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dc.contributor.advisorDykema, John
dc.contributor.advisorKeith, David
dc.contributor.authorVietri, Riccardo Paolo
dc.date.accessioned2019-11-26T16:05:49Z
dc.date.submitted2019-04
dc.identifier.citationVietri, Riccardo Paolo. 2019. Modular Composite Outrigger for Stratospheric Balloon Experiment. Bachelor's thesis, Harvard College.
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41940966*
dc.description.abstractCurrently, most flavor development is performed by chemists who experimentally iterate many times to find flavors that best fit specified requirements. In this study, three potential algorithms for automating and accelerating this process are examined and implemented, and the results are analyzed. The apriori algorithm, which generates probabilistic association rules, fails because it only predicts the coexistence of high-frequency features. The generative adversarial network (GAN) fails because the generator only exploits a small part of the solution space. However, the variational autoencoder (VAE) successfully recovers purposely omitted features 95.6% of the time, making it the most promising algorithm.en_US
dc.language.isoen_USen_US
dash.licenseLAA
dc.titleModular Composite Outrigger for Stratospheric Balloon Experimenten_US
dc.typeThesis or Dissertationen_US
dc.date.available2019-11-26T16:05:49Z
thesis.degree.date2019en_US
thesis.degree.disciplineMechanical Engineeringen_US
thesis.degree.grantorHarvard Collegeen_US
thesis.degree.levelUNDERGRADUATEen_US
thesis.degree.nameBachelor of Scienceen_US


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