• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.131-134
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• 2000

음성신호처리에서 스펙트럼 분석은 매우 중요하다. 하지만 스펙트럼 분석을 위해서 사용되는 윈도우에 의해 생기는 누설에러지 때문에 음성신호의 스펙트럼 정보가 왜곡된다. 본 논문에서는 스펙트럼 분석 시 발생되는 창함수 사용에 의해 생기는 누설에너지를 최소화하기 위한 새로운 창함수를 제안하고자 한다. 그 형태는 전체 창함수크기의 반을 방형창으로 나머지 반을 해밍창으로 하고 창의 처음 부분은 $\pm$20표본에서 영점을 찾아주는 것이다. 이 창함수의 특징은 신호분석에 있어서 왜곡은 크지만 그 형태에 있어서 가장 이상적인 방형창함수의 장점과 side lobe가 작아 비교적 왜곡이 적은 해밍창함수의 장점을 취한 것이라 하겠다. 실제 음성 신호에의 적용에 있어서 방형창과 해밍창의 적용비는 신호의 종류 및 용도에 따라 달리할 수 있다. 제안한 창함수는 해밍창함수 보다는 좁은 main lobe 특성으로 음성신호의 단구간 스펙트럼 분석시 음성의 빠른 변화특성을 적절히 보여줄 수 있고 방형창보다는 side lobe의 영향을 줄일 수 있다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.563-568
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• 2023

ECG(electrocardiogram) is used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the cause of all heart diseases can be found. Because the ECG signal obtained using the ECG-KIT includes noise in the ECG signal, noise must be removed from the ECG signal to apply to the deep learning. In this paper, Noise included in the ECG signal was removed by using a lowpass filter of the Digital FIR Hamming window function. When the performance evaluation of the three activation functions, sigmoid(), ReLU(), and tanh() functions, which was confirmed that the activation function with the smallest error was the tanh() function, the elapsed time was longer when the batch size was small than large. Also, it was confirmed that result of the performance evaluation for the GRU model was superior to that of the LSTM model.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.10
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• pp.783-790
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• 2018

The frequency-hopping technique is one of the spread-spectrum techniques. Frequency hopping is a communication system in which the carrier frequency channel is hopped within the wideband. Therefore, a frequency-hopping system has such advantages as antijamming and low probability of intercept. This system is often used in military communications. Because frequency-hopping signal detection is difficult, it is an important research issue. A novel detection technique is proposed that can improve detection probability. When the received signal is transformed to a frequency domain sample by fast Fourier transform, spectral leakage lowers the detection probability. This problem can be solved by using the Hamming window, and the detection probability can be increased. However, in a frequency-hopping environment, the windowing technique lowers the detection probability. The proposed method solves this weakness. The simulation results show that the proposed detection technique improves the detection probability by as much as 13 %.