• 전기학회논문지
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• 제67권6호
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• pp.794-798
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• 2018

There is a growing need for a care system that can continuously monitor, manage and effectively relieve stress for modern people. In recent years, mobile healthcare devices capable of measuring heart rate have become popular, and many stress monitoring techniques using heart rate variability analysis have been actively proposed and commercialized. In addition, respiratory biofeedback methods are used to provide stress relieving services in environments using mobile healthcare devices. In this case, breathing information should be measured well to assess whether the user is doing well in biofeedback training. In this study, we extracted the heart beat interval signal from the PPG and used the oscillator based notch filter based on the IIR band pass filter to track the strongest frequency in the heart beat interval signal. The respiration signal was then estimated by filtering the heart beat interval signal with this frequency as the center frequency. Experimental results showed that the number of breathing could be measured accurately when the subject was guided to take a deep breath. Also, in the timeing measurement of inspiration and expiration, a time delay of about 1 second occurred. It is expected that this will provide a respiratory biofeedback service that can assess whether or not breathing exercise are performed well.

• Computers and Concrete
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• 제32권5호
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.457-462
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• 2007

This paper reports on the research which investigates acoustic signals acquired in turning with rough and finish simultaneously. The material is aluminum thin pipe. Two acoustic sensors were set on CNC machine. One was set on the finish bite and the other the rough. Two signals were first analyzed in order to consider how much the acoustic signal from the finish bite was coupled by that from the rough. A simple data collecting system to acquire signals from the finish was then determined because two acoustic signals were little coupled. Second the fundamental experiments were accomplished to study the effects of machine vibration and material state. The signal characteristics due to surface defects were studied from the collected acoustic signal data. The signal analysis was based on real time data, root mean squared average and frequency spectrum by fast fourier transform. As a result, the acoustic signals were made effects by machine condition, material structure. The acoustic signal from the finish bite was closely correlated with surface quality. Two types surface micro defects were then evaluated by the signal characteristics.

• 대한전자공학회논문지SP
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• 제49권4호
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• pp.111-119
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• 2012

Time Stretched Pulse (TSP)는 공간적으로 임펄스(Impulse)를 효율적으로 전달하고 분석하기 위해서 사용되어 진다. 하지만 발신기와 수신기의 전달함수를 포함시키지 않으면, 시간 영역에서의 분석은 직, 간접 신호의 중첩으로 공간의 자유음장 특성을 파악하기 불가능하다. 일반적으로 공간의 자유음장(Free Field)은 표준 ISO 3745 Annex A에 의해서 평가되고 있는데, 일정 주파수 간격의 1/3 옥타브 밴드 신호를 연속적으로 발신 및 수신하여 거리별 신호 감소를 역자승 법칙(Inverse Square Law)을 적용하여 판단하고 있다. 본 논문은 자유음장 분석에서 TSP 신호를 적용하여 일반적인 ISO 3745의 1/3 옥타브 밴드 신호와 비교하였다. 역자승 모델 값과의 차이점을 분석한 결과 TSP 신호 또한 1/3 옥타브 밴드 신호와 유사한 결과를 보이고 있으며, 측정 시간 및 확장성에 대해서는 우수하게 판단되었다. 본 실험에서는 ISO 3745에 의해서 제한된 주파수 범위에서 자유음장과 반자유음장(Hemi-free Field)을 검증 받은 무향실을 사용하였다.

• 한국환경생태학회지
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• 제23권6호
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• pp.553-563
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• 2009

환경특성에 따른 집박쥐(Pipistrellus abramus)의 반향정위 변화를 분석하기 위하여 2009년 4월부터 8월까지 경상북도 경주시 천북면 일원의 주간 휴식장소를 대상으로 실시하였다. 환경특성 유형은 일몰 후 출현순간, 출현 후 이동, 논 경작지, 산림 가장자리, 개방공간, 주택단지 등 6개 유형으로 구분하여 비교하였다. 분석결과 환경특성에 따른 차이 및 서식지간 이동과 먹이포획을 위한 비행 사이에서는 차이가 있는 것으로 나타났다. 외부 환경으로의 출현 순간에서는 짧은 시간의 FM 시그널만 확인되었으며, 개방 공간에서는 긴 시간의 CF 시그널 형태의 음을 이용하는 것으로 나타났다. 그 외 환경특성에서는 펄스의 형태적인 차이는 있었지만 FM과 CF 시그널을 혼합하여 이용하는 것으로 확인되었다. 먹이포획을 위한 비행과 서식지간 이동을 위한 비행에서는 펄스의 지속시간을 제외한 펄스 간격, 최고 진동수, 개시부와 종결부 진동수에서는 두 가지 비행패턴 사이에서 유의적인 차이가 확인되었다. 출현순간을 제외하고 서식지간 이동을 위한 비행시에는 곤충의 반향을 감지하여 곤충의 탐색에 적합한 협대역의 FM 시그널과 긴 펄스 지속시간을 가지는 형태를 보였으며, 먹이포획을 위한 비행에서는 폭넓은 탐색과 정확한 위치파악을 위한 광대역의 FM 시그널과 짧은 시간의 펄스 지속시간을 갖는 것으로 확인되었다.

