{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108051"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108051","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Developing a disturbance source characterization technique for small satellite applications","abstract":"In this thesis we establish a framework with which to characterize candidate sources of disturbance for small satellite applications. By characterize we mean estimate disturbance source vibrational frequencies, and by candidate sources we mean sources previously determined with the ability to induce micro-vibrations. This framework centers on the operation of distributed sensors, and we present a set of components capable of performing a characterization effort of this nature. Our implementation of supervised learning enables us to predict actuator operational frequency values based on accelerometer readings. The standardized mean squared error (SMSE), a measure of error between the mean prediction and the true value, important for quantifying prediction performance, is shown to be a function of the Fourier transformation type used; and we conclude which considered Fourier transformation results in the lowest prediction errors. Furthermore, we analyze how different dataset sizes and sensor-actuator pairings affect the frequency predictions.","abstract_html":"In this thesis we establish a framework with which to characterize candidate sources of disturbance for small satellite applications. By characterize we mean estimate disturbance source vibrational frequencies, and by candidate sources we mean sources previously determined with the ability to induce micro-vibrations. This framework centers on the operation of distributed sensors, and we present a set of components capable of performing a characterization effort of this nature. Our implementation of supervised learning enables us to predict actuator operational frequency values based on accelerometer readings. The standardized mean squared error (SMSE), a measure of error between the mean prediction and the true value, important for quantifying prediction performance, is shown to be a function of the Fourier transformation type used; and we conclude which considered Fourier transformation results in the lowest prediction errors. Furthermore, we analyze how different dataset sizes and sensor-actuator pairings affect the frequency predictions.","abstract_has_math":false,"creators":["Augustyniak, Adam"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Bretl, Timothy W"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:58:06Z","date_published":"2020-08-26T21:58:06Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Small Satellite","Disturbance Source","Distributed Sensing","Supervised Learning"],"languages":["en"],"rights":["Copyright 2020 Adam Augustyniak"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108051","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bretl, Timothy W"]},{"key":"dc:creator","label":"Author","values":["Augustyniak, Adam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:58:06Z","2020-05-12","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Small Satellite","Disturbance Source","Distributed Sensing","Supervised Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Adam Augustyniak"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108051"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis we establish a framework with which to characterize candidate sources of disturbance for small satellite applications. By characterize we mean estimate disturbance source vibrational frequencies, and by candidate sources we mean sources previously determined with the ability to induce micro-vibrations. This framework centers on the operation of distributed sensors, and we present a set of components capable of performing a characterization effort of this nature. Our implementation of supervised learning enables us to predict actuator operational frequency values based on accelerometer readings. The standardized mean squared error (SMSE), a measure of error between the mean prediction and the true value, important for quantifying prediction performance, is shown to be a function of the Fourier transformation type used; and we conclude which considered Fourier transformation results in the lowest prediction errors. Furthermore, we analyze how different dataset sizes and sensor-actuator pairings affect the frequency predictions.","Submission original under an indefinite embargo labeled 'Open Access'. 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This framework centers on the operation of distributed sensors, and we present a set of components capable of performing a characterization effort of this nature. Our implementation of supervised learning enables us to predict actuator operational frequency values based on accelerometer readings. The standardized mean squared error (SMSE), a measure of error between the mean prediction and the true value, important for quantifying prediction performance, is shown to be a function of the Fourier transformation type used; and we conclude which considered Fourier transformation results in the lowest prediction errors. Furthermore, we analyze how different dataset sizes and sensor-actuator pairings affect the frequency predictions.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Adam Augustyniak, accepted the attached license on 2020-05-12 at 15:30.","The student, Adam Augustyniak, submitted this Thesis for approval on 2020-05-12 at 15:35.","This Thesis was approved for publication on 2020-05-12 at 21:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15364 on 2020-08-25 at 17:14:24","Made available in DSpace on 2020-08-26T21:58:06Z (GMT). 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