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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Speaker-targeted Synthetic Speech Detection

Text-to-speech technologies are evolving quickly towards realistic-sounding human-like voices. As this technology improves, so does the opportunity for malpractice in speaker identification (SID) via spoofing, the process of impersonating a voice biometric via synthesis. More data typically equates to a more realistic voice model, which poses an issue for well-known subjects, such as politicians and celebrities, who have vast amounts of multimedia available online. Detection of synthetic speech has relied on signal processing techniques that focus on the generation of new acoustic features and train deep learning models to detect when an audio file has been manipulated through the characterization of unnatural changes or artifacts. However, these techniques do not use any information from the speaker they are evaluating. This paper proposes to incorporate information from the speaker-of-interest (SoI) into the models to avoid specific spoofing attacks for certain vulnerable people. The wealth of data for well-known people can also be used to train a speaker-specific spoofing detector with a higher level of accuracy than a speaker-independent model. The paper proposes a new xResNet-PLDA system and compares it to three different baseline systems: a state-of-the-art speaker identification system, an xResNet system trained to discriminate between bona fide and fake speech, and a speaker identification system in which the PLDA and calibration models were trained with bona fide and fake speech. We evaluated the systems in two different scenarios — a cross-validation scenario and a hold-out scenario — with three different databases. We show how the proposed system outperforms dramatically the baseline systems in each scenario and for each database. Finally, we show how using a small amount of the SoI’s speech to adapt global calibration parameters improves the performance of the system, especially in unseen conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Why Seeding Works When Nucleation Barriers Vanish

This paper explains why seeds can speed up crystallization even when the formation of new crystal nuclei is not the slow step. In zeolite synthesis, local crystalline order can form relatively early, but the material may still appear poorly crystalline by X-ray diffraction because many small crystallites are misoriented and lack long-range coherence. Using coarse-grained molecular simulations, the study showed that seeds help solve this coherence problem. Rather than primarily lowering a nucleation barrier, seeds impose a common orientation on nearby growing crystallites, allowing them to merge into larger, better aligned domains. This accelerates the development of X-ray-detectable crystallinity even if the total amount of locally ordered material has not changed much. The work changes how induction times in zeolite synthesis should be interpreted: the delay before X-ray crystallinity appears can reflect the time needed to build long-range structural coherence, not simply the time needed to form crystalline material. This provides a new explanation for why seeding is effective in growth-limited crystallization and offers design principles for controlling crystallization rate and crystal quality.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterization of Rare-earth Doped Thermal Barrier Coatings for Phosphor Thermometry

Thermal Barrier Coatings have been extensively used to protect and insulate the metallic components in turbine engines from extreme environments to achieve higher turbine inlet temperatures, resulting in an increase in combined engine cycle efficiency, lowering NOx emissions and fuel consumption. Additionally, as the major failure mechanisms determining lifetime are thermally activated during engine operation, uncertainty in temperature measurements affects lifetime prediction. Early detection of Thermal Barrier Coatings spallation symptoms, associated with local delamination induced temperature changes, can reduce forced engine outages. Further improvements are envisioned with the development of more reliable measurement techniques. For this purpose, Phosphor Thermometry has been considered a promising method for precision monitoring of turbine blade coatings that contain embedded phosphor dopants. In this work, a modified four-flux Kubelka-Munk model for luminescent rare-earth doped Thermal Barrier Coating configurations supported the design and the fabrication of viable sensing coatings for potential industrial implementation. Phosphor Thermometry instrumentation was developed with the objective of expanding capabilities of the technique, using a synchronized acquisition method on sensing coatings. Experiments on an innovative Erbium-Europium co-doped Yttria-Stabilized Zirconia coating have demonstrated the effectiveness of this advanced Phosphor Thermometry instrument with enhanced sensitivity and extended temperature range. Delamination progression monitoring was achieved for the first time using a Thermal Barrier Coating configuration subjected to indentation that includes a thin luminescent layer deposited on the surface and associating the multi-layer configuration with a predictive model that evaluates the advancement of the degradation of the coatings. To further ensure the integrity of the phosphor doped coatings, synchrotron X-ray diffraction was performed to characterize the specific residual strains and thermal expansion of the materials, determining mechanical robustness and compatibility with state-of-the-art Thermal Barrier Coatings. The outcomes of this work pave the way for in-situ temperature measurements on phosphor-doped turbine blade coatings with increased performance and accuracy.

