• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2442-2447
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• 2015

Because an electric railway vehicle is a large scale moving load, it can cause various kinds of power quality problems in the railroad power system. The power quality impacts are considered as the strong instantaneous stresses to the related power systems and can cause an accelerating aging and a malfunction of the power supplying components. Therefore, it is necessary to detect the small and intermittent symptoms mixed in the voltage waveform. However, they cannot be detected by the triggering functions of the existing power analyzers installed in the railway systems. This paper will examine the drawback of some fast detection tools and propose an advanced detecting and analyzing method based on a combined TEO and STFT algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.259-263
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• 1996

In diagnosing of mechanical machinery, it is often improtant to get information about the movement inside the machine casing. If the values of internal tities may be derived from the measurement using sensors installed on the external casing, it would be much better in many senses. This paper discusses extracting internal gear movements byfrequencydemodulation from gear meshing force signatures which can be recovered from the vibrations though inverse filter. There are several way in demodulating signals. In this paper, especially, Hibert Transform, Wigner-Ville distribution, and Teager energy operator are examined and compared. Effects of noise on the frequency demodulation methods and the behavior of bandpass filtered noisy signal are discussed using simulated time-varying frequency signals.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.40-48
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• 2023

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.816-823
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• 2018

The Auto-synchronizer is essential equipment for synchronizing a generator to the power system. It is performing that measurement of the magnitude, frequency and phase of the voltage signal of the power system and generator. It is important to select the appropriate measurement algorithm for preventing various problem such as mechanical stress and Electrical problem. Teager Energy Operator(TEO) and Discrete separation algorithm(DESA) is measurable the instantaneous parameters of a sine wave using 5 samples and can be measured at a fast and with a simple operation. Therefore it has many advantages in measuring the parameters. In this paper, it confirmed measurement results using matlab simulations when there are synchronized in order of frequency, magnitude. Also it presented methods using digital filters and sample intervals to improve accuracy.