• Journal of KIBIM
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• v.9 no.2
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• pp.21-28
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• 2019

Recently, many scan projects are gradually increasing for maintenance, construction. The scan data contains useful data, which can be generated in the target application from the facility, space. However, modeling the scan data required for the application requires a lot of cost. In example, the converting 3D point cloud obtained from scan data into 3D object is a time-consuming task, and the modeling task is still very manual. This research proposes Scan-to-Geometry Mapping Rule Definition (S2G-MD) which maps point cloud data to geometry for irregular building plane objects. The S2G-MD considers user use case variability. The method to define rules for mapping scan to geometry is proposed. This research supports the reverse engineering semi-automatic process for the building planar geometry from the user perspective.

• Structural Engineering and Mechanics
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• v.40 no.2
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• pp.245-256
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• 2011

In this paper, the use of universal serendipity elements (USE) to eliminate node mapping distortions for dynamic problem is presented. Rectangular shaped elements for USE are being introduced by using a flexible master element with an adjustable edge node location. The shape functions of the universal serendipity formulation are used to derive the mass and damping matrices for the dynamic analyses. These matrices eliminate the node mapping distortion errors that occurs incase of the standard shape function formulations. The verification of new formulation will be tested and the errors encountered in the standard formulation will be studied for a dynamically loaded deep cantilever.

• ETRI Journal
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• v.43 no.4
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• pp.594-602
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• 2021

Recently, quantitative and repetitive inspections of the old urban area were conducted because many structures exceed their designed lifetime. The health of a building can be validated from the condition of the outer wall, while the slant angle of the wall widely serves as an indicator of urban regeneration projects. Mostly, the inspector directly measures the inclination of the wall or partially uses 3D point measurements using a static light detection and ranging (LiDAR). These approaches are costly, time-consuming, and only limited space can be measured. Therefore, we propose a mobile mapping system and automatic slant map generation algorithm, configured to capture urban environments online. Additionally, we use the LiDAR-inertial mapping algorithm to construct raw point clouds with gravity information. The proposed method extracts walls from raw point clouds and measures the slant angle of walls accurately. The generated slant angle map is evaluated in indoor and outdoor environments, and the accuracy is compared with real tiltmeter measurements.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.341-347
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• 2022

This paper presents a comparison result of two simultaneous localization and mapping (SLAM) algorithms for navigation that have been proposed in literature. The performances of Extended Kalman Filter (EKF) SLAM under Gaussian condition, FastSLAM algorithms using Rao-Blackwellised method for particle filtering are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on indoor environment. The results show that the Particle filter based FastSLAM has the better performance in terms of accuracy of localization and mapping. The experimental results are discussed and compared.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.397-406
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• 2022

Recently, research and development to revitalize smart construction are being actively carried out. Accordingly, 3D mapping technology that digitizes construction site is drawing attention. To create a 3D digital map for construction site a point cloud generation method based on LiDAR(Light detection and ranging) using MMS(Mobile mapping system) is mainly used. The purpose of this study is to analyze the accuracy of MMS LiDAR-based point cloud data. As a result, accuracy of MMS point cloud data was analyzed as dx = 0.048m, dy = 0.018m, dz = 0.045m on average. In future studies, accuracy comparison of point cloud data produced via UAV(Unmanned aerial vegicle) photogrammetry and MMS LiDAR should be studied.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.59-65
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• 2023

This paper proposes an Extended and Adaptive Inverse Perspective Mapping (EA-IPM) model that can obtain an accurate bird's-eye view (BEV) from the forward-looking monocular camera on the sidewalk with various curves. While Inverse Perspective Mapping (IPM) is a good way to obtain ground information, conventional methods assume a fixed relationship between the camera and the ground. Due to the nature of the driving environment of the mobile robot, there are more walking environments with frequent motion changes than flat roads, which have a fatal effect on IPM results. Therefore, we have developed an extended IPM process to be applicable in IPM on sidewalks by adding a formula for complementary Y-derive processes and roll motions to the existing adaptive IPM model that is robust to pitch motions. To convince the performance of the proposed method, we evaluated our results on both synthetic and real road and sidewalk datasets.

• ETRI Journal
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• v.30 no.6
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• pp.818-825
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• 2008

This paper proposes an exponentiation method with Frobenius mappings. The main target is an exponentiation in an extension field. This idea can be applied for scalar multiplication of a rational point of an elliptic curve defined over an extension field. The proposed method is closely related to so-called interleaving exponentiation. Unlike interleaving exponentiation methods, it can carry out several exponentiations of the same base at once. This happens in some pairing-based applications. The efficiency of using Frobenius mappings for exponentiation in an extension field was well demonstrated by Avanzi and Mihailescu. Their exponentiation method efficiently decreases the number of multiplications by inversely using many Frobenius mappings. Compared to their method, although the number of multiplications needed for the proposed method increases about 20%, the number of Frobenius mappings becomes small. The proposed method is efficient for cases in which Frobenius mapping cannot be carried out quickly.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.268-271
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• 2003

There are several methods for the design of 2D filter. Notable among them is McClellan transform method. This transform allows us to obtain a high order 2D FIR filter through mapping the 1D frequency points of a 1D prototype FIR filter onto 2D frequency contours. We design 2D filter using this transform. Then we notice for mapping deviation of the 2D filter. In this paper, Quadratic programming (QP) method allows us to obtain coefficients of McClellan transform. Then we compare deviation of QP method with least-squares(LS) method. Elliptic filter is used for comparison. The optimal cutoff frequencies of a 1D filter are obtained directly from the QP method. Also several problem of LS method are solved.

• Journal of The Korean Association For Science Education
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• v.30 no.8
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• pp.1044-1059
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• 2010

In this study, we investigated the influences of the method of using analogy on concept understanding, mapping understanding, and mapping error by analogical reasoning ability level. We also investigated students' perception of a role-playing analogy activity. Seventh graders (N=152) at a middle school were assigned to the comparison and the experimental groups. The students of the experimental group were taught about 'the relation between pressure and volume of gas' with experience-based role-playing analogy, while those of the comparison group were taught with explanation-centered analogy. Analyses of the results revealed that the instruction using roleplaying analogy was more effective in concept understanding and retention of mapping understanding than explanation-centered analogy instruction, regardless of analogical reasoning ability level. It was also found that the students of the experimental group had fewer mapping errors than those of the comparison group. However, there was little difference in t pes of mapping errors by the method of using analogy. The students of the experimental group answered that they did not have difficulties in performing the role-playing analogy activity and they actively engaged in the activity. They perceived that the role-playing analogy activity was interesting. Educational implication of these findings are discussed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.75-80
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• 2017

In this paper, we describe the effect of the regularization method and the network with identity mapping on the performance of the activation functions in deep convolutional neural networks. The activation functions act as nonlinear transformation. In early convolutional neural networks, a sigmoid function was used. To overcome the problem of the existing activation functions such as gradient vanishing, various activation functions were developed such as ReLU, Leaky ReLU, parametric ReLU, and ELU. To solve the overfitting problem, regularization methods such as dropout and batch normalization were developed on the sidelines of the activation functions. Additionally, data augmentation is usually applied to deep learning to avoid overfitting. The activation functions mentioned above have different characteristics, but the new regularization method and the network with identity mapping were validated only using ReLU. Therefore, we have experimentally shown the effect of the regularization method and the network with identity mapping on the performance of the activation functions. Through this analysis, we have presented the tendency of the performance of activation functions according to regularization and identity mapping. These results will reduce the number of training trials to find the best activation function.