Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data analysis”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Soil Organic Matter (SOM): Mass Spectrometry Data Analysis

Considered a general purpose tool for characterization of samples at the molecular level in environmental and life sciences, mass spectrometry (MS) is commonly used for measuring organic matter in air, water and soil. Broad base of commercially available instrumentation and data analysis tools provide functional analytical platforms for molecular characterization for a variety of applications and samples. MS instrumentation with ultrahigh resolving power (m/ΔmFWHM > 200 000) is necessary for broad identification of thousands of molecular components present in organic matter samples. This article discusses basic principles of data analysis for ultrahigh resolution MS measurements of small molecules of organic matter.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

XAFS studies on battery materials: Data analysis supported by a chemometric approach

The large amount of data collected during a typical operando X-ray absorption fine structure (XAFS) experiment and the interest to thoroughly investigate and comprehend a redox process occurring in a Li-ion battery need an advanced data analysis approach. In this context, multivariate curve resolution (MCR) has gained attention as chemometric technique that allows the identification of the number of the species and their existence range without any a priori information on the studied system. Herein the call for an advanced data treatment is answered by a joint XAFS-MCR approach, able to unveil the local structural dynamics of the selected species, providing a quick and efficient data interpretation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

High Resolution Data Analysis: Plans and Prospects [Book Chapter]

Herein, a report on the progress on the high resolution data analysis of the ADMX experimental results is presented. In this paper, tools are developed and tested on a blind injection mimicking a Maxwellian like signal in the frequency domain. This blind injection will be used as a test bed which can be later implemented on all the high resolution data. The high resolution data is stored in the Fermilab server. In this analysis a PostgreSQL query was made to ensure the blind injection is in the middle of the frequency spectrum and 19 such files were found. The time series data is read using a c++ program. An apodization function is applied on the time series data and zero filled to reduce the frequency spacing in order to achieve a better interpolation. A FFTW header is used to compute the Fourier transform of the time series data. A Savitzky–Golay filter is applied on the unnormalized power which then can be used to remove the spectral shape. Each frequency spectrum has a bandwidth of 50 kHz.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

What’s Driving Uncertainty? The Model or the Model Parameters (What’s Driving Uncertainty? The influences of model and model parameters in data analysis)

Here, one of the substantial improvements to the practice of data analysis in recent decades is the change from reporting just a point estimate for a parameter or characteristic, to now including a summary of uncertainty for that estimate. Understanding the precision of the estimate for the quantity of interest provides better understanding of what to expect and how well we are able to predict future behavior from the process. For example, when we report a sample average as an estimate of the population mean, it is good practice to also provide a confidence interval (or credible interval, if you are doing a Bayesian analysis) to accompany that summary. This helps to calibrate what ranges of values are reasonable given the variability observed in the sample and the amount of data that were included in producing the summary.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Columnar data analysis with ATLAS analysis formats

Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the "columnar analysis" paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A novel procedure on next generation sequencing data analysis using text mining algorithm

Background: Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Methods: We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. Results: The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. Conclusion: The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Efficient Implementation of Artificial Neural Networks for Sensor Data Analysis Based on a Genetic Algorithm

The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.

D estefani, André↗
DOE OSTI2013

Histogram data analysis for a three-dimensional diluted ferromagnetic 3- and 4-state potts models

On the basis of a histogram data analysis, phase transitions (PTs) in a three-dimensional diluted ferromagnetic 3- and 4-state Potts models are investigated. Systems with linear dimensions of L = 20-60 and spin concentrations of p = 1.00, 0.95, and 0.65 are studied. It is shown that the introduction of weak disorder (p {approx} 0.95) into the system in the three-dimensional Potts model with q = 3 is sufficient to change a first-order phase transition to a second-order one, whereas, in the three-dimensional Potts model with q = 4, the change of a first-order phase transition to a second-order one occurs only in the presence of strong disorder (p {approx} 0.65).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Con Connections: Detecting Fraud from Abstracts using Topological Data Analysis

In this paper we present a novel approach for identifying fraudulent papers from their titles and abstracts. The premise of the approach is that there are holes in the presentation of the approach and findings of fraudulent research papers. As an abstract is intended to highlight key features of the approach as well as important conclusions the authors seek to determine if the assumed existence of holes can be identified from analysis of abstracts alone. The data set considered is derived from papers sharing a single author with labels determined based on a formal linguistic analysis of the complete documents. To detect these logical and literary holes we utilize techniques from topological data analysis which summarizes data based on the presence of multi-dimensional, topological holes. We find that, in fact, topological features derived through a combination of techniques in natural language processing and time-series analysis allow for superior detection of the fraudulent papers than the natural language processing tools alone. Thus we conclude that the connections and holes present in the abstracts of research cons contributes to an ability to infer the scientific validity of the corresponding work.

