• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.43-54
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• 2012

This paper presents a new localization technique of an UGV(Unmanned Ground Vehicle) by matching ortho-edge images generated from a DSM (Digital Surface Map) which represents the 3D geometric information of an outdoor navigation environment and 3D range data which is obtained from a LIDAR (Light Detection and Ranging) sensor mounted at the UGV. Recent UGV localization techniques mostly try to combine positioning sensors such as GPS (Global Positioning System), IMU (Inertial Measurement Unit), and LIDAR. Especially, ICP (Iterative Closest Point)-based geometric registration techniques have been developed for UGV localization. However, the ICP-based geometric registration techniques are subject to fail to register 3D range data between LIDAR and DSM because the sensing directions of the two data are too different. In this paper, we introduce and match ortho-edge images between two different sensor data, 3D LIDAR and DSM, for the localization of the UGV. Details of new techniques to generating and matching ortho-edge images between LIDAR and DSM are presented which are followed by experimental results from four different navigation paths. The performance of the proposed technique is compared to a conventional ICP-based technique.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.577-587
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• 2023

The utilization of crowdsourced spatial data has been actively researched; however, issues stemming from the uncertainty of data quality have been raised. In particular, when low-quality data is mixed into drone imagery datasets, it can degrade the quality of spatial information output. In order to address these problems, the study presents a methodology for automatically validating the geometric quality of crowdsourced imagery. Key quality factors such as spatial resolution, resolution variation, matching point reprojection error, and bundle adjustment results are utilized. To classify imagery suitable for spatial information generation, training and validation datasets are constructed, and machine learning is conducted using a radial basis function (RBF)-based support vector machine (SVM) model. The trained SVM model achieved a classification accuracy of 99.1%. To evaluate the effectiveness of the quality validation model, imagery sets before and after applying the model to drone imagery not used in training and validation are compared by generating orthoimages. The results confirm that the application of the quality validation model reduces various distortions that can be included in orthoimages and enhances object identifiability. The proposed quality validation methodology is expected to increase the utility of crowdsourced data in spatial information generation by automatically selecting high-quality data from the multitude of crowdsourced data with varying qualities.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.19 no.1
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• pp.8-15
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• 2009

This study was examined to find out asbestos exposure level the factors which affected the level at asbestos abatement sites. We visited a total of thirteen building demolition sites(3 apartments, 3 schools, 4 stores, and 3 houses) were visited to collect samples and related data from August to November, 2006. The results of this study were as follows 1. The results of an analysis of bulk samples to identify types of asbestos at the asbestos abatement sites showed that the kinds of the asbestos detected were chrysotile by 50.0%, were tremolite by 2.6%, and were the contents of chrysotile by 3 to 20%. 2. The geometric mean concentration of asbestos was 0.007 f/cc(range 0.001-0.34 f/cc) and its geometric standard deviation was 5.83. Of the samples, however, 12 exceeded the Korean Occupational Exposure Limit(0.1f/cc). 3. Of the materials, textile material had the highest concentration with geometric mean of 0.016 f/cc. When asbestos-containing materials were removed using T type tools, the geometric mean concentration of asbestos was 0.061 f/cc. The level by this method was much higher than by other removal methods. In analysis by the type of building, the geometric mean concentration of asbestos in stores was 0.042 f/cc and was higher than in other buildings. 4. The Poisson regression analysis was applied to find out the factors that affect the airborne asbestos concentration. As a result of the analysis, removal using a T type tool was the most important factor affecting the asbestos concentration(p<0.01). In conclusion, the airborne asbestos concentration(geometric mean) in asbestos abatement sites was 0.007 f/cc(0.001~0.34 f/cc), and 12(14.6%) of all samples were over the 0.1 f/cc. These results showed that asbestos abatement workers have been exposed to the high level of airborne asbestos because they have not been keeping asbestos removal rule. In accordance with increases of the number of building demolition sites, the better government regulation on asbestos abatement methods should be made and be performed well at building demolition sites.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1143-1153
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• 2018

