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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

Shehadeh, Hana↗
NASA NTRS2004

ENSO Bred Vectors in Coupled Ocean-Atmosphere General Circulation Models

The breeding method has been implemented in the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Coupled General Circulation Model (CGCM) with the goal of improving operational seasonal to interannual climate predictions through ensemble forecasting and data assimilation. The coupled instability as cap'tured by the breeding method is the first attempt to isolate the evolving ENSO instability and its corresponding global atmospheric response in a fully coupled ocean-atmosphere GCM. Our results show that the growth rate of the coupled bred vectors (BV) peaks at about 3 months before a background ENSO event. The dominant growing BV modes are reminiscent of the background ENSO anomalies and show a strong tropical response with wind/SST/thermocline interrelated in a manner similar to the background ENSO mode. They exhibit larger amplitudes in the eastern tropical Pacific, reflecting the natural dynamical sensitivity associated with the presence of the shallow thermocline. Moreover, the extratropical perturbations associated with these coupled BV modes reveal the variations related to the atmospheric teleconnection patterns associated with background ENSO variability, e.g. over the North Pacific and North America. A similar experiment was carried out with the NCEP/CFS03 CGCM. Comparisons between bred vectors from the NSIPP CGCM and NCEP/CFS03 CGCM demonstrate the robustness of the results. Our results strongly suggest that the breeding method can serve as a natural filter to identify the slowly varying, coupled instabilities in a coupled GCM, which can be used to construct ensemble perturbations for ensemble forecasts and to estimate the coupled background error covariance for coupled data assimilation.

Yang, S. C.↗
NASA NTRS2008

Multichannel Networked Phasemeter Readout and Analysis

Netmeter software reads a data stream from up to 250 networked phasemeters, synchronizes the data, saves the reduced data to disk (after applying a low-pass filter), and provides a Web server interface for remote control. Unlike older phasemeter software that requires a special, real-time operating system, this program can run on any general-purpose computer. It needs about five percent of the CPU (central processing unit) to process 20 channels because it adds built-in data logging and network-based GUIs (graphical user interfaces) that are implemented in Scalable Vector Graphics (SVG). Netmeter runs on Linux and Windows. It displays the instantaneous displacements measured by several phasemeters at a user-selectable rate, up to 1 kHz. The program monitors the measure and reference channel frequencies. For ease of use, levels of status in Netmeter are color coded: green for normal operation, yellow for network errors, and red for optical misalignment problems. Netmeter includes user-selectable filters up to 4 k samples, and user-selectable averaging windows (after filtering). Before filtering, the program saves raw data to disk using a burst-write technique.

Edmonds, Karina↗
NASA NTRS1985

Vector magnetic field observations with the Haleakala polarimeter

Several enhancements were recently made to the Haleakala polarimeter. Linear array detectors provide simultaneous resolution over a 3-A wavelength range, with spectral resolution of 40 mA. Optical fibers are now used to carry the intensity-modulated light from the rotating quarter-wave plate polarimeter to the echelle spectrometer, permitting its removal from the spar to a more stable environment. These changes, together with improved quarter-wave plates, reduced systematic errors to a few parts in 10,000 for routine observations. Examples of Stokes profiles and derived magnetic field maps are presented.

