• 한국IT서비스학회:학술대회논문집
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• 2008.05a
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• pp.123-128
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• 2008

생활공간의 혁신적인 발전은 U-City라는 형태로 추진되고 가시화되고 있으며 특히 U-City의 옥외 공간에서 이뤄지는 서비스는 공간이 지능화되고 다양한 형태의 단말을 통해 보다 능동적이고 찾아가는 형태로 제시되고 있다. 그러나 이러한 서비스를 실현하기 위해서는 옥외공간에 다양한 서비스 장비가 설치, 운용되어야 하는데 KT는 이를 효과적으로 구성하고 구현할 수 있는 IT인프라로서 도시 야간 필수 시설물이며 도시미관을 해치지 않는 가로등 형태로 수용하여 서비스 구현에 필요한 장비를 수용하는 지능형 가로등 U-Pole을 개발해오고 있다. 본 논문에서는 U-Pole로 구성된 현장 서비스 제공 플랫폼과 옥외 공간지능화 서비스 사례를 소개하고자 한다.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.4
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• pp.28-35
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• 2012

Recently, as the social interest increase has been focused on new renewable energy system to build-up sustainable urban planning system, the related studies have been actively conducting. As well as in other areas, the construction area, which accounts for over 40% of the total energy consumption, clearly showed this tendency. Whereas, various landscape facilities applying renewable energy equipments have been manufactured and installed, systematic study available for planning and designing is rarely found in Korea. This study is expected to contribute to the landscape planning and designing by quantifying the energy-efficient and economic advantages of the renewable energy system for landscape facilities. For this purpose, the analysis on the energy-efficiency and economic values under the scenario that the current fossil power supply for the streetlights in urban parks in Seoul, Daegu, and Incheon were replaced by photovoltaic power generation were performed through RETScreen$^{(R)}$ a clean energy simulation program based on the NASA Satellite Meteorological Data. As a result, the generated power and the economic values vary depending on the climatic features of the appointed cities. This study will be used to build up the effective decision-making in applying the clean renewable system to the plan and design of landscaping.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.41-47
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• 2019

In this study, a smart assistive device is designed to recognize pedestrian signal and to provide audio instructions for visually impaired people in crossing streets safely. Walking alone is one of the biggest challenges to the visually impaired and it deteriorates their life quality. The proposed device has a camera attached on a pair of glasses which can detect traffic lights, recognize pedestrian signals in real-time using a machine learning algorithm on GPU board and provide audio instructions to the user. For the portability, the dimension of the device is designed to be compact and light but with sufficient battery life. The embedded processor of device is wired to the small camera which is attached on a pair of glasses. Also, on inner part of the leg of the glasses, a bone-conduction speaker is installed which can give audio instructions without blocking external sounds for safety reason. The performance of the proposed device was validated with experiments and it showed 87.0% recall and 100% precision for detecting pedestrian green light, and 94.4% recall and 97.1% precision for detecting pedestrian red light.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.325-331
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• 2016

In driving at evening or night, we are not able to recognize the speed bump and so stop suddenly. It could result in accidents. And also, we have a restriction of street light installation in farm road because it could be harmful to the crops and driver could not recognize the walking people. It needs to develop the speed bump with light and streetlight to be non harmful to the crops. So, we develop both the speed bump and streetlight with LED which could be non harmful to the crops and be increased recognition of walking people in farm road. For LED lighting power, we use the solar cells, and piezoelectric elements. It has automatic on/off according to power saving rates without illumination sensor. Minimization of circuit elements and design of minimum resisters and low power LED was used for power saving in assuring 3-days.

• 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.