• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.4
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• pp.148-158
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• 2011

This study examines visitors' image and behavior characteristics of Wolseong palace in Gyeongju. This area has been a royal palace during Silla periods. So many scholars dedicate to the protection of this historical-cultural heritage. The research process consists of two main steps, such as on-site field investigation and survey research. The data were collected in summer and autumn. Collected data is classified into three groups to describe visitors' behavior, time, space, and then processed by statistical methods. The results are as follows: First, there is a shortage of programs and facilities. The result shows most visitors consider Wolseong palace as a pathway for walking. Hence better functions should be developed to attract more visitors but with least effect to historical remains. The founding is that increasing programs for history exploration, enhancing lighting installation, facilities, plant arrangement, road condition and so forth would be suggested.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.127-132
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• 2020

In this study, the process of providing information necessary to remove physical barriers such as road slopes that obstruct the activities of the disabled is in progress. Through experiments, we implement a quantified road surface roughness index that enables the implementation of IoT-based systems necessary for the elderly and the disabled to safely move to their destination. As a preliminary study, a road surface measurement device using a gyroscope was devised. To check the roughness and flatness of the road surface, X, Y displacement, and acceleration displacement were measured using a gyroscope. By calculating the measured data, the roughness and flatness of the road surface were quantified from 0 to 100. We implemented an algorithm that divides this index into 4 stages, displays it on a map, and provides it to users. Finally, a system for the disabled and elderly electric wheelchair users to secure basic mobility was established.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.100-108
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• 2021

The purpose of this study is to analyze the reliability index of a design by applying the reduction factor of the recently developed fiber reinforced high strength concrete design guideline(draft). By collecting material and member test data performed for the development of the design guideline(draft), statistical characteristics of material strength and member strength analysis equations are obtained. A simul ation that appl ies the material statistical characteristics and the member anal ysis equation of the design guidel ine(draft) is performed, and the statistical characteristics of the section strength are calculated by combining the statistical characteristics of the analysis equation. Reliability analysis was performed by applying the load combination of the domestic highway bridge design code and concrete structural code, and it was confirmed that the design that applies the reduction factor for materials and members suggested in the design guideline(draft) satisfies the target reliability index.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.849-856
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• 2020

Recently, as the number of high-height vehicles such as double-decker buses has increased, collision accidents have occurred in bridges and tunnels due to the deviation from the designated routes and driver's carelessness. In the case of the existing front collision warning system, it is limited to vehicles and pedestrians, so it is difficult to use it as a pass height warning system for the high height vehicles. In this paper, we propose a system that generates a warning by determining the correlation and time series characteristics of data for each segment using multiple lidar sensors and then determining the possibility of collision in the upper part of the vehicle. Also, the proposed system confirmed the proper operation through a real-time driving test and a system performance evaluation by the Korea Automobile Testing & Research Institute.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1567-1577
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• 2020

Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.280-288
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• 2021

Understanding the occurrence characteristics of pine wilt disease (PWD) is essential for determining a suitable strategy to minimize the damage caused by PWD. Thus, in this study, we characterized various environmental conditions, including meteorological factors, geographical factors, and artificial factors influencing the occurrence of PWD. The occurrence data of PWD from May 2016 to April 2018 and spatial data of various environmental factors, including natural and anthropogenic factors, were collected. We evaluated the relative contribution of the environmental variables on the number of dead pine trees by PWD. In this study, among the 17 natural and anthropogenic factors, the factors affecting the occurrence of dead trees by PWD were verified. The results showed that altitude and temperature from May to August, among natural factors, and distance to building and forest road among anthropogenic factors were the most influential factors on the occurrence of PWD.

• Land and Housing Review
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• v.13 no.1
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• pp.113-129
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• 2022

A building's eco-friendliness is directly related to various values including the life cycle cost of a building. However, the conventional architectural design method has a limitation in that it cannot create an optimized case according to the surrounding environmental conditions. Therefore, the purpose of this research is to present a design assistance tool that can review planning cases optimized for the environmental conditions of the building site in the planning stage of architectural production. To achieve the purpose of the study, an algorithm for realizing 3D modeling of the region and analysis of the solar environment was produced based on the site contours, building, and road information from the digital topographic map provided by the National Geographic Information Institute. To examine the validity of the developed algorithm, a comparative experiment was conducted targeting the elevation direction of the existing building. As a result, it was found that the optimal elevation direction selected by the algorithm can receive higher insolation compared to the front facade of the main building.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.389-395
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• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.599-607
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• 2021

Street-view images, which are omnidirectional scenes centered on a specific location on the road, can provide various obstacle information for the pedestrians. Pedestrian network data for the navigation services should reflect the up-to-date obstacle information to ensure the mobility of pedestrians, including people with disabilities. In this study, the object detection model was trained for the bollard as a major obstacle in Seoul using street-view images and a deep learning algorithm. Also, a process for updating information about the presence and number of bollards as obstacle properties for the crosswalk node through spatial matching between the detected bollards and the pedestrian nodes was proposed. The missing crosswalk information can also be updated concurrently by the proposed process. The proposed approach is appropriate for crowdsourcing data as the model trained using the street-view images can be applied to photos taken with a smartphone while walking. Through additional training with various obstacles captured in the street-view images, it is expected to enable efficient information update about obstacles on the road.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.93-101
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• 2023

In this paper, we propose a method of creating a 3D bounding box for an object using a vanishing point to increase the accuracy of object recognition in an image when recognizing an traffic object using a video camera. Recently, when vehicles captured by a traffic video camera is to be detected using artificial intelligence, this 3D bounding box generation algorithm is applied. The vertical vanishing point (VP1) and horizontal vanishing point (VP2) are derived by analyzing the camera installation angle and the direction of the image captured by the camera, and based on this, the moving object in the video subject to analysis is specified. If this algorithm is applied, it is easy to detect object information such as the location, type, and size of the detected object, and when applied to a moving type such as a car, it is tracked to determine the location, coordinates, movement speed, and direction of each object by tracking it. Able to know. As a result of application to actual roads, tracking improved by 10%, in particular, the recognition rate and tracking of shaded areas (extremely small vehicle parts hidden by large cars) improved by 100%, and traffic data analysis accuracy was improved.