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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗
DOE OSTI2019

Workspace and Office Design: A Review of the Literature

The Systems Analysis & Decision Support (02150) group completed a review of the research literature on workspace and office design in 2016 for the Asset Management Department (04853). The goal was to characterize results and lessons learned from existing research to understand the effectiveness of current workspaces at Sandia National Laboratories and inform guidance for future workspace design. The study team reviewed 96 documents, published primarily since the year 2000, covering a range of factors associated with workspace design - workspace costs, acoustics, collaboration and privacy, generational preferences, employee health, performance and productivity, organizational retention, and workspace satisfaction. The research literature consistently highlighted the relative deficiencies of open-plan office spaces as compared to traditional private enclosed offices for knowledge workers. While open-plan offices can provide some cost savings, they may not be cost effective in the long term due to future hidden costs incurred by degradations in employee productivity, increased attrition, and increased sickness absences as well as any post-construction modifications needed to resolve emerging workspace issues. The chief deficiencies of open-plan offices include lower levels of employee satisfaction due to reduced visual and auditory privacy, increased interruptions, distractions from irrelevant background speech, less physical space, and more ambient noise. The drawbacks reported in the literature tend to outweigh any benefits associated with potential facilitation of coworker interactions and collaboration. Key suggestions identified in the literature to guide and optimize workspace and office design are provided.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

International ACM Symposium on High Performance Parallel and Distributed Computing Conference for 2017, 2018, 2019, and 2020

The 28th ACM HPDC Conference was held in Phoenix, Arizona, June 24 and 28, 2019 (hpdc.org/2019), that was colocated with ACM FCRC 2019 (fcrc.acm.org). During the conference, Prof. Geoffrey Fox, Indiana University, was given the HPDC Achievement Award for 2019. Prof gave a keynote speech entitled “Perspectives on High-Performance Computing in a Big Data World. In addition, to the keynote speakers from HPDC and FCRC conferences, the conference organized successfully five workshops and one Ph.D. forum. The ACM FCRC had a total of 2700 attendees, and HPDC had a total of 120 attendees that included 32 students. We have used the DOE sponsorship to support the conference proceedings that acknowledge the DoE support and partially supported the travel to the HPDC PC meeting, Keynote speaker accommodation, best papers, presentation and poster award.

42 ENGINEERING↗
DOE OSTI2022

Power System Waveform Classification Using Time-Frequency and CNN

Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Los Alamos National Laboratory Director's Strategic Resilience Initiative

The Director’s Strategic Resilience Initiative was created late in 2019 in recognition of the changing geo-political climate. The renewed strategic competition between Russia, China, and the United States, noted in the 2018 National Defense Strategy and the 2018 Nuclear Posture Review, put great power rivalry and competition at the forefront of national security considerations. At the same time, Russian President Vladimir Putin’s March 1, 2018, speech and China’s October 1, 2019, National Day Military Parade, underscored the nuclear dimensions of this new strategic competition. Accompanying this changed strategic environment are rapid advances in an expansive array of relevant technological sectors including affordable and comprehensive global communication and reconnaissance assets, autonomous vehicles, quantum computing, advanced manufacturing, and artificial intelligence.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Universal Fourier Attack for Time Series

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Characterization of Peripheral Neurophotonic Systems for High-Performance Human-Computer Interfaces (CRADA Final Report)

As part of the Cyclotron Road program, Morphosis Inc. sought to investigate a non-invasive neuromuscular sensing approach for use as an intuitive and secure human–computer interface. These highly miniaturized, wearable neural interfaces were completely non-invasive and maintained stable, high-bandwidth, long-term access to a user’s actions, intent, and identity, while offering an exceptionally high signal-to-noise ratio compared to contemporary neural recording technologies. The widespread adoption of neural interfaces had the potential to reshape how people interact with technology, with profound societal impacts. Millions worldwide suffered from movement and/or speech disabilities, and these tools had the potential to democratize access to technology to enhance autonomy and quality of life. More broadly, interfaces capable of accurately conveying intentions and safeguarding identities could serve as a cornerstone for privacy, trust, and personal authenticity in digital environments. The use of thought-driven control of digitally enabled devices and governance of digital identities had the potential to revolutionize relationships with technology, transforming how people learn, communicate, and interact with the world.

42 ENGINEERING↗
DOE OSTI2025

AI foundation models for experimental fusion tasks

Artificial Intelligence (AI) foundation models, while successful in various domains of language, speech, and vision, have not been adopted in production for fusion energy experiments. This brief paper presents how AI foundation models can be used for fusion energy diagnostics, enabling, for example, visual automated logbooks to provide greater insights into chains of plasma events in a discharge, in time for between-shot analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

The effect of simultaneous exposure on the attention selection and integration of segments and lexical tones by Urdu-Cantonese bilingual speakers

In the perceptual learning of lexical tones, an automatic and robust attention-to-phonology system enables native tonal listeners to adapt to acoustically non-optimal speech, such as phonetic conflicts in daily communications. Previous tone research reveals that non-native listeners who do not linguistically employ lexical tones in their mother tongue may find it challenging to attend to the tonal dimension or integrate it with the segmental features. However, it is unknown whether the attentional interference initially caused by a maternal attentional system would continue influencing the non-optimal tone perception for simultaneous bilingual teenagers. From an endpoint in the age of language acquisition, we investigate whether the tone-specific attention mechanism developed by the Urdu-Cantonese simultaneous bilinguals is automatic enough to assist them in adapting to a phonetically-conflicting environment. Three groups of teenagers engaged in a four-condition ABX task: Urdu-Cantonese simultaneous bilinguals, Cantonese native listeners, and Urdu-speaking, late learners of Cantonese. The results showed that although the simultaneous bilinguals could phonologically process Cantonese tones in a Cantonese-like way under a conflict-free listening condition, they still failed in adapting to the phonetic conflicts, especially the segment-induced ones. It thus demonstrated that the simultaneous exposure and years of regular education in Hong Kong local schools still could not automatically guarantee simultaneous bilingual processing of Cantonese tones. In interpreting the findings, it hypothesized that, except for simultaneous exposure, the development of a tone-specific attention mechanism is also likely to be L1-inhibitory, tone experience-driven, and language-specific for simultaneous bilinguals.

Ning, Jinghong↗
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗
NASA NTRS1965

Audio problems in space.

Audio signal processing techniques in future space exploration, discussing channel capacity, speech processing, bandwidth narrowing, etc

SPACE EXPLORATION↗
NASA NTRS1967

Analog voicing detector responds to pitch

Modified electronic voice encoder /Vocoder/ includes an independent analog mode of operation in addition to the conventional digital mode. The Vocoder is a bandwidth compression equipment that permits voice transmission over channels, having only a fraction of the bandwidth required for conventional telephone-quality speech transmission.

Abel, R. S.↗