• 대한의생명과학회지
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• 제17권4호
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• pp.297-304
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• 2011

Influenza A virus of the Orthomyxoviridae family is a contagious respiratory pathogen that continues to evolve and burden in the human public health. It is able to spread efficiently from human to human and have the potential to cause pandemics with significant morbidity and mortality. It has been estimated that every year about 500 million people are infected with this virus, causing about approximately 0.25 to 0.5 million people deaths worldwide. Influenza A viruses are classified into different subtypes by antigenicity based on their hemagglutinin (HA) and neuraminidase (NA) proteins. The sudden emergence of influenza A virus subtypes and access for epidemiological analysis of this subtypes demanded a rapid development of specific diagnostic tools. Also, rapid identification of the subtypes can help to determine the antiviral treatment, because the different subtypes have a different antiviral drug resistance patterns. In this study, our aim is to detect influenza A virus subtypes by using real-time nucleic acid sequence based amplification (NASBA) which has high sensitivity and specificity through molecular beacon. Real-time NASBA is a method that able to shorten the time compare to other molecular diagnostic tools and is performed by isothermal condition. We selected major pandemic influenza A virus subtypes, H3N2 and H5N1. Three influenza A virus gene fragments such as HA, NA and matrix protein (M) gene were targeted. M gene is distinguished influenza A virus from other influenza virus. We designed specific primers and molecular beacons for HA, NA and M gene, respectively. In brief, the results showed that the specificity of the real-time NASBA was higher than reverse transcription polymerase chain reaction (RT-PCR). In addition, time to positivity (TTP) of this method was shorter than real-time PCR. This study suggests that the rapid detection of neo-appearance pandemic influenza A virus using real-time NASBA has the potential to determine the subtypes.

• 대한임상검사과학회지
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• 제53권1호
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• pp.49-59
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• 2021

본 연구는 국내에서 가장 빈번히 검출되는 CPE의 유전자형 중 blaKPC 및 blaNDM 유전자의 진단으로 기존의 표현형 검사 및 일반 PCR 검사보다 시간 단축 및 사후 분석의 단점이 보완된 real-time PCR의 융해 곡선을 이용한 분석법에 대해 알아보았다. 표현형적 검사결과 MHT는 35균주 중 25균주에서 양성을 확인하고, CIT는 meropenem+PBA 및 meropenem+EDTA에서 각각 14균주의 양성을 확인하였다. PCR 검사결과 KPC 25균주에서 증폭 산물을 확인하였고, K. pneumoniae 10균주, E. coli 5균주, A. baumannii 5균주, P. aeruginosa 4균주, P. putida 1균주로 나타났다. NDM은 8균주에서 증폭 산물을 확인하였고, K. pneumoniae 2균주, E. coli 3균주, P. aeruginosa 1균주, E. cloacae 1균주, P. rettgeri 1균주로 나타났다. 실시간 중합효소연쇄반응(Real-time PCR)을 이용한 융해 곡선 분석결과 KPC 25균주, NDM은 8균주에서 증폭을 확인하였고, PCR 결과와 100% 일치함을 확인하였다. 결론적으로 real-time PCR을 이용한 신속하고 특이성이 높은 CRE의 조기진단은 공중보건 및 감염 중에 항균 관리접근법을 통한 병원 내 감염확산 방지 및 통제가 가능할 것으로 사료된다.

• 융합정보논문지
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• 제12권1호
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• pp.31-38
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• 2022

본 논문은 전문가 독립적 비지도 신경망 학습 기반 다변량 시계열 데이터 분석 모델인 MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder)의 실제 현장에서의 적용과 Auto-encoder 기반인 MSCRED 모델의 한계인, 학습 데이터가 오염되지 않아야 된다는 점을 극복하기 위한 학습 데이터 샘플링 기법인 Subset Sampling Validation을 제시한다. 라벨 분류가 되어있는 발전소 장비의 진동 데이터를 이용하여 1) 학습 데이터에 비정상 데이터가 섞여 있는 상황을 재현하고, 이를 학습한 경우 2) 1과 같은 상황에서 Subset Sampling Validation 기법을 통해 학습 데이터에서 비정상 데이터를 제거한 경우의 Anomaly Score를 비교하여 MSCRED와 Subset Sampling Validation 기법을 유효성을 평가한다. 이를 통해 본 논문은 전문가 독립적이며 오류 데이터에 강한 이상 진단 프레임워크를 제시해, 다양한 다변량 시계열 데이터 분야에서의 간결하고 정확한 해결 방법을 제시한다.

