• 대한유전성대사질환학회지
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• 제15권3호
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• pp.138-146
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• 2015

Purpose: If early diagnosis is not made, patients with metabolic disorders as homocystinemia rapidly progress to physical defect or mental retardation resulted in storage of the toxic material into the brain. Therefore, it is necessary to develop an analytical method for a rapid screening and/or correct confirmation diagnosis. Methods: The standard solution of sulfur amino acids spiked plasma was subjected to protein precipitation with methanol, and then consecutively derivatized with trimethylsilyl (TMS) and trifluoroacyl (TFA) and determined by GC-MS. The formation of TMS derivative of the hydroxyl and TFA derivative of amino functional group was performed by BSTFA and MBTFA, respectively. Selective ion monitoring (SIM) mode was used for quantification with selected specific ions. Results: A calibration curve on standard spiked pooled plasma showed a linear relationship with correlation coefficient of 0.9936-0.9992 for all compounds over the range of 0.1-300 ng. The precision and accuracy were within S.D. of 1-15% and RSD of 1-15% for intra-day assay at 2 ng/mL, 15 ng/mL and 30 ng/mL. LOD and LOQ was 0.4 ng/mL and 4 ng/mL respectively. Conclusion: A rapid analytical method was developed to quantify sulfur amino acids and methyl malonic acid, after two-step derivatization procedure with good sensitivity and specificity on human plasma. Advantages of a new method are simplicity and rapidity. The method could be useful for routine analysis, diagnosis of homocysteinemia.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제51권10호
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• pp.492-499
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• 2002

This paper presents the method for an estimation of discharge current waveform in short gap discharge by radiated electromagnetic fields. The method of current waveform estimation is to use the one antenna method (single field method) with a measured electric or magnetic field at given field point by a time domain antenna. In order to verify the availability of the estimation theory, the discharge current waveform estimation was performed by one antenna methods using the measured electric fields of Wilson & Ma and compared with experiments. In addition diagnosis technique for power equipments is able to prevent from large accidents by finding signs of tile accidents before they happen. From the results of the estimation of discharge current, we have a possibility for the application of insulation diagnosis technique for power equipments using S$F_6$ gas. From this point of view, this paper simulated discharge progress and partial discharge by using needle-plan electrodes system in S$F_6$, studied the distribution of frequency spectrum of the radiated electromagnetic waves using a biconical antenna and a spectrum analyzer. From the experimental results of this study, according to the consideration of the mutual relation between frequency spectrum of the radiated electromagnetic waves and discharge progress, it was confirmed that detecting Partial discharge and estimating discharge progress in S$F_6$ could be Possible.

• JSTS:Journal of Semiconductor Technology and Science
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• 제7권4호
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• pp.221-228
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• 2007

In this paper, we propose a new symbolic simulation for scan chain diagnosis to solve the diagnosis resolution problem. The proposed scan chain fault simulation, called the SF-simulation, is able to analyze the effects caused by faulty scan cells in good scan chains. A new scan chain fault simulation is performed with a modified logic ATPG pattern. In this simulation, we consider the effect of errors caused by scan shifting in the faulty scan chain. Therefore, for scan chain diagnosis, we use the faulty information in good scan chains which are not contaminated by the faults while unloading scan out responses. The SF-simulation can tighten the size of the candidate list and achieve a high diagnosis resolution by analyzing fault effects of good scan chains, which are ignored by most previous works. Experimental results demonstrate the effectiveness of the proposed method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.292-297
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• 2004

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) which is independent of the CNC vendor. The OAC and machine tools with OAC led the convenient environment where it is possible to implement user-defined application programs efficiently within CNC. Tis paper proposes a method of operational fault cause diagnosis which is based on the status of programmable logic controller (PLC) in machine tools with OAC. The operational fault is defined as a disability state occurring during normal operation of machine tools. The faults are occupied by over 70% of all faults and are also unpredictable as most of them occur without any warning. Two diagnosis models, the switching function (SF) and the step switching function (SSF), are propose in order to diagnose the fault cause quickly and exactly. The cause of an occurring fault is logically diagnosed through a fault diagnosis system (FDS) using the diagnosis models. A suitable interface environment between CNC and develope application modules is constructed in order to implement the diagnostic functions in the CNC domain. The diagnosed results were displayed on a CNC monitor for machine operators and provided to a remote site through a web browser. The result of his research could be a model of the fault cause diagnosis and the remote monitoring for machine tools with OAC.

