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Full-Text Articles in Engineering
Mitigation Of Catastrophic Interference In Neural Networks And Ensembles Using A Fixed Expansion Layer, Robert Austin Coop
Mitigation Of Catastrophic Interference In Neural Networks And Ensembles Using A Fixed Expansion Layer, Robert Austin Coop
Doctoral Dissertations
Catastrophic forgetting (also known in the literature as catastrophic interference) is the phenomenon by which learning systems exhibit a severe exponential loss of learned information when exposed to relatively small amounts of new training data. This loss of information is not caused by constraints due to the lack of resources available to the learning system, but rather is caused by representational overlap within the learning system and by side-effects of the training methods used. Catastrophic forgetting in auto-associative pattern recognition is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward …
Recognition System Of Indonesia Sign Language Based On Sensor And Artificial Neural Network, Endang Supriyati, Mohammad Iqbal
Recognition System Of Indonesia Sign Language Based On Sensor And Artificial Neural Network, Endang Supriyati, Mohammad Iqbal
Makara Journal of Technology
Sign language as a kind of gestures is one of the most natural ways of communication for most people in deaf community. The aim of the sign language recognition is to provide a translation for sign gestures into meaningful text or speech so that communication between deaf and hearing society can easily be made. In this research, the Indonesian sign language recognition system based on flex sensors and an accelerometer is developed. This recognition system uses a sensory glove to capture data. The sensor data that are processed into feature vector are the 5-fingers bending and the palm acceleration when …
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.
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 …
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 …