• Journal of Microbiology and Biotechnology
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• 제32권1호
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• pp.91-98
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• 2022

Tetanus is a potentially fatal public health illness resulted from the neurotoxins generated by Clostridium tetani. C. tetani is not easily culturable and culturing the relevant bacteria from infected wounds has rarely been useful in diagnosis; PCR-based assays can only be conducted at highly sophisticated laboratories. Therefore, a real-time recombinase polymerase amplification assay (Exo-RPA) was constructed to identify the fragments of the neurotoxin gene of C. tetani. Primers and the exo probe targeting the conserved region were designed, and the resulting amplicons could be detected in less than 20 min, with a detection limit of 20 copies/reaction. The RPA assay displayed good selectivity, and there were no cross-reactions with other infectious bacteria common in penetrating wounds. Tests of target-spiked serum and pus extract revealed that RPA is robust to interfering factors and has great potential for further development for biological sample analysis. This method has been confirmed to be reliable for discriminating between toxic and nontoxic C. tetani strains. The RPA assay dramatically improves the diagnostic efficacy with simplified device architecture and is a promising alternative to real-time PCR for tetanus detection.

• 한국가스학회지
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• 제18권3호
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• pp.53-59
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• 2014

본 연구에서는 비정상상태 운전을 기본으로 하는 CNG 충전소를 대상으로 다변량 통계분석방법 중의 하나인 다차원의 대용량 데이터 처리에 적합한 주성분분석(PCA) 기법을 사용하여 실시간 이상감지 및 진단이 가능한 모니터링 시스템을 제안하였다. CNG 충전소로부터 매초 간격으로 수집되는 7개의 압력센서 데이터와 5개의 온도센서 데이터의 주요 경향을 나타내는 변수들의 조합으로 주성분이라 불리는 새로운 특성변수들을 산출하고, 분산의 분포를 통해 특성변수의 계산으로부터 모델을 구축하였다. 모니터링은 구축된 모델을 통해 운전 중의 실시간 데이터를 반영하여 진행된다. 시스템 검증 및 정확성을 개선하기 위해 모니터링 테스트를 수행한 결과, 정상상태의 모든 데이터를 정상으로 판단하였고, 이상 데이터의 성공적인 검출 시 관련 변수를 추적하여 비정상 원인을 찾아낼 수 있었다.

• 한국소음진동공학회논문집
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• 제14권11호
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• pp.1091-1100
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• 2004

This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 추계학술대회 논문집 Vol.16
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• pp.575-578
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• 2003

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• 전기학회논문지
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• 제59권4호
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• pp.728-732
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• 2010

GIS(Gas insulated switching gear) is power equipment with excellent dielectric strength and is economy merit in high confidence and stability. Recently, because equipment of GIS was occurring problem of confidence used for a long time, partial discharge on-line diagnosis systems have been importantly recognized. Partial discharge (PD) detection is an effective means for monitoring and evaluation of dielectric condition of gas insulated system (GIS). The ultra-high-frequency (UHF) PD detection technique can detect and locate the PD sources inside GIS by detecting electromagnetic wave emitted from PD source. Therefore, real-time diagnostic system using UHF detection method has been developed for this application is being expanded gradually. However, the signal of partial discharge occurring in SF6 gas is very weak and susceptible to external noises which mainly consist of PD in air. Thus, it is important to distinguish the PD in SF6 gas more sensitively from the external noises. Unfortunately, these external noise signals and the partial discharge signals have very similar characteristics. Therefore, to solve this problem, we need the signal processing method for distinguish partial discharge signals with external noise signals for improvement of SNR(signal to noise ratio) and sensitivity. In this paper, we proposed internal signal processing method for removing external noise signals with built-in pre.amplifier and frequency conversion circuit.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 춘계학술대회 논문집 반도체재료
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• pp.75-78
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• 2001

Recently, the HFPD measurement testing is widely used in partial discharge measurement of HV machines because HFPD measurement testing receives less influence of external noise and has a merit of good sensitivity. Also HFPD testing is able to offer the judgement standard of degradation level of HV machine and can detect discharge signals in live-line. Therefore it is very useful method compare to previous conventional PD testing method and effective diagnosis method in power transformer that requires live-line diagnosis. But partial discharges have very complex characteristics of discharge pattern so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated transformer is manufactured and HFPD occurred from transformer is measured with broad band antenna in real time, the degradation grade of transformer is analyzed through produced patterns in simulated transformer according to applied voltages.