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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Studies in astronomical time series analysis. III - Fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data

This paper develops techniques to evaluate the discrete Fourier transform (DFT), the autocorrelation function (ACF), and the cross-correlation function (CCF) of time series which are not evenly sampled. The series may consist of quantized point data (e.g., yes/no processes such as photon arrival). The DFT, which can be inverted to recover the original data and the sampling, is used to compute correlation functions by means of a procedure which is effectively, but not explicitly, an interpolation. The CCF can be computed for two time series not even sampled at the same set of times. Techniques for removing the distortion of the correlation functions caused by the sampling, determining the value of a constant component to the data, and treating unequally weighted data are also discussed. FORTRAN code for the Fourier transform algorithm and numerical examples of the techniques are given.

Scargle, Jeffrey D.↗
DOE OSTI2021

Code Artifact for: Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data [SWR-21-96]

This repository contains data ingestion, feature extraction, and analysis code used in NREL Technical report "Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data." The code is written in Python. The ETL and feature extraction code must be run in a Spark context. The analysis code can be run without Spark, provided you have pre-computed features in a CSV file. Analysis code related to the NREL Technical Report NREL/TP-2C00-74212. Includes PySpark functions to perform trip segmentation and feature extraction over big time series data in Apache Spark. Includes "domain specific" features such as Aerodynamic Speed (ft/s), Characteristic Acceleration (ft/s2), Percent Below 55 (%), Percent Zero (%), Stops Per Mile, Average Speed (mph), Maximum Speed (mph), and Speed Standard Deviation (mph). Includes Pyspark UDF to compute "domain agnostic" features using the TSFresh library. This software record also includes the analysis notebooks and code to generate the results in the previously mentioned technical report.

Perr-Sauer, Jordan↗
DOE OSTI2021

Time-series dissolved oxygen, other bigeochemically-relevant analytes, and pressure gradients associated with the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”

This dataset contains time-series data from a vertical profile within the bed and banks of the Columbia river near Richland, WA. Water was sampled through 3 small tubes embedded in the sediment at 50,100, and 200 cm below the sediment-water interface. The goal of this study was to observe the correlations between hydraulic drivers and biogeochemical responses. The results of this study are published in the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”. The file types included in the data package are all time-series spreadsheet data, including hydraulic head gradients, physical parameters (temperature, pressure, SpC (specific conductivity)), and biogeochemical parameters (dissolved oxygen, pH, NO3 (nitrate) and ORP (oxidation-reduction potential)).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

Application of modern time series analysis to high stability oscillators

Techniques of modern time series analysis useful for investigating the characteristics of high-stability oscillators and identifying systematic perturbations are discussed with reference to an experiment in which the frequencies of superconducting cavity-stabilized oscillators and hydrogen masers were compared. The techniques examined include transformation to stationarity, autocorrelation and cross-correlation, superresolution, and transfer function determination.

Farrell, B. F.↗
DOE OSTI2023

Image-based time series analysis to establish differential disease progression for two Fusarium head blight pathogens in oat spikelets with variable resistance

Oat-based value-added products have increased their value as healthy foodstuff. Fusarium head blight (FHB) infections and the mycotoxins accumulated to the oat seeds, however, pose a challenge to oat production. The FHB infections are predicted to become more prevalent in the future changing climates and under more limited use of fungicides. Both these factors increase the pressure for breeding new resistant cultivars. Until now, however, genetic links in oats against FHB infection have been difficult to identify. Therefore, there is a great need for more effective breeding efforts, including improved phenotyping methods allowing time series analysis and the identification of molecular markers during disease progression. To these ends, dissected spikelets of several oat genotypes with different resistance profiles were studied by image-based methods during disease progression by Fusarium culmorum or F. langsethiae species. The chlorophyll fluorescence of each pixel in the spikelets was recorded after inoculation by the two Fusarium spp., and the progression of the infections was analyzed by calculating the mean maximum quantum yield of PSII (F v /F m ) values for each spikelet. The recorded values were (i) the change in the photosynthetically active area of the spikelet as percentage of its initial size, and (ii) the mean of F v /F m values of all fluorescent pixels per spikelet post inoculation, both indicative of the progression of the FHB disease. The disease progression was successfully monitored, and different stages of the infection could be defined along the time series. The data also confirmed the differential rate of disease progression by the two FHB causal agents. In addition, oat varieties with variable responses to the infections were indicated.

