• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.119-129
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• 2004

This paper is designed to report the results of development and validation procedures in relation to the Freeway Incident Management System (FIMS) prototype development as part of Intelligent Transportation Systems Research and Development program. The central core of the FIMS is an integration of the component parts and the modular, but the integrated system for freeway management. The whole approach has been component-orientated, with a secondary emphasis being placed on the traffic characteristics at the sites. The first action taken during the development process was the selection of the required data for each components within the existing infrastructure of Korean freeway system. After through review and analysis of vehicle detection data, the pilot site led to the utilization of different technologies in relation to the specific needs and character of the implementation. This meant that the existing system was tested in a different configuration at different sections of freeway, thereby increasing the validity and scope of the overall findings. The incident detection module has been performed according to predefined system validation specifications. The system validation specifications have identified two component data collection and analysis patterns which were outlined in the validation specifications; the on-line and off-line testing procedural frameworks. The off-line testing was achieved using asynchronous analysis, commonly in conjunction with simulation of device input data to take full advantage of the opportunity to test and calibrate the incident detection algorithms focused on APID, DES, DELOS and McMaster. The simulation was done with the use of synchronous analysis, thereby providing a means for testing the incident detection module.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Korean Journal of Poultry Science
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• v.50 no.1
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• pp.41-50
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• 2023

Over the last decade, avian influenza (AI) has been considered an emerging disease that would become the next pandemic, particularly in countries like South Korea, with continuous animal outbreaks. In this situation, risk assessment is highly needed to prevent and prepare for human infection with AI. Thus, we developed the risk assessment matrix for a high-risk area of human infection with AI in South Korea based on the notion that risk is the multiplication of hazards with vulnerability. This matrix consisted of highly pathogenic avian influenza (HPAI) in poultry farms and the number of poultry-associated production facilities assumed as hazards of avian influenza and vulnerability, respectively. The average number of HPAI in poultry farms at the 229-municipal level as the hazard axis of the matrix was predicted using a negative binomial regression with nationwide outbreaks data from 2003 to 2018. The two components of the matrix were classified into five groups using the K-means clustering algorithm and multiplied, consequently producing the area-specific risk level of human infection. As a result, Naju-si, Jeongeup-si, and Namwon-si were categorized as high-risk areas for human infection with AI. These findings would contribute to designing the policies for human infection to minimize socio-economic damages.

• 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.

• The Journal of Korean society of community based occupational therapy
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• v.8 no.1
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• pp.33-44
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• 2018

Objective : This study aims to review research trends in driving rehabilitation for the disabled in South Korea since 2000 suggesting research directions for clinicians and researchers. Methods : Fifty eight articles in 16 journals listed in accredited or candidate journal lists of National Research Foundation of Korea from January, 2000 to December, 2016 were reviewed. 'Driving rehabilitation' and 'driving for disabled' were used as search terms. Descriptive statistics were used to classify articles according to study methodology, levels of evidence, study participants, research topics, and academic associations or official journals. Results : Fifty percent of analyzed researches have been published since 2012. Twenty-two studies (37.9%) were published as group comparison and correlational research. Only seven studies (12.1%) were included in evidence level I. There were 19 studies (38.8%) conducted with brain-injured patients among 49 studies including participants. The Korean Society of Occupational Therapy Journal, having published 15 studies (25.9%) about driving rehabilitation, ranked first among the analyzed journals. In research topic, 15 (25.9%) studies were performed about clinical evaluation. Conclusion : The present study showed that the quality of driving rehabilitation-related studies has been increasing, but more intervention-based researches need to be carried out and it is also necessary to carry out various researches in related fields in order to establish efficient driving rehabilitation in Korea.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.3
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• pp.282-288
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• 2023

In this study, we analyzed the amount of roadside fine dust generated from newly constructed specially modified asphalt pavement and general asphalt pavement from existing roads. We collected the 1,000 g (100 g/day) of dust samples from the roadside of the express bus terminal and commercial facility area in Chungcheongnam-do's C site at three-day intervals during the summer of 2022 and 2023. The collected samples were separated from fine dust according to size in the 75-150 ㎛ range and, were separated only from Tire and Road Wear Particles through density separation. No.1-3 are general asphalt pavement section as an existing road. Fine dust and Tire and Road Wear Particles in No.1-3 were 24.27 g, 24.36 g, 0.53 g, and 0.53 g, respectively, and the quantitative results for 2022 and 2023 were similar. On the other hand, No.4-6 are newly constructed specially modified asphalt pavement section. Fine dust decreased by 14.8 % and tire and road wear particles decreased by 29.6 % in 2023 compared to 2022 in No.4-6. In addition, according to the results of thermogravimetric analysis, Tire and road wear particles in No.1-3 are tire and road components at 30 % and 70 %, respectively. And Tire and road wear particles in No.4-6 are tire and road components at 35 % and 65 % in 2023, respectively. From these results, it was confirmed that the newly constructed specially modified asphalt pavement can be effective in reducing roadside fine dust and Tire and Road Wear Particles. However, there may be some shortcomings in conclusive research results due to limited space and sample collection period. In the future, we plan to conduct various case studies.