• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1291-1295
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• 2004

This paper proposed a new approach for endpoint detection of isolated speech, which proves to significantly improve the endpoint detection performance. The proposed algorithm relies on the root mean square energy (rms energy), zero crossing rate and spectral characteristics of the speech signal where the Euclidean distance measure is adopted using cepstral coefficients to accurately detect the endpoint of isolated speech. The algorithm offers better performance than traditional energy-based algorithm. The vocabulary for the experiment includes English digit from one to nine. These experimental results were conducted by 360 utterances from a male speaker. Experimental results show that the accuracy of the algorithm is quite acceptable. Moreover, the computation overload of this algorithm is low since the cepstral coefficients parameters will be used in feature extraction later of speech recognition procedure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1222-1230
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• 2022

As the number of signal sources with low detectability by using various modulation techniques increases, research to classify signal modulation methods is steadily progressing. Recently, a Convolutional Neural Network (CNN) deep learning technique using FFT as a preprocessing process has been proposed to improve the performance of received signal classification in signal interference or noise environments. However, due to the characteristics of the FFT in which the window is fixed, it is not possible to accurately classify the change over time of the detection signal. Therefore, in this paper, we propose a CNN model that has high resolution in the time domain and frequency domain and uses wavelet transform as a preprocessing process that can express various types of signals simultaneously in time and frequency domains. It has been demonstrated that the proposed wavelet transform method through simulation shows superior performance regardless of the SNR change in terms of accuracy and learning speed compared to the FFT transform method, and shows a greater difference, especially when the SNR is low.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.2
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• pp.332-338
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• 2010

Annually, electrical fires caused by arcing phenomena in power system rapidly increase as the use of more electric appliances, but there is no established method for the prevention of the accidents. With this background, this paper dealt with the experimental results on a series arc detection technique and a device for air conditioners. Series arcing phenomena that is generated in incomplete connection of air conditioners was simulated, and the frequency spectrum was analyzed. The Fast Fourier Transform (FFT) of the arc pulse showed that the dominant frequency components exist in ranges of 190 kHz~250 kHz and 900 kHz~1.6 MHz. An arc detection circuit with low cut off frequency of 170 kHz to attenuate 60 Hz by 170 dB and a signal discriminator were designed. Also, an algorithm which separate series arc signal from unwanted noises produced by switching operation, inverter, and surge was proposed. Application experiment was carried out on several types of air-conditioners by using the arc generator specified in UL1699, and the results showed the over 99 % accuracy.

• Journal of IKEEE
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• v.23 no.1
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• pp.310-313
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• 2019

This paper used the majority function to digitally filter back-electromotive force as an explanation of the Brushless DC MOTOR control algorithm. The cause and improvement of motor noise, which are operating in close proximity to high frequency sources, did not use conventional low pass filter and comparator elements. Also, they repeatedly output a noise-free BEMF signal for the input value of the majority detection filtering. These filtering steps can help reduce costs and minimize the area of a PCB by requiring relatively little hardware.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.4
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• pp.309-314
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• 2013

This paper dealt with the radiated electromagnetic wave detection of partial discharge (PD) in oil for insulation diagnostics of oil-immersed transformers. Three types of electrode system were fabricated to simulate the insulation defects that could occur in oil-immersed transformers. Frequency components of radiated electromagnetic wave in oil was measured by broadband bi-conical antennas of 300 MHz~2 GHz and a spectrum analyzer of 9 kHz~3 GHz. Frequency component of electromagnetic waves from PD in oil were highly distributed at 500 MHz. From the result, a narrow-band monopole antenna with the center frequency of 500 MHz was fabricated. We could detect PD signal in insulation oil without an influence of external noise by a measurement system which consists of the prototype monopole antenna, a LNA (Low Noise Amplifier), an oscilloscope and a spectrum analyzer.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.119-122
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• 2003

This paper describes the sensorless technique for the surface-mounted permanent-magent synchronous motor(SPMSM or PMSM) drive based on magnetic saliency. The control technique is a sensorless control algorithm that injects the high frequency voltage to the stator terminal in order to estimate the rotor position and speed. The rotor position and speed for sensorless vector control is achieved by appropriate signal processing to extract the position information from the stator current in the low speed range including zero speed. Proposed sensorless algorithm using the double-band hysteresis controller and initial rotor position detection exhibits excellent reference tracking and increased robustness. Experimental results are presented to verify the feasibility of the proposed control schemes.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.45-51
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• 2004

Boiler tubes in power plants are often leaked due to various material degradations including creep and thermal fatigue damage under severe operating conditions such as high temperature and high pressure over an extended period of time. To monitor and diagnose the tubes on site and in real time, the acoustic emission (AE) technology was applied. We developed an AE leak detection system, and used it to study the variation of AE signal from the on-site tubes in response to the changes in the boiler operation condition and to detect the locations of leakage based on it. Detection of leak was performed by acquiring and evaluating the signals in separate regimes of high and low frequency signal. As a result of these studies, we found that on-line monitoring and detection of leak location for boiler tubes is possible using the developed system. Thus, the system is expected to contribute to the safe operation of power plants, and prevent economic losses due to potential leak.

• Korean Journal of Optics and Photonics
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• v.22 no.5
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• pp.223-229
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• 2011

We present a low jitter frequency up-conversion detector based on quasi-phase matched sum frequency generation in a periodically poled $LiNbO_3$ waveguide for efficient single-photon detection at 1.5 ${\mu}m$ telecommunication wavelengths. The maximum detection efficiency and the noise count rate using the pump power of 300 mW and the pump wavelength of 974 nm are about 7% and 480 kHz, respectively. We also characterize the timing jitter of the frequency up-conversion detector by analyzing the time distribution of the detection outputs for photons generated through a picosecond pump pulsed spontaneous parametric downconversion. The minimum timing jitter was measured to be about 39.1 ps. Coincidence measurement with a narrow time window for pulsed up-conversion photons can eliminate the unwanted noise counts and maximize signal to noise ratio.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2C
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• pp.238-245
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• 2010

For orthogonal frequency division multiplexing (OFDM) systems sharing the spectrum with narrow band primary devices, a fractional bandwidth (FBW) mode has been proposed recently to reduce the interference to the primary users. The FBW mode divides the total OFDM bandwidth into subbands and activates (or deactivates) a subset of the subbands according to the result of spectrum sensing. In this paper, we analyze the detection error probability of FBW mode information which is delivered by the sequence embedded in the preamble and evaluate the performance in wireless regional area network environments. The results show that the detection probability derived analytically estimates the actual value from simulation adequately and that a low detection error probability less than $10^{-3}$ is obtained at a low signal-to-noise power ratio.