• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.848-853
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• 2005

3D geometric modeling of an object of interest has been intensively investigated in many fields including CAD/CAM and computer graphics. Traditionally, CAD and geometric modeling tools are widely used to create geometric models that have nearly the same shape of 3D real objects or satisfy designers intent. Recently, with the help of the reverse engineering (RE) technology, we can easily acquire 3D point data from the objects and create 3D geometric models that perfectly fit the scanned data more easily and fast. In this paper, we present 3D geometric model generation based on a stereo vision system (SVS) using random pattern projection. A triangular mesh is considered as the resulting geometric model. In order to obtain reasonable results with the SVS-based geometric model generation, we deal with many steps including camera calibration, stereo matching, scanning from multiple views, noise handling, registration, and triangular mesh generation. To acquire reliable stere matching, we project random patterns onto the object. With experiments using various random patterns, we propose several tips helpful for the quality of the results. Some examples are given to show their usefulness.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.3
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• pp.19-25
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• 2022

Purpose: As interest in odor increases, odor complaints are also rapidly increasing. Traditional markets are not included in malodor control areas and are not easy to manage, so measure the odor substances generated in traditional markets and analyze the cause of high concentration points. Research design, data and methodology: The average value was presented by continuously measuring the combined odor, TVOC, hydrogen sulfide, and ammonia for 5 minutes at 100m intervals in Joong-ang traditional market, Jayu traditional market, Doraemi traditional market, and Sundae Alley in Wonju, Gangwon-do. In each market, up to the third highest concentration point for each measurement item was marked and analyzed. Results: The Joong-ang traditional market, Doraemi traditional market, and Sundae Alley had high readings at the intersection. The Jayu traditional market had high measurements around restaurants and clothing stores. In addition, the concentration of complex malodors was also high at the points where the hydrogen sulfide concentration was measured. Conclusions: Odor generated in traditional markets is an important indicator for merchants and consumers. Therefore, in future studies, analysis that can supplement the limitations of measurement data and seasonal effects is needed.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1349-1356
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• 2004

The interest of version management is increasing in electronic commerce requiring data mining and documents processing system related to digital governmentapplications. In this paper, we define a change set that is to manage historicalinformation and to maintain XML documents during a long period of time and propose several storage policies of XML documents using a change set. A change set includes a change oper-ation set and temporal dimensions and a change operation set is composed with schema change operations and data change operations. We pro-pose three storage policies using a change set. Three storage policies are (1) storing all the change sets, (2) storing the change sets and the versions periodically. (3) storing the aggregation of change sets and the versions at a point of proper time. Also, we compare the performance between the existing storage policy and the proposed storage policies. Though the performance evaluation, we show that the method to store the aggregation of change sets and the versions at a point of proper time outperforms others.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.37-51
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• 2020

With the development of machine learning and deep learning technologies, there has been increasing interest and attempt to apply these technologies to the detection of urban changes. However, the traditional methods of detecting changes and constructing spatial information are still often performed manually by humans, which is costly and time-consuming. Besides, a large number of people are needed to efficiently detect changes in buildings in urban areas. Therefore, in this study, a methodology that can detect changes by classifying road, building, and vegetation objects that are highly utilized in the geospatial information field was proposed by applying deep learning technology to point clouds. As a result of the experiment, roads, buildings, and vegetation were classified with an accuracy of 92% or more, and attributes information of the objects could be automatically constructed through this. In addition, if time-series data is constructed, it is thought that changes can be detected and attributes of existing digital maps can be inspected through the proposed methodology.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.325-336
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• 2006

For supporting LBS service, recent studies on spatial network databases (SNDB) have been done actively. In order to gain good performance on query processing in SNDB, we, in this paper. design efficient storage and index structures for spatial network data, point of interests (POIs), and moving objects on spatial networks. First, we design a spatial network file organization for maintaining the spatial network data itself consisting of both node and edges. Secondly, we design a POI storage and index structure which is used for gaining fast accesses to POIs, like restaurant, hotel, and gas station. Thirdly, we design a signature-based storage and index structure for efficiently maintaining past, current, and expected future trajectory information of moving objects. Finally, we show that the storage and index structures designed in this paper outperform the existing storage structures for spatial networks as well as the conventional trajectory index structures for moving objects.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Journal of Korean Public Health Nursing
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• v.19 no.1
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• pp.95-107
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• 2005

