• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.121-134
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• 2013

The transverse cylindrical projection has been used in Korea since 1910s when the nationwide geodetic network was firstly established. However, the projection has a number of different types of functions according to a way of its mathematical derivation as well as a section of its coefficients and terms, for instance Gauss- Schreiber(GS) and Gauss-Kruger(GK) types. Although the transverse cylindrical projection itself is assigned to a system, projected coordinates would be diverse with respect to the function used in the actual calculation. In order to investigate impact of functions used in the computation, five different equations (i.e., 2 GS and 3 GK) were implemented in this study by using MATLAB. A series of numerical simulation tests has been carried out to compare and characterize them in terms of projection accuracy, difference of projected coordinates and distortion. Furthermore, a comparison between GS and GK function was made under the Korean gridding system, consisting of four zones. Results from the numerical computations were qualitatively analyzed and summarized in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.74-81
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• 1997

This paper proposes an evolutionary learning algorithm to discipline the projection neural nctworks (PNNs) with special type of hidden nodes which can activate radial basis functions as well as sigmoid functions. The proposed algorithm not only trains the parameters and the connection weights hut also c~ptimizes the network structure. Through the structure optimization, the number of hidden node:; necessary to represent a given target function is determined and the role of each hidden node is decided whether it activates a radial basis function or a sigmoid function. To apply the algorithm, PNN is realized by a self-organizing genotype representation with a linked list data structure. Simulations show that the algorithm can build the PNN with less hidden nodes than thc existing learning algorithm using error hack propagation(EE3P) and network growing strategy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.519-522
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• 2004

본 논문에서는 외향 기반 접근법을 기반으로 한 로봇의 위치 추정 알고리즘을 제안한다. 로봇이 작업을 수행할 공간에서 강한 상관관계를 갖는 영상들을 취득하여 eigenspace로 투영 시킴으로써 주성분의 추출을 수행한다. 이 추출된 주성분은 신경 회로망을 이용해 eigenspace에서의 연속 외향 함수(continuous appearance function)로 나타낼 수 있다. 로봇의 위치 추정을 위해 새로운 영상이 주어지면 이것을 eigenspace로 투영 시킨 후 연속 외향 함수를 통해 로봇의 현재 위치를 추정한다. 최종적으로는, 영상안의 데이터에 칼만 필터를 적용함으로써 로봇의 정확한 위치와 영상으로 획득된 정보 사이의 오차를 이용하여 보다 정확한 이동 로봇의 위치를 추정하는 알고리즘을 제안한다.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.228-242
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• 2005

The projected clustering seeks to find clusters in different subspaces within a high dimensional dataset. We propose an algorithm to discover near optimal projected clusters without user specified parameters such as the number of output clusters and the average cardinality of subspaces of projected clusters. The objective function of the algorithm computes projected energy, quality, and the number of outliers in each process of clustering. In order to minimize the projected energy and to maximize the quality in clustering, we start to find best subspace of each cluster on the density of input points by comparing standard deviations of the full dimension. The weighting factor for each dimension of the subspace is used to get id of probable error in measuring projected distances. Our extensive experiments show that our algorithm discovers projected clusters accurately and it is scalable to large volume of data sets.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1
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• pp.49-54
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• 1998

본 논문에서는 잡음이 존재하는 환경에 강인한 것으로 알려져 있는 투영 방법을 우 도 측정에 가중 함수와 결합하여 사용하는 방법을 제안하였다. 반연속 HMM을 이용한 고립 단어의 인식 실험 결과, 제안한 방법이 실험에 사용된 잡음의 환경들에서 모두 좋은 성능을 나타내었다. 아울러 병렬 모델 결합 방법을 반연속 HMM에 적용하였는데 이는 코드북의 변 환반으로 쉽게 잡음의 특성을 반영할 수 있다. 가중 투영 우도 측정 방법을 병렬 모델 결합 방법에 적용한 경우에도 우수한 성능을 거둘 수 있었다.

• Tunnel and Underground Space
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• v.26 no.2
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• pp.100-109
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• 2016

This study suggests three spatial distribution functions of strength parameters, which can be adopted in the derivation of failure conditions for transversely isotropic rocks. All three proposed functions, which are the oblate spheroidal function, the exponential function, and the function based on the directional projection of the strength parameter tensor, consist of two model parameters. With assumption that the cohesion and friction angle can be described by the proposed distribution functions, the transversely isotropic Mohr-Coulomb criterion is formulated and used as a failure condition in the simulation of the conventional triaxial tests. The simulation results confirm that the failure criteria incorporating the proposed distribution functions could reproduce the general trend in the variations of the axial stress at failure and the directions of failure planes with varying inclination of the weankness planes and confining pressure. Among three distribution functions, the function based on the directional projection of the strength parameter tensor yields the highest axial strength, while the axial strength estimated by the oblate spheroidal distribution function is the lowest.

• Journal of the Korean Magnetics Society
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• v.22 no.3
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• pp.73-78
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• 2012

We calculated the electron spin resonance (ESR) line-profile function. The line-width of single-walled carbon nanotube (SWNT) was studied as a function of the temperature at a frequency of 9.5 GHz in the presence of external electromagnetic radiation. The temperature dependence of the line-widths is obtained with the projection operator method (POM) proposed by Argyres and Sigel. The scattering is little affected in the low-temperature region (T < 200 K). We conclude that the calculation process presented in this method is useful for optical transitions in SWNT.

• Proceedings of the Optical Society of Korea Conference
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• 2003.02a
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• pp.94-95
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• 2003

광학식 3차원 형상측정 기술은 산업현장과 의료분야 등에서 광범위하게 사용되어지고 있으며, 이에 대한 연구도 활발히 진행되고 있다. 본 연구에서는 푸리에 변환법에 의한 위상정보 추출 기술을 개발하고, 주파수 영역에서의 창함수 필터에 따른 위상추출 특성을 분석하였다. 광조사 장치로는 LCD 프로젝터를 이용한 투영방식(그림 1)과 레이저 간섭계 투영방식(그림 2)을 사용하였다. (중략)

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.530-537
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• 2019

In this paper we propose a novel approach to representation of the 3D reconstruction problem by employing a concept of system function that is defined as the ratio of the output to the input signal. Akin to determination of system function (or system response), this paper determines system function by choosing (or defining) appropriate input and output signals. In other words, the 3D reconstruction using structured circular light patterns is reformulated as determination of system function from input and output signals. This paper introduces two algorithms for the reconstruction. The one defines the input and output signals as projected circular light patterns and the images overlaid with the patterns and captured by camera, respectively. The other one defines input and output signals as 3D coordinates of the object surface and the image captured by camera. The first one leads to the problem as identifying the system function and the second one leads to the problem as estimation of an input signal employing concept of modulation-demodulation theory. This paper substantiate the proposed approach by providing experimental results.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.605-613
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• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.