• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.67-72
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• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.113-118
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• 2016

The walking speed of handicapped people generally is slower than that of normal people. So it is difficult for them to cross at crosswalks within the allotted time provided by the traffic light. This problem can be solved by expanding the time of the traffic light. However, if the latency of the traffic light is increased without distinguishing the handicapped among all other pedestrians, the efficiency of traffic signal lights will decrease. In this paper, we propose a smart traffic signal connecting mechanism between the previous pedestrian traffic signal and a pedestrian's device (smartphone). This Smart pedestrian traffic light, through this mechanism, minimizes traffic congestion by providing additional walking time only to the handicapped among pedestrians. This crosswalk traffic light recognizes the handicapped using a technique called Internet of things (IOT). In this paper, we extract the data necessary to build an effective smart crosswalk traffic light mechanism through simulation techniques. We have extracted different kinds of traffic signal times with our virtual simulation environment to verify the efficiency of the smart crosswalk pedestrian traffic light system. This approach can validate the effective delay time of the traffic signal time through a comparison based on number of pedestrians.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.867-878
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• 2021

Recently, there has been active research on smart street lamps that can collect real-time traffic data and provide traffic information by attaching images and radars to road lighting facilities. Smart street light technology can detect, identify, and provide dense information compared to existing technologies. In order to effectively utilize the smart streetlight as a high-resolution information delivery medium, a branch-type operation strategy that is different from the existing centralized operation strategy is required. This study presents dynamic information delivery strategies, release strategies, and their criteria for various purposes in a spatial range, separated by the context before and after the occurrence of smart street lights-based accidents. Through this, it is expected that smart road lighting facilities can be used more effectively.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.97-102
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• 2019

As the number of "Mobility Handicapped" increases, the incidence of "Mobility Handicapped" traffic accidents is also increasing. In order to reduce the incidence of traffic accidents in the "Mobility Handicapped", a service providing system for "Mobility Handicapped" is required. Since these services have different data formats, data heterogeneity occurs. Therefore, the system should resolve the data heterogeneity by mapping the format of the data. In this paper, we design DBaaS as a mobility handicapped system for data interoperability. This system provides a service to extend the flashing time of the traffic lights according to the condition of "Mobility Handicapped" on the occurrence of a fall or a crosswalk in a crosswalk where there is a risk of a traffic accident. These services can reduce the incidence of traffic accidents in "Mobility Handicapped".

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.1071-1073
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• 2022

본 논문은 항만 완전 자동화를 위하여, 자율주행 트랙터와 스마트 신호등을 도입한 IoT 기반 스마트 항만 교통제어 시스템을 메타버스를 통해 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.437-440
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• 2007

Recently embedded system developed a lot. Physically, embedded systems range from portable devices such as digital watches and MP3 players, to large stationary installations like traffic lights, factory controllers, or the systems controlling nuclear power plants. This paper focuses on implementation of portable device which is applicable to the child-kidnap or missing child prevention system in residential area or public area. To be specific, this device is to transmit video data which comes from the camera in the device into the host PC via WLAN. Embedded hardware platform consists of s3c2440 with ARM9 core, WindowsCE OS and other sensors. OS enables the platform to do multitasking jobs which are handling GPS data, taking video, capturing audio via microphone in the device and transfer all kind of realtime data to the host PC.

• 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 Internet Computing and Services
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• v.23 no.5
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• pp.47-58
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• 2022

In recent years, navigation aids, which plays as sea traffic lights, have been digitized, and are developing beyond simple sign purpose to provide various functions such as marine information collection, supervision, control, etc. For example, Busan Port which is located in South Korea is leading the application of the advanced technologies by installing cameras on buoys and recording video images to supervise maritime accidents. However, there are difficulties to perform their major functions since the advanced technologies require long-term battery operation and also management and maintenance of them are hampered by marine characteristics. This study proposes a system that can automatically notify maritime objects passing around buoys by analyzing image information. In the existing sensor-based accident prevention systems, the alarms are generated by a collision detection sensor. The system can identify the cause of the accident whilst even though it is difficult not possible to fundamentally prevent the accidents. Therefore, in order to overcome these limitations, the proposed a maritime object detection system is based on marine characteristics. The experiments demonstrate that the proposed system shows about 5 times faster processing speed than other existing algorithms.

• 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.