With the aim of diagnostic research on health education. the health promoting behaviors related to elementary school students' weight control. life satisfaction. health state. self-efficacy. parent's interest and knowledge about weight control and school health education of weight control were investigated on the basis of the PRECEDE model. The data for this study were collected from a sample at an elementary school in Chongju for 5 days in July. 2004. In analyzing the data. t-test. one-way ANOVA. chi-square test and multiple regression analysis were done by using SPSS 10.1 The results were as follows : 1. The elementary school students' level of life satisfaction was above half of the full point. The score difference of life satisfaction was statistically significant by grade and academic achievement(F=4.646. p=.010. F=16.042. p=.000). 2. The perceived level of health state was moderate for all students. Normal weight students' level was significantly higher than obese students' (t=3.667. p=.000). 3. The perceived level of health promoting behaviors related to weight control was above the moderate. The perceived level of health promoting behaviors related to weight control in the obese students was significantly higher than that in normal weight students(t=-2.225. p=.027). The students used computer for 1.48 hours and watched TV for 2.52 hours a day. 4. The score of health promoting behavior self-efficacy in this subject was 70.61. 5. The parents' level of interest in the obese students' weight control was significantly higher than that in the normal weight students(t=-4.86. p=.000). 6. Sixty-six percent of the students learned about weight control education in school. 7. The health promoting behavior self-efficacy among the educational diagnostic variables was the most influential variable in students' health promoting behaviors related to weight control. This research diagnosed the needs of weight control education in elementary school by assessing various factors related to weight control behaviors. The research findings suggest that we can enhance the prevention of childhood obesity by strengthening the related factors such as parents' knowledge and interest, health promoting behaviors and self-efficacy related to weight control in school health education.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.335-341
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• 2023

In this study, four algorithms (Standard, Bone, Dual volume, and Stent Follow up) were applied to the image of the aneurysm in cerebral angiography to reconstruct the image in 3D, and quantitatively evaluate Noise, SNR, and CNR based on the reconstructed image to find out the optimal algorithm. As an analysis method, Image J program, which can analyze images and calculate area and pixel values, was used for images reconstructed with four algorithms. In order to obtain Noise, SNR, and CNR, the region of interest (ROI) is measured by designating the point where the abnormal artery (aneurysm) is located and the surrounding normal artery in the image are measured, and the mean value and SD value are obtained. Background noise was set to two surrounding normal artery to increase reliability. The values of SNR and CNR were calculated based on the given formula. As a result, the noise was the lowest in the stent follow-up algorithm, and the SNR and CNR were the highest. Therefore, the stent follow-up algorithm is judged to be the most appropriate algorithm. The data of this study are expected to be useful as basic data for 3D image evaluation of the vascular and aneurysm in cerebral angiography, and it is believed that appropriate algorithm changes will serve as an opportunity to further improve image quality.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.55-62
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• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• Korean Journal of Geomatics
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• v.2 no.2
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• pp.123-130
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• 2002

LiDAR (Light Detection And Ranging) system has a profound impact on geoinformatics. The laser mapping system is now recognized as being a viable system to produce the digital surface model rapidly and efficiently. Indeed the number of its applications and users has grown at a surprising rate in recent years. Interest is now focused on the reconstruction of buildings in urban areas from LiDAR data. Although with present technology objects can be extracted and reconstructed automatically using LiDAR data, the quality issue of the results is still major concern in terms of geometric accuracy. It would be enormously beneficial to the geoinformatics industry if geometrically accurate modeling of topographic surface including man-made objects could be produced automatically. The objectives of this study are to reconstruct buildings using airborne LiDAR data and to evaluate accuracy of the result. In these regards, firstly systematic errors involved with ALS (Airborne Laser Scanning) system are introduced. Secondly, the overall LiDAR data quality was estimated based on the ground check points, then classifying the laser points was performed. In this study, buildings were reconstructed from the classified as building laser point clouds. The most likely planar surfaces were estimated by the least-square method using the laser points classified as being planes. Intersecting lines of the planes were then computed and these were defined as the building boundaries. Finally, quality of the reconstructed building was evaluated.