Fouliard, Quentin↗
DOE OSTI2020

Thin Film Hydrogen Sensor Development, Testing and Integration Into Low Cost Wireless Sensing Systems (SBIR Phase 1 Final Report)

Element One, Inc. is reporting on the results of its DOE SBIR Phase I project for the development and testing of low-cost thin film hydrogen sensors and their subsequent integration into wireless sensing systems and networks. This work is based on Element One’s chemo-chromic sensing technology which indicates hydrogen presence (leaks) colorimetrically and electronically with no power required. The lack of required power makes them particularly suitable for passive Radio Frequency Identification Device (RFID) technology which can be deployed affordably and abundantly to detect leaks in hazardous environments. Element One built upon its experience with chemochromic materials that change color in the presence of hydrogen. Both thin films and pigments have been used, but thin films have the ability to change conductivity by more than three orders of magnitude making them ideal for resistive sensors. Prototype sensors were fabricated, tested and characterized. For the wireless component, Element One partnered with Esensor, Inc. to construct and test a wireless sensing network. The network was tested and validated at the National Renewable Energy Laboratory (NREL) in February and March. The tests involved exposing the wireless system to hydrogen gas concentrations of 1% and 5%. There were no significant problems encountered, and the system is ready for commercial development. The system demonstrated the viability of wireless hydrogen sensing networks for hydrogen leak detection and other applications.

08 HYDROGEN↗
DOE OSTI2021

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

58 GEOSCIENCES↗
DOE OSTI2026

Search for the isospin-violating decays 𝜒 𝑐⁢𝐽 → $Λ\bar{⁢Σ}^0$ + c.c. and 𝜂 𝑐 → $Λ\bar{⁢Σ}^0$ + c.c.

Using a sample of (2712.4 ± 14.3) × 10 6 𝜓⁡(3686) events collected with the BESIII detector, we perform a search for the isospin-violating decays 𝜒 𝑐⁢𝐽 → $Λ\bar{⁢Σ}^0$ + c.c.(𝐽 = 0,1,2) and 𝜂 𝑐 → $Λ\bar{⁢Σ}^0$ + c.c. No significant signal for 𝜒 𝑐⁢𝐽 or 𝜂 𝑐 is observed in the $Λ\bar{⁢Σ}^0$ invariant mass distribution. The upper limits on the branching fractions at the 90% confidence level are set to be ℬ⁡(𝜒 𝑐⁢0 → $Λ\bar{⁢Σ}^0$ + c.c.) < 1.5 × 10 −6 , ℬ⁡(𝜒 𝑐⁢1 → $Λ\bar{⁢Σ}^0$ + c.c.) < 1.6 × 10 −6 , ℬ⁡(𝜒 𝑐⁢2 → $Λ\bar{⁢Σ}^0$ + c.c.) < 1.7 × 10 −6 , and ℬ⁡(𝜂 𝑐 → $Λ\bar{⁢Σ}^0$ + c.c.) < 6.2 × 10 −5 for the first time.

Ablikim, M. [Institute of High Energy Physics] (OR↗
DOE OSTI2022

Impacts of active satellite sensors' low-level cloud detection limitations on cloud radiative forcing in the Arctic

