rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Supporting Early Mission Concept Evaluation through Natural Language Processing
  name:
    name part: Simpson, Benjamin Cade
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Selva, Daniel
    role:
      role term: thesis advisor.
  name:
    name part: Texas A&M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Proposal evaluation of pre-Phase A mission concepts is largely based on the input from subject matter experts who determine the scientific merit of a mission concept based on a number of criteria including: the relevance of the mission objectives to national and international priorities; the existence of a complete set of measurement, instrument, and platform requirements that are traceable to the mission objectives; and several others. The Science Traceability Matrix is a standard tool used to articulate this relevance and traceability and therefore is a key input to this reviewing process. However, inconsistencies in the structure and vocabulary used in the Science Traceability Matrix and other sections of the proposal across organizations make this process challenging and time-consuming. At the same time, as part of the Digital Engineering revolution, NASA and other space organizations are starting to embrace key concepts of model-based systems engineering and understand the value of moving from unstructured text documents to more formal knowledge representations that are amenable to automated data processing. In this line, this thesis leverages transformer models, a recent advance in natural language processing, to demonstrate automatic extraction of science relevance and traceability information from unstructured mission concept proposals. By doing so, this work helps pave the way for future applications of natural language processing to support other systems engineering practices within mission/program development such as automated parsing of design documentation. The proposed tool, called AstroNLP, is evaluated with a case study based on the Astrophysics Decadal Survey. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/197266
  note: by Benjamin Simpson.
  note: "Major Subject: Aerospace Engineering"
  note: Includes vita.
  note: Master of Science Texas A&M University 2022 https://hdl.handle.net/1969.1/197266
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major Aerospace Engineering
  subject:
    topic: Natural Language Processing
  subject:
    topic: Systems Engineering
  subject:
    topic: Astronomy
  identifier: https://hdl.handle.net/1969.1/197266
  record info:
    record content source: TXA
    record creation date: 240819
    record change date: 20240820145859.5
    record identifier: in00005463628
    language of cataloging:
      language term: eng