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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Remote sensing data processing: Two years ago, today, and two years from today

Beginning with a survey of the state-of-the-art of processing remotely sensed data in early 1975, significant developments between that time and the present are chronicled, and technologies for early 1979 are projected. Current technical issues discussed include: training selection and labeling; classification and mensuration; use of satellite indicators to supplement predictions; small scale field structures; physical factors; ancillary data; geometric quality; and the cost of processing.

Holmes, Q. A.↗
NASA NTRS1979

Concepts leading to the IMAGE-100 hybrid interactive system

As LACIE Procedure 1 evolved from the Classification and Mensuration Subsystem smallfields procedures, it became evident that two computational systems would have merit-the LACIE/Earth Resources Interactive Processing System based on a large IBM-360 computer oriented for operational use with high computational throughput, and a smaller, highly interactive system based on a PDP 11-45 minicomputer and its display system, the IMAGE-100. The latter had advantages for certain phases; notably, interactive spectral aids could be implemented quite rapidly. This would allow testing and development of Procedure 1 before its implementation on the LACIE/Earth Resources Interactive Processing System. The resulting minicomputer system, called the Classification and Mensuration Subsystem IMAGE-100 Hybrid System, allowed Procedure-1 operations to be performed interactively, except for clustering, classification, and automatic selection of best acquisitions, which were offloaded to the LACIE/Earth Resources Interactive Processing System.

Mackin, T. F.↗
NASA NTRS1984

Feature selection and the information content of Thematic Mapper simulator data for forest structural assessment

An assessment is made of the information content of Thematic Mapper Simulator (TMS) data for the case of a forested region, in order to determine the sensitivity of such data to forest crown closure and tree size class. Principal components analysis and Monte Carlo simulation indicated that channels 4, 7, 5 and 3 were optimal for four-channel forest structure analysis. As the number of channels supplied to the Monte Carlo feature selection routine increased, classification accuracy increased. The greatest sensitivity to the forest structural parameters, which included succession within clearcuts as well as crown closure and size class, was obtained from the 7-channel TMS data.

Spanner, M. A.↗
NASA NTRS1992

Singularity classification as a design tool for multiblock grids

A major stumbling block in interactive design of 3-D multiblock grids is the difficulty of visualizing the design as a whole. One way to make this visualization task easier is to focus, at least in early design stages, on an aspect of the grid which is inherently easy to present graphically, and to conceptualize mentally, namely the nature and location of singularities in the grid. The topological behavior of a multiblock grid design is determined by what happens at its edges and vertices. Only a few of these are in any way exceptional. The exceptional behaviors lie along a singularity graph, which is a 1-D construct embedded in 3-D space. The varieties of singular behavior are limited enough to make useful symbology on a graphics device possible. Furthermore, some forms of block design manipulation that appear appropriate to the early conceptual-modeling phase can be accomplished on this level of abstraction. An overview of a proposed singularity classification scheme and selected examples of corresponding manipulation techniques is presented.

Jones, Alan K.↗
NASA NTRS2003

An Evaluation of Electronic Nose for Space Program Applications

The ability to monitor air contaminants in the Shuttle and the International Space Station is important to ensure the health and safety of astronauts. Three specific space applications have been identified that would benefit from a chemical monitor: organic contaminants in crew cabins, propellant contaminants in the airlock, and pre-combustion fire detection. NASA has assessed several commercial and developing electronic noses (e-noses) for these applications. A preliminary series of tests identified those e-noses that exhibited sufficient sensitivity to the vapors of interest. These e-noses were further tested to assess their ability to identify vapors, and in-house software has been developed to enhance identification. This paper describes the tests, the classification ability of selected e-noses, and the software improvements made to meet the requirements for these space program applications.

Young, Rebecca C.↗
NASA NTRS2016

Decision Manifold Approximation for Physics-Based Simulations

With the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.

Wong, Jay Ming↗
DOE OSTI2010

Simple Fully Automated Group Classification on Brain fMRI

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS1973

On minimizing the probability of misclassification for linear feature selection

The use of techniques for feature selection permits treatment of classification problems in spaces of reduced dimensions. A method is considered of linear feature selection for n-dimensional observation vectors which belong to one of two populations, where each population is described by a known multivariate normal density function. More specifically, the problem of finding a 1xn transformation matrix B for which the probability of misclassification with respect to the one-dimensional transformed density functions was minimized was considered. Theoretical results are presented which give rise to a numerically tractable expression for the variation in the probability of misclassification with respect to B. Using this expression a computational procedure is discussed for obtaining a B which minimizes the probability of misclassification. Preliminary numerical results are discussed.

Guseman, L. F., Jr.↗
DOE OSTI2013

Information Gain Based Dimensionality Selection for Classifying Text Documents

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexity is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

DeepWelding: a Deep Learning Enhanced Approach to GTAW Using Multi-source Sensing Images

Deep learning has great potential to reshape manufacturing industries. In this paper, we present DeepWelding, a novel framework that applies deep learning techniques to improve gas tungsten arc welding (GTAW) process monitoring and penetration detection using multi-source sensing images. The framework is capable of analyzing multiple types of optical sensing images synchronously and consists of three deep learning enhanced consecutive phases: image preprocessing, image selection, and weld penetration classification. Specifically, we adopted generative adversarial networks (pix2pix) for image denoising and classic convolutional neural networks (AlexNet) for image selection. Both pix2pix and AlexNet delivered satisfactory performance. However, five individual neural networks with heterogeneous architectures demonstrated inconsistent generalization capabilities in the classification phase when holding out multi-source images generated with specific experimental settings. Therefore, two ensemble methods combining multiple neural networks were designed to improve the model performance on unseen data collected from different experimental settings. In conclusion, we also found that the quality of model prediction was heavily influenced by the data stream collection environment. We think these findings are beneficial for the broad intelligent welding community.

