Genetic Tradespace Exploration for Team Generation
June 2024-November 2024
This research primarily revolved around the creation and improvement of a MATLAB application that leveraged tradespace exploration for the formation of teams for a Mechanical Engineering Senior Project course. In laymen’s terms we used the creation of several generations of solutions, or team combinations assigned to different projects, and imitated the process of natural selection to propagate more successful solutions through the generations to ultimately result in better and better solutions. A better solution was defined by the level of satisfaction that all members of a team have with the members that they are partnered with as well as the project they are assigned to. After performing this calculation for a number of generations, we then displayed all the solutions in the final generation for a human user to interact with and ultimately select which solution they found the most appealing performance wise.
Writeup on this research (soon to come)
Containerization and Improvement of the VISION Application
December 2024-Present
The VISION (or Visual Interaction tool for Seeking Inspiration based on Nonnegative Matrix Factorization) application seeks to do exactly what it’s name implies, provide inspiration for innovators and creators via NMF performed on a number of relevant, keyword based relationships between relevant engineering patent data. As of writing this I have done little work on this project more than beginning to understand what exactly NMF is and how the current system works to begin porting over said system to divorce it’s functionality from MATLAB and make it portable to allow for it’s integration with a web-based framework in the near future. As such, I defer to the paper written by my advising Professor Dr. Song linked here at the moment regarding its present state of functionality.