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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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RMSE based methods distort circular error estimates (up to 50% overestimation). The empirical approach is the only statistically unbiased estimator offered. Ager modification to Shultz approach is nearly unbiased, but cumbersome. All methods hover around 20% uncertainty (@ 95% confidence) for low geopositional bias error estimates. This requires careful consideration in assessment of higher accuracy products.
The term network surveillance is defined in general terms and illustrated with many examples. Statistical methodologies that can be used as tools for network surveillance are discussed. Details for 3 illustrative examples that address network security, surveillance for data network failures, and surveillance of email traffic flows are presented. Some open areas of research are identified.
The preliminary results on deep-mixing manifestations in stellar variability are tested using our improved method and extended data set. We measure the timescales τ {sub m} of the stochastic change in the spectral power of rotational harmonics with numbers m ≤ 3 in the light curves of 1361 main-sequence stars from the Kepler mission archive. We find that the gradient [log(τ{sub 2})−log(τ{sub 1})]/[log(2)−log(1)] has a histogram maximum at −2/3, demonstrating agreement with Kolmogorov’s theory of turbulence and therefore confirming the manifestation of deep mixing. The squared amplitudes of the first and second rotational harmonics, corrected for integral photometry distortion, also show a quasi-Kolmogorov character with spectral index ≈−5/3. Moreover, the reduction of τ {sub 1} and τ {sub 2} to the timescales τ {sub lam1} and τ {sub lam2} of laminar convection in the deep stellar layers reveals the proximity of both τ {sub lam1} and τ {sub lam2} to the turnover time τ {sub MLT} of standard mixing length theory. Considering this result, we use the obtained stellar variability timescales instead of τ {sub MLT} in our analysis of the relation between stellar activity and the Rossby number P/τ {sub MLT}. Comparison of our diagrams with previous results and theoretical expectations shows that best-fit correspondence is achieved for τ {sub lam1}, which can therefore be used as an analog of τ {sub MLT}. This means that the laminar component (giant cells) of stellar turbulent convection indeed plays an important role in the physics of stars. Additionally, we estimate the diffusivity of magnetic elements in stellar photospheres.
A method for treating atomic and molecular line opacities in cool stellar atmospheres by a statistical opacity sampling is investigated. Under the usual assumptions of plane-parallel geometry, radiative equilibrium, hydrostatic equilibrium, and LTE, each radiative quantity is computed monochromatically at each chosen frequency and depth without any averaging of the opacity. The number of frequencies needed to allow an accurate integration of the energy flux over a given spectral interval is investigated as a function of depth, including opacity for both CN and C2. This method is extended to the calculation of a model atmosphere of a star, and the effect of the number and placement of frequency points is studied. The method is applied to treating molecular lines of CO, C2, and CN in a cool carbon star. Significant advantages of the opacity sampling method are its flexibility, which permits computation of models having arbitrary variations of chemical composition and of opacity with wavelength and depth, and generalizability to include departures from LTE.
Purpose: Multi-tracer PET imaging is getting more attention in radiotherapy by providing additional tumor volume information such as glucose and oxygenation. However, automatic PET-based tumor segmentation is still a very challenging problem. We propose a statistical fusion approach to joint segment the sub-area of tumors from the two tracers FDG and FMISO PET images. Methods: Non-standardized Gamma distributions are convenient to model intensity distributions in PET. As a serious correlation exists in multi-tracer PET images, we proposed a new fusion method based on copula which is capable to represent dependency between different tracers. The Hidden Markov Field (HMF) model is used to represent spatial relationship between PET image voxels and statistical dynamics of intensities for each modality. Real PET images of five patients with FDG and FMISO are used to evaluate quantitatively and qualitatively our method. A comparison between individual and multi-tracer segmentations was conducted to show advantages of the proposed fusion method. Results: The segmentation results show that fusion with Gaussian copula can receive high Dice coefficient of 0.84 compared to that of 0.54 and 0.3 of monomodal segmentation results based on individual segmentation of FDG and FMISO PET images. In addition, high correlation coefficients (0.75 to 0.91) for the Gaussian copula for all five testing patients indicates the dependency between tumor regions in the multi-tracer PET images. Conclusion: This study shows that using multi-tracer PET imaging can efficiently improve the segmentation of tumor region where hypoxia and glucidic consumption are present at the same time. Introduction of copulas for modeling the dependency between two tracers can simultaneously take into account information from both tracers and deal with two pathological phenomena. Future work will be to consider other families of copula such as spherical and archimedian copulas, and to eliminate partial volume effect by considering dependency between neighboring voxels.
