Identification and Forecasting of Nuclear Power Plants Transients by Semi-Supervised Method with Change of Representation Technique, M.Sc. Thesis Sharif University of Technology ; Ghofrani, Mohamad Bagher (Supervisor) ; Moshkbar Bakhshayesh, Khalil (Supervisor)
Abstract
In this work, we aim to find a way to identify and forecast transients in nuclear power plants with the aid of semi-supervised machine learning algorithm. Forecasting and identifying transients in nuclear power plants at the early stages of formation are essential for safety considerations and precautionary measures. The use of machine learning algorithms provides an intelligent control mechanism that, along with the main operator of the power plant, raises the transient detection and identification rate. Our algorithm of choice is to change the way data is presented, which is a semi-supervised learning approach. The algorithm consists of two methods: quantum dynamics clustering...
Cataloging briefIdentification and Forecasting of Nuclear Power Plants Transients by Semi-Supervised Method with Change of Representation Technique, M.Sc. Thesis Sharif University of Technology ; Ghofrani, Mohamad Bagher (Supervisor) ; Moshkbar Bakhshayesh, Khalil (Supervisor)
Abstract
In this work, we aim to find a way to identify and forecast transients in nuclear power plants with the aid of semi-supervised machine learning algorithm. Forecasting and identifying transients in nuclear power plants at the early stages of formation are essential for safety considerations and precautionary measures. The use of machine learning algorithms provides an intelligent control mechanism that, along with the main operator of the power plant, raises the transient detection and identification rate. Our algorithm of choice is to change the way data is presented, which is a semi-supervised learning approach. The algorithm consists of two methods: quantum dynamics clustering...
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