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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Plutonium Retention by Crystalline Silicotitanate under Hyperalkaline Conditions Relevant to Tank-Side Cesium-Removal at the Hanford Site

Crystalline silicotitanate (CST) is used in Hanford’s Tank-Side Cesium-Removal (TSCR) process to selectively remove Cs-137 from highly caustic, nitrate-rich tank supernatants. Recent testing with actual waste samples suggests that CST can also retain measurable plutonium (Pu), which could affect radiological classification and disposal pathways for spent CST. To quantify this behavior, Pu partitioning to CST was studied under Hanford-relevant conditions using batch-contact experiments in a representative simulant (2 M NaNO3, 0.7 M NaOH). Isotherm data were measured and distribution ratios calculated, with Cs+ uptake used as benchmark. Under low-carbonate conditions, Pu was retained strongly by CST in systems initially contacted with either PuO2 nanoparticles (Pu(IV)) or aqueous Pu(VI), with distribution ratios of ~2,200–3,700 mL/g, generally exceeding those for Cs+ (~400–1,000 mL/g). Increasing carbonate concentration strongly reduced PuO2 nanoparticle retention; at [Na2CO3] = 1 M, distribution ratios decreased by up to one order of magnitude to roughly 100–300 mL/g. Electron microscopy suggests that Pu retention involves a combination of mechanisms such as PuO2 NP aggregation induced by CST leachate components, and association with CST bead surfaces.

Neumann, J.↗
DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗
NASA NTRS1973

An objective technique to estimate percentage of an ERTS-1 water boundary resolution element covered by water

The author has identified the following significant results. An objective technique was developed to measure the surface area of water bodies. Nineteen water bodies in the Houston and Galveston, Texas area were selected as a basis for the technique development. The actual surface area of each body was determined from rectified and enlarged NASA aircraft photography. A clustering algorithm was used to produce classification maps of the region from ERTS-1 data. Certain classes were identified as being 100% water. Other classes were identified as being mixtures of water with land or vegetation. The number of picture elements falling on each water body and its boundary were counted. A linear regression analysis was performed to relate the total number of picture elements and boundary elements counted to the actual surface area. The standard error of the estimate was 6.7 acres. The absolute error was not a function of the actual surface area of the water body.

Erb, R. B.↗
NASA NTRS1977

Computer-aided classification for remote sensing in agriculture and forestry in Northern Italy

A set of results concerning the processing and analysis of data from LANDSAT satellite and airborne scanner is presented. The possibility of performing inventories of irrigated crops-rice, planted groves-poplars, and natural forests in the mountians-beeches and chestnuts, is investigated in the Po valley and in an alphine site of Northern Italy. Accuracies around 95% or better, 70% and 60% respectively are achieved by using LANDSAT data and supervised classification. Discrimination of rice varieties is proved with 8 channels data from airborne scanner, processed after correction of the atmospheric effect due to the scanning angle, with and without linear feature selection of the data. The accuracies achieved range from 65% to more than 80%. The best results are obtained with the maximum likelihood classifier for normal parameters but rather close results are derived by using a modified version of the weighted euclidian distance between points, with consequent decrease in computing time around a factor 3.

Dejace, J.↗
NASA NTRS1978

The Ten-Ecosystem Study - Landsat ADP mapping of forest and rangeland in the United States

The Ten-Ecosystem Study was designed to assess the maximum information content of Landsat data and its utility for large area classification using a uniform technical approach on the 10 generalized forest and rangeland ecosystems of the United States. Conclusions on the feasibility of using Landsat remote sensing automatic data processing methods, selecting the best seasons, analyzing costs and the effects of site complexity, miscellaneous analysis, problems, and recommendations were derived from 2 years of study, the project being three-fourths completed.

Kan, E. P.↗
NASA NTRS1981

Evaluation of a segment-based LANDSAT full-frame approach to corp area estimation

As the registration of LANDSAT full frames enters the realm of current technology, sampling methods should be examined which utilize other than the segment data used for LACIE. The effect of separating the functions of sampling for training and sampling for area estimation. The frame selected for analysis was acquired over north central Iowa on August 9, 1978. A stratification of he full-frame was defined. Training data came from segments within the frame. Two classification and estimation procedures were compared: statistics developed on one segment were used to classify that segment, and pooled statistics from the segments were used to classify a systematic sample of pixels. Comparisons to USDA/ESCS estimates illustrate that the full-frame sampling approach can provide accurate and precise area estimates.

