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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Unobtrusive Sensor-Based Occupancy Facing Direction Detection and Tracking Using Advanced Machine Learning Algorithms

Facing direction detection plays a critical role in human-computer interaction and has a wide application in surveillance systems, driving awareness recognition, smart home appliances, computer games, and so on. Current detection methods are mainly focused on extracting specific patterns from user's optical images, which raises concerns on privacy invasion and these detection techniques do not usually work in a dark environment. To address these concerns, here this paper proposes an activity recognition system guided by an unobtrusive sensor (ARGUS). By using a low pixel infrared thermopile array sensor, ARGUS is capable of identifying five facing directions (left 45°/90°, right 45°/90°, and front) through the support vector machine classifier. Also two feature extraction methods are compared. One is manually-defined and the other is based on a pre-trained convolutional neural network (CNN) model. The facing direction detection accuracy resulting from manually-defined features reaches 85.3%, 90.6%, and 85.2% at detection distance of 0.6, 1.2, and 1.8 m, respectively. The level of accuracy resulted from using pre-trained CNN features demonstrates a much more reliable performance (89.1%, 95.3%, and 95.1% at distance of 0.6, 1.2, and 1.8 m, respectively). In addition, ARGUS has been successfully applied for occupancy tracking with a root mean square error of 0.19 m.

42 ENGINEERING↗
DOE OSTI2016

SU-F-J-44: Development of a Room Laser Based Real-Time Alignment Monitoring System Using An Array of Photodiodes

Purpose: To develop a real-time alignment monitoring system (RAMS) to compensate for the limitations of the conventional room laser based alignment system, and to verify the feasibility of the RAMS. Methods: The RAMS was composed of a room laser sensing array (RLSA), an analog-todigital converter, and a control PC. In the RLSA, seven photodiodes (each in 1 mm width) are arranged in a pattern that the RAMS provides alignment in 1 mm resolution. It works based on detecting laser light aligned on one of photodiodes. When misaligned, the laser would match with different photodiode(s) giving signal at unexpected location. Thus, how much displaced can be determined. To verify the reproducibility of the system with respect to time as well as repeated set-ups, temporal reproducibility and repeatability test was conducted. The accuracy of the system was tested by obtaining detection signals with varying laser-match positions. Results: The signal of the RAMS was found to be stable with respect to time. The repeatability test resulted in a maximum coefficient of variance of 1.14%, suggesting that the signal of the RAMS was stable over repeated set-ups. In the accuracy test, signals between when the laser was aligned and notaligned with any of sensors could be distinguished by signal intensity. The signals of not-aligned sensors were always below 75% of the signal of the aligned sensor. It was confirmed that the system could detect 1 mm of movement by monitoring the pattern of signals, and could observe the movement of the system in real-time. Conclusion: We developed a room laser based alignment monitoring system. The feasibility test verified that the system is capable of quantitative alignment monitoring in real time. The system is relatively simple, not expensive, and considered to be easily incorporated into conventional room laser systems for real-time alignment monitoring. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Multimodal sensor fusion framework for residential building occupancy detection

For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Graph neural networks for detecting anomalies in scientific workflows

Identifying and addressing anomalies in complex, distributed systems can be challenging for reliable execution of scientific workflows. We model these workflows as directed acyclic graphs (DAGs), where the nodes and edges of the DAGs represent jobs and their dependencies, respectively. We develop graph neural networks (GNNs) to learn patterns in the DAGs and to detect anomalies at the node (job) and graph (workflow) levels. We investigate workflow-specific GNN models that are trained on a particular workflow and workflow-agnostic GNN models that are trained across the workflows. Our GNN models, which incorporate both individual job features and topological information from the workflow, show improved accuracy and efficiency compared to conventional learning methods for detecting anomalies. While joint trained with multiple scientific workflows, our GNN models reached an accuracy more than 80% for workflow level and 75% for job level anomalies. In addition, we illustrate the importance of hyperparameter tuning method in our study that can significantly improve the metric(s) measure of evaluating the GNN models. Finally, we integrate explainable GNN methods to provide insights on job features in the workflow that cause an anomaly.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

