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At least 343 records · Page 19

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NASA NTRS2013

Data Mining for Anomaly Detection

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

Biswas, Gautam↗

DOE OSTI2025

Incorporating long-range dependence and fractal features in turbulence spectra

We introduce an advanced turbulence spectrum model developed from mathematical foundations from a covariance function class and empirically validated using extensive field data. This model captures the complex dynamics of long-range dependence, and fractal characteristics prevalent in riverine and atmospheric boundary layer (ABL) flows that are ignored by classical spectrum models, such as IEC (International Electrotechnical Commission) von Kármán and Kaimal model. The model delineates scaling behaviors across distinct frequency bands and offers substantial flexibility through five well-defined parameters each characterizing a distinct physical aspect of the velocity time series. A detailed procedure for obtaining each parameter from time series data is outlined. The comprehensive validations with field data from tidal currents and ABL flows substantiate the model’s fidelity in accurately replicating observed phenomena. This validation establishes the reliability of the proposed model and, when incorporated into stochastic full-field simulators such as TurbSim, demonstrates its potential to advance the predictive modeling and analysis of turbulent flows in environmental science and engineering contexts.

Cheng, Shyuan [Univ. of Illinois at Urbana-Champai↗

DOE OSTI2021

Identifying Light-Duty Vehicle Travel from Large-Scale Multimodal Wearable GPS Data with Novelty Detection Algorithms

Identifying travel mode within travel survey data sets, especially light-duty vehicle (LDV) travel, is foundational, though nontrivial, to travel behavior analysis and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. They are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the data requirements. This paper proposes an LDV travel mode detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique-one-class support vector machines (OCSVMs)-and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multimodal prediction.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Matrix Completion Using Alternating Minimization for Distribution System State Estimation

This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2020

Matrix Completion Using Alternating Minimization for Distribution System State Estimation: Preprint

41 EE - Solar Energy Technologies Office (EE-4S)↗

NASA NTRS1999

Large Field Visualization with Demand-Driven Calculation

We present a system designed for the interactive definition and visualization of fields derived from large data sets: the Demand-Driven Visualizer (DDV). The system allows the user to write arbitrary expressions to define new fields, and then apply a variety of visualization techniques to the result. Expressions can include differential operators and numerous other built-in functions, ail of which are evaluated at specific field locations completely on demand. The payoff of following a demand-driven design philosophy throughout becomes particularly evident when working with large time-series data, where the costs of eager evaluation alternatives can be prohibitive.

Moran, Patrick J.↗

NASA NTRS2003

Update on EMD and Hilbert-Spectra Analysis of Time Series

This method is especially well suited for analyzing time-series data that represent nonstationary and nonlinear physical phenomena. The method is based principally on the concept of empirical mode decomposition (EMD), according to which any complicated signal (as represented by digital samples) can be decomposed into a finite number of functions, called "intrinsic mode functions" (IMFs), that admit well-behaved Hilbert transforms. The local energies and the instantaneous frequencies derived from the IMFs through Hilbert transforms can be used to construct an energy-frequency-time distribution, denoted a Hilbert spectrum.

