Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “learning from home”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Home Page: The Mode of Transport through the Information Superhighway

The purpose of the project with the Aeroacoustics Branch was to create and submit a home page for the internet about branch information. In order to do this, one must also become familiar with the way that the internet operates. Learning HyperText Markup Language (HTML), and the ability to create a document using this language was the final objective in order to place a home page on the internet (World Wide Web). A manual of instructions regarding maintenance of the home page, and how to keep it up to date was also necessary in order to provide branch members with the opportunity to make any pertinent changes.

Lujan, Michelle R.↗
NASA NTRS2003

Learning New Techniques for Remediation of Contaminated Sites

The project emphasizes NASA's Missions of understanding and protecting our home planet as well as of inspiring the next generation of explorers. The project fellow worked as part of a team on the development of new emulsion-based technologies for the removal of Contaminants from soil, sediment, and groundwater media with the scientists in charge of the emulsion-based technologies. Hands-on chemistry formulation and analyses using a GCM, as well as field sampling was done. The fellow was tidy immersed in lab and fieldwork, as well as, training sessions to qualify her to do the required work. The principal outcome of the project is the motivation to create collaboration links between major research university (UCF) and an emerging research university (UT).

Lipsett-Ruiz, Teresa↗
DOE OSTI2023

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
DOE OSTI2021

Consumer Guide to Small Wind Energy Systems

Learn how to use a small wind energy system to produce electricity to power your home. This fact sheet from Energy Saver includes information on how small wind energy systems can work for homes and how to determine whether your site is a good candidate for a small wind turbine.

small wind energy system, wind turbine, Energy Sav↗
DOE OSTI2021

College-bound cowboys: The Los Alamos Ranch School Before the wartime lab, Los Alamos was home to a boarding school

Summer’s over and the school year’s soon to start. What’s a kid to do? Go West, young man! Escape the city that is “making you soft” and learn self-reliance from the western U.S. of A. – or so advertised the ranch school movement in America from roughly 1900-1960. This educational movement included Los Alamos. Before being developed into a wartime lab to create atomic weapons, the mesa was home to one of the most prominent and pricey ranch schools in America.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Consumer Guide to Cool Roofs

Learn how cool roofs can save you energy and money while improving the comfort of your home. This fact sheet from Energy Saver includes information on the benefits of cool roofs, how cool roofs work and can save energy and money, and how to select and buy cool roofing materials.

cool roofs, roofing materials, Energy Saver↗
DOE OSTI2021

Machine learning-based analysis of COVID-19 pandemic impact on US research networks

Here in this study we explore how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-at-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Solar Pathways in Federal Energy Assistance Programs: Expanding Low Income Home Energy Assistance Program (LIHEAP) and Weatherization Assistance Program (WAP)

How can solar best fit within your LIHEAP or WAP activities? Come join NREL and learn about the various pathways and new resources available to help implement solar in low-income programs. Panelists will share results from a multi-year research project, including survey results on LIHEAP and WAP solar adoption across the United States. This session will highlight case studies from early implementers, key lessons learned and resources developed based on stakeholder feedback for interested organizations. Attendees can expect gain a better understanding of the perceived barriers and opportunities to solar implementation, including the importance of partner coordination and complementary funding sources, and the next steps for how to get started. Additionally, attendees will hear from a local implementer of solar in WAP about their program and process.

Colorado↗
DOE OSTI2023

Energy Data from Heat Pumps Installed in Juneau, AK

Heat pumps offer a great low carbon emission method to heat homes. Thermalize Juneau 2021 was a clean energy campaign that helped homeowners in Juneau, Alaska install heat pumps into their homes. Juneau is located near the climactic limit of many heat pumps, and we were interested in how well the heat pumps can function in cold climates. This was done by using Sense meters to remotely monitor the energy usage of 10 homes with heat pumps. The Sense meters are small devices that connect to the electrical panel and monitor the energy use of multiple appliances through machine learning. First, we recorded the energy usage of a heat pump in the lab using both the Sense meter and the existing lab datalogger. Both recorded very similar energy usage data which confirmed that the Sense meter can accurately measure the fluctuations in the heat pump energy use. Next, we looked at the energy data from the Sense meters in the homes with heat pumps and paired it with local weather data to see how well the heat pumps functioned in the cold weather.

Alaska↗
DOE OSTI2023

AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience

The U.S. Department of Energy (DOE) has launched a Connected Communities program that supports projects that expand DOE's network of grid-interactive, efficient building communities nationwide. As an early pilot of Connected Communities, the National Renewable Energy Laboratory (NREL) and its partners have developed and demonstrated a community-scale, hierarchical control solution to address the potential grid issues arising from high penetration of solar photovoltaics (PV) as well as to improve grid reliability and resilience in this residential community. A field pilot study has been performed at an affordable housing development called the Basalt Vista Community, which was built for school teachers and other professionals in the local workforce and represents an autonomous energy grid with all-electric, energy-efficient homes. With no natural gas line in the community, this is the first all-electric net-zero community in rural Colorado. In the field pilot study, NREL has validated the performance of the hierarchical control solution, which consists of NREL's foresee home energy management systems (HEMS) and community aggregators, in increasing demand flexibility and self-consuming PV, reducing potential over-voltages, and supporting critical loads during emergency events. In this presentation, we will present the methodology, simulation and field pilot results, and lessons learned from the project.

