• Journal of IKEEE
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• v.24 no.4
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• pp.1081-1085
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• 2020

Instantaneous frequencies (IF) of the AM-FM signal is estimated based on the inflection point detection (IPD) method. Local maxima/minima are detected using the IPD, and they are exploited to find the IF of AM and FM components, respectively. The envelope of the maxima/minima is obtained to estimate the IF of the AM part. And the distance between neighboring maxima (or minima) is used to estimate the IF of the FM component. Computer simulation shows that the proposed method properly estimates the IF of the AM and FM when the signal has fixed frequencies for both parts. In the case of the time-varying IF of the FM part, the estimated IF shows some deviation from the true IF due to the rough sampling effect of the maximum/minimum points. Thus, the post-processing such as the lowpass filtering of the estimated IF is required to refine the resulting IF estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3001-3008
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• 2013

To diagnose the cardiac valve abnormalities using analysis of phonocardiogram, first of all, accurate detection of S1, S2 components is needed for heart sound segmentation. In this paper, a new method that uses the third moment characteristics of an envelope of the PCG is proposed for accurate detection of S1 and S2 components of the heart sound with cardiac murmurs. The envelope of the PCG is obtained from the short-time energy profile, and its third moment profile with slope information is used for accurate time gating of the S1, S2 components. Experimental results have shown that the proposed method is superior to the conventional second moment method for detection of S1 and S2 regions from the heart sound signals with cardiac murmurs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.11
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• pp.905-910
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• 2008

Vibration analysis is widely used in machinery diagnosis and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local fault, in local fault of gearboxes using the wavelet transform. Moreover, envelop analysis is well known as useful tool for the detection of rolling element bearing fault. In this paper, a acoustic emission (AE) sensor is employed to detect gearbox damage by installing them around bearing housing at driven-end side. Signal processing is conducted by wavelet transform and enveloping to detect her fault all at once gearbox using AE signal.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.47-52
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• 2011

We studied about the damaged tire decision algorithm that measured dominant period of ultrasonic signal due to a foreign material on the friction between tire and road surface. We computed the power spectrum about the envelope of ultrasonic signal acquired from the damaged tire, then proposed the dominant period decision algorithm by statistical power threshold value. As the result of simulation, when driving by the speed of 80km/h, the 100ms of dominant period that measured in the proposed algorithm is more accurate than the 97.6ms of power spectrum peak period referenced on the average period of ultrasonic signal envelope peak, 101.24ms.

• The Journal of the Acoustical Society of Korea
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• v.12 no.2E
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• pp.63-72
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• 1993

본 논문에서는 무제한 음성 생성을 위한 단위음성으로서의 다이폰을 2음절 자연음성으로부터 자동 추출하는 알고리즘을 제안한다. 입력음성을 개량 켑스트럼 파라미터로 분석하여 이로부터 다이폰 추출 파라미터들을 도출한다. 제안된 파라미터로는 에너지 레벨을 나타내는 0차 켑스트럼의 동적변화량, 스펙트럼의 시간 변화량 영교차율, 캡스트럼의 유클리디안 거리이다. 스펙트럼 포락의 변화가 완만한 모음 연쇄등의 음소 경계를 보다 효율적으로 검출하기 위해 스펙트럼의 시간 변화를 미세부분과 개형부분으로 나누어 각각을 파라미터로 사용한다. VV(모음연쇄), VCV(C: 반모음, 자음), VCCV형들로 이루어진 2음절 단어들에 대해 실험한 결과, 모음연쇄 등이 포함되어 있음에도 약 85% 정확도의 음소경계검출을 얻었다. 본 논문에 의한 다이폰을 이용한 합성음의 청취실험 결과 명료도가 높음을 확인하였다.

• Korean Journal of Optics and Photonics
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• v.29 no.3
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• pp.99-103
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• 2018

Optical coherence elastography (OCE) is based on optical coherence tomography (OCT), which is a noninvasive, high-resolution, cross-sectional imaging technique. In this paper, we have developed dynamic optical coherence elastography to measure elasticity, a mechanical property of tissue, by phase difference. A piezoelectric actuator was used for sinusoidal mechanical loading of samples. Before applying this method to biomaterial, we assessed the feasibility of OCE with samples of sponge, eraser, and sharp lead. Cross-sectional and phase-difference images of the sample were obtained under sinusoidal loading. The strain rate was calculated from the phase-difference information. To obtain the envelope of the phase-difference oscillations along the horizontal direction, Hilbert transformation was performed at each depth. The elevation of the envelope was represented by color mapping, and we could measure the relative elasticity within the sample by comparing the elevations. Finally, there was an advantage when we calculated the shear rate using self-interference in the sample arm, instead of the interference between sample and reference arms.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.65-68
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• 2002

현재 음성인식기는 다 채널의 음성입력방식을 사용하고 있는 추세이다. 이런 방법으로 음성인식기를 사용할 때에 자동적으로 음성을 검출하는 음성입력 방식은 발성자와 마이크간의 거리에 따라 Glottal Spectrum 성분이 변하는 특성을 가지고 있다. 이러한 Glottal Spectrum 성분은 a=R1/R0 (LPC 포락선의 기울기) 로 나타낼 수 있다. 본 논문에서는 발성자와 마이크 거리에 따른 Glottal Spectrum 성분을 비교 분석 하고자 한다.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.2
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• pp.258-266
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• 2012

Recently, Acoustic Emission (AE) technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the rolling element bearing problems and Wavelet transform is a powerful method to detect faults occurred on gearboxes. However, exact method for AE signal is not developed yet. Therefore, in this paper, two methods, which is Hilbert transforms (HT) and Hilbert-Huang transforms (HHT), will be compared for development a signal processing method for early fault detection system by using AE. AE signals were measured through a fatigue test. HHT has better advantages than HT because HHT can show the time-frequency domain result. But, HHT needs long time to process a signal, which has a lot of data, and has a disadvantage in de-noising filter.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.