Bauer, M. E.↗
NASA NTRS1988

Antarctic Meteorite Newsletter, Volume 11, Number 2, August 1988

Presented are classifications and descriptions of a large number of meteorites which include the last samples from the 1984 collection and the first samples from the 1987 collection. There is a particularly good selection of meteorites of special petrologic type in the 1987 collection. The achondrites include aubrites, ureilites, howardites, eucrites, and a diogenite. The howardites are particularly notable because of their size and previous scarcity in the Antarctic collection. Noteworthy among the 7 irons and 3 mesosiderities are 2 anamolous irons and 2 large mesosiderites. The carbonaceous chondrites include good suites of C2 and C4 meteorites, and 2 highly equilibrated carbonaceous chondrites tentatively identified as C5 and C6 meteorites. Also included are surveys of numerous meteorites for Al-26 and thermoluminescence. These studies provide information on the thermal and radiation histories of the meteorites and can be used as measures of their terrestrial ages.

Source record↗
NASA NTRS1990

Correlations between environmental parameters and nuclear activity

The occurrence and quantitative properties of Seyfert 1 and Seyfert 2 galaxies as a function of the galaxy environment were studied in a systematic way. For this investigation the authors selected from the Catalogue of Quasars and Active Nuclei (Veron & Veron, 1989) all Seyfert galaxies with the following properties: (1) m sub V less than or equal to 15; (2) listed classification as Sey 1, Sey 2, or Sey 3; (3) v sub rad less than or equal to 20,000 km s(sup -1). This results in a sample size of 242 Seyfert galaxies. For all these objects their galaxy environment was inspected on plates. The vicinity of each sample Seyfert galaxy was searched for companion galaxies out to at least 0.5 Mpc. Lacking redshift information, the authors adopted as a companion galaxy any galaxy in this area having a size between 20 and 200 percent of the Seyfert galaxy size. As an important environmental parameter for Seyfert activity, the authors consider the galaxy density within an environment of 500 kpc radius. They distinguish four density classes: D1: no companion (isolated Seyfert); D2: 1 companion; D3: 2 to 5 companions (group); and D4: less than or equal to 6 companions (dense group). The distribution of the various Seyfert classes among these density classes is listed in tabular form, both in absolute numbers and in percentages (given in brackets). From these statistics the environments of Sey 1, Sey 2, and Sey 3 galaxies do not differ in general with the exception that among the isolated galaxies the Seyfert 1 galaxies dominate conspicuously. No significant dependence of morphological type (de Vaucouleurs T-type) on density class is observed both for Sey 1 and Sey 2 galaxies. On the basis of an extensive search of the Seyfert literature the authors also investigated the dependence of the intrinsic parameters of the sample Seyfert galaxies on density class.

Kollatschny, W.↗
NASA NTRS1989

Sources of error in thematic classification of remotely sensed imagery

From a statistician's point of view, the input datasets are rarely examined to determine their underlying frequency distribution; it is just assumed that the data are normal enough, and that the deviations from normality are unimportant. It is not clear how deviations from a hypothetical multivariate normal might affect the power of the classification process, and there is ample evidence in the literature that, at a minimum, the spectral channels are correlated. From a practioner's point of view, in a supervised classification the number of training fields for developing a statistical description of a given class is usually arbitrary. It is unclear how small changes in the details of the training field selection process affect the quality of the derived thematic information. The start of an examination of this latter problem is discussed.

Star, Jeffrey L.↗
NASA NTRS1993

Do it yourself remote sensing: Generating an inexpensive, high tech, real science lake mapping project for the classroom

The utilization of modest equipment and software revealed bottom contours and water column conditions of a dynamic water body. Classroom discussions of field techniques and equipment capabilities followed by exercises with the data sets in cause-and-effect analysis all contributed to participatory education in the process of science. This project is presented as a case study of the value of engaging secondary and collegiate level students in planning, executing and appraising a real world investigation which they can directly relate to. A 1 km wide bay, experiencing marsh inflow, along an 8 km long lake situated 120 km north of Ottawa, Canada, on the glaciated Canadian Precambrian Shield was mapped in midsummer for submerged topography, bottom composition, temperature profile, turbudity, dissolved oxygen and biota distribution. Low level aerial photographs scanned into image processing software are permitting spatial classification of bottom variations in biology and geology. Instrumentation consisted of a portable sport fishing SONAR depth finder, an electronic lead line multiprobe with photocell, thermistor and dissolved oxygen sensors, a selective depth water sampler, portable pH meter, an underwater camera mounted on a home-made platform with a bottom-contact trigger and a disposable underwater camera for shallow survey work. Sampling transects were referenced using a Brunton hand transit triangulating several shore markers.

