• 오토저널
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• 제10권3호
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• pp.31-37
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• 1988

A phase different method to evaluate the instrument error of roundness measuring instrument and the form error of specimens for the calibration of the instrument is used. An instrument with a rotary table supported by an air bearing was calibrated by using the standard balls as a standard. The calibration was carried out repeatedly by setting the same ball in 12 phase angles(per 30.deg.) on the table and by recording their roundness errors with a magnification of 100,000 times. As a result of data analysis of all the observations, readout at each of 144 orientations(per 2.5.deg.) from recorded data file, the error of performance of the instrument and the specimens are separated. In the particular instrument used in the present experiment, the error of the instrument was determined with the accuracy of 0.0164 (.mu.m) and the form error of the specimens was determined with the accuracy of 0.0264,0.0172(.mu.m), respectively. If the instrument was calibrated by using the above specimens, then the accuracy of the measurement of roundness error can be improved to about 0.017 (.mu.m).

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.245-247
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• 2010

This research presents both system structure analysis to improve performance of 3D laser scanner, which has time of flight method, and scheme to minimize distance measurement errors during signal process. With the help of reference source, we minimized the instability of electronic signal processing time and possibility of distance measurement errors. Furthermore, it helps easy alignment and accuracy of system by using fiber delay line and coupler.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.555-559
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• 1998

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis have been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We proposed an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted four morphological features parameters such as centromeric index (C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.). These Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results shown that the chromosome classification error was reduced much more than that of the other classification methods.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 가을 학술발표회 논문집
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• pp.607-614
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• 2002

For the accurate analysis of crack problems, considerable nodal refinement near the crack tip to capture singular stress field with sufficient accuracy to provide a useful computation of stress intensity factor is required. So, in this paper, adaptive nodal refinement scheme is proposed where nodes in restricted cell regions centered at crack tip are arranged in array for enhanced spatial resolution and adaptivity. With only cell-wise adaptive refinement scheme around crack tip fields, singularity of crack tip is sufficiently described to expect a successive crack propagate direction. Through numerical tests, accuracy of the proposed adaptive scheme is investigated and compared with the finite element and experimental results. By this implementation, it is shown that high accuracy is achieved by using iterative cell-wise solution method fur analyzing crack propagation problems.

• 항공우주시스템공학회지
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• 제4권1호
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• pp.19-22
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• 2010

The accuracy and integrity of global navigation satellite systems (GNSS) can be improved by using GNSS augmentation systems. Large ionospheric spatial gradient, during ionosphere storm, is a major threat for using GNSS augmentation systems by increasing the spatial decorrelation between a reference system and users. Ionosphere decorrelation behavior can be analyzed by using dual frequency GPS data. GNSS receivers have their own biases, called inter-frequency bias (IFB) between dual(P1 and P2) frequencies and they must be accurately estimated before computing ionosphere delays. GPS network data in Korea is used to compute each receiver's IFB, and their estimation accuracy and variability are analyzed. IFB estimation methodology to apply for ionosphere gradient analysis is discussed.

• ETRI Journal
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• 제23권3호
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• pp.129-137
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• 2001

A random sampling or systematic sampling method is commonly used to assess the accuracy of classification results. In remote sensing, with these sampling methods, much time and tedious work are required to acquire sufficient ground truth data. So, a more effective sampling method that can represent the characteristics of the population is required. In this study, fractal analysis is adopted as an index for reference sampling. The fractal dimensions of the whole study area and the sub-regions are calculated to select sub-regions that have the most similar dimensionality to that of the whole area. Then the whole area's classification accuracy is compared with those of sub-regions, and it is verified that the accuracies of selected sub-regions are similar to that of whole area. A new kind of reference sampling method using the above procedure is proposed. The results show that it is possible to reduce sampling area and sample size, while keeping the same level of accuracy as the existing methods.

• 한국생산제조학회지
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• 제25권2호
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• pp.143-148
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• 2016

A micro end mill is one of the precise tools used in machining ultra-precision products such as microchannel and micropatterned mold. To achieve the required precision of these products, several studies investigated the cutting force, burr formation, and burr generation mechanism of micro end mills; however, there are few studies on the alignment of micro tools, which is the foundation of machining. Hence, in this investigation, relation expressions were derived to determine the relation between the misalignment parameters and the machining accuracy. At the same time, the effect of the machining parameters was analyzed using a multiple linear regression analysis and the analysis of variance. The results indicate that the tilting angle of a micro tool has more influence on the machining accuracy than other parameters.

• 한국컴퓨터정보학회논문지
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• 제21권12호
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• pp.125-130
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• 2016

Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.

• 한국측량학회지
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• 제18권2호
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• pp.135-141
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• 2000

본 연구에서는 수치근접사진측량기법에 의한 DCS 420 디지털 카메라의 석재표면 거칠기 측정정확도 파악을 위하여 윈도우 환경의 Microsoft Visual basic 6.0으로 표면거칠기측정시스템을 구축하였다. 본 시스템을 통하여 거칠기가 거의 없는 이상적인 평면과 곡면인 돌의 표면 거칠기를 최소제곱법으로 기준면을 적용, 측정한 결과 $\pm$0.1 mm이하의 정확도로 측정할 수 있었다.

• 지능정보연구
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• 제1권2호
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• pp.1-16
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• 1995

The field of human judgment and decision making provides useful methodologies for examining the human decision making process and substantive results. One of the methodologies is a lens model analysis which can examine valid nonlinearity in the human decision making process. Using the method, valid nonlinearity in human decision behavior can be successfully detected. Two linear(statistical) models of human experts and two nonlinear models of human experts are compared in terms of predictive accuracy (predictive validity). The results indicate that nonlinear models can capture factors(valid nonlinearity) that contribute to the expert's predictive accuracy, but not factors (inconsistency) that detract from their predictive accuracy. Then, it is argued that nonlinear models cab be more accurate than linear models, or as accurate as human experts, especially when human experts employ valid nonlinear strategies in decision making.