Search NASA⌕ Search

SEARCH · Search NASA

Results for “Histogram”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Microscopic theory, analysis, and interpretation of conductance histograms in molecular junctions

Molecular electronics break-junction experiments are widely used to investigate fundamental physics and chemistry at the nanoscale. Reproducibility in these experiments relies on measuring conductance on thousands of freshly formed molecular junctions, yielding a broad histogram of conductance events. Experiments typically focus on the most probable conductance, while the information content of the conductance histogram has remained unclear. Here we develop a microscopic theory for the conductance histogram by merging the theory of force-spectroscopy with molecular conductance. The procedure yields analytical equations that accurately fit the conductance histogram of a wide range of molecular junctions and augments the information content that can be extracted from them. Our formulation captures contributions to the conductance dispersion due to conductance changes during the mechanical elongation inherent to the experiments. In turn, the histogram shape is determined by the non-equilibrium stochastic features of junction rupture and formation. The microscopic parameters in the theory capture the junction’s electromechanical properties and can be isolated from separate conductance and rupture force (or junction-lifetime) measurements. The predicted behavior can be used to test the range of validity of the theory, understand the conductance histograms, design molecular junction experiments with enhanced resolution and molecular devices with more reproducible conductance properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splitting of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.

58 GEOSCIENCES↗
DOE OSTI2021

Open Radiation Monitoring: Histogram Builder Module Design

The Open Radiation Monitoring Project seeks to develop and demonstrate a modular radiation detection architecture designed specifically for use in arms control treaty verification (ACTV) applications that will facilitate rapid development of trusted systems to meet the needs of potential future treaties. A modular architecture can be used to reduce more complex systems to a series of single purpose building blocks, thereby facilitating equipment inspection and in turn building trust in the equipment by all treaty parties. Furthermore, a modular architecture can be used to control data flow within the measurement system, reducing the risk of "hidden switches" and constraining the amount of sensitive information that could potentially be inadvertently leaked. This report details the first revision of a prototype circuit that will convert analog pulses directly into a histogrammed data set for further processing. The circuit was designed with both spectroscopy and multiplicity analysis in mind but can, in principle, be used to reduce any raw data stream into a histogram. The number of output channels is limited, and the histogram bin ranges are user configurable to allow for non-uniform and discontinuous bins, which makes it possible to restrict the information being passed down stream if desired. Pulse processing relies entirely on analog circuitry and non- programmable logic, which enables operation without the need for a central processor or other programmable control unit. The circuit remains untested under the Open Radiation Monitoring project due to the closure of the sponsoring program. However, further development and testing is scheduled to take place in support of a purpose-built trusted verification system development effort known as COGNIZANT, which demonstrates the potential benefit of developing a suite of modular trusted system components.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Temporal Evolution and Dose-Volume Histogram Predictors of Visual Acuity After Proton Beam Radiation Therapy of Uveal Melanoma

Purpose: To perform an in-depth temporal analysis of visual acuity (VA) outcomes after proton beam radiation therapy (PBRT) in a large, uniformly treated cohort of uveal melanoma (UM) patients, to determine trends in VA evolution depending on pretreatment and temporally defined posttreatment VA measurements; and to investigate the relevance of specific patient, tumor and dose-volume parameters to posttreatment vision loss. Methods and Materials: Uveal melanoma patients receiving PBRT were identified from a prospectively maintained database. Included patients (n=645) received 56 GyE in 4 fractions, had pretreatment best corrected VA (BCVA) in the affected eye of count fingers (CF) or better, with posttreatment VA assessment at specified post-PBRT time point(s). Patients were grouped according to the pretreatment BCVA into favorable (≥20/40) or unfavorable (20/50-20/400) and poor (CF) strata. Temporal analysis of BCVA changes was described, and univariate and forward stepwise multivariate logistic regression analyses were performed to identify predictors for VA loss. Results: Median VA follow-up was 53 months (range, 3-213 months). At 60-month follow up, among evaluable treated eyes with favorable pretreatment BCVA, 45% retained BCVA ≥20/40, whereas among evaluable treated eyes with initially unfavorable/poor BCVA, 21% had vision ≥20/100. Among those with a favorable initial BCVA, attaining BCVA of ≥20/40 at any posttreatment time point was associated with subsequent maintenance of excellent BCVA. Multivariate analysis identified volume of the macula receiving 28GyE (P<.0001) and optic nerve (P=.0004) as independent dose-volume histogram predictors of 48-month post-PBRT vision loss among initially favorable treated eyes. Conclusions: Approximately half of PBRT-treated UM eyes with excellent pretreatment BCVA assessed at 5 years after treatment will retain excellent long-term vision. 28GyE macula and optic nerve dose-volume histogram parameters allow for rational treatment planning optimization that may lead to improved visual outcomes. The detailed temporal analysis with intermediate as well as long-term functional prognosis, and the relationship of outcomes with clinical and treatment planning parameters, is critical for informed care of UM patients before and after PBRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. Here, the results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Flux Histograms for 2017 Analysis

