• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.67-74
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• 1996

3차원좌표측정기(Coordinate Measuring Machine)의 공간오차(Volumetric error)의 측정을 위하여 홀-플레이트(Hole-Plate)를 이용하는 방법이 연구되었다. 티타늄 또는 세라믹으로 제작되는 홀-플레이트의 설계 예를 보였다. 홀-플레이트의 측정홀 숫자와 진원도(Roundness)의 영향이 연구되었으며, 또한 홀-플레이트의 설치시 발생하는 오차도 검토되었다. 3차원좌표측정기의 공간오차성분 모두를 별도로 측정하는 방법이 제안되었다. 홀-플레이트를 이용 2차원 및 3차원 공간의 길이 오차를 직접적으로 측정하는 방법도 소개되었다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.839-840
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• 2012

본 논문에서는 3 축 기계장비의 공간오차를 예측하기 위한 사전 단계로 각 축에 대하여 레일형상오차를 측정하였다. 전용 측정지그를 설계/제작하여 이 지그가 이동함에 따라 혼합축차이점법을 이용하여 레일형상오차를 측정하였다. 레일형상오차로부터 테이블 운동오차를 예측하고, 이와 더불어 각 축 사이의 직각도 오차를 측정한 후 이로부터 최종적으로 3 축 장비에 대한 공간오차를 평가할 예정이다. 예측된 공간오차는 실제 레이저를 이용한 공간오차 측정방법을 이용하여 검증할 예정이다.

• Journal of Korea Water Resources Association
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• v.35 no.1
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• pp.1-12
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• 2002

This study was intended to investigate the rainfall-runoff relationship with spatially distributed rainfall data, and then, to analyze and quantify the uncertainty induced by spatially averaging rainfall data. For constructing spatially distributed rainfall data, several historical rainfall events were extended spatially by simple kriging method based on the semivariogram as a function of the relative distance. Runoff was computed by two models; one was the modified Clark model with spatially distributed rainfall data and the other was the conventional Clark model with spatially averaged rainfall data. Rainfall errors and discharge errors occurred through this process were defined and analyzed with respect to various rain-gage network densities. The following conclusions were derived as the results of this work; 1) The conventional Clark parameters could be appropriate for translating spatially distributed rainfall data. 2) The parameters estimated by the modified Clark model are more stable than those of the conventional Clark model. 3) Rainfall and discharge errors are shown to be reduced exponentially as the density of rain-gage network is increased. 4) It was found that discharge errors were affected largely by rainfall errors as the rain-gage network density was small.

• Proceedings of the KGS Conference
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• 2003.05a
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• pp.207-210
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• 2003

지리학적 정보는 지구의 표면이나 가까이에 나타나는 현상과 사상에 대한 정보로서 정의된다(Goodchild et al., 1999). 지리학에서, 이러한 지리학적 정보는 특정한 현상을 연구하기 위한 공간자료로 이용되는데, 이는 공간적 패턴을 통해 유형화된다. 이러한 공간자료는 현지답사를 통해 수집ㆍ분석되며, 관찰자의 주관적 판단, 기술적인 오류로 인해, 오차의 필연적 발생 가능성을 안고 있다(Maffini, 1989; Bolstad, 1990; Dunn, 1990; Keeler, 1991). (중략)

• Spatial Information Research
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• v.21 no.2
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• pp.45-54
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• 2013

Analytical and simulation error models have the ability to describe (or realize) error-corrupted versions of spatial data. But the different approaches for modeling positional errors require an internal validation that ascertains whether the analytical and simulation error models predict correct positional errors in a defined set of conditions. This paper presents stochastic simulation models of a point and a line segm ent to be validated w ith analytical error models, which are an error ellipse and an error band model, respectively. The simulation error models populate positional errors by the Monte Carlo simulation, according to an assumed error distribution prescribed by given parameters of a variance-covariance matrix. In the validation process, a set of positional errors by the simulation models is compared to a theoretical description by the analytical error models. Results show that the proposed simulation models realize positional uncertainties of the same spatial data according to a defined level of positional quality.

• Journal of The Geomorphological Association of Korea
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• v.20 no.4
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• pp.85-99
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• 2013

The objective of this paper is to analyze the spatial distribution of errors in the DEM generated using waterlines from multi-temporal remote sensing data and to assess flood vulnerability. Unlike conventional research in which only global statistics of errors have been generated, this paper tries to quantitatively analyze the spatial distribution of errors from a probabilistic viewpoint using geostatistical simulation. The initial DEM in Baramarae tidal flats was generated by corrected tidal level values and waterlines extracted from multi-temporal Landsat data in 2010s. When compared with the ground measurement height data, overall the waterline-based DEM underestimated the actual heights and local variations of the errors were observed. By applying sequential Gaussian simulation based on spatial autocorrelation of DEM errors, multiple alternative error distributions were generated. After correcting errors in the initial DEM with simulated error distributions, probabilities for flood vulnerability were estimated under the sea level rise scenarios of IPCC SERS. The error analysis methodology based on geostatistical simulation could model both uncertainties of the error assessment and error propagation problems in a probabilistic framework. Therefore, it is expected that the error analysis methodology applied in this paper will be effectively used for the probabilistic assessment of errors included in various thematic maps as well as the error assessment of waterline-based DEMs in tidal flats.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.223-227
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• 1992

산업의 발전으로 정밀가공기술 및 측정기술 및 측정기술의 개발에 대한 요구가 급증하고 있으며, 특히 가공전 공작물의 장착정도, 팔레트 교환 위치정도와 가공후 가공정도 및 공작기계 자체의 정도 판별은 생산품의 정밀도와 직접 관계되는 중요한 요인이기 때문에 정확히 규명할 필요가 있다. 본 연구에서는 공작물의 장착오차와 기계오차가 존재하는 경우 Denavit-Hartenberg 접근법에 의하여 레퍼런스에 대한 공간상 linkage의 기하학적 표현을 Matrix Multiplication을 사용하여 Cutting Tool 과 Workpiece에 대한 공간상의 관계를 규명하였으며, 가공에 미치는 체적오차를 규명하고자 한다.

• Spatial Information Research
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• v.19 no.2
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• pp.37-46
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• 2011

Recently in the field of GIS(Geographic Information System), data integration from various sources has become an important topic in order to use spatial data effectively. In general, the integration of spatial data is accomplished by navigating corresponding space object and combining the information interacting with each object. But it is very difficult to navigate an object which has correspondence with one in another dataset. Many matching methods have been studied for navigating spatial object. The purpose of this paper is development of method for searching correspondent spatial object considering local position error which is remained even after coordinate transform ation when two different building data sets integrated. To achieve this goal, we performed coordinate transformation and overlapped two data sets and generated blocks which have similar position error. We matched building objects within each block using similarity and ICP algorithm. Finally, we tested this method in the aspect of applicability.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1431-1440
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• 1993

This paper presents a new NC machine performance test and calibration system. In order to measure NC machine erros in simpler, and less time-comsuming way, some indirect measuring systems such as circular disk system and double ball bar system have been developed instead of laser interferometer. But these indirect measuring systems have shown their limits in identifying each of NC machine error sources in absolute numerical value. Therefore, we developed an unique NC machine error measurement system which provides a simple measuring process like other conventional indirect methods and still can indentify each of NC machine error sources in absolute numerical value.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.561-568
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• 2015

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).