• 전기저널
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• 7호통권115호
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• pp.28-32
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• 1986

• 한국자동차공학회논문집
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• 제18권2호
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• pp.104-113
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• 2010

Domestic insurance claims were statistically investigated to analyze the elderly driver's accident patterns and injury types in side impact crashes. Medical treatment records and accident vehicle damage photos have been surveyed for 5,419 cases. The results of our statistical analysis showed that the thorax injury risk of the elderly drive group is 8.8 and 4.0 times higher than that of the young and middle age group respectively. Diagnosis showed that most thorax injuries were caused by rib fracture. The head injury risk of the elderly female driver group seemed to be higher than that of the younger female driver group, however, statistical test has not been conducted because of the lack of number of samples for elderly female accident.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1005-1018
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• 2017

This paper proposes an App Visualization (AppV) based on IoT Self-diagnosis Micro Control Unit (ISMCU) for accident prevention. It collects a current status of a vehicle through a sensor, visualizes it on a smart phone and prevents vehicles from accident. The AppV consists of 5 components. First, a Sensor Layer (SL) judges noxious gas from a current vehicle and a driver's driving habit by collecting data from various sensors such as an Accelerator Position Sensor, an O2 sensor, an Oil Pressure Sensor, etc. and computing the concentration of the CO collected by a semiconductor gas sensor. Second, a Wireless Sensor Communication Layer (WSCL) supports Zigbee, Wi-Fi, and Bluetooth protocol so that it may transfer the sensor data collected in the SL to ISMCU and the data in the ISMCU to a Mobile. Third, an ISMCU integrates the transferred sensor information and transfers the integrated result to a Mobile. Fourth, a Mobile App Block Programming Tool (MABPT) is an independent App generation tool that changes to visual data just the vehicle information which drivers want from a smart phone. Fifth, an Embedded Module (EM) records the data collected through a Smart Phone real time in a Cloud Server. Therefore, because the AppV checks a vehicle' fault and bad driving habits that are not known from sensors and performs self-diagnosis through a mobile, it can reduce time and cost spending on accidents caused by a vehicle's fault and noxious gas emitted to the outside.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.171-174
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• 2005

The circuit simulation analysis and diagnosis methods are used to instruments in detail when they give apparently abnormal readings. In this paper, a new simulator through an analysis of the important circuits modeling under severe accident conditions has been designed, the realization for a body work instead of the two sorts of the Labview & Pspice as an one order command in the Labview program. The program can be shown the output graph form the circuit modeling as an order commend. The procedure for the simulator design was divided into two design steps, of which the first step was the diagnosis methods, the second step was the circuit simulator for the signal processing tool. It has three main functions which are a signal processing tool, an accident management tool, and an additional guide from the initial screen.

• Korean Chemical Engineering Research
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• 제60권2호
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• pp.223-228
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• 2022

화학공정에서 의도되지 않게 발생하는 이상은 큰 사고를 유발할 수 있다. 이러한 문제를 해결하기 위해, 신속하게 이상의 원인을 감지하고 판별하는 이상 진단 모델이 필요하다. 하지만, 이상 진단을 연구하는 대부분 연구의 경우, 상용프로그램에서 공정 시뮬레이션을 이용하여 이상 데이터를 생성하고 이를 이용하여 연구한 방법론을 적용하고 있다. 이는 실제 공정상에서 이상을 포함하는 실제 데이터를 얻는 데 많은 제약이 있음을 의미한다. 본 연구에서는 실제 폴리스티렌 반응기에서 얻은 이상 데이터와 정상 데이터를 분석하여 적절한 이상 진단 모델을 설계하고자 하였다. 먼저, 정상 데이터를 분석하여 세 가지의 조업 모드가 존재함을 확인하였으며, 모드 판별을 위한 모델을 SVM (Support Vector Machine)을 이용하여 만들었다. 각 조업 모드 별로 PCA (Principal Component Analysis)를 이용하여 이상 진단 모델을 만들었으며, 실제 이상 데이터를 이용하여 계산한 결과 신속하게 이상을 진단할 수 있음을 확인하였다. 본 연구에서 제안한 모델을 통해, 실제 사고가 발생하는 경우 신속한 대처가 가능하며, 이는 잠재적인 손실의 감소에 기여할 수 있음을 의미한다.

• Journal of applied mathematics & informatics
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• 제8권2호
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• pp.505-518
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• 2001

The huge and complicated plants such as nuclear power stations are likely to cause the operators to make mistakes due to a variety of inexplicable reasons and symptoms in case of emergency. That’s why the prevention system assisting the operators is being developed for. First of all. I suggest an improved fuzzy diagnosis. Secondly, I want to demonstrate that a classification system of nuclear plant’s accident investigating the causes of accidents foresees possible problems, and maintains the reliability of the diagnostic reports in spite of improper working in part. In the event of emergency in a nuclear plant, a lot of operational steps enable the operators to find out what caused the problems based on an emergent operating plan. Our system is able to classify their types within twenty to thirty seconds. As so, we expect the system to put down the accidents right after the rapid detection of the damage control-method concerned.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
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• pp.315-317
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• 2009

When happen the electrical facilities accident, the one's diagnosis system of fault cause was constructed by FMEA method. From the verification of system, the one's diagnosis system agreed well with result that analyzed actual stale. Thus, the system is judged to be used effectively examine for accident cause of electrical facilities.

• 조명전기설비학회논문지
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• 제28권4호
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• pp.16-20
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• 2014

To prevent any accident in electric power utilities, many researches for inspection and diagnosis deteriorations occurring by corona discharges have been continuously studying. Inspection and diagnosis of electric power utility is very important to prevent an accident. This paper studies a measurement of ultra-violet(UV) ray of corona discharges on polymer insulators using an UV sensor array with an optic lens. The detection of an UV signal begins at 60kV, which is about 37.5% of the breakdown voltage of the polymer insulator and the stronger the high voltage increased to the polymer insulator was. It can be determined that the polymer insulator mounted on a live part must be examined when the discharge risk exceeds approximately 40%. In conclusion, the status of power utilities can be checked using an UV sensor.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2005년도 춘계학술발표회
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• pp.1259-1260
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• 2005