• 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.

• Journal of Embryo Transfer
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• v.28 no.3
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• pp.177-184
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• 2013

The objective of this study was to analyze the accuracy of estrus detection of heat detector and analysis of estrus behavior (mounting and mounted), and the evaluation of conditions required for improving reproductive efficiency in Holstein dairy cows fitted with a estrous detector. The heat detection system consists of estrous detector based on wireless sensor and an electric bulletin board displayed estrus behavior data. When cow mounting other cows, the accuracy of estrus behavior displayed an electric bulletin board were 87.5% (mounting other cows only), 100% (mounting other cows but not standing), 80.0% (mounting other cows with standing for 1~4 seconds), 90.0% (mounting other cows but not standing for 1~4 seconds), 80% (mounting other cows with standing for more than 5 seconds) and 90.0% (mounting other cows but not standing for more than 5 seconds). When cow mounted other cows, the accuracy of estrus behavior displayed an electric bulletin board were 100% (mounted other cows but not standing), 100% (mounted other cows with standing for 1~4 seconds), 100% (mounted other cows but not standing for 1~4 seconds) and 100% (mounted other cows with standing for more than 5 seconds). Circadian distribution of first observed in estrus were 59.1% (am 8~pm 6) and 40.9% (pm 6~am 8). Distribution for the number of estrus behavior were 40.9% (less than 3 times), 36.4% (4~6 times) and 22.7% (more than 4 times). The conception rates relative to interval from first estrus behavior to insemination for estrus periods were 23.1% (less than 11 hours) and 55.6% (12~20 hours).

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.