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Results for “Spectral feature extraction”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

APPL Hyperspectral_Imaging_Dataset_for_Heritability_Analysis_in_Populus_trichocarpa

This dataset contains hyperspectral imaging data collected at the Advanced Plant Phenotyping Laboratory (APPL) at Oak Ridge National Laboratory. Natural variants of Populus trichocarpa were imaged using a high-throughput hyperspectral phenotyping pipeline to quantify spectral reflectance traits for downstream quantitative genetics analyses. The dataset includes hyperspectral image files and derived reflectance data products suitable for extracting spectral features across the measured wavelength range (e.g., VNIR and/or SWIR, depending on instrument configuration), along with associated sample metadata (e.g., genotype identifiers, experimental design factors, and imaging run identifiers). These data were generated to support analyses of broad-sense heritability of hyperspectral traits and their relationships with biochemical phenotypes (including lignin traits from Py-MBMS).

APPL↗
DOE OSTI2026

ClassNMSW- a real-time classification approach for non-recycled municipal solid waste using hyperspectral imaging

Real-time classification of non-recycled municipal solid waste (NMSW) is essential for efficient valorization. This study introduces ClassNMSW, a comprehensive framework for classifying 22 NMSW subclasses under industrial constraints by using hyperspectral imaging (HSI). A primary innovation of this work is the development of a variance-controlled spectral extraction algorithm. Unlike traditional methods that rely on simple averaging, this approach systematically investigates the extent of pixel extraction to minimize the loss of critical chemical information while maximizing data reduction thus ensuring high spectral fidelity with low computational cost. The approach developed in this work integrates automated, computer-vision-based background removal, eliminating the need for the manual thresholding common in current literature. To resolve ambiguities among chemically similar subclasses, a tiered classification and multi-camera fusion strategy (NIR17 and NIR22) is implemented. Results demonstrate that ClassNMSW achieves an object-wise weighted accuracy of 98.70% for single-sensor configurations and 100% under sensor fusion. A novel rolling-window strategy satisfies desired end-to-end latency of <2 s, satisfying the strict deterministic requirements of high-speed industrial sorting environments. The ClassNMSW framework provides a scalable foundation for advancing circularity and resource recovery in large-scale waste valorization operations.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

Abstract Applying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults.

58 GEOSCIENCES↗
DOE OSTI2023

Detection of Grid-Signal Distortions Using the Spectral Correlation Function

This study proposes a novel method for signal detection and feature extraction based on the spectral correlation function, enabling improved characterization of grid-signal distortions. Our approach differs from existing treatments of signal distortion in its analysis of the varied spectral content of signals observed in real-world scenarios. The method we propose has state-of-the-art discriminative power that provides meaningful and understandable characterizations of various grid events and anomalies. In conclusion, to validate the approach, we use real world data from the Grid Event Signature Library, which is maintained jointly by Oak Ridge National Laboratory and Lawrence Livermore National Laboratory.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves

Abstract Background High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar ( Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves. Results The Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R 2 = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements. Conclusions The simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals.

09 BIOMASS FUELS↗
DOE OSTI2023

An Assessment of Persistent Acoustic Monitoring of a Nuclear Reactor during Full Power Generation

Persistent low-frequency (<180 Hz) acoustic detection took place within the boundaries of Oak Ridge National Laboratory to monitor full power operations of the High Flux Isotope Reactor. Three acoustic sensors were installed at distances of 69, 101, and 914 m from the northeast corner of the cooling towers to monitor and assess four reactor power generation cycles. Features were extracted from power spectral density calculations where data were collected during reactor on and off operations. Diverse spectral features were present during full reactor power, including a 21.4 Hz fundamental frequency and ascending harmonics. Using bandpass filters, these related frequencies were isolated and summed, and the root mean square energy was calculated. The method of isolating and summing characteristic features provided a significant improvement in identifying acoustic behavior related to reactor power when the raw signals were obscured by noise.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

STM/S Grid LDOS Data and Analysis Code for Deciphering Majorana Zero Modes in Topological Superconductor

