• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.10
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• pp.791-798
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• 2018

The CFAR algorithm is mainly used for target detection in radar systems. In particular, OS CFAR is used in a non-uniform noise environment. However, it requires a large amount of computation, because it should sort reference cells in ascending order. This makes it difficult to apply the radar system in real time. In this paper, we describe how to reduce the computational burden of OS CFAR. We compared the power of the test cell and reference cell to determine only the presence or absence of target detection. The common reference cells overlapping in the reference cells of the three test cells are obtained. We first compare the test cell with the highest power value among the three test cells to the common reference cells. Next, we compare each test cell to general reference cells, excluding the common reference cells. The computation time is shortened by reducing the power comparison computation amounts.

• Journal of the Korean Society of Safety
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• v.32 no.2
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• pp.38-45
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• 2017

This paper is an experimental study to find an optimal detection method for detecting fire early in a rack-type warehouse stored with goods. In this study, we constructed rack-type structure with the fourth floor of 13.5 m high and conducted fire experiments which were to measure flow of heat/smoke in rack-type structure and response time of fire detectors. The detectors used at experiments were fixed temperature type detectors, rate of rise detectors, photoelectric smoke detectors, air sampling smoke detectors and flame detectors. The used ignition sources are n-heptane fire for response of heat detection and cotton fire for response of smoke detection. The fixed temperature type detectors, rate of rise detectors and photoelectric detectors were installed to every rack level respectively. The results show that the rate of rise detector should be installed every 2 levels and photoelectric smoke detector should be installed every 4 levels for the early stage fire detection. Air sampling smoke detectors can detect fire early in response to control of sensitivity, but there is a problem in false alarm. The fixed temperature detector is not suitable for early stage fire detection in warehouse and flame detector not worked if flame is not visible, so it need to install combination with other detector.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.623-631
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• 2011

In this study, a new method which can detect incidents in interrupted traffic flow was suggested. The applied method of detecting the incident is the Latin Square Analysis Method by using traffic traits. In the Latin Square Analysis, unlike other previously tried methods, the traffic situation was analyzed, this time considering the changes in traffic traits for each lane and for each time period. The data used in this study were the data observed in the actual field with fine weather. The traffic volumes, the vehicle speed and the occupancy rate were collected on the interrupted flow road. The data were collected in normal and incident situations. The incidents occurred on the second lane, the time of persistent incidents was set to 10 minutes. The Latin Square Analyses were performed using the collected data with the traffic volume, with the vehicle speed or with the occupancy rate. As a result in this study, in case of detecting the traffic situations with Latin Square Analysis, it will be more successful to apply traffic volume to detect the traffic situations than to apply other factors.

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.523-530
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• 2009

Despite the growing need for customized operating system kernels for embedded devices, kernel development continues to suffer from insufficient reliability and high testing cost for several reasons such as the high complexity of the kernel code. To alleviate these difficulties, this study proposes the MOdel-based KERnel Testing (MOKERT) framework for detection of concurrency bugs in the kernel. MOKERT translates a given C program into a corresponding Promela model, and then tries to find a counter example with regard to a given requirement property, If found, MOKERT executes that counter example on the real kernel code to check whether the counter example is a false alarm or not, The MOKERT framework was applied to the Linux proc file system and confirmed that the bug reported in a ChangeLog actually caused a data race problem, In addition, a new data race bug in the Linux proc file system was found, which causes kernel panic.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.962-969
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• 2006

To address the importance of the process fault detection for productivity, support vector machines (SVMs) is employed to assist the decision to determine process faults in real-time. The reactive ion etching (RIE) tool data acquired from a production line consist of 59 variables, and each of them consists of 10 data points per second. Principal component analysis (PCA) is first performed to accommodate for real-time data processing by reducing the dimensionality or the data. SVMs for eleven steps or etching m are established with data acquired from baseline runs, and they are further verified with the data from controlled (acceptable) and perturbed (unacceptable) runs. Then, each SVM is further utilized for the fault detection purpose utilizing control limits which is well understood in statistical process control chart. Utilizing SVMs, fault detection of reactive ion etching process is demonstrated with zero false alarm rate of the controlled runs on a run to run basis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.427-430
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• 2007

Many victims and property damages are caused in fires every year. In this paper, flame and smoke detection algorithm by using image processing technique is proposed to early alarm fires. The first decision of proposed algorithms is to check candidate of flame region with its unique color distribution distinguished from artificial lights. If it is not a flame region then we can check to candidate of smoke region by measuring difference of brightness and chroma at present frame. If we just check flame and smoke with only simple brightness and hue, we will occasionally get false alarms. Therefore we also use motion information about candidate of flame and smoke regions. Finally, to determine the flame after motion detection, activity information is used. And in order to determine the smoke, edges detection method is adopted. As a result of simulation with real CCTV video signal, it is shown that the proposed algorithm is useful for early fire recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5522-5536
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• 2017

In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• Fire Science and Engineering
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• v.32 no.5
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• pp.57-66
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• 2018

A sports center fire in Jecheon that caused 29 deaths and 40 injuries was analyzed based on initial response and management in order to investigate the causes of the casualties. The aspects of initial response included delay of reporting 119, not being guided to the exit for the body scrubber on the $2^{nd}$ floor for escape, opening the fire doors of the exits on the $1^{st}$ floor and $3^{rd}$ floor, not closing the fire door of the main stairs on the $3^{rd}$ floor, and not doing an emergency broadcast. The management aspects included closing the alarm valve of the sprinkler system on the $1^{st}$ floor, starting-stop of the fire pump controller, neglecting leakage of water and electricity between the false ceiling and ceiling in the parking lot, hiding the exit light in the ladies bathroom lounge on the $2^{nd}$ floor and piling up things in the exit, neglecting the button of an automatic door on the $2^{nd}$ floor, conventional fire safety management, insufficient inspection of fire equipment, and not working fire shutters and smoke exhaust windows.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.49-56
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• 2012

Performance analysis on RAIM, which is one of the techniques for monitoring integrity to ensure the reliability of GPS, was conducted in this study. RAIM is such a method which allows its user to monitor integrity in the stand-alone mode. Among the existing RAIM procedures, the representative methods including the RCM (Range Comparison Method), LSRM (Least Square Residual Method), Parity approach and WRAIM (Weighted RAIM) were evaluated, and their performance was analyzed. To validate the performance of the implemented algorithms, fault detection was tried on the clock malfunctioning event of PRN 23 occurred on January 1st, 2004. As a result, it was identified that the LSRM and the WRAIM detected all the faults happened in the event. In the case of RCM, all the states of fault were detected except for the error which occurred as a false alarm at one epoch. Furthermore, simulated biases were added for each satellite to analyze the sensitivity of each algorithm. Consequently, when biases of the 9-13 meters range were simulated for the RCM and LSRM algorithm, all the malfunctions were detected. For the WRAIM method, it could detect range biases greater than 15 meters.