Mickey, D. L.↗
DOE OSTI2012

Prostate Bed Motion During Intensity-Modulated Radiotherapy Treatment

Purpose: Conformal radiation therapy in the postprostatectomy setting requires accurate setup and localization of the prostatic fossa. In this series, we report prostate bed localization and motion characteristics, using data collected from implanted radiofrequency transponders. Methods and Materials: The Calypso four-dimensional localization system uses three implanted radiofrequency transponders for daily target localization and real-time tracking throughout a course of radiation therapy. We reviewed the localization and tracking reports for 20 patients who received ultrasonography-guided placement of Calypso transponders within the prostate bed prior to a course of intensity-modulated radiation therapy at Fox Chase Cancer Center. Results: At localization, prostate bed displacement relative to bony anatomy exceeded 5 mm in 9% of fractions in the anterior-posterior (A-P) direction and 21% of fractions in the superior-inferior (S-I) direction. The three-dimensional vector length from skin marks to Calypso alignment exceeded 1 cm in 24% of all 652 fractions with available setup data. During treatment, the target exceeded the 5-mm tracking limit for at least 30 sec in 11% of all fractions, generally in the A-P or S-I direction. In the A-P direction, target motion was twice as likely to move posteriorly, toward the rectum, than anteriorly. Fifteen percent of all treatments were interrupted for repositioning, and 70% of patients were repositioned at least once during their treatment course. Conclusion: Set-up errors and motion of the prostatic fossa during radiotherapy are nontrivial, leading to potential undertreatment of target and excess normal tissue toxicity if not taken into account during treatment planning. Localization and real-time tracking of the prostate bed via implanted Calypso transponders can be used to improve the accuracy of plan delivery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Determining the photon polarization of the b{yields}s{gamma} using the B{yields}K{sub 1}(1270){gamma}{yields}(K{pi}{pi}){gamma} decay

Recently the radiative B decay to the strange axial-vector mesons, B{yields}K{sub 1}(1270){gamma}, has been observed with a rather large branching ratio. This process is particularly interesting as the subsequent K{sub 1} decay into its three-body final state allows us to determine the polarization of the photon, which is mostly left- (right-)handed for B(B) in the SM while various new physics models predict additional right- (left-)handed components. A new method is proposed to determine the polarization, exploiting the full Dalitz plot distribution, which seems to reduce significantly the statistical errors. This polarization measurement requires, however, detailed knowledge of the K{sub 1{yields}}K{pi}{pi} strong interaction decays, namely, the various partial wave amplitudes into the several possible quasi-two-body channels, as well as their relative phases. The pattern of partial waves is especially complex for the K{sub 1}(1270). We attempt to obtain the information through the combination of an experimental input and a theoretical one, provided by the {sup 3}P{sub 0} quark-pair-creation model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

New Precision Limit on the Strange Vector Form Factors of the Proton

The parity-violating cross-section asymmetry in the elastic scattering of polarized electrons from unpolarized protons has been measured at a four-momentum transfer squared Q 2 = 0.624 GeV 2 and beam energy E b = 3.48 GeV to be A PV = -23.80 ± 0.78 (stat) ± 0.36 (syst) parts per million. This result is consistent with zero contribution of strange quarks to the combination of electric and magnetic form factors G E s + 0.517 G M s = 0.003 ± 0.010 (stat) ± 0.004 (syst) ± 0.009 (ff), where the third error is due to the limits of precision on the electromagnetic form factors and radiative corrections. With this measurement, the world data on strange contributions to nucleon form factors are seen to be consistent with zero and not more than a few percent of the proton form factors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

SU-E-J-230: Effect of Metal Hip Prosthesis On the Accuracy of Electromagnetic Localization and Tracking

Purpose: To quantify the effect of metal hip prosthesis on the ability to track and localize electromagnetic transponders. Methods: Three Calypso transponders were implanted into two prostate phantoms. The geometric center of the transponders were identified on computed tomography and set as the isocenter. With the phantom stationary on the treatment table and the tracking array 14-cm above the isocenter, data was acquired by the Calypso system at 10 Hz to establish the uncertainty in measurements. Transponder positional data was acquired with unilateral hip prostheses of different metallic compositions and then with bilateral hips placed at variable separation from the phantom. Results: Regardless of hip prosthesis composition, the average vector displacement in the presence of a unilateral prosthesis was < 0.5 mm. The greatest contribution to overall vector displacement occurred in the lateral dimension. With bilateral hip prosthesis, the average vector displacement was 0.3 mm. The displacement in the lateral dimension was markedly reduced compared with a unilateral hip, suggesting that there was a countervailing effect with bilateral hip prosthesis. The greatest average vector displacement was 0.6 mm and occurred when bilateral hip prostheses were placed within 4 cm of the detector array. Conclusion: Unilateral and bilateral hip prostheses did not have any meaningful effect on the ability to accurately track electromagnetic transponders implanted in a prostate phantom. At clinically realistic distances between the hip and detection array, the average tracking error is negligible.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2015

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.