Previous studies revealed that satellites sensors with the best detection capability identify 25 %–40 % and 0 %–25 % fewer clouds below 0.5 and between 0.5–1.0 km, respectively, over the Arctic. Quantifying the impacts of cloud detection limitations on the radiation flux are critical especially over the Arctic Ocean considering the dramatic changes in Arctic sea ice. In this study, the proxies of the space-based radar, CloudSat, and lidar, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), cloud masks are derived based on simulated radar reflectivity with QuickBeam and cloud optical thickness using retrieved cloud properties from surface-based radar and lidar during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment. Limitations in low-level cloud detection by the space-based active sensors, and the impact of these limitations on the radiation fluxes at the surface and the top of the atmosphere (TOA), are estimated with radiative transfer model Streamer. The results show that the combined CloudSat and CALIPSO product generally detects all clouds above 1 km, while detecting 25 % (9 %) fewer in absolute values below 600 m (600 m to 1 km) than surface observations. These detection limitations lead to uncertainties in the monthly mean cloud radiative forcing (CRF), with maximum absolute monthly mean values of 2.5 and 3.4 Wm –2 at the surface and TOA, respectively. Cloud information from only CALIPSO or CloudSat lead to larger cloud detection differences compared to the surface observations and larger CRF uncertainties with absolute monthly means larger than 10.0 Wm –2 at the surface and TOA. The uncertainties for individual cases are larger – up to 30 Wm –2 . These uncertainties need to be considered when radiation flux products from CloudSat and CALIPSO are used in climate and weather studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. Furthermore, this paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Probing the Origin of Changing-look Quasar Transitions with Chandra

Extremely variable quasars can also show strong changes in broad-line emission strength and are known as changing-look quasars (CLQs). To study the CLQ transition mechanism, we present a pilot sample of CLQs with X-ray observations in both the bright and faint states. From a sample of quasars with bright-state archival SDSS spectra and (Chandra or XMM-Newton) X-ray data, we identified five new CLQs via optical spectroscopic follow-up and then obtained new target-of-opportunity X-ray observations with Chandra. No strong absorption is detected in either the bright- or the faint-state X-ray spectra. The intrinsic X-ray flux generally changes along with the optical variability, and the X-ray power-law slope becomes harder in the faint state. Large-amplitude mid-infrared variability is detected in all five CLQs, and it echoes the variability in the optical with a time lag expected from the light-crossing time of the dusty torus for CLQs with robust lag measurements. The changing-obscuration model is not consistent with the observed X-ray spectra and spectral energy distribution changes seen in these CLQs. It is highly likely that the observed changes are due to the changing accretion rate of the supermassive black hole, so the multiwavelength emission varies accordingly, with promising analogies to the accretion states of X-ray binaries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Selection of optimal wavelengths for optical soiling modelling and detection in photovoltaic modules

Soiling impacts the photovoltaic (PV) module performance by decreasing the amount of light reaching the photovoltaic cells and by changing their external spectral response. Currently, the soiling monitoring market is moving toward optical sensors that measure transmittance or reflectance, rather than directly measuring the impact of soiling on the performance of photovoltaic modules. These sensors, which use a single optical measurement, are not able to correct the soiling losses that depend on the solar irradiance spectra and on the spectral response of the monitored PV material. This work investigates methods that can improve the optical detection of soiling by extracting the full soiling spectrum profiles using only two or three monochromatic measurements. Spectral transmittance data, measured with a spectrophotometer and collected during a 46-week experimental soiling study carried out in Jaén, Spain, was analysed in this work. The use of a spectral profile for the hemispherical transmittance of soiled PV glass is found to significantly improve the soiling detection, returning the lowest errors independently of the PV materials and irradiance conditions. In addition, this work shows that it is also possible to select the measurement wavelengths to minimize the soiling loss detection error depending on the monitored PV semiconductor material (silicon, CdTe, a-Si, CIGS and a representative perovskite). The approaches discussed in this work are also found to be more robust to potential measurement errors compared to single wavelength measurement techniques.

