• Journal of Advanced Marine Engineering and Technology
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• v.34 no.2
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• pp.332-338
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• 2010

Annually, electrical fires caused by arcing phenomena in power system rapidly increase as the use of more electric appliances, but there is no established method for the prevention of the accidents. With this background, this paper dealt with the experimental results on a series arc detection technique and a device for air conditioners. Series arcing phenomena that is generated in incomplete connection of air conditioners was simulated, and the frequency spectrum was analyzed. The Fast Fourier Transform (FFT) of the arc pulse showed that the dominant frequency components exist in ranges of 190 kHz~250 kHz and 900 kHz~1.6 MHz. An arc detection circuit with low cut off frequency of 170 kHz to attenuate 60 Hz by 170 dB and a signal discriminator were designed. Also, an algorithm which separate series arc signal from unwanted noises produced by switching operation, inverter, and surge was proposed. Application experiment was carried out on several types of air-conditioners by using the arc generator specified in UL1699, and the results showed the over 99 % accuracy.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.163-174
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• 2002

Speeding has been reported as one of the major causes for fatal traffic accidents in Korea. The resolution against this dangerous speeding comes to make the automated speed enforcement system an enforcement tool. The speed detection device, which measures speeds of each incoming vehicles using double loop sensors, requires high accuracy. The object of this study is to develop an accurate speed measurement algorithm using double loop detectors. Some important findings are summarized as follows: 1) It was found that speed measurement errors are caused by scanning rate, distance of two loops, irregular vehicle trajectories, multiple vehicles in detection zone. 2) A proposed algorithm using two signal set proved to reduce variance as well as mean of speed measurement. 3) A proposed filtering algorithm was effective to filter irregular driving vehicles and multiple vehicles in detection zone. A comprehensive field test of developed algorithm resulted in significant improvement of speed measurement accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1758-1764
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• 2012

A large number of wire rope has been used in various inderstiries as Cranes and Elevators from expanding the scale of the industrial market. But now, the management of wire rope is used as manually operated by rope replacement from over time or after the accident.It is caused to major accidents as well as economic losses and personal injury. Therefore its time to need periodic fault diagnosis of wire rope or supply of real-time monitoring system. Currently, there are several methods has been reported for fault diagnosis method of the wire rope, to find out the feature point from extracting method is becoming more common compared to time wave and model-based system. This method has implemented a deterministic modeling like the observer and neural network through considering the state of the system as a deterministic signal. However, the out-put of real system has probability characteristics, and if it is used as a current method on this system, the performance will be decreased at the real time. And if the random noise is occurred from unstable measure/experiment environment in wire rope system, diagnostic criterion becomes unclear and accuracy of diagnosis becomes blurred. Thus, more sophisticated techniques are required rather than deterministic fault diagnosis algorithm. In this paper, we developed the fault diagnosis of the wire rope using probability density estimation techniques algorithm. At first, The steady-state wire rope fault signal detection is defined as the probability model through probability distribution estimate. Wire rope defects signal is detected by a hall sensor in real-time, it is estimated by proposed probability estimation algorithm. we judge whether wire rope has defection or not using the error value from comparing two probability distribution.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.605-610
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• 2016

The ELCB(: Earth Leakage Circuit Breaker) is the only way being used to prevent accidents from happening caused by electrical disaster. However, the existing ELCB has a limit to prevent damages to life and property due to a electric fire and a human body electric shock caused by the resistive leakage current, because of detecting the total leakage current in the block range of 15mA~30mA. It also has problems such as reduced productivity and reliability due to malfunctions by capacitive leakage currents. In this study, we have implemented the algorithm for the resistive leakage current detection technique and developed the resistive leakage current detection module based on a MSP430 processor, 16bit signal processor and this module can be operated in a desired block threshold within 0.03 seconds as specified in KS C 4613.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.957-966
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• 2020

According to the recent statistics of the National Industrial Security Center, about 80% of the confidential leak are caused by former and current employees in the case of domestic confidential leak accidents. Most of the information leak incidents by these insiders are due to poor security management system and information leak detection technology. Blocking confidential leak of insiders is a very important issue in the corporate security sector, but many previous researches have focused on responding to intrusions by external threats rather than by insider threats. Therefore, in this research, we design an internal information leak scenario to effectively and efficiently detect various abnormalities occurring in the enterprise, analyze the key indicators of the leak symptoms derived from the scenarios by using data analytics and propose a model that accurately detects leak activities.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.1
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• pp.45-52
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• 2016

Blast furnaces are crucial equipment for steel production. A typical furnace risks unexpected accidents caused by contraction and expansion of the walls under an environment of high temperature and pressure. In this study, an acoustic emission (AE) monitoring system was tested for evaluating the large-scale structural health of a blast furnace. Based on the growth of shell cracks with the emission of high energy levels, severe damage can be detected by monitoring increases in the AE energy parameter. Using this monitoring system, steel mill operators can establish a maintenance period, in which actual shell cracks can be verified by cross-checking the UT. From this study, we expect that AE systems permit early fault detection for structural health monitoring by establishing evaluation criteria based on the severity of shell cracking.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.197-203
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• 2015

Nuclear power plant has increased continuously for power production in all over the world and the interest about nuclear accident and the dismantling of aging nuclear power plant has been a growing. The leaked radioactive source that is generated by radiation accidents must detect and remove to minimized the damage as soon as possible. Gamma-ray detection system that have been developed until now cannot provide the precise position of radioactive sources because they detect and imaging the position of radiation sources in just two dimensions. In this paper, stereo gamma ray detection system has developed and the algorithm for calculation of the distance has implemented to be able to measure the distribution of the leakage gamma ray source for the system. Stereo camera calibration for distance detection was conducted with the correction pattern and LED light and we carried out performance test of the system for the LED light source and a gamma ray source. In both experiments the results of the performance test, it was confirmed to have a 5% error. The results of this paper is used as a material for the development of gamma-ray imaging device.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.211-218
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• 2021

A recent spread of personal mobility devices such as electric kickboards has brought about a rapid increase in accident cases. Such vehicles are susceptible to falling accidents due to their low dynamic stability and lack of outer protection chassis. This paper presents the development of an automatic emergency braking system and a safe starting system as driving assistance devices for electric kickboards. The braking system employed artificial intelligence to detect nearby threaening objects. The starting system was developed to disable powder to the motor until when the driver's boarding is confirmed. This study is meaningful in that it proposes the convergence technology of advanced driver assistance systems specialized for personal mobility devices.