• 농업과학연구
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• 제37권3호
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• pp.471-479
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• 2010

Forest roads of 16,424km have been constructed as infrastructure for efficient management of forest. The demand of forest road have been also increased steadily with SOC conception for forest management and wood production. But, accuracy verification by completion drawing of forest road needed aspects extration of geographic information to sound like forest road construction and completion drawing. However, verification for completion drawing has not ascertained. This study carried out the evaluation for position accuracy about constructed forest road in Chungcheongnam-do for evaluating horizontal position accuracy of completion drawing of forest road. In result, first of distance of completion drawing and real route designed completion drawing longer than the real route as Gongju 83m, Seosan 66m, Nonsan 27m and Dangjin 19m, respectively. Second, RMSE by point-correspondence was 11m~14.7m, buffering analysis appeared difference of 18~24m. Finally, index of shape was the similar completion and real route through 6.5~7.4 and data information of forest road corresponds to be perfect. For such reasons, the existing completion drawings have a problem that it cannot use graphic information for drawing digital map according to the regulation, and there is an urgent need for improvement to solve this problem in the process of design and construction.

• 디지털융복합연구
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• 제10권3호
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• pp.161-166
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• 2012

본 논문은 미군이 수행중인 미래전투 환경과, 네트워크 중심의 현대화 전쟁 배경 하에, 정보보증 이슈를 중심으로 정보보증 정책 및 로드맵에 대해 연구 분석하였다.

• 한국측량학회지
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• 제29권1호
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• pp.11-20
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• 2011

이 연구의 목적은 자전거 이용과 관련하여 주변의 환경요소를 고려한 안전하고 편리한 자전거 도로 설계 개선 및 정보제공방안에 대하여 연구하는데 있다. 이 연구에서는 기존 자전거 도로 설계 및 시공사례 분석을 통하여 현장여건을 반영한 자전거 도로 설계 방안을 수립하고 자전거 도로의 유형별 교통사고 유형 분석, 현황측량, 현지조사 등을 통하여 안전하고 편리한 자전거 도로 건설을 위한 자전거 도로의 유형별 최적 설계방안을 제시하였다. 안전한 자전거 도로 설계를 위해서는 주변환경 요소를 고려한 제원, 폭원, 곡선반경, 오르막차로 등에 대한 자전거 도로 최적설계와 안전표지 안전시설 분리시설 설치를 통하여 교통수단별 이용자간의 통행분리가 이루어져야 함을 알 수 있었다. 또한 안전한 자전거 도로 설계를 위해서는 최소한의 자전거 이용자의 주행 공간 및 자전거 도로 간의 연결성이 확보되어야 하며 자전거 이용자의 활성화를 위해서는 경사, 노선정보, 편의시설위치, 대중교통 연계정보 등 자전거도 도로의 안전 및 편의관련정보를 제공하여야 할 것으로 판단된다.

• 자동차안전학회지
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• 제13권4호
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1621-1624
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• 2003

This paper presents a new method that generates a path that has no collision with the obstacles or the characters by using the three motion parameters, and automatically creates natural motions of characters that are confined to the path. Our method consists of three parameters: the joint information parameter, the behavior information parameter, and the environment information parameter. The joint information parameters are extracted from the joint angle data of the character and this information is used when creating a path following motion by finding the relation-function of the parameters on each joint. A user can set the behavior information parameter such as velocity, status, and preference and this information is used for creating different paths, motions, and collision avoidance patterns. A user can create the virtual environment such as road and obstacle, also. The environment is stored as environment information parameters to be used later in generating a path without collision. The path is generated using Hermit-curve and each control point is set at important places.

• 디지털융복합연구
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• 제18권4호
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• pp.221-228
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• 2020

산업 기술이 발달함에 따라 편리함을 추구하게 되면서 교통수단 역시 발달하고 있다. 대도시에 거주하는 많은 사람들은 버스, 택시, 자가용 등의 교통수단을 이용하여 출퇴근을 하고 있고 여가를 즐기므로 이동시 발생하는 교통사고의 피해를 줄이기 위한 연구가 필요하다. 본 연구는 실시간으로 도로단위 강우량을 추정하는 법을 다루고 있다. 이를 위해 기상청에서 제공하는 강우 관측 자료와 강우 레이더자료를 실시간으로 수집하여 통합 데이터베이스를 만들고 이를 크리깅 방법을 통해 도로단위 강우량으로 추정하였다. 이 외에도 도로의 실시간 교통소통정보도 강우정보와 융합하여 인터렉티브하게 시각화하는 연구를 수행하였다.

• 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.

• 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.

• 한국대기환경학회지
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• 제34권5호
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.