14 SOLAR ENERGY↗
DOE OSTI2022

A Sparse and Low Rank Penalized Signal Decomposition Model with Constraints: Anomaly Detection in PV Systems

Recently, robust PCA has seen its wide application in various industries for its ability to perform the task of anomaly detection. The essence of robust PCA approach is to break down the signal into a low rank component and sparse component. In many applications, a simple breakdown of the signal without accounting for the signs of low rank components and sparse components would violate the physical constraints of the decomposed signal. In addition, often times, the signals in the real world collected for a long duration has smooth changes within a day and between days. As an example, the power signals collected in a photovoltaic (PV) system are cyclostationary, exhibiting these characteristics. Neglecting the smoothness of signals would result in miss detection of anomalous signals which are smooth within a day but non-smooth between days and vice versa. In this paper, we developed a signal decomposition approach for the purpose of anomaly detection based on the idea of low rank and sparse decomposition taking into consideration the signs of the decomposed low rank and sparse components and the within-day and between-day smooth changes in the original signals. The proposed unsupervised approach for fault detection eliminates the need for faulty samples required by other machine learning methods. It does not require the full I-V characteristics to work. Furthermore, there is no need for complex modelling of PV systems as in the case of power loss analysis. Using Monte Carlo simulations, we demonstrate the ability of our proposed approach for detecting anomalies of different duration and severity in PV systems.

14 SOLAR ENERGY↗
DOE OSTI2025

Real-Time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-Negative Matrix Factorization

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems because the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate or sacrifice detection sensitivity to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) is a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation, it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. Here, we have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Anomaly detection↗
DOE OSTI2023

Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes

Complex mechanisms regulate the cellular distribution of cholesterol, a critical component of eukaryote membranes involved in regulation of membrane protein functions directly and through the physiochemical properties of membranes. StarD4, a member of the steroidogenic acute regulator-related lipid-transfer (StART) domain (StARD)-containing protein family, is a highly efficient sterol-specific transfer protein involved in cholesterol homeostasis. Its mechanism of cargo loading and release remains unknown despite recent insights into the key role of phosphatidylinositol phosphates in modulating its interactions with target membranes. We have used large-scale atomistic Molecular dynamics (MD) simulations to study how the dynamics of cholesterol bound to the StarD4 protein can affect interaction with target membranes, and cargo delivery. We identify the two major cholesterol (CHL) binding modes in the hydrophobic pocket of StarD4, one near S136&S147 (the Ser-mode), and another closer to the putative release gate located near W171, R92&Y117 (the Trp-mode). We show that conformational changes of StarD4 associated directly with the transition between these binding modes facilitate the opening of the gate. To understand the dynamics of this connection we apply a machine-learning algorithm for the detection of rare events in MD trajectories (RED), which reveals the structural motifs involved in the opening of a front gate and a back corridor in the StarD4 structure occurring together with the spontaneous transition of CHL from the Ser-mode of binding to the Trp-mode. Further analysis of MD trajectory data with the information-theory based NbIT method reveals the allosteric network connecting the CHL binding site to the functionally important structural components of the gate and corridor. Mutations of residues in the allosteric network are shown to affect the performance of the allosteric connection. These findings outline an allosteric mechanism which prepares the CHL-bound StarD4 to release and deliver the cargo when it is bound to the target membrane.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Tracing the impacts of Mount Pinatubo eruption on regional climate using spatially-varying changepoint detection

Significant events, such as volcanic eruptions, can have global and long-lasting impacts on climate. These global impacts, however, are not uniform across space and time. Understanding how the Mt. Pinatubo eruption affects global and regional climate is of great interest for predicting the impact on climate due to similar events as well as understanding the possible effect of the stratospheric aerosol injections proposed to combat climate change. While many studies illustrated the impact of the Pinatubo eruption on a global scale, studies at a fine regional scale are scarce. Here, we propose a Bayesian spatially-varying changepoint detection and estimation method to trace the impact of Mt. Pinatubo eruption on regional climate. Our approach takes into account the diffusing nature and spatial correlation of the climate changes attributed to the volcanic eruption. We illustrate our method and demonstrate its advantages over an existing changepoint detection method through simulations. Finally, we apply our method to monthly stratospheric aerosol optical depth and surface temperature data from 1985 to 1995 to detect and estimate changepoints following the 1991 Mt. Pinatubo eruption. Our results quantitatively characterize the spatial pattern of the eruption’s impact on regional climate, complementing the previous studies on the global impact of the Pinatubo eruption.