42 ENGINEERING↗
DOE OSTI2014

A benchmark concept for simulation in radiographic testing

The new standard ISO 17636–2:2013 “NDT of welds: Radiographic testing - Part 2: X- and gamma ray techniques with digital detectors” describes a complex procedure for film replacement by phosphor imaging plates and digital detector arrays. RT modeling software should consider these detector types, X-ray film, and the standard requirements for image quality. Practitioners expect the same visibility of image quality indicators (IQI) in the simulated radiographs as in the experimental exposures. The proposed benchmark test is based on the comparison of experimental radiographs taken at BAM with modeled ones of participants. The experimental setup and the determination of the equivalent penetrameter sensitivity (EPS) as described in the procedure of ASTM E 746 are used for quantitative evaluation of the achievable contrast sensitivity for step hole IQIs as considered in Annex B of ISO 17636–2. System classification data for Computed Radiography (CR) and film systems will be provided by BAM according to ISO 11699–1 for selected film systems and according to ASTM E 2446 for selected CR systems. The classification of films and digital detectors is based on the measurement of the dose response function, the basic spatial resolution (SR{sub b}) of the image, and the measured image noise, which depends on the detector efficiency, the quantum statistics, and the detector fixed pattern noise.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1987

Evaluation of several classification schemes for mapping forest cover types in Michigan

Landsat MSS data were evaluated for mapping forest cover types in the northern Lower Peninsula of Michigan. The study examined seasonal variations, interpretation procedures and vegetation composition/distribution and their effect on overall classification accuracy and ability to identify individual pine species. Photographic images were used for visual interpretations while digital analysis was performed using a common (ERDAS) microcomputer image processing system. The classification schemes were evaluated using contingency tables and were ranked using the KAPPA statistic. The various classification schemes were ranked differentially according to study site location. Visual interpretation procedures ranked best, or least accurate, depending on the spatial distribution and complexity of the forest cover. Supervised classification techniques were more accurate than unsupervised clustering over all sites and seasons. Maximum likelihood classification of June data was superior to any digital classification technique of February data. The study indicates that classification accuracy is more dependent on the composition and distribution of forests in the northern lower Peninsula of Michigan than on the selection of a particular classification scheme.

Hudson, W. D.↗
NASA NTRS1986

Landsat Thematic Mapper digital information content for agricultural environments

Landsat Thematic Mapper (TM) data collected for Imperial Valley, California in December, 1982 were digitally examined to assess their utility to distinguish among agricultural and other land-covers. Statistics for thirty-seven training sites representing a variety of crops plus urban, water and desert land-covers were obtained and analyzed using transformed divergence (TD) calculations. TD values were employed to assess intraclass variability and the best bands for classification. Four subscenes were selected for clustering or unsupervised signature extraction. These areas were agriculture, urban, desert and water land-covers. The number of clusters for these subscenes were examined and the best TM bands for interclass separability were identified. The results of the clustering and training site analyses for interclass separability were compared. The TM data were useful for the digital delimitation of most crops and other cover types in this analysis. Four bands of data are adequate for classification with the best results obtained by the selection of one band from each of the available portions of the electromagnetic spectrum. Different band combinations are best for various land-cover intraclass separability.

Haack, Barry↗
DOE OSTI2014

Optical spectroscopic observations of γ-ray blazar candidates. I. Preliminary results

A significant fraction (∼30%) of the γ-ray sources listed in the second Fermi/LAT (2FGL) catalog is still of unknown origin, being not yet associated with counterparts at lower energies. Using the available information at lower energies and optical spectroscopy on the selected counterparts of these γ-ray objects, we can pinpoint their exact nature. Here, we present a pilot project pointing to assess the effectiveness of the several classification methods developed to select γ-ray blazar candidates. To this end, we report optical spectroscopic observations of a sample of five γ-ray blazar candidates selected on the basis of their infrared Wide-field Infrared Survey Explorer (WISE) colors or of their low-frequency radio properties. Blazars come in two main classes, BL Lac objects and FSRQs, showing similar optical spectra except for the stronger emission lines of the latter. For three of our sources, the almost featureless optical spectra obtained confirm their BL Lac nature, while for the source WISEJ022051.24+250927.6 we observe emission lines with equivalent width EW ∼ 31 Å, identifying it as a FSRQ with z = 0.48. The source WISEJ064459.38+603131.7, although not featuring a clear radio counterpart, shows a blazar-like spectrum with weak emission lines with EW ∼ 7 Å, yielding a redshift estimate of z = 0.36. In addition, we report optical spectroscopic observations of four WISE sources associated with known γ-ray blazars without a firm classification or redshift estimate. For the latter sources, we confirm a BL Lac classification, with a tentative redshift estimate for the source WISEJ100800.81+062121.2 of z = 0.65.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1977

Toward fully automated agricultural inventory from satellites

Conventional approaches to inventories from satellites rely heavily on the judgment of image analysts, who 'train' the computer classification program. The fully automated approach to this problem, described in the present paper, eliminates the need for human judgment in training the computer, by using fully automated techniques for selecting spectral characteristics and classification parameters to optimize agreement with ground truth in specified test areas.

Ungar, S. G.↗
DOE OSTI2011

Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.

99 GENERAL AND MISCELLANEOUS↗