• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.122-129
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• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

• Journal of Korean Society on Water Environment
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• v.33 no.2
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• pp.123-129
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• 2017

The purpose of this study is to identify potential methodologies to reasonably estimate the effectiveness of road vacuum cleaning in terms of pollution loads reduction. In this context, this study proposes two empirical equations to estimate the amount of diffuse pollution loads removed by road vacuum cleaning. The proposed equations estimate the removed amount of pollution loads respectively taking into consideration of: a) the distance of road vacuum cleaning; and b) the amount of road-deposited sediment(RDS). All of the parameters in these equations were evaluated based on results of field monitoring and laboratory analyses, except for the RDS generation rate. The results of this study suggest that pollutant removal efficiency is 46.3% for $BOD_5$ and 56.4% for TP; discharge ratios for particulate and dissolved $BOD_5$ are 35.0% and 21.2%, respectively; discharge ratios for particulate and dissolved TP are 35.0% and 19.4%, respectively. Average concentrations of pollutants in RDS are $BOD_5$ 977.3 mg/kg and TP 317.6 mg/kg. Some results of a case study imply that both equations can be potentially useful if the adopted parameters are reasonably evaluated. In particular, the RDS generation rate should be evaluated based on monitoring data collected from various road conditions.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.143-150
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• 2015

PURPOSES : The authors set out to estimate the related carbon emissions, energy use, and costs of the national freeways and highways in Korea. To achieve this goal, a macro-level methodology for estimating those amounts by road type, road structure type, and road life cycle was developed. METHODS : The carbon emissions, energy use, and costs associated with roads vary according to the road type, road structure type, and road life cycle. Therefore, in this study, the road type, road structure type, and road life cycle were classified into two or three categories based on criteria determined by the authors. The unit amounts of carbon emissions and energy use per unit road length by classification were estimated using data gathered from actual road samples. The unit amounts of cost per unit road length by classification were acquired from the standard cost values provided in the 2013 road business manual. The total carbon emissions, energy use, and cost of the national freeways and highways were calculated by multiplying the road length by the corresponding unit amounts. RESULTS: The total carbon emissions, energy use, and costs associated with the national freeways and highways in Korea were estimated by applying the estimated unit amounts and the developed method. CONCLUSIONS: The developed method can be employed in the road planning and design stage when decision makers need to consider the impact of road construction from an environmental and economic point of view.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.2
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• pp.131-139
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• 2003

The links operating interrupted flow are intend to yield the traffic between the out flow and inflow part effect zone of street section, we build the delay model using the time gap between under construction and not. We review the applicability of interrupted flow, and thus we can put this data to practical use as the basis data to compute the inducement charge for traffic delay. Also building about traffic effect zone of interrupted flow wouldn't produce at the section beside occupying roads and construction cross section, thus we must review the plan to minimize traffic delay by the construction occupying road. In future there must be advanced the incomplete in this study, and groping for the various alternatives to minimize the traffic delay by the road occupying construction, with developing the various sets of detailed analyzing models, that is analysis on the street strength, crossroads geometrical forms of crossroads, public traffics, pedestrians, occupying types.

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.37-46
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• 2016

The difference between definition time of GPS (Global Positioning System) position data and actual display time of car positions on a map could reduce the accuracy of car positions displayed in PND (Portable Navigation Device)-type CNS (Car Navigation System). Due to the time difference, the position of the car displayed on the map is not its current position, so an improved method to fix these problems is required. It is expected that a method that uses predicted future positionsto compensate for the delay caused by processing and display of the received GPS signals could mitigate these problems. Therefore, in this study an analysis was conducted to correct late processing problems of map positions by mapmatching using a Kalman filter with only GPS position data and a RRF (Road Reduction Filter) technique in a light-weight CNS. The effects on routing services are examined by analyzing differences that are decomposed into along and across the road elements relative to the direction of advancing car. The results indicate that it is possible to improve the positional accuracy in the along-the-road direction of a light-weight CNS device that uses only GPS position data, by applying a Kalman filter and RRF.

