• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.49-55
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• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.66-70
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• 2017

Radioactive materials are used in medicine, non-destructive testing, and nuclear plants. Source localization is especially important during nuclear decommissioning and decontamination because the actual location of the radioactive source within nuclear waste is often unknown. The coded-aperture imaging technique started with space exploration and moved into X-ray and gamma ray imaging, which have imaging process characteristics similar to each other. In this study, we simulated $21{\times}21$ and $37{\times}37$ coded aperture collimators based on a modified uniformly redundant array (MURA) pattern to make a gamma imaging system that can localize a gamma-ray source. We designed a $21{\times}21$ coded aperture collimator that matches our gamma imaging detector and did feasibility experiments with the coded aperture imaging system. We evaluated the performance of each collimator, from 2 mm to 10 mm thicknesses (at 2 mm intervals) using root mean square error (RMSE) and sensitivity in a simulation. In experimental results, the full width half maximum (FWHM) of the point source was $5.09^{\circ}$ at the center and $4.82^{\circ}$ at the location of the source was $9^{\circ}$. We will continue to improve the decoding algorithm and optimize the collimator for high-energy gamma rays emitted from a nuclear power plant.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.123-128
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• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.112-125
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• 2019

An acoustic signal-based, tunnel-incident detection system was developed and evaluated. The system was comprised of three components: algorithm, acoustic signal collector, and server system. The algorithm, which was based on nonnegative tensor factorization and a hidden Markov model, processes the acoustic signals to attenuate noise and detect incident-related signals. The acoustic signal collector gathers the tunnel sounds, digitalizes them, and transmits the digitalized acoustic signals to the center server. The server system issues an alert once the algorithm identifies an incident. The performance of the system was evaluated thoroughly in two steps: first, in a controlled tunnel environment using the recorded incident sounds, and second, in an uncontrolled tunnel environment using real-world incident sounds. As a result, the detection rates ranged from 80 to 95% at distances from 50 to 10 m in the controlled environment, and 94 % in the uncontrolled environment. The superiority of the developed system to the existing video image and loop detector-based systems lies in its instantaneous detection capability with less than 2 s.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.25-40
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• 2003

The purpose of this thesis is development of detection algorithm for stop-line detector. Detail detection area is set in basing detection area($1.8{\times}4.0m$) and traffic information(volume, occupancy, nonoccupancy) is collected by passive infrared detector at designing detection area. The basis detection area($1.8{\times}4.0m$) is named existing PIR and detection area applied on development algorithm is named proposal PIR. The proposal PIR is collected data such volume, occupancy, nonoccupancy, speed and lane change, but this thesis is limited to evaluate for volume, occupancy and nonoccupancy The procedure and each step of being developed algorithm is described in the next (1) The detection area of proposal PIR is made up of 2 of $1.8{\times}0.6m$ size(the detection area is named 1 and 3) and 1 of $1.8{\times}1.78m$ size(the detection area is named 2) (2) The image detection area is set on monitor to analyze outdoor photographing data then video frame analysis has been done by analyzer. (3) The occupancy, nonoccupancy and speed data of vehicle have been collected with the detection area 1 and 3 and lane change has been collected with combination of detection area 1, 2 and 3 The MAD and MAPE have been utilized to being compared with volume, occupancy and nonoccupancy for the field application and evaluation of a algorithm As the result, the proposal PIR data have been identified superior to the existing PIR data and the effect has been improved its information(volume, occupancy and nonoccupancy)

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.33-43
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• 2005

Traffic condition monitoring system serves as the foundation for all intelligent transportation system operation. Loop detectors and Video Image Processing are the most widely common technology approach to condition monitoring in korea Highways. Lane Usage is defined as the proportion of total link volume served by each lane. In this research, the lane Usage(LU) of two lane link for one day. Interval is 56% : 44%. The LU of three lane link is 39% : 37% : 24%. The LU of four lane link is 25% : 29% : 26% : 21%. These analysis reveal that each lane distributions of link are not same. This research investigates the general concept of lane usage by using collected loop detector data and the investigated that lane distribution is different by traffic lane and lane usage is consistent by time of day.