oats↗
NASA NTRS1998

Tropical Tropospheric Ozone (TTO) Maps from Nimbus 7 and Earth-Probe TOMS by the Modified-Residual Method: Validation, Evaluation and Trends based on Atlantic Regional Time Series - 1

The well-known wave-one pattern seen in tropical total ozone [Shiotani, 1992; Ziemke et al., 1996, 1998] has been used to develop a modified-residual (MR) method for retrieving time-averaged stratospheric ozone and tropospheric ozone column amount from TOMS (Total Ozone Mapping Spectrometer) over the 14 complete calendar years of Nimbus 7 observations (1979-1992) and from TOMS on the Earth-Probe (1996-present) and ADEOS platforms (1996- 1997). Nine- to sixteen-day averaged tropical tropospheric ozone (TTO) maps, validated with ozonesondes, show a seasonality expected from dynamical and chemical influences. The maps may be viewed on a homepage: http://metosrv2.umd.edu/~tropo. Stratospheric column ozone, which is also derived by the modified-residual method, compares well with sondes (to within 6-7 DU) and with stratospheric ozone column derived from other satellites (within 8-10 DU). Validation of the TTO time-series is presently limited to ozonesonde comparisons with Atlantic stations and sites on the adjacent continents (Ascension Island, Natal, Brazil; Brazzaville); for the sounding periods, TTO at all locations agrees with the sonde record to +/-7 DU. TTO time-series and the magnitude of the wave-one pattern show ENSO signals in the strongest El Nifio periods from 1979-1998. From 12degN and 12degS, zonally averaged tropospheric ozone shows no significant trend from 1980-1990. Trends are also not significant during this period in localized regions, e.g. from just west of South America across to southern Africa. This is consistent with the ozonesonde record at Natal, Brazil (the only tropical ozone data publicly available for the 1980's), which shows a not statistically significant increase. The lack of trend in tropospheric ozone agrees with a statistical analysis based on another method for deriving TTO from TOMS, the so-called Convective-Cloud-Differential approach of Ziemke et al. [1998].

Thompson, Anne M.↗
NASA NTRS2013

Adaptive Sensing of Time Series with Application to Remote Exploration

We address the problem of adaptive informationoptimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility -- all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.

artificial intelligence↗
DOE OSTI2024

Multivariate Time Series Intermittent Fault Detectionin Controller Area Network CAN

Fault detection in Controller Area Network (CAN) systems is crucial for ensuring the reliability and safety of automotive and industrial applications. This study investigates and compares the effectiveness of time series classification models for supervised fault detection in CAN data. This repository contains the code and data for our benchmarking experiment aimed at detecting intermittent faults in automotive Controller Area Network (CAN) data. The goal of this project is to compare various machine learning (ML) and deep learning (DL) models using different Time Series Cross-Validation (TSCV) techniques to evaluate their effectiveness in a streaming environment for fault detection.

Hespeler, Steven [Oak Ridge National Laboratory (O↗
DOE OSTI2023

Logarithm-Based Methods for Interpolating Quaternion Time Series

In this paper, we discuss a modified quaternion interpolation method based on interpolations performed on the logarithmic form. This builds on prior work that demonstrated this approach maintains C 2 continuity for prescriptive rotation. However, we develop and extend this method to descriptive interpolation, i.e., interpolating an arbitrary quaternion time series. To accomplish this, we provide a robust method of taking the logarithm of a quaternion time series such that the variables $\theta$ and $\hat{n}$ have a consistent and continuous axis-angle representation. We then demonstrate how logarithmic quaternion interpolation out-performs Renormalized Quaternion Bezier interpolation by orders of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Time-series elemental imaging reveals CAX-dependent redistribution patterns for anoxia recovery