A study of the early detection of insect infestations and density/distribution of host plants

The author has identified the following significant results. With comparative observations of film types and seasonal influences on reflectance characteristics, many crop varieties can be identified. This study shows that citrus, sugar cane, brush, some winter vegetables, and grain crops could be identified. Vegetative patterns in border areas can be detected. This information can be useful in detecting avenues of entry of pest species and areas of stress that require vigilance in stopping the spread of destructive species. Influence of some environmental factors on crops that may be confused with pest injury, or related factors, can be detected and identified with Skylab data (S-190B).

Hart, W. G.↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Enamel ribbons, surface nodules, and octacalcium phosphate in C57BL/6 Amelx -/- mice and Amelx +/- lyonization

Amelogenin is required for normal enamel formation and is the most abundant protein in developing enamel. Methods. Amelx +/+ , Amelx +/- and Amelx -/- molars and incisors from C57BL/6 mice were characterized using RT-PCR, Western blotting, dissecting and light microscopy, immunohistochemistry (IHC), transmission electron microscopy (TEM), scanning electron microscopy (SEM), backscattered SEM (bSEM), nanohardness testing, and X-ray diffraction. No amelogenin protein was detected by Western blot analyses of enamel extracts from Amelx -/- mice. Amelx -/- incisor enamel averaged 20.3±3.3 μm in thickness, or only 1/6th that of the wild-type (122.3±7.9 μm). Amelx -/- incisor enamel nanohardness was 1.6 Gpa, less than half that of wild-type enamel (3.6 Gpa). Amelx +/- incisors and molars showed vertical banding patterns unique to each tooth. IHC detected no amelogenin in Amelx -/- enamel and varied levels of amelogenin in Amelx +/- incisors, which correlated positively with enamel thickness, strongly supporting lyonization as the cause of the variations in enamel thickness. TEM analyses showed characteristic mineral ribbons in Amelx +/+ and Amelx -/- enamel extending from mineralized dentin collagen to the ameloblast. The Amelx -/- enamel ribbons were not well-separated by matrix and appeared to fuse together, forming plates. Furthermore, x-ray diffraction determined that the predominant mineral in Amelx -/- enamel is octacalcium phosphate (not calcium hydroxyapatite). Amelx -/- ameloblasts were similar to wild-type ameloblasts except no Tomes’ processes extended into the thin enamel. Amelx -/- and Amelx +/- molars both showed calcified nodules on their occlusal surfaces. Histology of D5 and D11 developing molars showed nodules forming during the maturation stage. Amelogenin forms a resorbable matrix that separates and supports, but does not shape early secretory stage enamel ribbons. Amelogenin may facilitate the conversion of enamel ribbons into hydroxyapatite by inhibiting the formation of octacalcium phosphate. Finally, amelogenin is necessary for thickening the enamel layer, which helps maintain ribbon organization and development and maintenance of the Tomes process.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Negative Selection Algorithm for Aircraft Fault Detection

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

Dasgupta, D.↗
DOE OSTI2016

Trends in atmospheric patterns conducive to seasonal precipitation and temperature extremes in California

Recent evidence suggests that changes in atmospheric circulation have altered the probability of extreme climate events in the Northern Hemisphere. We investigate northeastern Pacific atmospheric circulation patterns that have historically (1949–2015) been associated with cool-season (October-May) precipitation and temperature extremes in California. We identify changes in occurrence of atmospheric circulation patterns by measuring the similarity of the cool-season atmospheric configuration that occurred in each year of the 1949–2015 period with the configuration that occurred during each of the five driest, wettest, warmest, and coolest years. Our analysis detects statistically significant changes in the occurrence of atmospheric patterns associated with seasonal precipitation and temperature extremes. We also find a robust increase in the magnitude and subseasonal persistence of the cool-season West Coast ridge, resulting in an amplification of the background state. Changes in both seasonal mean and extreme event configurations appear to be caused by a combination of spatially nonuniform thermal expansion of the atmosphere and reinforcing trends in the pattern of sea level pressure. In particular, both thermal expansion and sea level pressure trends contribute to a notable increase in anomalous northeastern Pacific ridging patterns similar to that observed during the 2012–2015 California drought. Collectively, our empirical findings suggest that the frequency of atmospheric conditions like those during California’s most severely dry and hot years has increased in recent decades, but not necessarily at the expense of patterns associated with extremely wet years.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