Tymochko, Sarah J.↗
DOE OSTI2018

Data Analysis for the Born Qualified Grand LDRD Project

This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processes for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.

42 ENGINEERING↗
DOE OSTI2015

A Data Analysis Center for Electromagnetic and Hadronic Interaction. Products of the DAC members

The Data Analysis Center (DAC) of the Center for Nuclear Studies (CNS) at the George Washington University (GW) has made significant progress in its program to enhance and expand the partial-wave (and multipole) analyses of fundamental two- and three-body reactions (such as pion-nucleon, photon-nucleon, and nucleon-nucleon scattering) by maintaining and augmenting the analysis codes and databases associated with these reactions. These efforts provide guidance to experimental groups at the international level, forming an important link between theory and experiment. A renaissance in light hadron spectroscopy is underway as a continuous stream of polarization data issues from existing precision electromagnetic facilities and the coming Jefferson Lab 12 GeV Upgrade. Our principal goals have been focused on supporting the national N* resonance physics program. We have also continued to study topics more generally related to the problems associated with partial-wave analysis. On the Experimental side of the CNS DAC. Its primary goal is the enhancement of the body of data necessary for our analyses of fundamental γ - N reactions. We perform experiments that study the dynamics responsible for the internal structure of the nucleon and its excitations. Our principal focus is on the N* programs at JLab and MAMI. At JLab we study spin-polarization observables using polarized photons, protons and neutrons and yielding charged final states. Similarly at MAMI we study neutral meson photoproduction off polarized protons and neutrons. We use the Crystal Ball and TAPS spectrometers (CBT) to detect photons and neutrons to measure the photoproduction of π0, η, 2π0, π0η, and K0 off the neutron. The CBT program complements our program at JLab, which studies reactions resulting in charged final states. We are also involved in a renewed effort to make neutral pion photoproduction measurements close to threshold at Mainz. In addition to the programs underway, we are contributing to the future by participation in preparations for the coming JLab 12 GeV Upgrade. GW students are involved in tests of the detectors proposed to be used with CLAS12, i.e., for the CentralTime-of-Flight Barrel (CTOF). WJB is heavily involved in the MUSE quest at PSI to solve the Proton Radius Puzzle.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Adapt: A Weather Radar Data Analysis and Nowcasting Platform for Informed Adaptive Scanning

SF-26-021 Adapt is a data processing platform for real-time data analysis, short term prediction of targets convective cells and tracking for archived data. It provides tools for downloading, processing, segmenting, projecting, analyzing, and visualizing storm cell data from weather radar. The pipeline includes cell detection, motion estimation using optical flow, cell property extraction, and persistence to NetCDF and SQLite/Parquet for guiding adaptive scanning.

Raut, Bhupendra Ashokrao [Argonne National Laborat↗
DOE OSTI2020

ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data

The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Data Analysis for NEPA Supplement Analysis of Chemistry and Metallurgy Research Building Replacement Project at LANL - 16017

The Department of Energy/National Nuclear Security Administration (DOE/NNSA) is proposing to provide analytical chemistry (AC) and materials characterization (MC) capabilities at the Los Alamos National Laboratory (LANL) by using a combination of existing space in two existing buildings: The Radiological Laboratory/Utility/Office Building (RLUOB) and the Plutonium Facility, Building 4 (PF-4) in TA-55. This represents a change from decisions made by DOE as informed by previous National Environmental Policy Act (NEPA) analyses. In accordance with Council on Environmental Quality and DOE requirements, NNSA prepared a Supplement Analysis to evaluate the potential environmental impacts of the proposed action. The focus of this analysis is on determining whether the proposal to provide AC and MC laboratory capabilities in existing space in RLUOB and PF-4 rather than building a new nuclear facility is a substantial change that is relevant to environmental concerns or whether new circumstance or information relevant to environmental concerns and bearing on the proposed action or its impacts are significant. The determination of the analysis is that the existing Chemistry and Metallurgy Research Building Replacement Environmental Impact Statement (CMRR EIS) need not be supplemented, nor is a new EIS required, because 'the proposed changes do not represent a substantial change that is relevant to environmental concerns.'[1 p.49] No further NEPA analysis is necessary. This report describes the analysis used to create data needed for the Supplement Analysis. Los Alamos subject matter experts estimated equipment lists, facility modifications, waste quantities, labor needs and radiological doses. Los Alamos NEPA experts assisted in compiling existing data from the LANL Site-Wide EIS and CMRR EIS for public and other impacts. Bounding conditions were used to determine NEPA estimates. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