Search NASA⌕ Search

SEARCH · Search NASA

Results for “Kernel methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

A Scalable Gaussian Process Analysis Algorithm for Biomass Monitoring

Biomass monitoring is vital for studying the carbon cycle of earth's ecosystem and has several significant implications, especially in the context of understanding climate change and its impacts. Recently, several change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments, but do not satisfy one or both of the two requirements of the biomass monitoring problem, i.e., {\em operating in online mode} and {\em handling periodic time series}. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) have been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. We focus on addressing the scalability issues associated with the proposed GP based change detection algorithm. This paper makes several significant contributions. First, we propose a GP based online time series change detection algorithm and demonstrate its effectiveness in detecting different types of changes in {\em Normalized Difference Vegetation Index} (NDVI) data obtained from a study area in Iowa, USA. Second, we propose an efficient Toeplitz matrix based solution which significantly improves the computational complexity and memory requirements of the proposed GP based method. Specifically, the proposed solution can analyze a time series of length $t$ in $O(t^2)$ time while maintaining a $O(t)$ memory footprint, compared to the $O(t^3)$ time and $O(t^2)$ memory requirement of standard matrix manipulation based methods. Third, we describe a parallel version of the proposed solution which can be used to simultaneously analyze a large number of time series. We study three different parallel implementations: using threads, MPI, and a hybrid implementation using threads and MPI. Experimental results show that the hybrid implementation scales better than the multi-threaded and MPI based implementations. The application of the proposed scalable algorithm is demonstrated in analyzing massive remote sensing observation data. The hybrid implementation, using 1536 computing cores, can analyze an NDVI data set for the Iowa study area in nearly 5 seconds, while a serial algorithm, using standard Cholesky decomposition based routines, takes several days to process the same data set.

09 BIOMASS FUELS↗
DOE OSTI2013

Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation

This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. Here, this paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Distributed Data-Driven Optimization for Voltage Regulation in Distribution Systems

Here, this paper proposes a distributed data-driven optimization framework for voltage regulation in distribution systems. The recursive kernel regression and alternating direction method of multipliers (ADMM) are selected to cover the system learning and distributed optimization tasks. The proposed distributed data-driven framework is capable of having a rapid response to system or load changes while considering the operation optimality. Besides, the distributed algorithm parallels the computation tasks and reduces the computational expense of a single agent. To validate the performance of the proposed method, a hypothetical 7-Bus system and the IEEE 123-Bus system are selected to show the effectiveness of the proposed data-driven framework. According to the numerical study results, the proposed method offers great flexibility for selecting customized kernel models for different regions and can effectively improve the system voltage profile in a distributed manner.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Data-driven probability concentration and sampling on manifold

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Automated Defect Identification For Triso Fuels

The developed code is to be used to identify manufacturing defects of nuclear fuel kernels using image processing methods. Past batches of TRi-structural ISOtropic particle (TRISO) fuel kernels have on occasion contained fissures that result in the fuel batch not meeting specifications. The developed code automates the inspection process of these kernels. The code analyzes micrographs of TRISO fuel kernels and outputs a count of total kernels in the sample, a count of the number of defective particles in the sample, as well as processed images for more effective manual inspection. This information output will be used to help identify if defective kernels are present in a fuel batch and quantify the countable fissure fraction.

Oncken, JosephE.↗
DOE OSTI2014

Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects

Purpose/Objective(s): To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials: Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variation modes in the dose distribution. The functional principal components were then tested for association with RB using logistic regression adapted to functional covariates (FLR). For comparison, 3 other normal tissue complication probability models were considered: the Lyman-Kutcher-Burman model, logistic model based on standard dosimetric parameters (LM), and logistic model based on multivariate principal component analysis (PCA). Results: The incidence rate of grade ≥2 RB was 14%. V{sub 65Gy} was the most predictive factor for the LM (P=.058). The best fit for the Lyman-Kutcher-Burman model was obtained with n=0.12, m = 0.17, and TD50 = 72.6 Gy. In PCA and FLR, the components that describe the interdependence between the relative volumes exposed at intermediate and high doses were the most correlated to the complication. The FLR parameter function leads to a better understanding of the volume effect by including the treatment specificity in the delivered mechanistic information. For RB grade ≥2, patients with advanced age are significantly at risk (odds ratio, 1.123; 95% confidence interval, 1.03-1.22), and the fits of the LM, PCA, and functional principal component analysis models are significantly improved by including this clinical factor. Conclusion: Functional data analysis provides an attractive method for flexibly estimating the dose-volume effect for normal tissues in external radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Adrastea: An Efficient FPGA Design Environment for Heterogeneous Scientific Computing and Machine Learning