• Advances in nano research
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• v.16 no.6
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• pp.531-548
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• 2024

The study focuses on using remote sensing to gather data about the Earth's surface, particularly in urban environments, using satellites and aircraft-mounted sensors. It aims to develop a classification framework for road targets using multi-spectral imagery. By integrating Convolutional Neural Networks (CNNs) with XGBoost, the study seeks to enhance the accuracy and efficiency of road target identification, aiding urban infrastructure management and transportation planning. A novel aspect of the research is the incorporation of quantum sensors, which improve the resolution and sensitivity of the data. The model achieved high predictive accuracy with an MSE of 0.025, R-squared of 0.85, RMSE of 0.158, and MAE of 0.12. The CNN model showed excellent performance in road detection with 92% accuracy, 88% precision, 90% recall, and an f1-score of 89%. These results demonstrate the model's robustness and applicability in real-world urban planning scenarios, further enhanced by data augmentation and early stopping techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.109-118
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• 2019

Road information is one of the most significant geospatial data for applications such as transportation, city planning, map generation, LBS (Location-Based Service), and GIS (Geographic Information System) database updates. Robust technologies to acquire and update accurate road information can contribute significantly to geospatial industries. In this study, we analyze the limitations of ACO (Ant Colony Optimization) road extraction, which is a recently introduced object-based road extraction method using high-resolution satellite images. Object-based ACO road extraction can efficiently extract road areas using both spectral and morphological information. This method, however, is highly dependent on object descriptor information and requires manual designations of descriptors. Moreover, reasonable iteration closing point needs to be specified. In this study, we perform improved ACO road extraction on VHR (Very High Resolution) optical satellite image by proposing an optimization stopping criteria and descriptors that complements the limitations of the existing method. The proposed method revealed 52.51% completeness, 6.12% correctness, and a 51.53% quality improvement over the existing algorithm.

• Journal of Environmental Impact Assessment
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• v.19 no.1
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• pp.99-106
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• 2010

The project of Cheonggyecheon revived the 5.8 kilometer stream and it removed the cover of stream and Cheonggye elevated road. It was begin October of 2003 and completed October of 2005. The purpose of this study is to analyze the air pollution change of Cheonggyecheon area and neighboring area from before and after the project. The change of concentration is compared with an air monitoring station data and measurement data. The analyzed pollutants are $NO_2$, $PM_{10}$, heavy metal, VOC which are measured at Cheonggyecheon and neighboring area. As the results, $NO_2$ concentration shows 10 % decreases in Cheonggyecheon area and neighboring area shows 16 % decreases by Chenoggyecheon restoration, and $PM_{10}$ concentration shows 15 % decreases in Cheonggyecheon area and neighboring area shows 16 % increases. One of VOC, benzene is increased in Cheonggyecheon area compared with neighboring area but Toluene, Ethylbenzene, m+p Xylene increased in neighboring area. After the Cheonggyecheon restoration, The heavy metals are not shows the improvement, but $PM_{10}$ and $NO_2$ concentration improved more than the changes of neighboring area. These improvements of pollution due to reduction of transportation and clearing of elevated road by Cheonggyecheon restoration project.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.185-193
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• 2006

The on-going pre-environmental investigation at present is performed by separate numerical analysis of each provision which makes integrated pre-environmental investigation is difficult. The application of numerical data is insufficient, which results to the deterioration of environmental investigation result's objectivity. A lot of time and money is required for the investigation. In this study, the spacial analysis function of GIS was applied on the 8 pre-environmental investigation factors. Pre-environmental investigation GIS DMS(Decision Making System) was constructed to make integrated investigation possible through the use of investigation results for each factor. Through the use of the developed pre-environmental investigation GIS DMS and the pre-constructed GIS data, the objectivity of environmental investigation is sufficient and time and cost are reduced. Therefore, this system can be used for pre-environmental investigation during route selection in the initial stages of road construction. Through the numerical and visual data obtained from the system developed in this paper, it is easier to gain the approval of the public. Furthermore, environmental problems due to road construction can be investigated with less time and money during the initial stages of road construction.