• 대한청각학회지
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• 제23권3호
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• pp.160-166
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• 2019

Lots of neuroimaging and animal studies have revealed that tinnitus and hyperacusis share the same patterns in the bottom up central auditory process. The aim was to identify the abnormal central patterns commonly observed in both tinnitus and hyperacusis in humans. We investigated two cases of normal hearing: a tinnitus patient and a hyperacusis patient. We compared the differences between the severe temporal hyper-activated state (STHS), with spikes, fast beta and gamma frequencies after noise exposure, and the mild temporal hyperactivated state (MTHS), in no sound exposed condition. The power of the gamma band in the two cases was increased in both auditory cortices compared to the other brain regions. Our results of human with normal hearing were the first to identify how tinnitus and hyperacusis caused by sound are abnormally active and how they maintain constant pathological states.

• Journal of Audiology & Otology
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• 제23권3호
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• pp.160-166
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• 2019

Lots of neuroimaging and animal studies have revealed that tinnitus and hyperacusis share the same patterns in the bottom up central auditory process. The aim was to identify the abnormal central patterns commonly observed in both tinnitus and hyperacusis in humans. We investigated two cases of normal hearing: a tinnitus patient and a hyperacusis patient. We compared the differences between the severe temporal hyper-activated state (STHS), with spikes, fast beta and gamma frequencies after noise exposure, and the mild temporal hyperactivated state (MTHS), in no sound exposed condition. The power of the gamma band in the two cases was increased in both auditory cortices compared to the other brain regions. Our results of human with normal hearing were the first to identify how tinnitus and hyperacusis caused by sound are abnormally active and how they maintain constant pathological states.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.69-74
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• 2006

It is known that the various noise sources which are engine, transmission, tire, intake system, etc exist at vehicle driving status. Specially noises which cannot be expected by a driver induce unpleasantness to all passengers. These noises are difficult to distinguish noise sources or specifications because of too many vehicle parts. Therefore in this paper, study on abnormal noise of bush on suspension system is performed by the measurement and analysis of the noises of bushings that are generated artificially. The measured noises are analyzed by two points-view of spectrum and sound quality. Finally, it is shown that the noise sources of bushings on the suspension system which are the pillow ball joint bush of a control arm and the rubber bush of a lower arm could be distinguished by the spectrum distribution and a index value based on tonality.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• 대한후두음성언어의학회지
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• 제4권1호
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• pp.24-32
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• 1991

Cleft palate or velopharyngeal incompetence shows many disorders and disabilities affecting speech transmission. including distortion. substitution. and the nasalization of the vowels. The nasalized vowels are produced primarily by lowering of the velum. resulting in opening a side passage for the air flow through the nasal cavity. These abnormal movements give rise to complex modification of the physical property of the sound or in the sound spectrum. The authors employed Sonagraph$^{\circledR}$ as a sound analyzer in order to ascertain the features which characterize the nasalization of vowels. Twenty healthy Korean male adult voluteers were analyzed in artificial conditions of anterior and posterior nasal obstruction. and velo-pharyngeal incompetence. The results were as follows : 1) Fundamental frequency was not changed by nasal obstruction or velopharyngeal incompetence. 2) There was no significant difference of the formant intensity between normal and nasal vowels. 3) In VPI, a decrease of the frequency of $F_2$ was observed in /e/ and /i/ vowels(p<0.001). 4) In VPI, the $F_2$ was frequently missed in /o/ and /u/ vowels. 5) In the consonant spectra of VPI, the 'release burst' was usually not observed.

• ETRI Journal
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• 제37권4호
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• pp.824-832
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• 2015

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.

• 한국정밀공학회지
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• 제10권4호
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• pp.142-151
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• 1993

An intelligent mulitiple monitoring system to monitor tool/machining states synthetically was proposed and developed. It consists of 2 fundamental subsystems : the multiple sensor detection unit and the intellignet integrated diagnosis unit. Three signals, that is, spindle motor current, Z-axis motor current, and machining sound were adopted to detect tool/machining states more reliably. Based on the multiple sensor information, the diagnosis unit judges either tool breakage or degree of tool wear state using fuzzy reasoning. Tool breakage is diagnosed by the level of spindle/z-axis motor current. Tool wear is diagnosed by both the result of fuzzy pattern recognition for motor currents and the result of pattern matching for machining sound. Fuzzy c-means algorithm was used for fuzzy pattern recognition. Experiments carried out for drill operation in the machining center have shown that the developed system monitors abnormal drill/states drilling very reliably.

• 한국음향학회지
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• 제28권1호
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• pp.32-43
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• 2009

본 논문은 자동 분할과 extreme learning machine (ELM)을 이용하여 연속 심음신호에 의한 심장질환 분류의 성능을 개선한다. 자동 분할을 위한 전처리 단계에서 비정상적인 심음신호는 심잡음 (murmur)과 클릭음 (click)을 포함하고 있기 때문에 제1음 (S1)과 제2음 (S2) 시작점 검출 결과가 부정확하거나 누락되어 기존의 심장질환 분류 시스템의 정확도를 저하시키게된다. 이러한 분할 오류에 의한 성능 저하를 감소하기 위해 S1 및 S2의 위치를 찾고, S1 및 S2의 시간 차이를 이용하여 부정확한 시작점을 교정한 다음 한 주기 심음 신호를 추출한다. 특징벡터로는 단일 주기의 심음 신호로부터 추출된 멜척도 필터뱅크 로그 에너지 계수와 포락선을 사용한다. 심장질환을 분류하기 위하여 한 개의 은닉층을 가진 ELM 알고리듬을 사용한다. 9가지 심장질환 분류 실험을 수행한 결과, 제안 방법은 81.6%의 분류 정확도를 나타내며, multi-layer perceptron(MLP), support vector machine (SVM), hidden Markov model (HMM) 중에서 가장 높은 분류 정확도를 보여준다.

• 대한관절경학회지
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• 제2권2호
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• pp.168-172
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• 1998

저자들은 관절운동의 장애 없이 양측 슬관절의 탄발음과 동통 및 파행을 주소로 한 6세 소아에서 관절경을 통해, 일반적인 탄발음의 원인이 아닌 외측 원판형 연골의 비후된 근위부착형의 리스버그 인대와 전방십자인대의 확장된 기시부의 충돌에 의해 탄발음이 유발된 1례를 발견하고 이를 관절경적으로 절제함으로서 치료하여 좋은 결과를 얻었기에 문헌 고찰과 함께 보고하는 바이다.