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Elliott, Shiloh N

Publications and source records attributed to Elliott, Shiloh N.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Review on the State of the Art of Machine Learning and Satellite Imaging: Detecting Scene Changes in Selected Nuclear Fuel Cycle Datasets

The timely detection of clandestine nuclear facilities is one of the greatest challenges faced by the International Atomic Energy Agency’s (IAEA). Idaho National Laboratory is currently applying machine learning (ML) to existing satellite imagery (SI) datasets to find facilities within the nuclear fuel life cycle, with primary focus placed on identifying critical predecessor (i.e., fuel fabrication and fuel enrichment) and successor (i.e., nuclear power plants) facilities. This could provide a satellite image methodology that the IAEA could leverage to discover clandestine facilities. The work presented in this paper describes the evolution of a workflow developed by this team for object detection related to critical infrastructure by expanding that workflow for the purpose of identifying nuclear fuel cycle components and automating dependency assessments. This will be done using two methods housed within a single pipeline. The first method involves implementing a DenseNet161 convolutional neural network to classify the images and explain the results using Local Interpretable Model-Agnostic Explanations (LIME). The second method implements You Only Look Once version 5 (YoloV5), to detect objects within images, provide a probability for the detection, and provide a bounding box that corresponds to the object of interest. The results of this work are anticipated to provide a clear picture of this portion of the nuclear fuel cycle and perform as a stand-alone tool for image assessment that can be expanded to additional fuel cycle components and implemented in international safeguards and national security domains. This capability addresses the IAEA’s need to detect undeclared nuclear materials and activities within a state while encompassing the entire nuclear fuel cycle.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Assessment Framework of Marine Hydrokinetic Technologies for Microgrid Applications

Communities in Alaska that are not connected to a regional grid (referred to hereafter as grid-islanded Alaskan communities) rely heavily on stand-alone generators with imported diesel fuel as the primary source of energy. Several of these grid-islanded Alaskan communities have the potential to harness significant wave, tidal, and hydrokinetic power; many also have hydropower or wind potential that could complement these resources. The implementation of marine and hydrokinetic energy focused microgrids in these communities would diversify their energy profiles, with the potential in many communities to keep costs flat while reducing dependance on diesel. This would enhance resilience and reduce environmental impacts. This seedling proposal will systematically identify grid-islanded coastal communities in Alaska with wave, tidal, and hydrokinetic energy potential to add fuel diversity to generation, applying microgrid integration methods and strategies that will meet the community’s needs. This proposal is in partnership with the Alaska Center for Energy and Power (ACEP) and XENDEE Corporation who bring extensive expertise in Alaskan communities and microgrid technoeconomic assessment, respectively. This project will deliver: (1) a database of grid-islanded communities that are candidates for MHK based microgrids, (2) an assessment of possible microgrid integration methods and strategies related to tidal, wave, and hydrokinetic technologies, and (3) a MHK development plan for the XENDEE Microgrid design platform and planning tool that can serve these grid-islanded communities and project developers. In the final stage, the planning tool will allow for the comprehensive comparison between diesel based and MHK based renewable generation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Developing a Decision Support Engine to Enable Irrigation Modernization - Poster

Irrigation systems in the United States are some of the oldest continually utilized infrastructure in existence today, with some systems exceeding 100 years in age. They are operated to meet farming demands but are managed through a balance of varying influence: policies limiting water usage, stakeholder interests, and environmental impacts. Irrigation modernization is defined as a set of activities that update and improve existing irrigation systems, including, but not limited to improving water quantity, development of distributed energy resources for surrounding communities, ecosystem services, and improved agricultural yields. Modernizing an existing irrigation system can enable stakeholders to combat changing environmental and population demands but is difficult because the complexity involved in determining the potential benefits and consequences of irrigation modernization is high. We are combining a large amount of various geospatial, tabular, and temporal data with subject matter expertise into a decision support engine that will enable stakeholders to determine the benefits and consequences of irrigation modernization in their irrigation systems. A web-based GIS will allow the user to construct the modifications out of a palette of modernization options, which will be sent to the analytics engine for computations, and back to the web client for a graphical display and comparison of relevant metrics. Our development process involves four phases: 1) identify mechanisms of modernization, 2) identify data requirements, data streams, first principles and applicable algorithms necessary to quantify modernization mechanisms, 3) create ‘modules’ for each modernization mechanism, these modules will form the decision support engine, each capable of performing independently but can also inform other modules when needed, 4) Merge the decision support engine with a user interface, capable of ingesting user inputs and returning insights into the impacts of a modernization project as they relate to economic, environmental, monetary, and energy generation. Once complete, it is our intention that this tool will be fundamental in irrigation modernization projects, providing a strong analytical basis from which stakeholders can quickly make informed decisions regarding project development.

13 HYDRO ENERGY↗