• Asian-Australasian Journal of Animal Sciences
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• 제10권6호
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• pp.603-608
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• 1997

To improve animal production, a simple and accurate pregnancy diagnosis plays a very important role. Therefore, the purpose of this study was to develop an on-farm blood progesterone enzyme immunoassay (EIA) system for monitoring the early pregnancy in sows. Star tubes coated with mouse monoclonal anti-progesterone antibody were used for this proposed EIA system which was tested in field trials. The results could be obtained within 30 minutes either by spectrophotometry or the naked eye. Heparinized fresh blood samples collected from the ear vein of sows 17-22 days after breeding (day 0) were tested qualitatively to diagnose sows as pregnant or non-pregnant with high ( > 3 ng/ml) or low ($${{\leq_-}}3ng/ml$$) progesterone in the blood. To provided a double check data, plasma progesterone levels were also measured quantitatively by the same EIA system with some modification. Total agreement of diagnosis by the on-farm EIA kit and by farrowing or abortion from 128 tested sows was found to be 92.2% accuracy (93.1% on pregnant diagnosis and 83.3% on non-pregnant diagnosis). It was concluded that the on-farm EIA blood progesterone test is a very useful method for monitoring the early pregnancy status of sows.

• Journal of Power Electronics
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• 제16권5호
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• pp.1981-1989
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• 2016

Battery diagnosis is vital to battery-based applications because it ensures system reliability by avoiding battery failure. This paper presents a novel intelligent battery charger with an online diagnosis function to circumvent interruptions in system operation. The charger operates in normal charging and diagnosing modes. The diagnosis function is performed with the impedance spectroscopy technique, which is achieved by injecting a sinusoidal voltage excitation signal to the battery terminals without the need for additional hardware. The impedance spectrum of the battery is calculated based on voltage excitation and current response with the aid of an embedded digital lock in amplifier in a digital signal processor. The measured impedance data are utilized in the application of the complex nonlinear least squares method to extract the battery parameters of the equivalent circuit. These parameters are then compared with the reference values to reach a diagnosis. A prototype of the proposed charger is applied to four valve-regulated lead-acid batteries to measure AC impedance. The results are discussed.

• 한국소음진동공학회논문집
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• 제16권1호
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• pp.27-33
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• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

• 정보처리학회논문지B
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• 제16B권2호
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• pp.165-172
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• 2009

최근 ACS에 따르면 여성에게 유방암은 가장 많이 발병하는 암으로서 그 사망자 수도 두 번째로 많은 암이다. 유방 X선 영상의 종괴나 석회 환부는 진단을 위한 가장 중요한 단서로서 알려져 있으므로 유방암의 조기진단을 위하여 디지털 유방 X선 영상을 컴퓨터에서 처리하는 연구가 진행되고 있다. 본 논문에서는 퍼지 클러스터 지식베이스에 기반을 둔 진단시스템을 제안한다. 제안된 시스템은 듀얼 OFUN-NET에 두 가지 종류의 특징 데이터를 처리하여 진단결과와 그 가능성을 알려준다. 실세계 의료기관으로부터 수집되고 공개적으로 제공되는 유방 X선 데이터베이스 DDSM으로부터 획득한 종괴와 석회 환부의 데이터를 사용하여 실험한다. 실험결과는 제안된 시스템이 기존의 방법보다 높은 분류 정확도와 유방 X선 영상 진단시스템으로서 전문가의 의사 결정을 도울 수 있는 타당한 결과를 보여준다.

• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• 한국컴퓨터정보학회논문지
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• 제26권2호
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• pp.39-44
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• 2021

본 논문에서는 가정에서 많이 기르는 반려견을 바탕으로 반려견 질병에 대한 전문적인 수의학 지식이 부족한 일반인들을 대상으로 자신의 반련견의 건강 상태를 파악할 수 있는 진단 시스템을 제안한다. 제안된 진단 시스템은 50가지 질병과 각 질병의 증상을 데이터베이스에 구축하여 입력된 증상을 통해서 반려견의 질병을 도출한다. 각 질병 데이터베이스에는 질병에 해당하는 증상 코드들을 가지고 있으며, 이러한 질병에 대한 데이터베이스를 이용하여 군집화 기법인 FCM 클러스터링 기법을 적용하여 질병을 클러스터링하고 그 결과 값인 소속도를 바탕으로 입력된 증상과 가까운 질병들을 도출하여 반려견의 진단 결과를 제공한다. 제안된 반려견 진단의 구현 결과에서는 선택한 증상들의 개수와 선택된 증상들이 포함된 질병들의 가능성 값을 구하여 내림차순으로 정렬하여 반려견의 증상과 가장 가까운 질병 상위 3가지를 도출하였다.