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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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A few state-of-the-art optical image processing techniques are described and their potential applications to the ACPL experiments are suggested. The discussion also includes the selection of recording media in the present system and the enhancement of signal-to-noise ratio for the expected data from ACPL.
Development of a scheme for utilizing remote sensing technology in an operational program for regional land use planning and land resource management program applications. The scheme utilizes remote sensing imagery as one of several potential inputs to derive desired and necessary data, and considers several alternative approaches to the expansion and/or reduction and analysis of data, using automated data handling techniques. Within this scheme is a five-stage program development which includes: (1) preliminary coordination, (2) interpretation and encoding, (3) creation of data base files, (4) data analysis and generation of desired products, and (5) applications.
Statistical encoding techniques enable the reduction of the number of bits required to encode a set of symbols, and are derived from their probabilities. Huffman encoding is an example of statistical encoding that has been used for error-free data compression. The degree of compression given by Huffman encoding in this application can be improved by the use of prediction methods. These replace the set of elevations by a set of corrections that have a more advantageous probability distribution. In particular, the method of Lagrange Multipliers for minimization of the mean square error has been applied to local geometrical predictors. Using this technique, an 8-point predictor achieved about a 7 percent improvement over an existing simple triangular predictor.
This paper presents empirical results of cable noise reduction techniques as demonstrated in a TEM cell operating with radiated fields from 2 - 200 MHz. It is the first part of a two-paper series. This first paper discusses cable types and shield connections. In the second paper, the effects of load and source resistances and chassis connections are examined. For each topic, well established theories are compared to data from a real-world physical system. Finally, recommendations for minimizing cable susceptibility (and thus cable emissions) are presented. There are numerous papers and textbooks that present theoretical analyses of cable noise reduction techniques. However, empirical data is often targeted to low frequencies (e.g. <50 KHz) or high frequencies (>100 MHz). Additionally, a comprehensive study showing the relative effects of various noise reduction techniques is needed. These include the use of dedicated return wires, twisted wiring, cable shielding, shield connections, changing load or source impedances, and implementing load- or source-to-chassis isolation. We have created an experimental setup that emulates a real-world electrical system, while still allowing us to independently vary a host of parameters. The goal of the experiment was to determine the relative effectiveness of various noise reduction techniques when the cable is in the presence of radiated emissions from 2 MHz to 200 MHz. The electronic system (Fig. 1) consisted of two Hammond shielded electrical enclosures, one containing the source resistance, and the other containing the load resistance. The boxes were mounted on a large aluminium plate acting as the chassis. Cables connecting the two boxes measured 81 cm in length and were attached to the boxes using standard D38999 military-style connectors. The test setup is shown in Fig. 2. Electromagnetic fields were created using an HP8657B signal generator, MiniCircuits ZHL-42W-SMA amplifier, and an EMCO 5103 TEM cell. Measurements were taken using an Agilent E4401B spectrum analyzer and HP1141a differential probes.
This paper presents empirical results of cable noise reduction techniques as demonstrated in a TEM cell operating with radiated fields from 2 - 200 MHz. It is the second part of a two-paper series. The first paper discussed cable types and shield connections. In this second paper, the effects of load and source resistances and chassis connections are examined. For each topic, well established theories are compared to data from a real-world physical system. Finally, recommendations for minimizing cable susceptibility (and thus cable emissions) are presented. There are numerous papers and textbooks that present theoretical analyses of cable noise reduction techniques. However, empirical data is often targeted to low frequencies (e.g. <50 KHz) or high frequencies (>100 MHz). Additionally, a comprehensive study showing the relative effects of various noise reduction techniques is needed. These include the use of dedicated return wires, twisted wiring, cable shielding, shield connections, changing load or source impedances, and implementing load- or source-to-chassis isolation. We have created an experimental setup that emulates a real-world electrical system, while still allowing us to independently vary a host of parameters. The goal of the experiment was to determine the relative effectiveness of various noise reduction techniques when the cable is in the presence of radiated emissions from 2 MHz to 200 MHz.
The problem of the calculation of ambient densities from San Marco-C neutral atmospheric composition experiment data is considered. A brief description is given of the data measurement method, followed by a description of both the theoretical and experimental data curves. Geometry, electrometer distortions, and noise effects are then studied in terms of their effects on the ideal data form. From these considerations two data reduction methods are evolved. The first is an iterative integration technique that exploits the symmetry of the experimental data about the minimum angle of attack. For the analysis of geometry effects, a second method using interval averaging was developed and studied.
Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).
In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. Here, the proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.