Purpose: The low contrast detectability (LCD) of a CT scanner is its ability to detect and display faint lesions. The current approach to quantify LCD is achieved using vendor-specific methods and phantoms, typically by subjectively observing the smallest size object at a contrast level above phantom background. However, this approach does not yield clinically applicable values for LCD. The current study proposes a statistical LCD metric using software tools to not only to assess scanner performance, but also to quantify the key factors affecting LCD. This approach was developed using uniform QC phantoms, and its applicability was then extended under simulated clinical conditions. Methods: MATLAB software was developed to compute LCD using a uniform image of a QC phantom. For a given virtual object size, the software randomly samples the image within a selected area, and uses statistical analysis based on Student’s t-distribution to compute the LCD as the minimal Hounsfield Unit’s that can be distinguished from the background at the 95% confidence level. Its validity was assessed by comparison with the behavior of a known QC phantom under various scan protocols and a tissue-mimicking phantom. The contributions of beam quality and scattered radiation upon the computed LCD were quantified by using various external beam-hardening filters and phantom lengths. Results: As expected, the LCD was inversely related to object size under all scan conditions. The type of image reconstruction kernel filter and tissue/organ type strongly influenced the background noise characteristics and therefore, the computed LCD for the associated image. Conclusion: The proposed metric and its associated software tools are vendor-independent and can be used to analyze any LCD scanner performance. Furthermore, the method employed can be used in conjunction with the relationships established in this study between LCD and tissue type to extend these concepts to patients’ clinical CT images.
Bibliography of subjective probability and Bayesian procedures - regression analyses
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We aim to test whether a blazar candidate of uncertain type (BCU) in the third Fermi catalog of active galactic nuclei (3LAC) can be potentially classified as a BL Lac object or a flat-spectrum radio quasar (FSRQ) by performing a statistical analysis of its broadband spectral properties. We find that 34% of the radio-selected BCUs (583 BCUs) are BL Lac-like and 20% are FSRQ-like, at a 90% level of confidence. Similarly, 77.3% of the X-ray-selected BCUs (176 BCUs) are evaluated as BL Lac-like and 6.8% may be FSRQ-like sources. And 88.7% of the BL Lac-like BCUs that have synchrotron peak frequencies available are high synchrotron peaked BL Lacs in the X-ray-selected BCUs. The percentages are accordingly 62% and 7.3% in the sample of 124 optically selected BCUs. The high ratio of the number of BL Lac-like sources to the number of FSRQ-like BCUs in the X-ray-selected and optically selected BCU samples is due to selection effects. Examining the consistency between our evaluation and spectroscopic identification case by case with a sample of 78 radio-selected BCUs, it is found that the statistical analysis and its resulting classifications agree with the results of the optical follow-up spectroscopic observations. Our observation campaign for high-|ρ{sub s}| BCUs selected with our method, i.e., |ρ{sub s}|>0.8, is ongoing.
Research studies of residents' responses to noise consist of interviews with samples of individuals who are drawn from a number of different compact study areas. The statistical techniques developed provide a basis for those sample design decisions. These techniques are suitable for a wide range of sample survey applications. A sample may consist of a random sample of residents selected from a sample of compact study areas, or in a more complex design, of a sample of residents selected from a sample of larger areas (e.g., cities). The techniques may be applied to estimates of the effects on annoyance of noise level, numbers of noise events, the time-of-day of the events, ambient noise levels, or other factors. Methods are provided for determining, in advance, how accurately these effects can be estimated for different sample sizes and study designs. Using a simple cost function, they also provide for optimum allocation of the sample across the stages of the design for estimating these effects. These techniques are developed via a regression model in which the regression coefficients are assumed to be random, with components of variance associated with the various stages of a multi-stage sample design.