Huang, Norden E.↗

NASA NTRS2003

NASA Tech Briefs, November 2003

Topics covered include: Computer Program Recognizes Patterns in Time-Series Data; Program for User-Friendly Management of Input and Output Data Sets; Noncoherent Tracking of a Source of a Data-Modulated Signal; Software for Acquiring Image Data for PIV; Detecting Edges in Images by Use of Fuzzy Reasoning; A Timer for Synchronous Digital Systems; Prototype Parts of a Digital Beam-Forming Wide-Band Receiver; High-Voltage Droplet Dispenser; Network Extender for MIL-STD-1553 Bus; MMIC HEMT Power Amplifier for 140 to 170 GHz; Piezoelectric Diffraction-Based Optical Switches; Numerical Modeling of Nanoelectronic Devices; Organizing Diverse, Distributed Project Information; Eigensolver for a Sparse, Large Hermitian Matrix; Modified Polar-Format Software for Processing SAR Data; e-Stars Template Builder; Software for Acoustic Rendering; Functionally Graded Nanophase Beryllium/Carbon Composites; Thin Thermal-Insulation Blankets for Very High Temperatures; Prolonging Microgravity on Parabolic Airplane Flights; Device for Locking a Control Knob; Cable-Dispensing Cart; Foam Sensor Structures Would be Self-Deployable and Survive Hard Landings; Real-Gas Effects on Binary Mixing Layers; Earth-Space Link Attenuation Estimation via Ground Radar Kdp; Wedge Heat-Flux Indicators for Flash Thermography; Measuring Diffusion of Liquids by Common-Path Interferometry; Zero-Shear, Low-Disturbance Optical Delay Line; Whispering-Gallery Mode-Locked Lasers; Spatial Light Modulators as Optical Crossbar Switches; Update on EMD and Hilbert-Spectra Analysis of Time Series; Quad-Tree Visual-Calculus Analysis of Satellite Coverage; Dyakonov-Perel Effect on Spin Dephasing in n-Type GaAs; Update on Area Production in Mixing of Supercritical Fluids; and Quasi-Sun-Pointing of Spacecraft Using Radiation Pressure.

Source record↗

DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Conservative solute transport processes and associated transient storage mechanisms: Comparing streams with contrasting channel morphologies, land use and land cover

Abstract Land use within a watershed impacts stream channel morphology and hydrology and, therefore, in‐stream solute transport processes and associated transient storage mechanisms. This study evaluated transport processes in two contrasting stream sites where channel morphology was influenced by the surrounding land use, land cover, climate and geologic controls: Como Creek, CO, a relatively undisturbed, high gradient, forested stream with a gravel bed and complex channel morphology, and Clear Creek, IA, an incised, low‐gradient stream with low‐permeability substrate draining an agricultural landscape. We performed conservative stream tracer injections at these sites to address the following questions: (1) How does solute transport vary between streams with differing morphologies? and (2) How does solute transport at each stream site change as a function of discharge? We analysed in‐stream tracer time series data and compared results quantifying solute attenuation in surface and subsurface transient storage zones. Significant trends were observed in these metrics with varying discharge conditions at the forested site but not at the agricultural site. There was a broad range of transport mechanisms and evidence of substantial exchange with both surface and hyporheic transient storage in the relatively undisturbed, forested stream. Changing discharge conditions activated or deactivated different solute transport mechanisms in the forested site and greatly impacted advective travel time. Conversely in the simplified agricultural stream, there was a narrow range of solute transport behaviour across flows and predominantly surface transient storage at all measured discharge conditions. These results demonstrate how channel simplification inhibits available solute transport mechanisms across varying discharge conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2004

Use of EO-1 Hyperion Data for Inter-Sensor Calibration of Vegetation Indices

Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been launched and their derived products are increasingly being used in regional and global vegetation studies. The increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multiresolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group of sensors that have produced a long-term, archived data set of the Earth s surface, at fine resolution and since 1972, capable of being processed into useful information for global change studies (Hall et al., 1991).

Huete, Alfredo↗

DOE OSTI2020

Correction for Neutral Pressure-Driven Signal in Radiated Power Measurements on Proto-MPEX

A pinhole camera using four-channel resistive bolometers was designed and implemented on the Prototype Material Plasma Exposure eXperiment (Proto-MPEX) to measure the plasma radiated power. Quantification of the radiated power is required for comprehensive power balance assessments but faces unique challenges on linear plasma devices due to weak absolute signal, compared to typical use in toroidal fusion plasmas, and high neutral pressure changes. Around 1 mTorr, the pressure-driven signal contribution was shown to overwhelm the radiated power signal in Proto-MPEX, found to be ~3 μW . Since the pressure-driven signals could not be predicted from first principle arguments, the signals were corrected using empirical models that take two experimental pressure measurements and the raw bolometer signal to reveal the radiated power signals. Four “gas-only” Proto-MPEX shots were taken, without the radio frequency (rf) plasma discharge, to train, optimize, and validate empirical models. The data included two pressure measurements at different locations along with the bolometer signal. Multiple models were tested; partial least-squares regression with a cross correlation method to account for the time offset in the pressure measurements provided the best combination of accuracy and stability. The model was validated to have a modeling error of 0.0036 V, which was lower than 10% of the expected radiated power signal. The model was tested on a different “gas-only” shot with a complete time-series data. For plasma operation shots, the corrected bolometer measurements were compared with an absolute extreme ultraviolet photodiode measurement and similarities and differences are discussed.