An intelligent control system for failure detection and controller reconfiguration

We present an architecture of an intelligent restructurable control system to automatically detect failure of system components, assess its impact on system performance and safety, and reconfigure the controller for performance recovery. Fault detection is based on neural network associative memories and pattern classifiers, and is implemented using a multilayer feedforward network. Details of the fault detection network along with simulation results on health monitoring of a dc motor have been presented. Conceptual developments for fault assessment using an expert system and controller reconfiguration using a neural network are outlined.

Biswas, Saroj K.↗
NASA NTRS2004

Carrier Modulation Via Waveform Probability Density Function

Beyond the classic modes of carrier modulation by varying amplitude (AM), phase (PM), or frequency (FM), we extend the modulation domain of an analog carrier signal to include a class of general modulations which are distinguished by their probability density function histogram. Separate waveform states are easily created by varying the pdf of the transmitted waveform. Individual waveform states are assignable as proxies for digital ONEs or ZEROs. At the receiver, these states are easily detected by accumulating sampled waveform statistics and performing periodic pattern matching, correlation, or statistical filtering. No fundamental natural laws are broken in the detection process. We show how a typical modulation scheme would work in the digital domain and suggest how to build an analog version. We propose that clever variations of the modulating waveform (and thus the histogram) can provide simple steganographic encoding.

Williams, Glenn L.↗
NASA NTRS2006

Carrier Modulation Via Waveform Probability Density Function

Beyond the classic modes of carrier modulation by varying amplitude (AM), phase (PM), or frequency (FM), we extend the modulation domain of an analog carrier signal to include a class of general modulations which are distinguished by their probability density function histogram. Separate waveform states are easily created by varying the pdf of the transmitted waveform. Individual waveform states are assignable as proxies for digital one's or zero's. At the receiver, these states are easily detected by accumulating sampled waveform statistics and performing periodic pattern matching, correlation, or statistical filtering. No fundamental physical laws are broken in the detection process. We show how a typical modulation scheme would work in the digital domain and suggest how to build an analog version. We propose that clever variations of the modulating waveform (and thus the histogram) can provide simple steganographic encoding.

Williams, Glenn L.↗
NASA NTRS1990

Distributed proximity sensor system having embedded light emitters and detectors

A distributed proximity sensor system is provided with multiple photosensitive devices and light emitters embedded on the surface of a robot hand or other moving member in a geometric pattern. By distributing sensors and emitters capable of detecting distances and angles to points on the surface of an object from known points in the geometric pattern, information is obtained for achieving noncontacting shape and distance perception, i.e., for automatic determination of the object's shape, direction and distance, as well as the orientation of the object relative to the robot hand or other moving member.

Lee, Sukhan↗
DOE OSTI2017

AHR and CYP1A expression link historical contamination events to modern day developmental effects in the American alligator

