• 한국ITS학회 논문지
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• 제18권2호
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• pp.104-113
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• 2019

도로 관리 주체는 도로 파손을 보수하기 위해 적지 않은 비용을 투입한다. 이러한 파손은 자연 요인과 노후화로 인하여 필연적으로 발생을 하는데, 효율적인 보수를 위한 유지보수 기술이 필요하다. 이런 수요에 대응하기 위해 여러 가지 기술들이 개발되고 적용되고 있지만, 최근 들어서는 차량용 블랙박스 형태로 수집한 영상 정보를 바탕으로 도로 노면 파손 유지 보수기술이 개발되고 있다. 이 파손 영역을 추출하는 방법에는 여러 가지가 있지만, 본 논문에서는 최근 활발히 연구되고 있는 심층 신경망 구조의 영상인식 기술에 대해 논하고자 한다. 특히 영역 기반의 합성곱 알고리즘을 이용하여 영상 내에서 도로 파손 유무와 그 영역을 추정할 수 있는 새로운 심층 신경망을 소개한다. 이를 개발하기 위해 실제 주행을 통해서 600여장의 영상 데이터를 수집하였고, 이를 활용하여 학습을 수행하였다. 그 결과 기존 모델과 성능을 비교하여 10.67% 향상된 신경망을 개발하였다.

• 기술사
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• 제33권4호
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• pp.38-43
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• 2000

ITS(Intelligent Transport System), a technology based on the recent and remarkable development in control, communications, computer and general information technologies is generally expected to be the most promising solution to the traffic congestion on urban road networks. The objective of this paper is to suggest the plan of ITS information service, current trends of ITS strategy, and technology standards.

• 대한교통학회:학술대회논문집
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• 대한교통학회 2007년도 제56회 학술발표회논문집
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• pp.98-107
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• 2007

• 대한원격탐사학회지
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• 제33권4호
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• 융합보안논문지
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• 제13권4호
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• pp.47-52
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• 2013

VANET(Vehicular Ad-hoc Network)은 원활한 교통 소통, 사고 방지 등 여러 가지 편리한 기능들을 제공하지만 그 기반을 애드혹 네트워크에 두고 있기 때문에 애드혹 망에서 발생하는 보안 문제를 가지고 있다. VANET에서 사용자의 프라이버시를 보호하기 위해 그룹 서명방식 등이 연구되어졌다. 그러나 그룹 간에 그룹 키 생성 단계 및 그룹 키 갱신단계에서 RSU(Road-Side Unit) 및 차량의 계산상 오버헤드가 발생한다. 본 논문에서는 효율적인 그룹 키 관리 기술을 제안한다. 즉, 그룹 키 생성 및 갱신 단계에서 CBF(Counting Bloom Filter)를 사용하므로 RSU 및 차량의 계산상 오버헤드를 감소시킨다. 또한, RSU와 차량에서 그룹 키를 자체적으로 갱신하여 관리하는 기법이다.

• 대한교통학회지
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• 제31권3호
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• pp.55-64
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• 2013

교통부문에서 온실가스 배출량은 주로 연료사용량에 의해 산정되었다(Tier 1 방식). 그러나 이 방법은 연료사용량을 측정할 수 없는 도로 구간에서 발생하는 배출량 산정에 사용되기 어렵다. 도로구간의 대기오염물질 배출량 또한 연료사용량에 의해 정확히 측정되어질 수 없는데 이는 대기오염물질 배출량이 속도, 차종, 차령, 유종 등의 함수이기 때문이다. 이러한 배경에서 본 연구의 목적은 ITS 정보를 이용하여 지역 간 도로에서 발생되는 이산화탄소와 질소산화물의 배출량을 Tier 3 수준으로 산정하는 방법론을 정립하는 것이다. 이 방법론은 집계단위가 작은 ITS 검지기 정보를 이용하기 때문에 배출계수의 오목한 형태에서 기인하는 과소추정의 오류를 피할 수 있는 장점을 갖는다. 제시된 방법론을 4개 사례 도로구간에 적용한 결과는 중차량의 속도관리가 이산화탄소 또는 질소산화물 배출량 관리에 매우 중요함을 시사하였다.

• Journal of Korean Society for Atmospheric Environment
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• 제17권E1호
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• pp.25-33
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• 2001

This paper presents an overview of the UK Hydrocarbon Monitoring Network and summarises some of the lessons learnt from running and automated VOC monitoring network in th UK. The paper will describe the operation of the network and the Quality Control and Quality Assurance (QA/QC) procedures used to ensure that the data qality objectives are met. The provision of accurate measurements of ambient air pollutant concentrations is a valuable and high-profile service of Governments, assisting policy decisions and allowing members of the public to be well-informed. The need for such measurements has been increased in the UK by the National Air Quality Strategy and European Air Quality Directives, with the National Networks playing a central role in delivering the information. The Hydrocarbon Network provides measurements directly in support of monitoring requirements for benzene and 1,3-butadiene, and of 23 other hydrocarbon species important for their role in ozone and secondary particulate formation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.41-60
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• 2018

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.

• 디지털산업정보학회논문지
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• 제18권2호
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• pp.9-16
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• 2022

The nodes constituting the vehicle ad hoc network (VANET) are vehicles moving along the road and road side units (RSUs) installed around the road. The vehicle ad hoc network is used to collect the status, speed, and location information of vehicles driving on the road, and to communicate with vehicles, vehicles, and RSUs. Today, as the number of vehicles continues to increase, urban roads are suffering from traffic jams, which cause various problems such as time, fuel, and the environment. In this paper, we propose a method to solve traffic congestion problems on urban roads and demonstrate that the method can be applied to solve traffic congestion problems through performance evaluation using two typical protocols of vehicle ad hoc networks, AODV and GPSR. The performance evaluation used ns-2 simulator, and the average number of traffic jams and the waiting time due to the average traffic congestion were measured. Through this, we demonstrate that the vehicle ad hoc-based traffic congestion management technique proposed in this paper can be applied to urban roads in smart cities.

• Smart Structures and Systems
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• 제32권5호
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.