• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 추계학술대회 논문집
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• pp.409-412
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• 2000

A high performance EEG signal measurement system is fabricated. It consists of high precision pre-amplifier and auto identification bandwidth unit. High precision pre-amplifier is composed of signal generator, signal amplifier with a impedance converter, body driver and isolation amplifier. The pre-amplifier is designed for low noise characteristics, high CMRR, high input impedance, high IMRR and safety, Auto identification bandwidth unit is composed of AD-converter and PIC micro-controller for real time processing EEG signal. The performance of EEG signal measurement system has been shown the classified bandwidth through the clinical demonstrations.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• Structural Engineering and Mechanics
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• 제15권3호
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• pp.299-314
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• 2003

This investigation presents an efficient method for identifying modal characteristics from the measured displacement, velocity and acceleration signals of multiple channels on structural systems. A Vector Backward Auto-Regressive model (VBAR) that describes the relationship between the output information in different time steps is used to establish a backward state equation. Generally, the accuracy of the identified dynamic characteristics can be improved by increasing the order of the Auto-Regressive model (AR) in cases of measurement of data under noisy circumstances. However, a higher-order AR model also induces more numerical modes, only some of which are the system modes. The proposed VBAR model provides a clear characteristic boundary to separate the system modes from the spurious modes. A numerical example of a lumped-mass model with three DOFs was established to verify the applicability and effectiveness of the proposed method. Finally, an offshore platform model was experimentally employed as an application case to confirm the proposed VBAR method can be applied to real-world structures.

• 자동차안전학회지
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• 제9권4호
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• pp.48-54
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• 2017

This paper presents the motorcycle inspection standards development on the vehicle identification, engine and transmission, tyres and wheels, steering and suspension, and brake system. 187 real-world motorcycles are visually and mechanically inspected according to the developed inspection standards. The non-compliance rate of the vehicle identification is 20.3% and main causes are insecure, damaged, and not clearly visible number plate. The non-compliance rate of the brake system is 15.5% and main cause is failing to meet the brake performance requirements. The motorcycle inspections standards are improved reflecting 187 cases of real-world motorcycle inspection results.

• 한국압력기기공학회 논문집
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• 제13권1호
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• pp.40-47
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• 2017

In this paper, we investigate the feasibility of a strategy of failure detection and identification. The point of proposed strategy includes a pattern extraction approach for failure identification using Auto-Associative Kernel Regression (AAKR). We consider a simulation data concerning 605 signals of a Generic Pressurized Water Reactor(GPWR). In the application, the reconstructions are provided by a set of AAKR models, whose input signals have been selected by Correlation Analysis(CA) for the identification of the groups. The failure pattern is extracted by analyzing the residuals of observations and reconstructions. We present the possibility of extraction of patterns for six failure.

• 대한세포병리학회지
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• 제9권2호
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• pp.139-146
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• 1998

The AutoPap 300 QC System is an automated device for the analysis and classification of conventional cervical cytology slides for quality control purpose. These studies evaluated the sensitivity of the AutoPap 300 QC System, and estimated morphologic features other than epithelial abnormality to identify a high quality control(QC) score with the AutoPap 300 QC System. The sensitivity of the AutoPap 300 QC System at 10% review rate for 210 cases of cervicovaginal cytology with low grade squamous intraepithelial lesion(LSIL) and higher grade lesion was assessed, and compared with a 10% random rescreening. The morphologic features, such as presence of endocervical component, dirty background, atrophy, abnormal ceil size, and celluiarity of single atypical cells were estimated in 45 cases of no review and 30 cases of QC review cases. The AutoPap 300 QC System identified 119(56.7%) out of 210 cases with LSIL and higher grade lesion at 10% review rate. It was more sensitive to squamous cell lesions$(50{\sim}62%)$ than to glandular lesions(10%). The dirty background and the scanty cellularity of single atypical cells were significantly related to low QC score. Conclusively, AutoPap 300 QC System is superior to human random rescreen for the identification of false negative smears. The upgrading of this device is required to enhance the defection of glandular lesion and certain Inadequate conditions of the slides.