Of the two 584 A channels in the helium glow detector, channel #1 appeared to provide data with erratic count rates and undue susceptibility to dayglow and solar contamination possibly because of filter fatigue or failure. Channel #3 data appear normal and of high quality. For this reason only data from this last channel was analyzed and used for detailed comparison with theory. Reduction and fitting techniques are described, as well as applications of the data in the study of nighttime and daytime Hel 584 A emission. A hot model of the interstellar medium is presented. Topics covered in the appendix include: observations of interstellar helium with a gas absorption cell: implications for the structure of the local interstellar medium; EUV dayglow observations with a helium gas absorption cell; and EUV scattering from local interstellar helium at nonzero temperatures: implications for the derivations of interstellar medium parameters.
In this paper, we summarize the results of the sensitivity analysis and data reduction carried out to determine the information content of AIRS and IASI channels. The analysis and data reduction was based on the use of subset selection techniques developed in the linear algebra and statistical community to study linear dependencies in high dimensional data sets. We applied the subset selection method to study dependency among channels by studying the dependency among their weighting functions. Also, we applied the technique to study the information provided by the different levels in which the atmosphere is discretized for retrievals and analysis. Results from the method correlate well with intuition in many respects and point out to possible modifications for band selection in sensor design and number and location of levels in the analysis process.
Topics discussed include the need for: the conception and development of a wide spectrum of experiments, instruments, and vehicles in order to derive the proper return from an exploration program; the effective use of alternative methods of data acquisition involving ground-based, airborne and near Earth orbital techniques to supplement spacraft mission; and continued reduction and analysis of existing data including laboratory and theoretical studies in order to benefit fully from experiments and to build on the past programs toward a logical and efficient exploration of the solar system.
Abstract In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.
The Pioneer Venus Orbiter has provided a vast amount of gravity data, since March 1979. High resolution results have revealed the high correlation between topography and gravity. These data were acquired at relatively low spacecraft altitudes (150 km) where atmospheric effects and high frequency variations were significant and modelling with global spherical harmonics was difficult. During 1982 data were acquired over a complete Venus rotation where the lowest altitudes were 1000 km or more, thus removing atmospherics and high frequency gravity effects. These data that have been reduced to produce a tenth degree and order spherical harmonic model of the global gravity field of Venus. The reduction technique used a least squares where 78 independent arcs of data were combined in a simultaneous inversion. The tenth degree and order gravity field solution has 117 parameters describing the spherical harmonic coefficients. This provides approximately 1800 km feature resolution. These coefficients are presented as a geoid map.
CV4Quantum is a statistical technique for reducing the sampling overhead in probabilistic error cancellation, which is an error mitigation technique used in quantum computing. CV4Quantum is based on the control variates method, which is a Monte Carlo variance reduction technique. This repository contains the code and data associated with a demonstration of CV4Quantum using simulation experiments.
Tables and graphs are presented for a review of oceanographic studies using satellite-borne instruments. The topics considered include sensor requirements, error analysis for wind determination from glitter pattern measurements, coverage frequency plots, ground station rise and set times, a technique for reduction and analysis of ocean spectral data, rationale for the selection of a 2 PM descending orbit, and a priority analysis.
The results of the launching of two Marshall-University of Michigan Probes (MUMP 9 and MUMP 10), Nike-Tomahawk sounding rocket payloads, are summarized. The MUMP 9 paylaod included an omegatron mass analyzer, a molecular fluorescence densitometer, a mini-tilty filter, and a lunar position sensor. This complement of instruments permitted the determination of the molecular nitrogen density and temperature in the altitude range from approximately 143 to 297 km over Wallops Island, Virginia, during January 1971. The MUMP 10 payload included an omegatron mass analyzer, an electron temperature probe (Spencer, Brace, and Carignan, 1962), a cryogenic densitometer, and a solar position sensor. This complement of instruments permitted the determination of the molecular nitrogen density and temperature and the charged particle density and temperature in the altitude range from approximately 145 to 290 km over Wallops Island, Virginia, during the afternoon preceding the MUMP 9 launch in January 1971. A general description of the payload kinematics, orientation analysis, and the technique for the reduction and analysis of the data is given.
The Radio and Plasma Wave Science (RPWS) experiment being built for the Cassini spacecraft will study a wide range of plasma and radio wave phenomena in the magnetosphere of Saturn and will also make valuable measurements during the cruise phase and at other encounters. A feature of data from wave receivers is the capability of producing vastly more data than the spacecraft telemetry link is capable of transmitting back to the Earth. Thus, techniques of on-board data compression and data reduction are important. The RPWS instrument has one processor dedicated to data compression tasks.
The Radio and Plasma Wave Science (RPWS) experiment being built for the Cassini spacecraft will study a wide range of plasma and radio wave phenomena in the magnetosphere of Saturn and will also make valuable measurements during the cruise phase and at other encounters. A feature of data from wave receivers is the capability of producing vastly more data than the spacecraft telemetry link is capable of transmitting back to the Earth. Thus, techniques of on-board data compression and data reduction are important. The RPWS instrument has one processor dedicated to data compression tasks.