58 GEOSCIENCES↗
NASA NTRS1972

An isoparametric quadrilateral membrane element for NASTRAN

The geometric and kinematic properties of the element are described, along with development of the matrices and vectors which characterize the element. The necessity for considering small deviations from element planeness is discussed, and the approach taken to account for these deviations is described. The improved accuracy over the existing quadrilateral membrane is indicated by a sample calculation for which an analytical solution is available from beam theory. For the same finite element idealization, the errors in maximum displacement and stress were significantly reduced.

Adelman, H. M.↗
NASA NTRS1991

Solution-adaptive program SADAP3D

A generic solution-adaptive grid program based on an error equi-distribution concept has been developed for use in complex multidimensional flows. The capability of a generic, user-friendly, multidimensional algorithm is demonstrated using several complex 3D flows in high-speed regimes. A simple scheme enforces the continuity of grid spacing along each currently adapted grid line as the computation proceeds. Calculation of normal vectors at each point of a grid plane is enhanced. The grid movement is automatically controlled by reinstating grid-spacing continuity locally in the mainstream of the unidirectional computation. It is concluded that the scheme can be readily coupled with many flow solvers to enhance accuracy and efficiency of solutions.

Djomehri, M. J.↗
NASA NTRS1995

A Bayesian approach to microwave precipitation profile retrieval

A multichannel passive microwave precipitation retrieval algorithm is developed. Bayes theorem is used to combine statistical information from numerical cloud models with forward radiative transfer modeling. A multivariate lognormal prior probability distribution contains the covariance information about hydrometeor distribution that resolves the nonuniqueness inherent in the inversion process. Hydrometeor profiles are retrieved by maximizing the posterior probability density for each vector of observations. The hydrometeor profile retrieval method is tested with data from the Advanced Microwave Precipitation Radiometer (10, 19, 37, and 85 GHz) of convection over ocean and land in Florida. The CP-2 multiparameter radar data are used to verify the retrieved profiles. The results show that the method can retrieve approximate hydrometeor profiles, with larger errors over land than water. There is considerably greater accuracy in the retrieval of integrated hydrometeor contents than of profiles. Many of the retrieval errors are traced to problems with the cloud model microphysical information, and future improvements to the algorithm are suggested.

Evans, K. Franklin↗
NASA NTRS2000

Enhancement of Directional Ambiguity Removal Skill in Scatterometer Data Processing Using Planetary Boundary Layer Models

Planetary boundary layer (PBL) models are utilized to enhance directional ambiguity removal skill in scatterometer data processing. The ambiguity in wind direction retrieved from scatterometer measurements is removed with the aid of physical directional information obtained from PBL models. This technique is based on the observation that sea level pressure is scalar and its field is more coherent than the corresponding wind. An initial wind field obtained from the scatterometer measurements is used to derive a pressure field with a PBL model. After filtering small-scale noise in the derived pressure field, a wind field is generated with an inverted PBL model. This derived wind information is then used to remove wind vector ambiguities in the scatterometer data. It is found that the ambiguity removal skill can be improved when the new technique is used properly in conjunction with the median filter being used for scatterometer wind dealiasing at JPL. The new technique is applied to regions of cyclone systems which are important for accurate weather prediction but where the errors of ambiguity removal are often large.

Kim, Young-Joon↗
NASA NTRS2001

Analysis of the Westland Data Set

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗
NASA NTRS2000

Condition Monitoring for Helicopter Data

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1992

The spin vector of Venus determined from Magellan data

A control network of the north polar region of Venus has been established by selecting and measuring control points on full-resolution radar strips. The measurements were incorporated into a least-squares adjustment program that improved initial estimates of the coordinates of the control points, pole direction, and rotation rate of Venus. The current dataset contains 4206 measurements of 606 points on 619 radar strips. The accuracy of the determination is driven by spacecraft ephemeris errors. An accurate estimate of the rotation period of Venus was obtained by applying an ephemeris improvement technique. The second cycle closure orbits improved ephemeris solutions for 40 orbits (376-384, 520-528, 588-592, 658-668, 1002-1010, 1408-1412, 1746-1764, and 2166-2170) are included and fixed in the geodetic control computations, thus trying the network to the J2000 coordinate system.

Davies, M. E.↗