This dataset provides raw millikelvin scanning tunneling microscopy/spectroscopy (STM/S) grid spectroscopy data and Python analysis scripts supporting the manuscript “Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution.” The dataset includes a raw grid spectroscopy file acquired on FeTe0.55Se0.45 at 40 mK under magnetic field, together with Python/Jupytext analysis scripts used for STM/S data processing, visualization, spectral deconvolution, Lorentzian peak fitting, feature extraction, machine-learning-assisted clustering, and figure generation. These files support the analysis of vortex-core local density of states and the identification of zero-bias-peak-related spectral components from complex in-gap states. The dataset is intended to provide a citable archival record of the data and analysis code associated with the published manuscript and to support transparency and reproducibility of the reported STM/S and machine-learning workflow.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Detecting Anomalies for Fire Prevention in Distribution Systems: Challenges and Analytical Techniques

Electric utilities in California have historically been linked to up to 10% of wildfires. To mitigate this risk, Southern California Edison has invested significantly in wildfire prevention strategies, including undergrounding cables and enhancing equipment inspections. This article explores a novel approach to fire prevention by detecting anomalies in the distribution system that may indicate potential fire hazards. The focus is on identifying arcing conditions through high-resolution point-on-wave (POW) measurements. Arcing, a precursor to fires, is challenging to detect due to its subtle transients and complex system topology. The article discusses the use of advanced signal processing and machine learning techniques, such as spectral correlation function and discrete wavelet transform, to extract features from POW data and accurately identify arcing events. The study demonstrates a high accuracy rate in detecting arcing, paving the way for improved fire prevention measures in electric distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
DOE OSTI2026

UV–Vis–NIR Reflectance Spectroscopy and Chemometrics for Monitoring Pu Directly on an Ion Exchange Column

Here, we present a fiber-optic UV–vis–NIR reflectance spectroscopy method for direct, noninvasive monitoring of Pu(IV) in a glass ion exchange column during dynamic loading and elution in a glovebox. A movable probe enables spatially resolved spectral acquisition along the column axis, capturing distinct features associated with Pu(IV) nitrate complexes during loading and free ions during elution. Principal component analysis was applied to extract the dominant spectral variance and resolve relative concentration profiles without requiring precise knowledge of optical penetration depth or species identity. This in situ approach reveals spatial gradients and speciation dynamics in real time, which provides actionable insight into Pu(IV) ion migration, resin saturation, and breakthrough behavior under evolving flow conditions. The method offers a practical, fiber-compatible strategy to monitor glass column–based separations for Pu and other lanthanides or actinides and to characterize metal–resin interactions in flow-through systems.

actinide↗
DOE OSTI2024

Extracting doubly excited state lifetimes in helium directly in the time domain with attosecond noncollinear four-wave-mixing spectroscopy

The helium atom, with one nucleus and two electrons, is a prototypical system to study quantum many-body dynamics. Doubly excited states, or quantum states in which both electrons are excited by one photon, showcase electronic-correlation mediated effects. In this paper, the natural lifetimes of the doubly excited 1 P o 2 s n p Rydberg series and the 1 S e 2 p 2 dark state in helium in the 60–65 eV region are measured directly in the time domain with extreme-ultraviolet/near-infrared noncollinear attosecond four-wave-mixing (FWM) spectroscopy. The measured lifetimes agree with lifetimes deduced from spectral linewidths and theoretical predictions, and the roles of specific decay mechanisms are considered. While complex spectral line shapes in the form of Fano resonances are common in absorption spectroscopy of autoionizing states, the background-free and thus homodyned character of noncollinear FWM results exclusively in Lorentzian spectral features in the absence of strong-field effects. The onset of strong-field effects that would affect the extraction of accurate natural lifetimes in helium by FWM is determined to be approximately 0.3 Rabi cycles. This study provides a systematic understanding of the FWM parameters necessary to enable accurate lifetime extractions, which can be utilized in more complex quantum systems in the future. Published by the American Physical Society 2024

Rupprecht, Patrick (ORCID:000000026491793X)↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗
DOE OSTI2024

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. Here, in this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories

Non-targeted liquid chromatography tandem highresolution mass spectrometry (LC−MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC−MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and featurebased molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC−MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.

DOM↗
DOE OSTI2026

Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element using one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. Here, in this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.

battery↗