Aerosol optical depth↗
DOE OSTI2023

A Randomization-Based, Zero-Trust Cyberattack Detection Method for Hierarchical Systems

This paper demonstrates a novel randomization-based approach for verifying power system control signals with application to detecting cyberattacks. We consider fully connected hierarchical systems containing multiple local agents and a global "trust" agent. The global agent uses a time-varying randomized assignment scheme to identify corrupt network links based on principles of zero trust and majority rule. To evaluate the performance of this detection approach, we implement our algorithm in MATLAB and run it against nearly 43 million unique attack scenarios spanning a range of system sizes. For each scenario, the algorithm determines whether the identified corruptions satisfy a set of validity constraints reflecting network topology and uses that result to say whether the recovered state value for one or more local agents is malicious. We compare the algorithm's determination to the true state of the system to assess performance and find that classification accuracy converges to 100% as system size increases, suggesting that the validity constraints become more difficult to satisfy for larger systems. We further explore the scenarios that evade detection to understand practical implications for employing this detection approach.

cybersecurity↗
DOE OSTI2024

LandScan Global 2023: Silver Edition

For a quarter of a century, the LandScan Global (LSG) project has annually released a global, high-resolution gridded population dataset representing the ambient or unwarned population at a 30 arcsecond resolution. LSG supports a range of applications such as emergency management, disaster response, and human health and security for understanding populations at risk. The 2023 release of LSG, the LandScan Silver Edition, represents a major methodological leap forward while also leveraging previous knowledge—the previous year was the baseline for the current annual update carrying forward valuable knowledge of the built environment for the past quarter century—to train the machine learning models. Compared with annual releases over the past 24years, multiple advancements were made to different aspects of the methodology to achieve reproducibility, transparency, and consistent global propagation of solutions to modeling or population distribution issues identified during the review process. These novel changes include incorporation of the latest available geospatial inputs across the globe, machine learning models instead of manual modifications, population feature importance analysis, open-source solutions vs. proprietary software, generation of multiple global versions, analytic validations, and human-in-the-loop revisions to produce the final version. Additionally, algorithms—such as anomaly detection—were introduced to quickly identify areas of focus to develop a new and robust systematic review. Significant changes in modeled population distributions were observed between the 2022 and 2023 releases, largely attributable to improvements in data and methods and discussed thoroughly within this report. In summation, the LandScan Silver Edition leverages the best of the past quarter century of LSG legacy knowledge and continues a tradition of applying cutting-edge enhancements to serve as a new benchmark for accurate, actionable gridded population data

Lebakula, Viswadeep↗
DOE OSTI2023

Two disulfide-reducing pathways are required for the maturation of plastid c -type cytochromes in Chlamydomonas reinhardtii

Abstract In plastids, conversion of light energy into ATP relies on cytochrome f, a key electron carrier with a heme covalently attached to a CXXCH motif. Covalent heme attachment requires reduction of the disulfide-bonded CXXCH by CCS5 and CCS4. CCS5 receives electrons from the oxidoreductase CCDA, while CCS4 is a protein of unknown function. In Chlamydomonas reinhardtii, loss of CCS4 or CCS5 yields a partial cytochrome f assembly defect. Here, we report that the ccs4ccs5 double mutant displays a synthetic photosynthetic defect characterized by a complete loss of holocytochrome f assembly. This defect is chemically corrected by reducing agents, confirming the placement of CCS4 and CCS5 in a reducing pathway. CCS4-like proteins occur in the green lineage, and we show that HCF153, a distant ortholog from Arabidopsis thaliana, can substitute for Chlamydomonas CCS4. Dominant suppressor mutations mapping to the CCS4 gene were identified in photosynthetic revertants of the ccs4ccs5 mutants. The suppressor mutations yield changes in the stroma-facing domain of CCS4 that restore holocytochrome f assembly above the residual levels detected in ccs5. Because the CCDA protein accumulation is decreased specifically in the ccs4 mutant, we hypothesize the suppressor mutations enhance the supply of reducing power through CCDA in the absence of CCS5. We discuss the operation of a CCS5-dependent and a CCS5-independent pathway controlling the redox status of the heme-binding cysteines of apocytochrome f.

Genetics & Heredity↗