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Erdinç Türkcan

Researcher at Istanbul Technical University

Publications -  14
Citations -  217

Erdinç Türkcan is an academic researcher from Istanbul Technical University. The author has contributed to research in topics: Artificial neural network & Recurrent neural network. The author has an hindex of 5, co-authored 14 publications receiving 194 citations. Previous affiliations of Erdinç Türkcan include Delft University of Technology.

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Elman's recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery

TL;DR: The high performance of Elman's RNN was shown by means of two different applications: detecting anomalies introduced from the simulated power operation of a high-temperature gas cooled nuclear reactor and detecting motor bearing damage using a coherence function approach for induction motors.
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Real-time nuclear power plant monitoring with neural network

TL;DR: A new learning technique adopted here compensates for the drawback of the conventional backpropagation algorithm, and is presented to make plant dynamic models on the ANN to detect anomalies of nuclear power plants in operation.
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Comparisons between the various types of neural networks with the data of wide range operational conditions of the Borssele NPP

TL;DR: The reactor system, the real time data collection and the merits of the three types of the neural network applied while in the learning and continuous processing of the changing of the operational conditions are presented.
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On-line neuro-expert monitoring system for Borssele Nuclear Power Plant

TL;DR: It was shown that the neuro-expert system successfully monitored the plant status at Borssele Nuclear Power Plant in the Netherlands and worked satisfactorily in diagnosing the system status by using the outputs of the neural networks and a priori knowledge base from the PWR simulator.
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Real time reactor noise diagnostics for the Borssele (PWR) nuclear power plant

TL;DR: In this article, the authors presented the measuring system, the operational tasks, and the results obtained so far on the real-time core-barrel motions (CBM) and the two-primary coolant pump vibrations measured through the reactor noise analysis.