Synthetic Renewable Data generation for grid reliability assessment with energy storage systems
Explore the source record for details and available documents.
Lopez Ramirez, Andres Stephen↗
SEARCH · Engineering Papers
Results for “Synthetic Data”
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.
A Data Driven hybrid Framework for Generating Synthetic Variable Energy Data
Explore the source record for details and available documents.
Lopez Ramirez, Andres Stephen [Sandia National Lab↗
Nominal and adversarial synthetic PMU data for standard IEEE test systems
GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.
99 GENERAL AND MISCELLANEOUS↗
Generating synthetic cosmological data with GalSampler
Explore the source record for details and available documents.
Andrew Hearin↗
A method of classification for multisource data in remote sensing based on interval-valued probabilities
An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.
Kim, Hakil↗
A comparison of clustering algorithms for use in large scale gene expression analysis using synthetic microarray data
Explore the source record for details and available documents.
analysis↗
Characterization of Potential Springs in the Lower Colorado Desert of Southern California using Satellite Radar and Landsat Time Series Analysis
Renewable energy development in southern California is receiving increasing attention due to potential impacts on wildlife habitats and water sources. This study was designed to quantify and map, for the first time, sub-surface water sources from springs in the Lower Colorado Desert area of southern California using satellite radar data and 30 years of Landsat satellite image data. Synthetic Aperture Radar (SAR) data was used to identify sub-surface water sources not already documented as springs or seeps in the National Hydrography Database. Landsat imagery starting in 1985 was used to characterize vegetation growth patterns at these suspected spring locations detected from SAR analysis. Results showed a total of 104 potential spring locations across the Lower Colorado Desert study area, 19 of which were detected within Solar Energy Zone (SEZ) development boundaries, roughly evenly split between Riverside East and Imperial East, and 13 of which (both inside and outside SEZs) showed relatively high green vegetation index values over the period of 1985 to 2015 that would depend on non-precipitation water sources associated with active springs.
DRECP↗
Planetary Probe Entry Atmosphere Estimation Using Synthetic Air Data System
This paper develops an atmospheric state estimator based on inertial acceleration and angular rate measurements combined with an assumed vehicle aerodynamic model. The approach utilizes the full navigation state of the vehicle (position, velocity, and attitude) to recast the vehicle aerodynamic model to be a function solely of the atmospheric state (density, pressure, and winds). Force and moment measurements are based on vehicle sensed accelerations and angular rates. These measurements are combined with an aerodynamic model and a Kalman-Schmidt filter to estimate the atmospheric conditions. The new method is applied to data from the Mars Science Laboratory mission, which landed the Curiosity rover on the surface of Mars in August 2012. The results of the new estimation algorithm are compared with results from a Flush Air Data Sensing algorithm based on onboard pressure measurements on the vehicle forebody. The comparison indicates that the new proposed estimation method provides estimates consistent with the air data measurements, without the use of pressure measurements. Implications for future missions such as the Mars 2020 entry capsule are described.
Karlgaard, Chris↗
Planetary Probe Entry Atmosphere Estimation Using Synthetic Air Data System
This paper develops an atmospheric state estimator based on inertial acceleration and angular rate measurements combined with a vehicle aerodynamic model. The approach uses the navigation state of the vehicle to recast the vehicle aerodynamic model to be a function solely of the atmospheric state. Force and moment measurements are based on vehicle sensed accelerations and angular rates. These measurements are combined with an aerodynamic model and a Kalman–Schmidt filter to estimate the atmospheric conditions. The method is applied to data from the Mars Science Laboratory mission, which landed the Curiosity rover on the surface of Mars in August 2012. The results of the estimation algorithm are compared with results from a flush air data sensing algorithm based on onboard pressure measurements on the vehicle forebody. The comparison indicates that the proposed method provides estimates consistent with the air data measurements, without the use of pressure transducers. Implications for future missions such as the Mars 2020 entry capsule are described.