Flooding-induced oxygen deprivation (anoxia) is a challenge to plant survival, necessitating adaptive mechanisms for recovery. This study investigated elemental redistribution during anoxia recovery using time-series elemental imaging to show changes in nutrient distribution. Focusing on the role of Cation/H + Exchangers (CAXs) in Arabidopsis thaliana, we show how mutants deficient in specific CAX transporters (cax1 and the cax1-4 quadruple mutant) respond to anoxia and metal stress. Mutants showed reduced lipid peroxidation and increased expression of flood-tolerance proteins during recovery. X-ray fluorescence microscopy and laser ablation–inductively coupled plasma mass spectrometry were used to show elemental redistribution over time. In wild-type plants (Col-0), post-anoxia elemental distribution resembled the elemental distribution of CAX mutants under normoxic conditions, suggesting that CAX-mediated elemental distribution before anoxia enables faster recovery post-anoxia, rather than affecting remobilization post-anoxia. Although CAX mutants had altered tolerance to excess manganese and copper, leaf metal distribution during metal stress was not altered. Here, these findings introduce the potential utility of time-series elemental imaging to show stress-response phenotypes and the importance of elemental distribution to recovery after anoxia. The novelty of this work lies in resolving spatial distribution patterns in a non-static system to gain insight into mechanisms of stress resilience in plants.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Modified superposition: A simple time series approach to closed-loop manual controller identification

Single-channel pilot manual control output in closed-tracking tasks is modeled in terms of linear discrete transfer functions which are parsimonious and guaranteed stable. The transfer functions are found by applying a modified super-position time series generation technique. A Levinson-Durbin algorithm is used to determine the filter which prewhitens the input and a projective (least squares) fit of pulse response estimates is used to guarantee identified model stability. Results from two case studies are compared to previous findings, where the source of data are relatively short data records, approximately 25 seconds long. Time delay effects and pilot seasonalities are discussed and analyzed. It is concluded that single-channel time series controller modeling is feasible on short records, and that it is important for the analyst to determine a criterion for best time domain fit which allows association of model parameter values, such as pure time delay, with actual physical and physiological constraints. The purpose of the modeling is thus paramount.

Biezad, D. J.↗
DOE OSTI2022

QA/QC of the East River, Colorado, discharge and geochemical time series datasets (Almont, BCC, and Pump House) to be used for modeling of hydrogeochemical balance

The following datasets were QA/QC-ed (Quality Assurance/Quality Control): 1. Brush Creek Confluence (BCC) discharge data (from Helen Malenda, USGS, Colorado School of Mines), which were calculated using the pressure transducer data and rating curves. The original 15 min time series data were presented as mean daily discharge. 2. Almont discharge data from United States Geological Survey (USGS). The original data were in 15 min time intervals, and were averaged to mean daily discharge time series. 3. Pump House discharge data as mean daily discharge (downloaded from the SFA portal). 4. BCC and Pump House chemistry data from SFA data portal and/or original spreadsheets provided by Roelof Versteeg. The following challenging QA/QC problems of the datasets were resolved: Missing data with the duration of gaps up to >1 month; Duplicated dates; Anomalies and outliers of discharge and concentrations; Time stamps of measurements of the discharge and concentrations are not aligned (hydrogeochemical balance calculations require the timestamps to be aligned). All QA/QC-ed datasets are given as csv files. The csv files were prepared using the xts files with multiple worksheets, which are also included in the data packages. Figures of the QA/QC-ed datasets are given in the jpeg and pdf formats. The QA/QC-ed datasets have been used to quantify discharge and chemical concentrations in river water in order to understand riverine exports of water and dissolved constituents in the East River watershed. These datasets served as a basis in the presentation given by P. Fox et al. at the 2021 Goldschmidt Conference.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Computer Program Recognizes Patterns in Time-Series Data

A computer program recognizes selected patterns in time-series data like digitized samples of seismic or electrophysiological signals. The program implements an artificial neural network (ANN) and a set of N clocks for the purpose of determining whether N or more instances of a certain waveform, W, occur within a given time interval, T. The ANN must be trained to recognize W in the incoming stream of data. The first time the ANN recognizes W, it sets clock 1 to count down from T to zero; the second time it recognizes W, it sets clock 2 to count down from T to zero, and so forth through the Nth instance. On the N + 1st instance, the cycle is repeated, starting with clock 1. If any clock has not reached zero when it is reset, then N instances of W have been detected within time T, and the program so indicates. The program can readily be encoded in a field-programmable gate array or an application-specific integrated circuit that could be used, for example, to detect electroencephalographic or electrocardiographic waveforms indicative of epileptic seizures or heart attacks, respectively.

Hand, Charles↗
NASA NTRS2021

Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

Trace gas↗
NASA NTRS2022

Time-Series Ratio Algorithm for Nisar Soil Moisture Retrieval

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and will provide global L-band radar observations that will be helpful for various soil moisture applications. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using NISAR simulated UAVSAR data collected during the SMAPVEX12 field experiment. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.

Jeonghwan Park↗
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