Flux for 2017 Analysis histograms for FHC and RHC:- The fluxes are integrated over the fiducial volume limited by -176 < X <177, -172 < Y < 179 and 25 < Z < 1150 cm. The fluxes are PPFX corrected, and both, the hadron production and focusing systematics are included (see docs 23441 and 17608 for more details).- These fluxes have been made with the official FHC (RHC) ND MC CAFAna files using the function DeriveFlux from CAFAna/XSec/Flux.*.- The root files have two types of histograms: flux_N and fractional_uncertainty_N, where N is the neutrino type. The entries are normalized by 10^6 POT, m2 and the bin width.- The text files contain tables of the flux normalized by 10^6 POT and m2.- The fractional uncertainties are made by the hadron production and beam focusing uncertainties added in quadrature. This corresponds to the sigma fraction respect to the central value and it meant to be used to make an uncertainty band (the covariance matrix is not included yet).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Improved Figure of Merit for Feynman Histograms

Typically a FOM that takes into account the differences between each bin of the histograms, as compared to the magnitude of the combination of the corresponding uncertainties, is used. Because the uncertainties corresponding to the larger multiplet bins are inherently larger than those of the smaller multiplet bins, this type of FOM puts more weight on differences between smaller multiplet bins. New proposed FOM also takes into account sensitivity of leakage multiplication (which is most sensitive to higher multiplet bins) to each bin in the histogram.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

NMF-Based Anomaly Detection in CMS 2D Tracking Occupancy Histograms

The CMS experiment relies on Data Quality Monitoring (DQM) to ensure that recorded collision data are suitable for physics analysis. During LHC Run 3, each run contains many lumisections and tracking monitoring elements, making offline inspection challenging, especially for localized detector effects that may appear only for short periods of time. This poster presents an unsupervised machine-learning approach to identify anomalous lumisections in CMS tracking occupancy histograms using Non-Negative Matrix Factorization (NMF). The workflow uses offline CMS DQMIO tracking histograms retrieved with the CMS DIALS API and organized as two-dimensional occupancy maps for each lumisection. After selecting stable lumisections, the occupancy maps are normalized and arranged into a non-negative data matrix. The NMF model learns a compact set of basis patterns describing normal tracking occupancy. Each lumisection is then reconstructed from these learned components, and the reconstruction error is used as an anomaly score. Large residuals indicate occupancy patterns that deviate from normal detector behavior and are flagged for further inspection. This NMF-based approach provides a fast and interpretable way to flag lumisections whose tracking occupancy patterns differ from normal detector behavior. Preliminary studies show sensitivity to known tracking anomalies, and ongoing work is focused on validating the method across additional Run 3 Pixel and Strip detector issues.

Rodríguez Ramos, Iliomar [Puerto Rico U., Mayaguez↗
DOE OSTI2021

Method for determining a histogram of variable sample rate waveforms

A computer-implemented method comprises receiving a plurality of sampled data points, each data point including a y value and a t value; defining an array of bins, each bin identified by a unique number and including histogram data for a range of y values; for each consecutive pair of data points including a current data point and a next data point, determining a corresponding one of a plurality of linear equations, each linear equation defining a line between the current data point and the next data point; for each line, determining an amount of time that the y value of the line is within the range of values for each bin from the current data point to the next data point; and adding the time to the histogram data for each bin.

Tohlen, Michael Aaron↗
DOE OSTI2024

Understanding atom probe’s analytical performance for iron oxides using correlation histograms and ab initio calculations

Abstract Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (10 10 Vm −1 ), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT). For instance, neutral molecules formed through dissociation may not be detected at all or with a time of flight no longer related to their mass, causing their loss from the analysis. Here, we evaluated the changes in the measured composition of FeO, Fe 2 O 3 and Fe 3 O 4 across a wide range of analysis conditions. Possible dissociation reactions are predicted by density-functional theory calculations considering the spin states of the molecules. The energetically favoured reactions are traced on to the multi-hit ion correlation histograms, to confirm their existence within experiments, using an automated Python-based routine. The detected reactions are carefully analyzed to reflect upon the influence of these neutrals from dissociation reactions on the performance of APT for analysing iron oxides.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Dose–volume histogram evaluation scheme for beam orientations in intensity-modulated radiation therapy

