• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.255-262
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• 2018

In order to determine the three-dimensional position in photogrammetry, a spatial resection is a pre-requisite step to determine exterior orientation parameters. The existing spatial resection method is a non-linear equation that requires initial values of exterior orientation parameters and has a problem that a gimbal lock phenomenon may occur. On the other hand, the spatial resection using quaternion is a closed form solution that does not require initial values of EOP (Exterior Orientation Parameters) and is a method that can eliminate the problem of gimbal lock. In this study, to analyze the stability of the quaternion-based spatial resection, the exterior orientation parameters were determined according to the different layout of control points and were compared with the determined values using existing non-linear equation. As a result, it can be seen that the quaternionbased spatial resection is affected by the layout of the control points. Therefore, if the initial value of exterior orientation parameters could not be obtained, it would be more effective to estimate the initial exterior orientation values using the quaternion-based spatial resection and apply it to the collinearity equation-based spatial resection method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Journal of Engineering Geology
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• v.24 no.1
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• pp.53-67
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• 2014

Numerical modeling is commonly used to reproduce the physical phenomena of dam-break and to compile resulting flood hazard maps. The accuracy of a dam-break model depends on the physical structure that describes the volume of storage, breach formation and progress, input variables, and model parameters. Model input and parameters are subjective in that they are prescribed; hence, caution is needed when interpreting the results. This study focuses on three parameters (breach degree ${\theta}$, shape factor P, and collapse rate k) used when the dam-break model is coupled with FLO-2D (a two-dimensional flood simulation model) to estimate flood coverage and depth etc. The results show that the simulation is sensitive to the shape factor P and the collapse rate k but not to the breach degree ${\theta}$. This study will contribute to reducing flood damage from dam-break disasters in the future.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.73-80
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• 2012

Recently, medical equipments are developed and used for diagnosis or studies. In addition, demand of techniques which automatically deal with three dimensional medical images obtained from the medical equipments is growing. One of the techniques is automatic bone segmentation which is expected to enhance the diagnosis efficiency of osteoporosis, fracture, and other bone diseases. Although various researches have been proposed to solve it, they are unable to be used in practice since a size of the medical data is large and there are many low contrast boundaries with other tissues. In this paper, we present a fast and accurate automatic framework for bone segmentation based on multi-resolutions. On a low resolution step, a position of the bone is roughly detected using constrained branch and mincut which find the optimal template from the training set. Then, the segmentation and the registration are iteratively conducted on the multiple resolutions. To evaluate the performance of the proposed method, we make an experiment with femur and tibia from 50 test knee magnetic resonance images using 100 training set. The proposed method outperformed the constrained branch and mincut in aspect of segmentation accuracy and implementation time.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.609-616
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• 2017

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.

• Journal of Advanced Navigation Technology
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• v.22 no.4
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• pp.271-278
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• 2018

For ionospheric correction of low earth orbiter (LEO) satellites using single frequency global navigation satellite system (GNSS) receiver, ionospheric scale factor should be applied to the ground-based ionosphere model. The ionospheric scale factor can be calculated by using a NeQuick model, which provides a three-dimensional ionospheric distribution. In this study, the ionospheric scale factor is calculated by using NeQuick G model during 2015, and it is compared with the scale factor computed from the combination of LEO satellite measurements and international GNSS service (IGS) global ionosphere map (GIM). The accuracy of the ionospheric delay calculated by the NeQuick G model and IGS GIM with NeQuick G scale factor is analyzed. In addition, ionospheric delay errors calculated by the NeQuick G model and IGS GIM with the NeQuick G scale factor are compared. The ionospheric delay error variations along to latitude and solar activity are also analyzed. The mean ionospheric scale factor from the NeQuick G model is 0.269 in 2015. The ionospheric delay error of IGS GIM with NeQuick G scale factor is 23.7% less than that of NeQuick G model.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.59-66
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• 2016

