• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2012년도 추계학술대회 논문집
• /
• pp.603-604
• /
• 2012

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2005년도 창립60주년 기념 추계 학술대회
• /
• pp.99.2-99.2
• /
• 2005

• 산업경영시스템학회지
• /
• 제32권4호
• /
• pp.215-222
• /
• 2009

Human voice reacts very sensitively to human's minute physical condition. For instance, human voice disorders affect patients profoundly especially in the case of Parkinson's disease. Acoustic tools such as MDVP, can function as an equipment that measures various voice in different objects. Many different approaches have been applied for analyzing the voice disorders for diagnosis of Parkinson's disease. According to the voice data of suspected Parkinson's patients from UCI Machine Learning Repository, it is reported to have 23 people with Parkinson's disease and 8 healthy people. Applying Mahalanobis Taguchi System (MTS) for diagnosis of Parkinson's disease, the correct diagnosis performance is compared to previous research results.

• 대한기관식도과학회지
• /
• 제7권2호
• /
• pp.161-167
• /
• 2001

Background and Objectives： Total laryngectomy is often required for advanced cases. But this operation induced the many inconvenience of basic daily life. Early diagnosis of laryngeal cancer is very important to prevent from this disastrous condition. In this point of view, mass screening test for early detection of laryngeal cancer is necessary. Screening test using voice has many advantages such as simple, less interventional. Voice collection by Automatic Response System(ARS) is comfortable and easy to got acoustic sample. Thus author tried to got the acoustic parameters which can differentiate normal, benign. and malignant laryngeal diseases and also checked the availability of parameters on neural network system. Materials and Methods: Author has evaluated the voice from 17 laryngeal cancer patients and 45 benign laryngeal disease patients who visited at Department of Otolaryngology, Pusan National University Hospital from May 1998 to April 2001, and 15 normal control. Author chose the sir Parameters (Jitt. vFo, Shim, vAm, NHR, SPI) that was thought to be related with voice collected by ARS among thirty-three parameters analysed by a Multi-Dimensional Voice Program (MDVP). Two-step neural network was used for the availability of six parameters. Results: The detection rate of normal voice by ARS voice analysis is 78.5% and detection rate of abnormal voice was 97.1 o/o. Among abnormal voice, the detection rate of benign laryngeal diseases and laryngeal cancers were 82.4 o/o, 70.6% respectively. Conclusion: Author concluded that six parameters and Matlab based neural network software may be effective in development of acoustic screening system for laryngeal cancer and further study should be necessary for development of new acoustic parameters.

• 동력기계공학회지
• /
• 제9권1호
• /
• pp.5-13
• /
• 2005

Acoustic emission(AE) is defined as the transient elastic waves thar are generated by the rapid release of energy. The advantage of AE is that very early crack growth can be detected well before a highly stressed component may fail. At present, an exact diagnosis is the most reliable means for determining the soundness of structures during power plant operations. AE monitoring has been applied successfully in power plants to determine mechanical problems, pressure vessel integrity and external valves leaks, vacuum leaks, the onset of cavitation in pumps and valves, the presence of flow(or no flow) in piping and heat exchange equipment, etc. Acoustic emission(AE) technology has recently strengthened its application base, and practitioners' understanding of the technique's fundamentals. This paper introduces the methods of a survey and assessment on AE monitoring applications in nuclear, fossil and hydraulic power plant. The main objective of this paper was to obtain information on various applications of AE technology in power plant.

• 대한후두음성언어의학회지
• /
• 제7권1호
• /
• pp.11-19
• /
• 1996

Reinke's edema is used for describing varying degrees of chronic swelling of the vocal folds. The acoustic analysis of Reinke's edema has not been reported so far in this country. The purpose of this study is to clarify acoustic and aerodynamic characteristics of the Reinke's edema. Several acoustic evaluations ＆ aerodynamic studies were done in 20 Reinke's edema patients and the data was compared with those of 20 normal controls. Videolaryngoscopy also was done to classify the severity in grading. We used C-Speech, Doctor speech science, and Phonatory function analyser. In C-Speech, we compared jitter, shimmer, and SNR(signal to noise ratio) of normal and Rrinke's edema patient. In Doctor speech science, we compared NNE(Glottal noise energy), speech fundamental frequency, voice quality between two groups. And in phonatory function analyser for aerodynamic function test, we compared speech intensity, airflow rate, and expiratory pressure between two groups. In conclusion, Reinke's edema patients showed lower voice pitches than normal, additionally jitter, shimmer, SNR(signal to noise ratio), NNE(Glottal noise energy), airflow rate, and expiratory pressure may be meaningful parameters for diagnosis and prognosis for treatment.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 하계학술대회 논문집 E
• /
• pp.1758-1760
• /
• 1997

Automatic detection system to detect acoustic emission pulse and fractal dimension were developed, to observe tree deterioration phenomena in LDPE. The purpose of our work are to use acoustic emission system and fractal dimension and to investigate the treeing phenomena in polymeric insulation under applied AC voltage 11[kV] with an artificial needle-shaped void(1.5[mm]) using the above system. We analyzed and phase angle-acoustic emission pulse amplitude-deterioration time ($\Phi$-AEA-t) pattern and phase angle-acoustic emission pulse number-deterioration time($\Phi$-AEN-t) pattern using statistical operators such as skewness, fractal dimension. In this paper show that the correlation of $\Phi$-AEA-t, $\Phi$-AEN-t, fractal dimension using regression analysis by the method of least squares can be used to predict the breakdown just before the breakdown occurs.

• 대한전기학회논문지:전기물성ㆍ응용부문C
• /
• 제51권6호
• /
• pp.246-252
• /
• 2002

This paper describes the acoustic emission measurement method to diagnose GIS for particles. We measured and analyzed the signals of acoustic waves using acoustic two types sensors with 125KHz and 50KHz resonant frequency respectively when the particles were dropped on the surfaces of circular plates and inside of GlS tanks. We found that the difference between peak outputs of two sensors depended on the types and materials of particles and the conditions of dropping position. These results showed that the outputs of 125KHz sensor were higher than those of 50KHz sensor in circular plate and 362㎸ GIS tank made of steel and vice versa in circular plate and 800㎸ GIS tank made of aluminum. The ratios outputs of 125KHz sensor to those of 50KHz sensor were 1.4 - 2.37 in 800㎸ GIS tank and were 0.5 - 1.0 in 362㎸ GIS tank. Therefore we knew that adaption of two types acoustic sensors which had different resonant frequencies as a very useful method in diagnosis of GIS.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2000년도 하계학술대회 논문집
• /
• pp.585-591
• /
• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• 센서학회지
• /
• 제32권3호
• /
• pp.199-206
• /
• 2023

This study presents a novel monitoring technique for underwater high-voltage direct current (HVDC) cables based on the Distributed Acoustic Sensor (DAS). The proposed technique utilizes vibration and acoustic signals generated on HVDC cables to monitor their condition and detect events such as earthquakes, shipments, tidal currents, and construction activities. To implement the monitoring system, a DAS based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) system was designed, fabricated, and validated for performance. For the HVDC cable monitoring experiments, a testbed was constructed on land, mimicking the cable burial method and protective equipment used underwater. Defined various scenarios that could cause cable damage and conducted experiments accordingly. The developed DAS system achieved a maximum measurement distance of 50 km, a distance measurement interval of 2 m, and a measurement repetition rate of 1 kHz. Extensive experiments conducted on HVDC cables and protective facilities demonstrated the practical potential of the DAS system for monitoring underwater and underground areas.