{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1137"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1137","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Using machine learning methods to aid scientists in laboratory environments","abstract":"As machine learning gains popularity as a scientific instrument, we look to create methods to implement it as a laboratory tool for researchers. In the first of two projects, we discuss creating a real-time interference monitor for use at a radio observatory. We show how deep neural networks can be used to assist with the detection of radio-frequency interference around the site, and consider methods of unsupervised learning to identify patterns in the detections. In the second project, we show how a reinforcement learning agent can build an internal hypothesis of its environment, using experience from past measurements, that it can then act on. We demonstrate how our newly developed method can be used to learn the dynamics of physics-based models and exploit the knowledge gained to achieve a given objective with measurable confidence. We also demonstrate how the agent's behaviour changes when the frequency of certain measurements is limited.","abstract_html":"As machine learning gains popularity as a scientific instrument, we look to create methods to implement it as a laboratory tool for researchers. In the first of two projects, we discuss creating a real-time interference monitor for use at a radio observatory. We show how deep neural networks can be used to assist with the detection of radio-frequency interference around the site, and consider methods of unsupervised learning to identify patterns in the detections. In the second project, we show how a reinforcement learning agent can build an internal hypothesis of its environment, using experience from past measurements, that it can then act on. We demonstrate how our newly developed method can be used to learn the dynamics of physics-based models and exploit the knowledge gained to achieve a given objective with measurable confidence. 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