. Besides changes in color and gloss, the induced degradation included parallel or mudflat cracks on 11 backsheets, sometimes in combination with delamination or blistering. Similar degradation has been observed in previous studies and is concerning since cracks compromise the mechanical integrity and electrical safety of backsheets. Quantitative parameters are desirable to reliably classify categories of cracks and supply unbiased features for statistical analysis in predictive lifetime models. We developed an analysis technique that utilizes surface profilometry data to quantify the depth, width, area, spacing, and number of cracks. Parameters are automatically extracted from the raw data by an algorithm running on a high performance distributed computing cluster. Our algorithm excelled at characterizing parallel cracks with minimal de-adhesion, and only an estimated 4% of crack detections were false positives. The addition of humidity and temperature variation formed up to three times as many cracks on a photodose basis compared to dry, constant temperature exposures. Cracks in real-world and accelerated exposures propagated to similar depths with equivalent photodoses; however, the number of cracks formed in accelerated exposures was far greater on a photodose basis. Of samples that cracked, the best performing backsheet configuration was polyvinyl fluoride/poly (ethylene-terephthalate)/polyethylene (PVF/PET/PE) while the least durable was PET/PET/ethylene-vinyl acetate. Finally, none of the six PVF/PET/PVF backsheets cracked in any of the exposures.

14 SOLAR ENERGY↗
DOE OSTI2018

Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

To further improve fault diagnosis accuracy, a new hybrid feature selection approach combined with a genetic algorithm (GA) and support vector machine (SVM) is presented in this study. Adaptive synthetic technique and arctangent transformation method are adopted to improve the statistical property of the training set (IEC TC10 dataset). Five filter methods based on different evaluation metrics are employed to rank 48 input features derived from dissolved gas analysis (DGA). Then, feature combination methods are applied to aggregate feature ranks and form a lower-dimension candidate feature subset. The GA-SVM model is implemented to optimise parameters and select optimal feature subsets. 5-fold cross-validation accuracy of the GA-SVM is used to evaluate fault diagnosis capability of feature subsets and finally, a novel subset is determined as the optimal feature subset. Accuracy comparison manifests the superiority of the optimal feature subsets over that of conventional approaches. Besides, generalisation and robustness of the optimal subset are validated by testing DGA samples from the local power utility. Results indicate that the optimal feature subset obtained by the proposed method can significantly improve the accuracies of power transformer fault diagnosis.

Kari, Tusongjiang↗
DOE OSTI2017

Machine learning to analyze images of shocked materials for precise and accurate measurements

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

47 OTHER INSTRUMENTATION↗