We present Adrastea, an efficient FPGA design environment for developing scientific machine learning applications. FPGA development is challenging, from deployment, proper toolchain setup, programming methods, interfacing FPGA kernels, and more importantly, the need to explore design space choices to get the best performance and area usage from the FPGA kernel design. Adrastea provides an automated and scalable design flow to parameterize, implement, and optimize complex FPGA kernels and associated interfaces. We show how virtualization of the development environment via virtual machines is leveraged to simplify the setup of the FPGA toolchain while deploying the FPGA boards and while scaling up the automated design space exploration to leverage multiple machines concurrently. Adrastea provides an automated build and test environment of FPGA kernels. By exposing design space hyper-parameters, Adrastea can automatically search the design space in parallel to optimize the FPGA design for a given metric, usually performance or area. Adrastea simplifies the task of interfacing with the FPGA kernels with a simplified interface API. To demonstrate the capabilities of Adrastea, we implement a complex random forest machine learning kernel with 10,000 input features while achieving extremely low computing latency without loss of prediction accuracy, which is required by a scientific edge application at SNS. We also demonstrate Adrastea using an FFT kernel and show that for both applications Adrastea is able to systematically and efficiently evaluate different design options, which reduced the time and effort required to develop the kernel from months of manual work to days of automatic builds.

Young, Aaron↗
DOE OSTI2020

Target Detection via Cognitive Radars Using Change-Point Detection, Learning, and Adaptation

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case. Herein, we also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements

42 ENGINEERING↗
DOE OSTI2013

A global ab initio potential energy surface for the X{sup 2}A{sup ′} ground state of the Si + OH → SiO + H reaction

We report the first global potential energy surface (PES) for the X{sup 2}A{sup ′} ground electronic state of the Si({sup 3}P) + OH(X{sup 2}Π) → SiO(X{sup 1}Σ{sub g}{sup +}) + H({sup 2}S) reaction. The PES is based on a large number of ab initio energies obtained from multireference configuration interaction calculations plus Davidson correction (MRCI+Q) using basis sets of quadruple zeta quality. Corrections were applied to the ab initio energies in the reactant channel allowing a proper description of long-range interactions between Si({sup 3}P) and OH(X{sup 2}Π). An analytical representation of the global PES has been developed by means of the reproducing kernel Hilbert space method. The reaction is found barrierless. Two minima, corresponding to the SiOH and HSiO isomers, and six saddle points, among which the isomerization transition state, have been characterized on the PES. The vibrational spectra of the SiOH/HSiO radicals have been computed from second-order perturbation theory and quantum dynamics methods. The structural, energetic, and spectroscopic properties of the two isomers are in good agreement with experimental data and previous high quality calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Preparation and characterization of plasticized palm-based polyurethane solid polymer electrolyte

Palm-based polyurethane solid polymer electrolyte was prepared via prepolymerization method between palm kernel oil based polyols (PKO-p) and 2,4’-diphenylmethane diisocyanate (2,4’-MDI) in acetone at room temperature with the vary amount of lithium trifuoromethanesulfonate (LiCF{sub 3}SO{sub 3}) salt and polyethylene glycol (PEG). The film was analyzed using attenuated total reflection infrared (ATR-IR) spectroscopy, electrochemical impedance spectroscopy (EIS) and X-ray diffractometry (XRD). EIS result indicated ionic conductivity obtained with 30 wt% LiCF3SO3 increased to 6.55 × 10{sup −6} S cm{sup −1} when 10 wt.% of plasticizer was added into the system. FTIR analysis showed the interaction between lithium ions and amine (-N-H) at 3600–3100 cm{sup −1}, carbonyl (-C=O) at 1750–1650 cm{sup −1} and ether (-C-O-C-) at 1150–1000 cm{sup −1} of the polyurethane forming polymer-salt complexes. The XRD result confirmed that LiCF{sub 3}SO{sub 3} salt completely dissociated within the polyurethane film with the absence of crystalline peaks of LiCF{sub 3}SO{sub 3}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Computational study of collisions between O({sup 3}P) and NO({sup 2}Π) at temperatures relevant to the hypersonic flight regime

Reactions involving N and O atoms dominate the energetics of the reactive air flow around spacecraft when reentering the atmosphere in the hypersonic flight regime. For this reason, the thermal rate coefficients for reactive processes involving O({sup 3}P) and NO({sup 2}Π) are relevant over a wide range of temperatures. For this purpose, a potential energy surface (PES) for the ground state of the NO{sub 2} molecule is constructed based on high-level ab initio calculations. These ab initio energies are represented using the reproducible kernel Hilbert space method and Legendre polynomials. The global PES of NO{sub 2} in the ground state is constructed by smoothly connecting the surfaces of the grids of various channels around the equilibrium NO{sub 2} geometry by a distance-dependent weighting function. The rate coefficients were calculated using Monte Carlo integration. The results indicate that at high temperatures only the lowest A-symmetry PES is relevant. At the highest temperatures investigated (20 000 K), the rate coefficient for the “O1O2+N” channel becomes comparable (to within a factor of around three) to the rate coefficient of the oxygen exchange reaction. A state resolved analysis shows that the smaller the vibrational quantum number of NO in the reactants, the higher the relative translational energy required to open it and conversely with higher vibrational quantum number, less translational energy is required. This is in accordance with Polanyi's rules. However, the oxygen exchange channel (NO2+O1) is accessible at any collision energy. Finally, this work introduces an efficient computational protocol for the investigation of three-atom collisions in general.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A Probabilistic Data-Driven Method for Photovoltaic Pseudo-Measurement Generation in Distribution Systems