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that initiates a transcriptional pathway responsible for the expression of CYP1A subfamily members, key to the metabolism of xenobiotic compounds. Toxic planar halogenated aromatic hydrocarbons, including dioxin and PCBs, are capable of activating the AHR, and while dioxin and PCB inputs into the environment have been dramatically curbed following strict regulatory efforts in the United States, they are still detected in the environment and exposures remain relevant today. Little is known regarding the effects that long-term chronic exposures to dioxin or dioxin-like compounds might have on the development and subsequent health of offspring from exposed individuals, nor is much known regarding AHR expression in reptilians. In this work, we characterize AHR and CYP1A gene expression in embryonic and juvenile specimen of a long-lived, apex predator, the American alligator (Alligator mississippiensis), and investigate variation in gene expression profiles in offspring collected from sites in the southeastern U.S. conveying differential exposures to environmental contaminants. Both age- and tissue-dependent patterning of AHR isoform expression was detected; receptor isoforms are highly expressed in liver, kidney, thyroid, and intestine. We then characterized two downstream transcriptional targets of the AHR, CYP1A1 and CYP1A2, and describe conserved elements of their genomic architecture. When comparisons across different sites were made, hepatic expression of CYP1A2, a direct target of the AHR, was elevated in embryos from a site associated with a dioxin point source and previously characterized PCB contamination. These effects were not persistent, as site-specific variation was absent in juveniles originating from field-collected eggs but reared under lab conditions. Our results illustrate the patterning of AHR gene expression in a long-lived environmental model species, and indicate a potential contemporary influence of historical contamination. This research presents a novel opportunity to link historical contamination events to contemporary effects on critical genetic pathways during embryonic development, and carries significant potential to inform our understanding of potential health effects in wildlife and humans.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS2018

Fast, Large Area, Wide Band GAP UV Photodetector for Cherenkov Light Detection

A solid-state radiation detector includes a pattern of conductive metal on a zinc oxide substrate. The pattern provides an arrangement of interdigitated electrode fingers. The wide band gap substrate is sensitive to UV light, which can cause a conductivity change in the substrate. The electrode fingers are configured to sense a difference in substrate conductivity resulting from UV light. The detector has a fast response time, which enables it to detect Cherenkov light. The compact detector provides savings on size, weight, and required power. Thus, the solid-state detector can be used to replace photomultiplier tubes in Cherenkov detectors. The features of the novel detector allow for an improved system to detect and monitor UV radiation, especially during deep space missions.

Wrbanek, John D.↗
NASA NTRS1983

What does the eye see best?

The quantum efficiency of the human eye is measured for detecting a wide variety of spatiotemporal patterns using foveal vision in bright light. The performance of an ideal visual detector whose weighting function is matched to the signal is analyzed in the abstract, and the conditions of a two-interval forced-choice experiment are described. The best stimulus found is a small, briefly exposed circular patch of sinusoidal grating having a spatial frequency of about 7 c/deg, drifting at about 4 Hz. It is proposed that this is the weighting function of the most efficient human contrast detector. This function resembles the receptive field sensitivity profiles of many cortical neurons. The detectors resemble those derived from other recent experiments on both detection and discrimination and may account for a broad range of previous psychophysical results.

Watson, A. B.↗
DOE OSTI2015

Using spatial statistical methods to determine an effective minimum detectable activity - 15681

Instrument minimum detectable activity (MDA) is generally established by collecting multiple measurements over time of a single representative background location to estimate a probability distribution of instrument readings. A reading that is considered to be greater than would be expected from the background probability distribution (i.e., a low-probability event) is deemed to be sufficiently unlikely that it must be attributable to added radioactivity and is therefore used as the instrument MDA. An effective MDA can be determined from spatially distributed data that are much lower than the instrument MDA. This has been accomplished using a large number of spatially distributed data points (from a radiation survey) where patterns of elevated survey readings can be distinguished from background at levels. Given the variation of the instrument response and the relatively low regulatory action levels, it is vital to establish MDA thresholds that are well below corrective action levels. Processing of radiological survey data with the use of histogram binning and geo-statistics in enabling pattern recognition can be used to establish effective field MDAs that are much lower than instrument MDAs. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

High-dimensional methods for quantum homodyne tomography

We provide optimized recursion relations for homodyne tomography. We improve previous methods by mitigating the divergences intrinsic in the calculation of the pattern functions used previously, and detail how to implement the data analysis through Monte Carlo simulations. Our refinements are necessary for the reconstruction of excited quantum states which populate a high-dimensional subspace of the electromagnetic field Hilbert space. •Stabilization of numerics for quantum homodyne tomography reconstructions.•High dimensional quantum homodyne tomography reconstruction.•Monte Carlo simulations of quantum homodyne tomography.•Quantum Homodyne tomography simulations.

Mosco, Nicola↗