• 대한기계학회논문집B
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• 제41권5호
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• pp.321-328
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• 2017

본 연구에서는 고온측과 저온측의 온도 예측을 위한 볼텍스 튜브 모델을 개발하였다. 볼텍스 튜브 모델은 시스템 식별 방법을 기반으로 개발하였으며, 개발된 볼텍스 튜브 모델은 ARX(Auto-Regressive with eXtra inputs)모델을 기반으로 하여 설계되었다. 본 연구에서 유도된 다항식 모델은 모델의 정확성을 확인하기 위해 실험데이터와 검증하였다. 또한, 유도된 모델은 안정성 검사 통과를 보여준다. 저온측 스로틀 밸브 각도를 변경하였을 때, 적절히 온도 분리가 이루어지는 것을 확인하였으며, 동적응답을 확인하기 위해 저온측 스로틀 밸브 각도를 변경 시켰을 경우, 볼텍스 튜브 모델의 온도가 적절히 분리 되는 것을 확인할 수 있다. 결론적으로, 개발된 볼텍스 튜브 모델을 저온측 스로틀 밸브 각도에 따라 온도 분리 예측이 가능하다는 것을 확인할 수 있다.

• 한국정보통신학회논문지
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• 제9권4호
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• pp.712-719
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• 2005

인터넷과 컴퓨터 네트워크의 발전에 힘입어 차세대 컴퓨팅 패러다임으로 유비쿼터스 컴퓨팅이 등장하였고, 이를 실현하기 위한 핵심 기술로 RFID(Radio Frequency Identification) 기술이 주목받고 있다. 이러한 RFID 응용 시스템에서 상호간의 데이터 교환을 위해 MIT Auto-ID Center 에서는 물리적 객체의 정보를 표현하기 위한 표준 언어로 XML(Extensible Markup Language) 기반의 PML(Physical Markup Language)을 제안하여 사용하고 있다. 하지만 Auto-ID Center에서 제시한 PML은 객체의 정보를 기술하기 위한 핵심부분만 정의 되어 있고, 실제 적용에 필요한 부분은 별도로 확장 정의하여 사용하도록 되어 있다. 이에 본 논문에서는 Auto-ID Center의 PML Core에 기반하여 RFID 응용 서비스에서 전자태그 정보 처리를 위해 사용될 수 있는 객체 타입을 정의하고 물류 유통 서비스 분야에 적용할 수 있도록 객체 정보 데이터 모델을 설계하였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.763-766
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• 2005

인터넷과 컴퓨터 네트워크의 발전에 힘입어 차세대 컴퓨팅 패러다임으로 유비쿼터스 컴퓨팅이 등장하였고, 이를 실현하기 위한 핵심 기술로 RFID(Radio Frequency Identification) 기술이 주목받고 있다. 이러한 RFID 응용 시스템에서 상호간의 데이터 교환을 위해 MIT Auto-lD Center 에서는 물리적 객체의 정보를 표현하기 위한 표준 언어로 XML(Extensible Markup Language) 기반의 PML(Physical Markup Language)을 제안하여 사용하고 있다. 하지만 Auto-lD Center에서 제시한 PML은 객체의 정보를 기술하기 위한 핵심부분만 정의 되어 있고, 실제 적용에 필요한 부분은 별도로 확장 정의하여 사용하도록 되어 있다. 이에 본 논문에서는 Auto-lD Center의 PML Core에 기반하여 RFID 응용 서비스에서 전자태그 정보 처리를 위해 사용될 수 있는 객체 타입을 정의하고 물류 유통 서비스 분야에 적용할 수 있도록 객체 정보 데이터 모델을 설계하였다.

• Structural Engineering and Mechanics
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• 제15권3호
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• pp.285-297
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• 2003

Complementing recent advances made in the field of structural health monitoring and damage detection, the concept of a wireless sensing network with distributed computational power is proposed. The fundamental building block of the proposed sensing network is a wireless sensing unit capable of acquiring measurement data, interrogating the data and transmitting the data in real time. The computational core of a prototype wireless sensing unit can potentially be utilized for execution of embedded engineering analyses such as damage detection and system identification. To illustrate the computational capabilities of the proposed wireless sensing unit, the fast Fourier transform and auto-regressive time-series modeling are locally executed by the unit. Fast Fourier transforms and auto-regressive models are two important techniques that have been previously used for the identification of damage in structural systems. Their embedment illustrates the computational capabilities of the prototype wireless sensing unit and suggests strong potential for unit installation in automated structural health monitoring systems.