• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.263-263
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• 2023

블랙아이스는 눈에 쉽게 구분되지 않아 많은 교통사고를 초래하고 있다. 한국교통연구원 교통사고분석시스템에 따르면, 2017년부터 2021년까지 5년간의 서리/결빙으로 인한 교통사고 사망자는 122명, 적설로 인한 교통사고 사망자는 40명으로, 블랙아이스는 적설에 비해 위험성이 높은 것으로 나타난다. 과거의 다양한 연구에서 블랙아이스 생성조건을 기압과 한기 축적등의 조건에서 예측해왔지만, 이러한 기상학적 모델은 봄철 해빙기의 일교차로 인한 눈의 해동과 재냉각과 같은 다양한 기상 조건에서의 블랙아이스 탐지가 어렵다는 한계가 있어 최근에는 이미지 판별과 딥러닝모델(YOLO 등)을 기반으로 한 센서가 제시되고 있다. 그러나, 이러한 방법은 충분한 컴퓨팅 자원이 뒷받침되어야 하며, 블랙아이스 탐지까지 걸리는 속도가 빠르지 못한 편으로, 블랙아이스 초입 구간에서의 제동에 취약하다는 잠재적인 약점을 가지고 있다. 그러므로 본 연구에서는 블랙아이스의 주 원인인 서리나 어는비가 발생하기 위해서 주변 공기가 이슬점 온도 이하, 노면온도와 이슬점이 어는점보다 낮아야 함을 이용, IoT 센서 모듈을 통해 Magnus 방정식으로 계산한 이슬점 온도와 노면 온도를 사용하는 이동식 블랙아이스 추정 장치를 제시한다. 본 장치는 대기압, 온도, 습도로부터 계산된 이슬점 온도와 노면 온도를 통한 서리발생 가능성과 대기 온도, 노면 온도를 통해 어는비의 발생환경 여부를 계산한다. 본 연구 결과를 통해 블랙아이스 추정과 기상정보 생산을 동시에 가능케 하며, 추정 결과를 통합 수집서버에 전송함으로서 운전자에게 전방 블랙아이스 위험 구간을 조기에 전달하는 시스템과 이를 관리하기 위한 인프라를 구축하여 운전 시 결빙 미끄러짐 사고를 저감하고자 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.1
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• pp.9-17
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• 2005

The objective of the study is to extract information about the road surfaces to be studied by analyzing asphalt concrete-paved road surface images photographed with a digital video camera. To analyze the accuracy of road surface information gained using a digital imagery processing method, it was compared and analyzed with the outcomes of control surveying. As a result, an average error of 0.0427 m in the X-axis direction, that of 0.0527 m in the Y-axis direction, and that of 0.1539 m in the Z-axis direction were found, good enough for mapping at a scale of 1:1,000 or less and GIS data. Besides, information on road surface assessment factors such as crack ratio, the amount of rutting and profile index was gained by analyzing processed digital imagery. This information made it possible to conduct road surface assessment by generating PSI and MCI. As quality digital image information has been gathered from roads and stored, important fundamental data on PMS (Pavement Management System) will become available in the future.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.6
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• pp.143-154
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• 2012

The wireless low-floored tram is an innovative transportation system which is environment-friendly and highly energy-efficient. In addition, the system has various advantages such as low construction cost, improvement of urban landscape, revitalization of surrounding commercial area, elevated convenience for passengers, etc. Therefore, more than ten local governments have proposed tram construction projects in Korea. Accordingly, many research and development projects are ongoing funded by government including the developments of tram vehicle, tram trackway, signal system, etc. The embedded rail system are commonly used in order to provide leveled roadway surface in urban area. It is effective to reduce the noise and vibration, caused at the interface between the wheel and track, to minimize the construction period, and to lower the maintenance cost. This paper investigated the design and construction processes for tram trackway and figured out the constructability for the test track with embedded rail system for the first time in Korea. The performance to reduce the noise and vibration were quantitatively measured in the test track with embedded rail system. In addition, the results were compared to the ones for track with conventional rail system.

• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.16 no.2
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• pp.45-53
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• 2012

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.273-286
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• 2013

Recently, due to problems with subway and bus operations, a need has emerged for a solution such as the introduction of an Advanced Transit System. In several municipalities, systems have been introduced using light rail as AGT urban aesthetics construction. There is high inhibition and civil cases are delayed by more environmentally friendly and accessible remedies. For the purpose of this study, Changwon city would be transformed to have an environmentally friendly transportation system such as a tram with an overview. Features, advantages, and disadvantages are analyzed, and systems are established the existing public transport routes by transfer system. Changwon city's tram plan is first step in open in year 2018, and second step with the opening goal of the year 2021, and the total line length of 33.9 km, the station will be built in the 38 locations. and also in 2011 a feasibility study, evaluated a low economic as B/C to 0.88, but it evaluated the high value of the policy analysis as AHP to 0.502. However, introduction of a tram project that should precede the as following condition. The first step in Changwon city's tram plan would be as follows : The introduction of the tram system would demand traffic management along with a restructuring of the bus system, and the tram system would be selected for domestic realities. Secondly, the introduction of trams would comprehend the advanced traffic composition in accordance with the consensus of the citizenship, and a legal system should be established for the introduction of the trams.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.121-122
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• 2016

본 논문에서는 노면상에 발생하는 다양한 유형의 유고정보를 센싱 및 처리하기 위한 미들웨어를 제안한다. 유고정보란 도로 및 노면에서 발생될 수 있는 센싱자료들을 분석하여 제공하는 정보로서 유고정보 처리 및 분석을 위한 기초 데이터들을 수집하는 목적지향 미들웨어 시스템을 구축하였다. 유고정보 분석을 위해 인터넷과 센서 수집을 통하여 미들웨어로 데이터를 수집한다. 이때 인터넷을 통한 수집을 위해 공개키를 사용하여 인터넷의 공공데이터들을 수집한다. 또한 수집된 데이터들을 미들웨어에서 관리 및 제어를 할 수 있다.

• Korean Journal of Construction Engineering and Management
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• v.5 no.4 s.20
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• pp.139-146
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• 2004

Resent removal work for road signs has been labor intensive and required times since it has been done manually using shaving type equipment. While traditional process is conducting, there are traffic jams caused by the passing control, and happened unexpected accidents to workers working at dangerous road circumstance. Besides, in current shaving method, there are high potentialities on the air pollution as well as the explosive accident occurred by using a propane gas. So, as an alternative, we have studied to develop the automatic erasing equipment made up with a high pressure water-jet system and automatic control system, mobile system; Wate-rjet system consists of an intensifier and nozzles to give a high pressure and spray on the sign, and automatic control system is composed of one axis robot using a hydraulic servo actuator controlled by a lever, And as a mobile system, a truck plays an important role for the transport of equipment and the forward movement in a removal process. In this paper, we have analyzed the characteristics of road signs and have investigated current erasing methods in the field. And we have organized and designed automatic erasing equipment, and we have made a basic experiment to find out the optimal spray condition as like the spray distance, spray angle and injection pressure.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.