• Korean Journal of Agricultural Science
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• v.37 no.3
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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.

• Journal of Digital Convergence
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• v.10 no.3
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• pp.161-166
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• 2012

This paper study and analysis about road map and information assurance policy in center of information assurance issues, under the future combat environment, which is performing US military, and the background of modernization war of network centric.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.11-20
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• 2011

This study aimed to devise the method to construct safe and convenient bicycle road by considering the peripheral environmental factor related to using bicycle. Analyzing the existing design of bicycle road and construction case, this study established the method to design bicycle road that reflects site condition and presented the optimal design method for each type of bicycle road to construct safe and convenient bicycle road by analyzing the type of traffic accident for each type of bicycle road, surveying present situation and local survey. It was found that the optimum design of bicycle road for specification, width, curve radius, ascending slope, etc in consideration of peripheral environment and separating traffic between users of traffic means should be done by installing safety sign, safe facilities and separation facilities to design safe bicycle road. Further, the minimum traffic space of bicycle users and connection between bicycle roads should be ensured to design safe bicycle road. It is judged that information related to safety and convenience of bicycle road such as slope, route information, location of convenience facilities, information to the public traffic should be provided so as to activate the users of bicycle.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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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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• 2003.10a
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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.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.221-228
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• 2020

As industrial technology advances, convenience is also being developed. Many people living in big cities are commuting using transportation such as buses, taxis, cars, etc. and enjoy leisure, so research is needed to reduce the damages caused by traffic accidents. This study deals with estimating road-level rainfall in real-time. A rainfall observation data and radar data provided by the Korea meteorological administration were collected in real-time to create an integrated database, which was estimated as road-level rainfall by universal kriging method. Besides, we conducted a study to interactively visualization of mash-up road traffic information in real-time with integrating rainfall information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.2
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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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• v.17 no.E1
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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.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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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.