For TOPS InSAR processing, high-precision image co-registration is required. We propose an image co-registration method suitable for the TOPS mode by comparing the performance of cross correlation method, the geometric co-registration and the enhanced spectral diversity (ESD) matching algorithm based on the spectral diversity (SD) on the Sentinel-1 TOPS mode image. Using 23 pairs of interferometric pairs generated from 25 Sentinel-1 TOPS images, we applied the cross correlation (CC), geometric correction with only orbit information (GC1), geometric correction combined with iterative cross-correlation (GC2, GC3, GC4), and ESD iteration (ESD_GC, ESD_1, ESD_2). The mean of co-registration errors in azimuth direction by cross correlation and geometric matching are 0.0041 pixels and 0.0016 pixels, respectively. Although the ESD method shows the most accurate result with the error of less than 0.0005 pixels, the error of geometric co-registration is reduced to 0.001 pixels by repetition through additional cross correlation matching between the reference and resampled slave image. The ESD method is not applicable when the coherence of the burst overlap areas is low. Therefore, the geometric co-registration method through iterative processing is a suitable alternative for time series analysis using multiple SAR data or generating interferogram with long time intervals.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.2
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• pp.209-216
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• 1997

A feature is well known as a medium to integrate CAD, CAPP and CAM systems. For a part drawing including both simple geometry and compound geometry, a process plan such as the selection of process, machine tool, cutting tool etc. normally needs simple geometry data and non-geometry data of the feature as the input. However, a extended process plan such as the generation of process sequence, operation sequence, jig & fixture, NC program etc. necessarily needs the compound geometry data as well as the simple geometry data and non-geometry data. In this paper, we propose a feature data model according to the result of analyzing necessary data, including the compound geometry data, the simple geometry data and the non-geometry data. Also, an example of the feature data model in milling process planning is described.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.215-223
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• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.

• Journal of Ocean Engineering and Technology
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• v.13 no.3 s.33
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• pp.12-20
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• 1999

In this paper, method for mapping a three-dimensional shape into the two-dimensional plane will be introduced. This method is referred to geometric modelling and means a transformation between the flat sheet and final surface. The initial blank shape represents the original configuration of the final shape formed into three dimensional surface. The initial constant constant area mapping hypothesis was used in this paper. This technique will be applied to the basic data for an interactive computer design capable of dealing with typical stamping process, including deep parts and complex shapes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.301-308
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• 2005

In this study, we implement a framework that directly links a general tensor-based shell finite element to NURBS geometric modeling. Generally, in CAD system the surfaces are represented by B-splines or non-uniform rational B-spline(NURBS) blending functions and control points. Here, NURBS blending functions are composed by two parameters defined in local region. A general tensor-based shell element also has a two-parameter representation in the surfaces, and all the computations of geometric quantities can be performed in local surface patch. Naturally, B-spline surface or NURBS function could be directly linked to the shell analysis routine. In our study, we use NLib(NURBS libraray) to generate NURBS for shell finite analysis. The NURBS can be easily generated by interpolating or approximating given set of data points through NLib.

• Korean Journal of Computational Design and Engineering
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• v.15 no.3
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• pp.234-242
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• 2010

3D CAD technology has been extended to a medical area including dental clinic beyond industrial design. The 2D images obtained by CT(Computerized Tomography) and MRI(Magnetic Resonance Imaging) are not intuitive, and thus the volume rendering technique, which transforms 2D data into 3D anatomic information, has been in practical use. This paper has focused on a method and its implementation for forming 3D geometric surface model from laminated CT images of the pubis. The implemented system could support a dental clinic to observe and examine the status of a patient's pubis before implant surgery. The supplement of 3D implant model would help dental surgeons settle operation plans more safely and confidently. It also would be utilized with teaching materials for a practice and training.

• 한국전산유체공학회:학술대회논문집
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• 2009.11a
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• pp.34-37
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• 2009

Deforming mesh should be used when bodies are deforming or moving relative to each other due to the presence of aerodynamic forces and moments. Also, the flow solver for such a flow problem should satisfy the geometric conservation law to ensure the accuracy of the solutions. In this paper, a RANS(Reynolds Averaged Navier-Stokes) solver including automatic mesh capability using TFI(Transfinite Interpolation) method and GCL is developed and applied to flows induced by oscillating wings with given frequencies. The computations are performed both on deforming meshes and on rigid meshes. The computational results are compared with experimental data, which shows a good agreement.