This paper presents an empirical evaluation on dimensionality reduction strategies by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is the high dimensionality of the dissimilarity space when a lots of objects are treated. To address this issue, two kinds of solutions have been proposed in the literature: prototype selection (PS)-based methods and dimension reduction (DR)-based methods. In this paper, instead of utilizing the PS-based or DR-based methods, a way of performing DBC in Eigen spaces (ES) is considered and empirically compared. In ES-based DBC, classifications are performed as follows: first, a set of principal eigenvectors is extracted from the training data set using a principal component analysis; second, an Eigen space is expanded using a subset of the extracted and selected Eigen vectors; third, after measuring distances among the projected objects in the Eigen space using $l_p$-norms as the dissimilarity, classification is performed. The experimental results, which are obtained using the nearest neighbor rule with artificial and real-life benchmark data sets, demonstrate that when the dimensionality of the Eigen spaces has been selected appropriately, compared to the PS-based and DR-based methods, the performance of the ES-based DBC can be improved in terms of the classification accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.125-132
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• 2019

Because the water exposed to shock waves caused by an underwater explosion cannot withstand the appreciable tension induced by the change in both pressure and velocity, the surrounding water is cavitated. This cavitating water changes the transferring circumstance of the shock loading. Three phenomena contribute to hull-plate damage; initial shock loading and its interaction with the hull plate, local cavitation, and local cavitation closure then shock reloading. Because the main concern of this paper is local cavitation due to a near-field underwater explosion, the water surface and the waves reflected from the sea bottom were not considered. A set of governing equations for the structure and the fluid were derived. A simple one-dimensional infinite plate problem was considered to verify this uncoupled solution approach compared with the analytic solution, which is well known in this area of interest. The uncoupled solution approach herein would be useful for obtaining a relatively high level of accuracy despite its simplicity and high computational efficiency compared to the conventional coupled method. This paper will help improve the understanding of fluid-structure interaction phenomena and provide a schematic explanation of the practical problem.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1425-1434
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• 2021

Continuous and periodic data acquisition must be preceded to maintain and manage the river facilities effectively. Adapting the existing general facilities methods, which include river surveying methods such as terrestrial laser scanners, total stations, and Global Navigation Satellite System (GNSS), has limitation in terms of its costs, manpower, and times to acquire spatial information since the river facilities are distributed across the wide and long area. On the other hand, the Mobile Mapping System (MMS) has comparative advantage in acquiring the data of river facilities since it constructs three-dimensional spatial information while moving. By using the MMS, 184,646,009 points could be attained for Anyang stream with a length of 4 kilometers only in 20 minutes. Levee points were divided at intervals of 10 meters so that about 378 levee cross sections were generated. In addition, the waterside maximum and average slope could be automatically calculated by separating slope plane form levee point cloud, and the accuracy of RMSE was confirmed by comparing with manually calculated slope. The reference slope was calculated manually by plotting point cloud of levee slope plane and selecting two points that use location information when calculating the slope. Also, as a result of comparing the water side slope with slope standard in basic river plan for Anyang stream, it is confirmed that inspecting the river facilities with the MMS point cloud is highly recommended than the existing river survey.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.4
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• pp.223-233
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• 2021

Systematic and continuous monitoring and management of the energy consumption of buildings are important for estimating building energy efficiency, and ultimately aim to cope with climate change and establish effective policies for environment, and energy supply and demand policies. Globally, buildings consume 36% of total energy and account for 39% of carbon dioxide emissions. The purpose of this study is to generate three-dimensional thermo-spatial building models with photogrammetric technique using drone TIR (Thermal Infrared) images to measure the temperature emitted from a building, that is essential for the building energy rating system. The aerial triangulation was performed with both optical and TIR images taken from the sensor mounted on the drone, and the accuracy of the models was analyzed. In addition, the thermo-spatial models of temperature distribution of the buildings in three-dimension were visualized. Although shape of the objects 3D building modeling is relatively inaccurate as the spatial and radiometric resolution of the TIR images are lower than that of optical images, TIR imagery could be used effectively to measure the thermal energy of the buildings based on spatial information. This paper could be meaningful to present extension of photogrammetry to various application. The energy consumption could be quantitatively estimated using the temperature emitted from the individual buildings that eventually would be uses as essential information for building energy efficiency rating system.