• Biomedical Science Letters
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• v.17 no.4
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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.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.1
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• pp.49-59
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• 2021

This study investigated the use of real-time PCR melting curves for the diagnosis of blaKPC and blaNDM genes among the most frequently detected carbapenemase-producing Enterobacteriaceae in Korea. As a means of addressing the shortcomings of phenotype tests and conventional PCR. The modified Hodge test confirmed positivity in 25 of 35 strains, and carbapenemase inhibition testing confirmed positivity in 14 strains by meropenem+PBA or meropenem+EDTA. PCR analysis showed amplification products in 25 strains of Klebsiella pneumoniae carbapenemases (KPC), 10 of K. pneumoniae, 5 of E. coli, 5 of A. baumannii, 4 of P. aeruginosa, and 1 of P. putida. New Delhi metallo β-lactamase (NDM) identified amplification products in 8 strains, that is, 2 K. pneumoniae, 3 E. coli, 1 P. aeruginosa, 1 E. cloacae, and 1 P. retgeri strains. Real-time PCR melting curve analysis confirmed amplification in 25 strains of KPC and 8 strains of NDM, and these results were 100% consistent with PCR results. In conclusion, our findings suggest early diagnosis of carbapenem resistant Enterobacteriaceae by real-time PCR offers a potential means of antibacterial management that can prevent and control nosocomial infection spread.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.31-38
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• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.

• Journal of Microbiology and Biotechnology
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• v.32 no.1
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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.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.11
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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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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• v.55 no.2
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05b
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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.