all-electric community↗
NASA NTRS1994

Learning random networks for compression of still and moving images

Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

Gelenbe, Erol↗
NASA NTRSDate not supplied

A Newly Developing Community-Oriented Data System from NASA GES DISC

Data services are essential to facilitate data access and to aid efficiency of conducting research and application activities. With emerging technologies such as cloud computing and AI/ML (Artificial Intelligence/Machine Learning) leading the pace of the data world, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), home to the permanent archive for multidisciplinary Earth Observation (EO) geospatial data to study atmospheric composition, weather and climate variability, and water and energy cycles is no exception.Interfacing directly with users as part of data center work, we understand the challenges for the required time and effort to discover, visualize, and analyze large varieties and quantities of Earth Observation information for research, monitoring, and decision-making, largely due to the existing data and information systems aim to support experienced users, but has been proved difficult for non-earth scientists and new users that are unfamiliar with the variety of formats and structures in which data, metadata, and information are stored, as well as the required methods to use them. To address these challenges, I will update our latest activities with regard to water-and energy-related products and community-oriented and user-friendly services at the GES DISC, including our plans for the emerging technologies.

Jennifer Wei↗
NASA NTRS2018

Adventures in Low Disc Loading VTOL Design

This memoir covers the first eight years of my 37 year career in VTOL aircraft design. It starts with family and how I came to be an engineer with a passion for aviation and a desire to make a difference. At MIT I acquired a solid understanding of basic physics, learned the basics of the various engineering disciplines and gained design experience. After over a decade on the East Coast I was homesick for Northern California. I decided to take a chance on working for the government instead of industry in order to return home. I was hired by Dr. Richard M. Carlson in March 1975 and joined a wonderful Army/NASA technical environment. The Interservice Helicopter Commonality Study was an important introduction to Joint Service aircraft design. The Advanced Attack Helicopter Source Selection Evaluation Board was an opportunity to learn acquisition system fundamentals and to lead a small team in a major technical evaluation. The Advanced Scout Helicopter Concept Formulation was an opportunity to learn how an aircraft development program is created and it formed a partnership between Dr. Carlson's Labs and Charlie Crawford's Development and Qualification directorate. The Army was Executive Service for the first year (1982) of the Joint Services Advanced Vertical Lift Aircraft (JVX) program. The JVX Joint Technology Assessment concluded that there was at least one design configuration, the tilt rotor, which could satisfy all JVX mission requirements with a high degree of inter-service commonality. The Navy became Executive Service at the end of the year and promptly released a JVX RFP to industry. This RFP resulted in the V-22 Osprey tilt rotor as the third type of VTOL aircraft to enter production and service. I was very lucky to have a useful role early in this program.

VTOL Design↗
DOE OSTI2024

Achieving Cooperative Community Equitable Solar Sources (ACCESS) (Final Technical Report)

Since 2011, solar has grown from a niche technology to a widely accessible source of power for homes and businesses across the United States and has become a fundamental part of the modern grid. There are still challenges, however, in learning how to integrate and use PV most effectively and how to make PV universally available. Most low- and moderate-income (LMI) customers cannot currently afford PV; capital costs and financing costs are too high to drive significant penetration. Providing access to LMI individuals and communities is a critical and immediate priority, and the focus of this project. The overall objective of the Achieving Cooperative Community Equitable Solar Sources (ACCESS) project is to explore and amplify the use of innovative, cost-effective energy access programs to serve co-ops’ LMI members1. ACCESS will research at least three financing mechanisms and at least six LMI program designs including LMI engagement strategies to maximize participation for these hard-to-reach audiences. ACCESS evaluated the financial mechanisms and program designs from field tests sited at diverse co-ops that provide recognizable models for the broader co-op community to identify optimal solutions for small utilities. The research with these cooperatives allowed testing of concepts and development of models and tools for other utilities to adapt to their own program designs and expansions. ACCESS published results and developed an “ACCESS Solar Access Toolkit” consisting of program designs, LMI engagement strategies, how-to guidance, and other tools to facilitate replication at small utilities across the country. Through the dissemination practices of the ACCESS project team, all NRECA member co-ops (~900) were made aware of the “ACCESS Solar Access Toolkit” and all other ACCESS resources. NRECA and its partners developed innovations to expand co-ops’ solar energy offerings to provide all of a co-op’s members—especially those who struggle to pay their bills—with cost-effective options that meet their needs. ACCESS specifically explored utility financing mechanisms and program designs that, independently or used in combination, increase solar access for rural electric cooperatives’ LMI members/ratepayers and that reduce LMI member/ratepayers’ electricity costs by at least 10%. LMI engagement strategies focused on maximizing the number of members who receive benefits and on the cost savings to LMI participants.

14 SOLAR ENERGY↗
DOE OSTI2021

Universal image representation based on a multimodal graph

A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.

Bremer, Peer-Timo↗
DOE OSTI2023

Universal image representation based on a multimodal graph

A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.

Bremer, Peer-Timo↗