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Full-Text Articles in Computer Engineering
Feature-Based Fault Detection Of Industrial Gas Turbines Using Neural Networks, Abbas Rasaienia, Behzad Moshiri, Mohammadamin Moezzi
Feature-Based Fault Detection Of Industrial Gas Turbines Using Neural Networks, Abbas Rasaienia, Behzad Moshiri, Mohammadamin Moezzi
Turkish Journal of Electrical Engineering and Computer Sciences
Gas turbine (GT) fault detection plays a vital role in the minimization of power plant operation costs associated with power plant overhaul time intervals. In other words, it is helpful in generating pre-alarms and paves the way for corrective actions in due time before incurring major equipment failures. Hence, finding an efficient fault detection technique that is applicable in the online operation of power plants involved with minor computations is an urgent need in the power generation industry. Such a method is studied in this paper for the V94.2 class of GTs. As the most leading stage for developing a …
Review Of Distinctive Phonetic Features And The Arabic Share In Related Modern Research, Yousef Alotaibi, Ali Meftah
Review Of Distinctive Phonetic Features And The Arabic Share In Related Modern Research, Yousef Alotaibi, Ali Meftah
Turkish Journal of Electrical Engineering and Computer Sciences
Most research in the field of digital speech technology has traditionally been conducted in only a few languages, such as English, French, Spanish, or Chinese. Numerous studies using distinctive phonetic features (DPFs) with different techniques and algorithms have been carried out during the last 3 decades, mainly in English, Japanese, and other languages of industrialized countries. DPF elements are based on a technique used by linguists and digital speech and language experts to distinguish between different phones by considering the lowest level of actual features during phonation. These studies have investigated the best performances, outcomes, and theories, especially those regarding …
Controlling The Chaotic Discrete-Hénon System Using A Feedforward Neural Network With An Adaptive Learning Rate, Kürşad Gökce, Yilmaz Uyaroğlu
Controlling The Chaotic Discrete-Hénon System Using A Feedforward Neural Network With An Adaptive Learning Rate, Kürşad Gökce, Yilmaz Uyaroğlu
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a feedforward neural network-based control scheme to control the chaotic trajectories of a discrete-Hénon map in order to stay within an acceptable distance from the stable fixed point. An adaptive learning back propagation algorithm with online training is employed to improve the effectiveness of the proposed method. The simulation study carried in the discrete-Hénon system verifies the validity of the proposed control system.