Metzger, Stephen M.↗
DOE OSTI2021

Feature Extraction: Improving Remote Sensor Classification of Non-Proliferation

This research focuses on developing algorithms for nuclear non-proliferation detection using remote sensor modeling. To improve the performance of classification models, we implemented a data pipeline with feature extraction. This pipeline takes raw data and transforms it into smaller data points called features that still describe the model. Improving this classification works towards the departments of energy’s missions of ensuring American’s security and prosperity by creating technology that addresses nuclear challenges. To conduct this analysis, we used the Python programming language and some key packages, including tsfresh and TSFEL. Originally tsfresh was selected because it has the most statistical features out of all the packages. Later TSFEL was incorporated due to the additional features it can extract from data, such as temporal and spectral. However, feature extraction becomes challenging in the presence of missing values. In this case, two additional Python packages were added to our workflow, NumPy and pandas, allowing for the feature extraction process to handle unknown values. Our data pipeline was tested on data collected from a simulation that describes the process state of a physical example. The results show the pipeline’s capability to consume and extract a total 17 features from tabular data. Future work includes producing classifications using decision tree-based models such as XGBoost and improving data collection by analyzing feature importance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2011

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

pattern recognition↗
DOE OSTI2022

Identifying Transient Candidates in the Dark Energy Survey Using Convolutional Neural Networks

The ability to discover new transient candidates via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine learning techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success. In this work, we present the results of an automated transient candidate identification on images with CNNs for an extant data set from the Dark Energy Survey Supernova program, whose main focus was on using Type Ia supernovae for cosmology. By performing an architecture search of CNNs, we identify networks that efficiently select non-artifacts (e.g., supernovae, variable stars, AGN, etc.) from artifacts (image defects, mis-subtractions, etc.), achieving the efficiency of previous work performed with random Forests, without the need to expend any effort in feature identification. The CNNs also help us identify a subset of mislabeled images. Performing a relabeling of the images in this subset, the resulting classification with CNNs is significantly better than previous results, lowering the false positive rate by 27% at a fixed missed detection rate of 0.05.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Functional protein mining with conformal guarantees

Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1973

The use of the temporal dimension in classifying and mapping ERTS-1 MSS data

Multispectral data from two ERTS-1 scenes of the same central Pennsylvania area were brought into registration by translation and then merged. The two scenes were viewed on different dates, but from adjacent ground tracks, as frequent cloud cover in Pennsylvania made it impossible to choose two scenes from the same track. Targets selected to be mapped included river water, railroad yards, creeks, urban areas, industrial areas, and vegetation. Equivalent training areas were chosen from each of the original scenes and from the merged data. Classification maps were produced for each, and a comparison was made. Scene brightness was found to have the most important effect on classification differences.

Borden, F. Y.↗
NASA NTRS1995

ROSAT observation program

In this annual progress report (November 1, 1993 to October 31, 1994), the geminga pulsar was observed by the ROSAT PSPC for 37,000 s in September 1993, in order to make a more detailed study than was previously possible of the pulse profile and two-component spectrum, and to do phase-resolved spectroscopy. This exposure was 2.5 times longer than the original discovery observation. In addition, a shorter 4,000 s exposure was made in October 1992, simultaneously with a GRO observation of Geminga, in order to verify the absolute phasing of the X-ray and gamma-ray peaks. We verified that the spectrum can be described as the sum of two black bodies, whose temperatures are 6 x 10(exp 5) K and 3 - 4 x 10(exp 6) K, with the latter covering 3 x 10(exp -5) the area of the former. The pulse profiles indicate that the intensity of the two emitting regions peak is approximately 90 deg out of phase in rotation, but that the temperatures are otherwise independent of phase. An improved estimate of the distance can be made from the cooler (larger) blackbody component, yielding d = 440 +/- 120 pc. Another program of this report was to obtain PSPC spectra of an important class of Seyfert galaxies which have narrow lines and stron permitted Fe II emission. Sometines called I Zw 1 objects, or narrow-line Seyfert 1s, they are crucial to our understanding of Seyfert classification and models of Seyfert unification. We observed four new objects and, in addition, obtained data on 17 more from the ROSAT archive. A third program combined PSPC and HRI observations of selected Seyfert galaxies which have unusual and variable spectra. The purpose was to disentangle diffuse X-ray emission from the nuclear source, in order to properly interpret the soft X-ray spectral shapes in terms of partial covering and/or warm-absorber models. The targets of the program are NGC 3516, NGC 3227, and NGC 7314. So far, we have only performed a dtailed analysis on NGC 3516. The fourth program of this report is the NGC 1672, the second brightest Seyfert galaxy and one or the original 'composite' Seyfert/starburst galaxies which have evidence for both Seyfert activity and H II regions in their optical spectra. It is one of the lowest luminosity Seyfert 2 galaxies that can be studied in detail.

Halpern, Jules P.↗
NASA NTRS1993

(abstract) Reddening Measurements using CCD uvby (Stromgren) Photometry

Reddening measurements for interstellar clouds have usually relied on one of two techniques: Star counts or spectro-photometric measurements. We present preliminary results for a technique to determine the extinction using CCD uvby (Stromgren) photometry, without the need to perform special classification of the stars. The method relies on the fact that the uvby system allows two independent measurements of the reddening through the m(sub 1)and c(sub 1) indices to select the true intrensic colors for each star. Using observations from the Burrell Schmidt on Kitt Peak, we find that the technique allows reliable reddening measurements to be made down to at least V=15&supm;.

measurements↗