• 한국터널지하공간학회 논문집
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• 제10권2호
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• pp.105-118
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• 2008

숏크리트는 터널에서 사용되는 중요한 지보재이다. 숏크리트와 암반의 접착상태는 터널의 안정성 및 사용성에 큰 영향을 끼치는 중요한 평가 요소이다. NATM공법을 이용한 터널 굴착시 굴착면 빛 벤치부에서 발파에 의해 숏크리트가 부착력을 잃고 암반으로부터 탈락되거나 공동이 형성되는 경우 숏크리트 자체의 파괴뿐만 아니라 터널의 전체적인 안정에도 악영향을 미친다. 숏크리트의 접착상태는 완전 접착, 접착력 상실, 그리고 공동으로 분류할 수 있다. 본 연구에서는 비파괴 시험인 충격반향기법(Impact-Echo)을 이용하여 숏크기트와 암반의 접착상태를 평가하고자 하였다. 범용 유한요소 해석 프로그램인 ABAQUS를 이용하여 충격반향시험에 대한 수치해석을 수행하였다. 수치해석으로부터 획득된 신호를 시간영역, 주파수 영역 및 시간-주파수 영역에서 각각 해석하여 숏크리트와 암반의 접착상태에 따른 신호특성을 분석하였다. 분석결과 능동적 신호 처리 기법인 Short-Time Fourier Transform(STFT)을 이용하여 숏크리트 배면의 접착상태를 효과적으로 예측할 수 있었다. 숏크리트 배면의 접착상태가 불량할수록 시간영역 신호의 최대 진폭 이후 첫 진폭이 커지며, 주파수 영역에서 최대 에너지가 커진다. 또한 뚜렷한 공진 주파수가 나타나므로 숏크리트의 두께의 역산이 가능해진다. 시간-주파수 영역에서 윤곽선은 시간축에 평행한 형상을 나타낸다. 또한 완전 접착조건에서 지반 종류에 따른 신호특성도 분석하였다. 지반조건이 불량할수록 시간-주파수 영역에서 시간축과 평행한 윤곽선의 길이가 길어지며 그 주파수 대역은 10 kHz 이하의 저주파수 영역에서 나타난다.

• 대한기계학회논문집A
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• 제27권4호
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• pp.537-543
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• 2003

Transient vibration generated by developing localized fault in gear can be used as indicators in gear fault detection. This vibration signal suffers from the background noise such as gear meshing frequency and its harmonics and broadband noise. Thus in order to extract the information about the only gear fault from the raw vibration signal measured on the gearbox this signal is processed to reduce the background noise with many kinds of signal-processing tools. However, these signal-processing tools are often very complex and time waste. Thus. in this paper. we propose a novel approach detecting the damage of gearbox and analyzing its pattern using the raw vibration signal. In order to do this, the residual signal. which consists of the sideband components of the gear meshing frequent) and its harmonics frequencies, is extracted from the raw signal by the power spectral density (PSD) to obtain the information about the fault and is used as the input data of the artificial neural network (ANN) for analysis of the pattern of gear fault. This novel approach has been very successfully applied to the damage analysis of a laboratory gearbox.

• 센서학회지
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• 제29권4호
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• pp.249-254
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• 2020

It is difficult to control children who exhibit negative behavior in dental clinics. Various methods are used for preventing pediatric dental patients from being afraid and for eliminating the factors that cause psychological anxiety. However, when it is difficult to apply this routine behavioral control technique, sedation therapy is used to provide quality treatment. When the sleep anesthesia treatment is performed at the dentist's clinic, it is challenging to identify emergencies using the current breath detection method. When a dentist treats a patient that is under the influence of an anesthetic, the patient is unconscious and cannot immediately respond, even if the airway is blocked, which can cause unstable breathing or even death in severe cases. During emergencies, respiratory instability is not easily detected with first aid using conventional methods owing to time lag or noise from medical devices. Therefore, abnormal breathing needs to be evaluated in real-time using an intuitive method. In this paper, we propose a method for identifying abnormal breathing in real-time using an intuitive method. Respiration signals were measured using a 3M Littman electronic stethoscope when the patient's posture was supine. The characteristics of the signals were analyzed by applying the signal processing theory to distinguish abnormal breathing from normal breathing. By applying a short-time Fourier transform to the respiratory signals, the frequency range for each patient was found to be different, and the frequency of abnormal breathing was distributed across a broader range than that of normal breathing. From the wavelet transform, time-frequency information could be identified simultaneously, and the change in the amplitude with the time could also be determined. When the difference between the amplitude of normal breathing and abnormal breathing in the time domain was very large, abnormal breathing could be identified.

• Journal of Welding and Joining
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• 제33권4호
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• pp.24-29
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• 2015

The current waveform was analysed to monitor the weld quality in real time process. The acquired current waveform was discretely analysed for the top and bottom limits of peaks as well as the pulse frequency measurement. Fast Fourier Transform was implemented in the program to monitor the pulse frequency in real time. The developed algorithm or program was tested for the validation purpose. The cross-section of weld profile was compared to the current waveform profile to correlate the monitored signal and the actual parts. Pulse frequency was also used as auxiliary tool for the quality monitoring. Based on the results, it was possible to evaluate the quality of welding by measure the current waveform profile and frequency measurement.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 추계학술대회 논문집 전력기술부문
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• pp.167-170
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• 2002

This paper presents a mode estimation for the analysis of small signal stability in power system. The low frequency oscillation mode estimation is based on Prony method that is able to accurately compute the modal parameters (frequency and damping) of oscillation mode from time series. The time series or time domain data is obtained in TSA process. The method applied to a large scale power systems and compared on the eigenanalysis results.