Beam orientations in intensity-modulated radiation therapy (IMRT) is an important, but difficult NP-hard optimization problem. A considerable part of this difficulty is due to the essential prerequisite of defining, appropriately, counter-intuitive criteria for a mathematical plan evaluation that coincides with the clinical judgment of the considered plan. Moreover, the quality of beam directions depends heavily on their intensities. Usually a stochastic selector is utilized for optimizing beam orientations, and the intensities are thus calculated many thousands of times, each time for a different selection of beam directions, resulting in excessive time complexity. Therefore, selecting an appropriate set of beam directions in IMRT is still a time-consuming manual trial-and-error search procedure that depends on intuition and empirical knowledge. To overcome these difficulties, this work utilizes the concept of dose–volume histogram (DVH), which is one of the main recognized quantitative measurement tools used for plan judgment, and presents a DVH evaluation scheme that parallelizes plan evaluation with clinical plan judgment. This scheme is then utilized to optimize the intensities of each individual beam, enabling it to be represented by its DVHs, produced for the different irradiated volumes. Finally, an evolutionary algorithm is employed to search for the best combination of those DVHs reflecting the clinical optimal. The presented methods have been applied to three real clinical examples. In comparison to the standard equally spaced beam plans, improvements were reported in all three cases. In comparison with the greedy algorithm, improvements were reported in those examples requiring more than five beam directions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Histogram-free multicanonical Monte Carlo sampling to calculate the density of states

Here, we report a new multicanonical Monte Carlo algorithm to obtain the density of states for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain a closed-form expression for the density of states expressed in a chosen basis set, instead of a numerical array of finite resolution as in previous variants of this class of MC methods such as the multicanonical sampling and Wang–Landau sampling. This is enabled by storing the visited states directly and avoiding the explicit collection of a histogram. This practice also has the advantage of avoiding undesirable artificial errors caused by the discretization and binning of continuous state variables. Our results show that this scheme is capable of obtaining converged results with a much reduced number of Monte Carlo steps, leading to a significant speedup over existing algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer

The objective of this study is finding an intensity based histogram (IBH) signature to predict pathologic complete response (pCR) probability using only pre-treatment magnetic resonance (MR) and validate it externally in order to create a workflow for the external validation of an MR IBH signature and to apply the model out of the environment where it has been tuned. The impact of pCR and the final predictors on the survival outcome were also evaluated.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Daily Rectal Dose-volume Histogram Variation in Prostate Intensity-modulated Radiation Therapy: Is It Clinically Significant in the Era of Image Guidance?

Because of the steep dose gradient associated with dose-escalated intensity-modulated radiation therapy, interfraction motion and variation in rectal volume may result in the rectum receiving a larger dose than predicted at treatment planning. This study aims to quantify the variation in daily rectal dose-volume histograms (DVHs) from the treatment plan and to discuss the potential clinical significance of this variation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Flat-Histogram Monte Carlo as an Efficient Tool To Evaluate Adsorption Processes Involving Rigid and Deformable Molecules

Monte Carlo simulations are the foundational technique for predicting thermodynamic properties of open systems where the process of interest involves the exchange of particles. Thus, they have been used extensively to computationally evaluate the adsorption properties of nanoporous materials and are critical for the in silico identification of promising materials for a variety of gas storage and chemical separation applications. In this work we demonstrate that a well-known biasing technique, known as “flat-histogram” sampling, can be combined with temperature extrapolation of the free energy landscape to efficiently provide significantly more useful thermodynamic information than standard open ensemble MC simulations. Namely, we can accurately compute the isosteric heat of adsorption and number of particles adsorbed for various adsorbates over an extremely wide range of temperatures and pressures from a set of simulations at just one temperature. We extend this derivation of the temperature extrapolation to adsorbates with intramolecular degrees of freedom when Rosenbluth sampling is employed. Consequently, the working capacity and isosteric heat can be computed for any given combined temperature/pressure swing adsorption process for a large range of operating conditions with both rigid and deformable adsorbates. Finally, continuous thermodynamic properties can be computed with this technique at very moderate computational cost, thereby providing a strong case for its application to the in silico identification of promising nanoporous adsorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A Histogram-Free Multicanonical Monte Carlo Algorithm for the Basis Expansion of Density of States

We report a new multicanonical Monte Carlo (MC) algorithm to obtain the density of states (DOS) for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain an analytical form for the DOS expressed in a chosen basis set, instead of a numerical array of finite resolution as in previous variants of this class of MC methods such as the multicanonical (MUCA) sampling and Wang-Landau (WL) sampling. This is enabled by storing the visited states directly in a data set and avoiding the explicit collection of a histogram. This practice also has the advantage of avoiding undesirable artificial errors caused by the discretization and binning of continuous state variables. Our results show that this scheme is capable of obtaining converged results with a much reduced number of Monte Carlo steps, leading to a significant speedup over existing algorithms.

97 MATHEMATICS AND COMPUTING↗