Lee, Seungsup↗

NASA NTRS2010

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Wagstaff, Kiri L.↗

DOE OSTI2022

Toward Forecasting Geomagnetic Storms

We present a selection of attempts to infer geomagnetic storm properties from time series data. Initially, we unsuccessfully attempt to predict the duration of a geomagnetic storm based on data preceding the onset of a geomagnetic storm. Similar techniques are also used to attempt to predict the maximum depth of a geomagnetic storm. Additionally, we present a case study in Gaussian Process regression, where we are able to accurately interpolate between geomagnetic storm parameters, but are unable to predict future storm behavior. Lastly, we describe attempts at using random forest regression with sliding windows to infer geomagnetic storm behavior at future times. These results did not provide robust forecasts of geomagnetic storm behavior, but did occasionally correctly predict a storm within the margin of error. Techniques presented in this paper include machine learning classification and regression, Gaussian Process regression, and machine learning regression with sliding windows. Many of the attempts presented in this document did not result in meaningful predictions of geomagnetic storm behavior. We hope this document can be used as a guide for future readers attempting to predict geomagnetic storm properties.

58 GEOSCIENCES↗

DOE OSTI2025

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗

DOE OSTI2023

Recent streamflow trends across permafrost basins of North America: Datasets

Climate change impacts, including changing temperatures, precipitation, and vegetation, are widely anticipated to cause major shifts to the permafrost with resulting impacts to hydro-ecosystems across the high latitudes of the globe. However, it is challenging to examine streamflow shifts in these regions owing to a paucity of data, discontinuity of records, and other issues related to data consistency and accuracy. We looked at recent changes in streamflow over 1976-2021 in watersheds affected by varying degrees of permafrost coverage to characterize trends and drivers for a range of watersheds across North America. Data sets are described in detail in the paper associated with this data set, Bennett et al. 2023, Front. Water - Water and Critical Zone, DOI: 10.3389/frwa.2023.1099660.These data contain CSV files of the streamflow, climate, and land surface characteristics for several sites located across the high latitude regions of North America. Both observed and reanalysis data products are provided. These files can be opened using Excel or a text editor, or they can be read, and analyzed in software tools such as Python or R. A brief description of the files is below, and more details can be found in the Methods section.rabpro_stats_north_select_74_55m.csv - This file describes the observed gages used in the analysis.GF31_23_metadat.csv - This file describes the 23 permafrost systems. Columns are as described in rabpro_stats_north_select_74_55m.csv above, with rabpro_id, the id used for the timeseries file mapping in GF31_23_time_series.csv.GF31_23_time_series.csv - This file contains the time series data for the stations described in GF31_23_metadat.csv.GF31_random_reaches_1583.csv - This file describes the 1583 randomly selected permafrost-dominant sites for machine learning analysis.era5_GF31_monthly_vars_random_reaches_1583.csv - This file contains the monthly ERA5 land data for the 1583 randomly selected permafrost-dominant sites.observed.zip: USGS and Hydat station data for the 74 gages analyzed in this study. Monthly, seasonal, and annual streamflow observations for minimum streamflow, mean streamflow, and maximum streamflow. Units are m3/sec. 1975-2022.streamflow_daily_GF31_infilled_1979_2022.csv - infilled daily streamflow data (infilled using GloFAS v 3.1) for 55 gages. Units are m3/sec. 1979-2022.glofas_23.zip - Glofas v3.1 file for the 23 permafrost-dominant gages in the study. Monthly, seasonal, and annual streamflow observations for minimum streamflow, mean streamflow, and maximum streamflow. Units are m3/sec. 1979-2021.glofas_1583.zip - Glofas v3.1 file for the 1583 randomly selected permafrost-dominant gages in the study. Monthly, seasonal, and annual streamflow observations for minimum streamflow, mean streamflow, and maximum streamflow. Units are m3/sec. 1979-2021.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