This paper presents a probabilistic data-driven method to model the uncertainty of distributed photovoltaic (PV) generation to enhance the observability of distribution grids with a high penetration of renewable resources. The proposed method employs a kernel density estimation (KDE) technique to develop the conditional probability density function (PDF) of PV outputs given the time of the day, using the measurement data from observed PV systems. Then time series of output power of unmeasured PV generators, located at the same grid, are generated according to the corresponding PDF and used as pseudo-measurements in distribution system state estimation (DSSE). The proposed model has been tested and verified using real PV data and utility networks.

Yuan, Yuxuan↗
DOE OSTI2016

Towards Achieving Performance Portability Using Directives for Accelerators

In this paper we explore the performance portability of directives provided by OpenMP 4 and OpenACC to program various types of node architectures with attached accelerators, both self-hosted multicore and offload multicore/GPU. Our goal is to examine how successful OpenACC and the newer offload features of OpenMP 4.5 are for moving codes between architectures, how much tuning might be required and what lessons we can learn from this experience. To do this, we use examples of algorithms with varying computational intensities for our evaluation, as both compute and data access efficiency are important considerations for overall application performance. We implement these kernels using various methods provided by newer OpenACC and OpenMP implementations, and we evaluate their performance on various platforms including both X86_64 with attached NVIDIA GPUs, self-hosted Intel Xeon Phi KNL, as well as an X86_64 host system with Intel Xeon Phi coprocessors. In this paper, we explain what factors affected the performance portability such as how to pick the right programming model, its programming style, its availability on different platforms, and how well compilers can optimize and target to multiple platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

PRACTICAL METHOD FOR ESTIMATING NEUTRON CROSS SECTION COVARIANCES IN THE RESONANCE REGION

Recent evaluations of neutron cross section covariances in the resolved resonance region reveal the need for further research in this area. Major issues include declining uncertainties in multigroup representations and proper treatment of scattering radius uncertainty. To address these issues, the present work introduces a practical method based on kernel approximation using resonance parameter uncertainties from the Atlas of Neutron Resonances. Analytical expressions derived for average cross sections in broader energy bins along with their sensitivities provide transparent tool for determining cross section uncertainties. The role of resonance-resonance and bin-bin correlations is specifically studied. As an example we apply this approach to estimate (n,{gamma}) and (n,el) covariances for the structural material {sup 55}Mn.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Discovery of a Protocluster Associated with a Lyα Blob Pair at z = 2.3

Bright Lyα blobs (LABs)—extended nebulae with sizes of ∼100 kpc and Lyα luminosities of ∼10{sup 44} erg s{sup −1}—often reside in overdensities of compact Lyα emitters (LAEs) that may be galaxy protoclusters. The number density, variance, and internal kinematics of LABs suggest that they themselves trace group-like halos. Here, we test this hierarchical picture, presenting deep, wide-field Lyα narrowband imaging of a 1° × 0.°5 region around a LAB pair at z = 2.3 discovered previously by a blind survey. We find 183 Lyα emitters, including the original LAB pair and three new LABs with Lyα luminosities of (0.9--1.3)×10{sup 43} erg s{sup −1} and isophotal areas of 16–24 arcsec{sup 2}. Using the LAEs as tracers and a new kernel density estimation method, we discover a large-scale overdensity (Boötes J1430+3522) with a surface density contrast of δ{sub Σ}=2.7, a volume density contrast of δ ∼10.4, and a projected diameter of ≈20 comoving Mpc. Comparing with cosmological simulations, we conclude that this LAE overdensity will evolve into a present-day Coma-like cluster with log(M/M{sub ⊙}) ∼ 15.1 ± 0.2. In this and three other wide-field LAE surveys re-analyzed here, the extents and peak amplitudes of the largest LAE overdensities are similar, not increasing with survey size, and implying that they were indeed the largest structures then and today evolve into rich clusters. Intriguingly, LABs favor the outskirts of the densest LAE concentrations, i.e., intermediate LAE overdensities of δ{sub Σ}=1--2. We speculate that these LABs mark infalling protogroups being accreted by the more massive protocluster.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint

One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year; (ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted to characterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.

17 WIND ENERGY↗