Nonstationary adjoint algorithm for determining launch vehicle flight loads due to winds and turbulence
Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.
The cost and safety goals for NASA s next generation of reusable launch vehicle (RLV) will require that rapid high-fidelity aerothermodynamic design tools be used early in the design cycle. To meet these requirements, it is desirable to identify adequate statistical models that quantify and improve the accuracy, extend the applicability, and enable combined analyses using existing prediction tools. The initial research work focused on establishing suitable candidate models for these purposes. The second phase is focused on assessing the performance of these models to accurately predict the heat rate for a given candidate data set. This validation work compared models and methods that may be useful in predicting the heat rate.
The cost and safety goals for NASA's next generation of reusable launch vehicle (RLV) will require that rapid high-fidelity aerothermodynamic design tools be used early in the design cycle. To meet these requirements, it is desirable to establish statistical models that quantify and improve the accuracy, extend the applicability, and enable combined analyses using existing prediction tools. The research work was focused on establishing the suitable mathematical/statistical models for these purposes. It is anticipated that the resulting models can be incorporated into a software tool to provide rapid, variable-fidelity, aerothermal environments to predict heating along an arbitrary trajectory. This work will support development of an integrated design tool to perform automated thermal protection system (TPS) sizing and material selection.
A typical goal for any detector testing program is to ascertain not only the performance of the detector systems under test, but also the confidence that systems accepted using that testing program’s acceptance criteria will exceed a minimum acceptable performance (which is usually expressed as the minimum acceptable success probability, p). A similar problem often arises in statistics, where we would like to ascertain the fraction, p, of a population of items that possess a property that may take one of two possible values. Typically, the problem is approached by drawing a fixed sample of size n, with the number of items out of n that possess the desired property, x, being termed successes. The sample mean gives an estimate of the population mean p ≈ x/n, although usually it is desirable to accompany such an estimate with a statement concerning the range within which p may fall and the confidence associated with that range. Procedures for establishing such ranges and confidence limits are described in detail by Clopper, Brown, and Agresti for two-sided symmetric confidence intervals.
In file-search techniques, the distribution of mismatches quantitatively measures the fit of a particular unknown against a given library. A simple theoretical method is developed to predict, a priori, this distribution. With this procedure, for any unknown code and library, the mean number of mismatches can be calculated exactly. For a given library and coding scheme, the mismatch distribution for most unknown codes closely follows a common curve which is easily calculated from statistical properties of the library. When this common curve is normalized to a constant mean, the observed matching behavior is well predicted. The theory should permit the user to more clearly assess the effects of errors on file search performance and suggest techniques by which recognition performance may be optimized.
This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.
“Dark energy” is a phenomenon of unknown origin that pervades the vacuum of space and is causing the present-day expansion of the universe to accelerate. Understanding the nature of dark energy is one of the most fundamental challenges to modern physics, having the potential to revolutionize our theories of both gravity and elementary particle physics. The effects of dark energy are currently only accessible to astronomical survey experiments that probe large volumes of the cosmos. One of the most powerful forms of experiment for studying dark energy is the cosmological “redshift survey,” which maps the large-scale structure of the universe as traced by the positions of galaxies, quasars, and intergalactic hydrogen clouds. The signature of dark energy and other cosmological parameters is imprinted on this large-scale structure as it evolves over cosmic time. This project focused on maximizing the accurate extraction of information from redshift surveys by improving the algorithms and statistics applied across the chain of inference from raw pixels to high-level cosmological parameter estimation. At the low level, this project developed new techniques for calibrating and analyzing high-background, low signal-to-noise data from astronomical survey spectrographs to provide reliable three-dimensional maps of galaxies and quasars throughout the universe. At the high level, this project developed new statistical frameworks for understanding the completeness of redshift surveys and the underlying demographics of the galaxies that they observe, thereby allowing for a more accurate connection between survey results and theoretical models. This work contributes to maximizing the scientific reach of existing and planned surveys, and opens new potential feasibility for more ambitious surveys in the future.