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MY SPACE INDUSTRY EXPERIENCE

MY SPACE INDUSTRY EXPERIENCE. Kamara Brown The George Washington University School of Engineering and Applied Science Department of Electrical Engineering. An Assessment of System Engineering Conceptual Design Laboratory The John Hopkins University/Applied Physics Laboratory (JHU/APL)

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MY SPACE INDUSTRY EXPERIENCE

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  1. MY SPACE INDUSTRY EXPERIENCE Kamara Brown The George Washington University School of Engineering and Applied Science Department of Electrical Engineering

  2. An Assessment of System Engineering Conceptual Design Laboratory The John Hopkins University/Applied Physics Laboratory (JHU/APL) Space Department, Space Systems Applications, Mission and Space Systems Engineering Kamara Brown 1, James Leary 2, Richard Anderson 3 1 Research Associate, George Washington University 2 Principal Investigator, Section Supervisor Space System Engineer , JHU/APL 3 Co-Principal Investigator, Space System Engineer, JHU/APL Background Research Problem Statement • As APL moves towards planning deep space missions, there is a need for improving their practices for conceptual spacecraft design. The SE Lab is being developed to better position APL in collaborating more effectively with NASA centers as well as other potential partners who have created similar design centers as an standard approach to conducting business. The SE Lab is being developed to better position APL in collaborating more effectively with NASA centers as well as other potential partners who have created similar design centers as an standard approach to conducting business. Defining the System Engineering Laboratory • What is the SE Lab? • Collaboration conceptual tool • Enhance existing system engineering and system • Architecture processes • Characteristics / Intent of the SE Laboratory • Tool to assist and increase quality of proposals • Mission Concept Studies, System Test Planning • Key Spacecraft Mission Question: • How do the different Spacecraft Subsystems interact to perform one or more missions?

  3. Data Analysis Developed Flow Chart for Subsystem to model in Phoenix Integration

  4. Relationship Models Developed Subsystem Relationship Models to identify Top Level Mission Needs

  5. Software Analysis Spearhead a study on Phoenix Integration to identify whether it will serve the SE Lab in developing Deep Space missions studies and prototypes.

  6. An Assessment of System Engineering Conceptual Design Laboratory Results / Recommendations Phoenix Integration • Need standardized forms from spacecraft subsystem leads in order to have a super decision-making system • File Wrapping Tools present more reusability for the scripts between Different system models Future Phases • Further investigation on defining requirements and constraints • Validate against additional software toolsets that develop comprehensive models • Research cost trends and estimating relationships • Developing cost estimating methods and models Acknowledgments The John Hopkins University Applied Physics Laboratory James Leary, Richard Anderson Dr. Ralph McNutt Grant Tregre, Patrick Hill Andre Smith, Donna Williams David Artis, Richard Conde Special Thanks to: Dave Rosage Joseph Dolan Linda Butler Dipak Srinivasan Karen Kirby SEA-3 Mission and Space System Engineering Group 2006 NASA / APL Research Associates

  7. An Assessment of Artificial Intelligence Technologies for Vehicle Management Systems Spacecraft and Vehicles Systems Department, Advanced Sensors & Health Management Systems, Code EV23 Kamara Brown 1 , Dr. Mike Watson 2 , Dr. Luis Trevino 3 1 Research Associate, George Washington University 2 Principal Investigator, Branch Chief, NASA MSFC 3 Principal Investigator, Artificial Intelligence Scientist, NASA MSFC Research Problem Statement Results Data Analysis As NASA moves towards planning deep space missions, there is a need for examining applications utilizing autonomous systems and AI technologies. This will allow space vehicle systems that can make decisions on its on. The intelligent system must dynamically select the “optimum” configurations for supporting such critical subsystems like crew environment, electrical power systems, propulsion systems. Research Objective • To spearhead a study on how AI Techniques can create intelligent (decision-making) space vehicle systems • To analyze and determine the most prominent techniques Recommendations Acknowledgments • Bayesian Belief Network • Appears to be the most prominent for space applications • Why? • Allows flexible • Does not need any previous knowledge (very user friendly) • Graphical representation with strong mathematical foundation NASA Marshall Space Flight Center Dr. Mike Watson, Dr. Luis Trevino, Dr. Deidre Paris Dr. Katherine Chavis John Wiley, Amanda Duffell, Valentin Korman Linda Brewster, Ricky Howard Special Thanks to: Dr. Frank Six Dr. Gerry Karr Dr. Ruth Jones Jessica Culler, Omar Mireles NASA Academy Staff 2005 NASA Academy Research Associates

  8. NAAA, NASA, Committees Quality Assurance Collaborations Giving Back Visiting NASA Academies Newsletters Strategic Planning Meetings Projects

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