{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/40587"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/40587","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Transient response of temperature sensors","abstract":"Transient of time constant 10 seconds will be within 2 % of its final value in 40· seconds. Cancelling this response and iritroducing one of time constant 1 second enables the 2 % band to be reached in just 5 seconds. It was also possible to correct transient responses made up of two exponentials to speed them up. Recursive estimators, which is a technique derived from successive approximations, namely the linear recursive estimator was used to estimate temperature changes in real time by basically weighing the new estimate according to how good the previous estimate was. Temperature measurement in the presence of noise is possible by using the Kalman filter which is a very powerful and impressive recursive estimator. The Kalman filter is basically a simple low-pass digital filter. The processed output is simply the weighted sum of the current input and the previous output and the results obtained were quite remarkable indeed.","abstract_html":"Transient of time constant 10 seconds will be within 2 % of its final value in 40· seconds. Cancelling this response and iritroducing one of time constant 1 second enables the 2 % band to be reached in just 5 seconds. It was also possible to correct transient responses made up of two exponentials to speed them up. Recursive estimators, which is a technique derived from successive approximations, namely the linear recursive estimator was used to estimate temperature changes in real time by basically weighing the new estimate according to how good the previous estimate was. Temperature measurement in the presence of noise is possible by using the Kalman filter which is a very powerful and impressive recursive estimator. The Kalman filter is basically a simple low-pass digital filter. The processed output is simply the weighted sum of the current input and the previous output and the results obtained were quite remarkable indeed.","abstract_has_math":false,"creators":["Prefumo, Jean-Pierre Guito"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Bell, J F W"],"committee_chairs":[],"committee_members":[],"year":1994,"date_issued":"1994","date_published":"1994","updated_at":"2026-07-24T01:34:01Z","subjects":["Electrical and Electronic Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/40587","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Bell, J F W"]},{"key":"dc:creator","label":"Author","values":["Prefumo, Jean-Pierre Guito"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-10-18T11:13:10Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-10-18T11:13:10Z"]},{"key":"dc:date.issued","label":"Date","values":["1994"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical and Electronic Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/40587"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Transient of time constant 10 seconds will be within 2 % of its final value in 40· seconds. Cancelling this response and iritroducing one of time constant 1 second enables the 2 % band to be reached in just 5 seconds. It was also possible to correct transient responses made up of two exponentials to speed them up. Recursive estimators, which is a technique derived from successive approximations, namely the linear recursive estimator was used to estimate temperature changes in real time by basically weighing the new estimate according to how good the previous estimate was. Temperature measurement in the presence of noise is possible by using the Kalman filter which is a very powerful and impressive recursive estimator. The Kalman filter is basically a simple low-pass digital filter. The processed output is simply the weighted sum of the current input and the previous output and the results obtained were quite remarkable indeed."]},{"key":"dc:title","label":"Title","values":["Transient response of temperature sensors"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bell, J F W"],"dc:creator":["Prefumo, Jean-Pierre Guito"],"dc:date.accessioned":["2024-10-18T11:13:10Z"],"dc:date.available":["2024-10-18T11:13:10Z"],"dc:date.issued":["1994"],"dc:description.abstract":["Transient of time constant 10 seconds will be within 2 % of its final value in 40· seconds. Cancelling this response and iritroducing one of time constant 1 second enables the 2 % band to be reached in just 5 seconds. It was also possible to correct transient responses made up of two exponentials to speed them up. Recursive estimators, which is a technique derived from successive approximations, namely the linear recursive estimator was used to estimate temperature changes in real time by basically weighing the new estimate according to how good the previous estimate was. Temperature measurement in the presence of noise is possible by using the Kalman filter which is a very powerful and impressive recursive estimator. The Kalman filter is basically a simple low-pass digital filter. The processed output is simply the weighted sum of the current input and the previous output and the results obtained were quite remarkable indeed."],"dc:identifier.uri":["http://hdl.handle.net/11427/40587"],"dc:publisher.department":["Department of Electrical Engineering"],"dc:subject":["Electrical and Electronic Engineering"],"dc:title":["Transient response of temperature sensors"],"dc:type":["Thesis / Dissertation"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-24T01:34:01Z"}