Karlgaard, Chris↗
Future Orbiting and In-Situ Exploration of Venus: Mount Etna as Terrestrial Analog
The exploration of Venus will soon experience a new golden era thanks to the recently selected NASA Deep Atmosphere of Venus Investigation of Noble gases, Chemistry and Imaging (DAVINCI) mission, NASA Venus Emissivity, Radio Science, InSAR, Topography & Spectroscopy (VERITAS) mission, and ESA EnVision mission. The DAVINCI mission will focus on the analysis of the the atmospheric vertical structure and composition of Earth’s twin planet and on the geologic structure of a tesserae terrain. The VERITAS mission will investigate the geologic features of its surface as well as geodynamic characteristics of the subsurface, providing high-resolution emissivity data, a global radar map at an approximate resolution of 30 meters/pixel, and estimation of the gravity anomaly of the shallow crust of the planet. The ESA EnVision mission will be complementary to the two NASA missions, providing high resolution 0.8-2.5 micron emissivity data, Synthetic Aperture Radar (SAR) data, and Subsurface Radar Sounder (SRS) data. Beyond those, the proposed Roscosmos-NASA Venera-D mission will also be equipped with an orbiter that will investigate the atmospheric composition and circulation, as well as a lander that will analize the in-situ chemical composition and the surface-atmosphere interactions.
P. D’Incecco↗
Hybrid Flush and Synthetic Air Data Filter for Entry Vehicle Atmospheric State Estimation
A hybrid flush/synthetic air data sensing filter for entry vehicle atmosphere estimation is developed. The approach makes use of a Kalman-Schmidt and Rauch-Tung-Streibel smoother. The filter/smoother blends information from pressure sensors distributed on the heatshield with pseudo-measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars 2020 entry, descent, and landing at Jezero crater on February 18th, 2021. The final paper will also include results from the Mars Science Laboratory entry, descent, and landing.
Chris D. Karlgaard↗
Sim-to-real supervised domain adaptation for radioisotope identification
Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.
Lalor, Peter W.↗
Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation
Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.
artificial intelligence↗
GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files
This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.
15 GEOTHERMAL ENERGY↗
Analysis of urban area land cover using SEASAT Synthetic Aperture Radar data
Digitally processed SEASAT synthetic aperture raar (SAR) imagery of the Denver, Colorado urban area was examined to explore the potential of SAR data for mapping urban land cover and the compatability of SAR derived land cover classes with the United States Geological Survey classification system. The imagery is examined at three different scales to determine the effect of image enlargement on accuracy and level of detail extractable. At each scale the value of employing a simplistic preprocessing smoothing algorithm to improve image interpretation is addressed. A visual interpretation approach and an automated machine/visual approach are employed to evaluate the feasibility of producing a semiautomated land cover classification from SAR data. Confusion matrices of omission and commission errors are employed to define classification accuracies for each interpretation approach and image scale.
Henderson, F. M.↗
Potentials for change detection using Seasat synthetic aperture radar data
The use of synthetic aperture radar (SAR) images for detecting change on the earth's surface is highly dependent on target orientation, azimuth angle, and sensor depression angle. SAR data can be used for change detection when consistency is maintained in radar wavelength, polarization, azimuth directions, and off-nadir depression angle. The interaction of these parameters and the imaged surface for change detection are shown in examples drawn from (1) Los Angeles, CA, (2) southern Florida, (3) Imperial Valley, CA, (4) a desert region west of Tucson, AZ, and (5) western Kansas. SAR imagery is used to emphasize the geometric form, and roughness, of the earth's surface. As changes in the roughness of the surface occur over time, temporal SAR images will indicate those differences. Several guidelines for change detection studies using imaging radar are derived from the examples.
Bryan, M. L.↗
Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images
The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.
25 ENERGY STORAGE↗
Hiperclust
This software leverages transfer learning to analyze atom probe tomography (APT) data. It is trained on synthetic data and then applies this knowledge to predict the optimal number of clusters for a given APT dataset. Initially, the software used preliminary clustering to estimate the general structure of the data. Based on this, it provides suggestions for key parameters like minimum cluster size and minimum number of points. These parameters are critical for algorithms like HDBSCAN, ensuring accurate cluster formation without the need for trial-and-error testing. The software runs on High-Performance computing (HPC) systems, enabling fast, scalable analysis of large APT datasets, ultimately saving time and improving the reliability of clustering outcomes.
Tang, Yalei [Idaho National Laboratory (INL), Idah↗