• Journal of the Korean GEO-environmental Society
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• v.10 no.7
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• pp.7-14
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• 2009

The correlation equation empirically proposed to obtain compression indexes has been proposed to conveniently obtain the value using the soil parameter that can be obtained through simple tests when the number of time of consolidation testing is low or the distribution is large but most of the analyzed regions are limited to certain regions abroad or in the country and multiple data were integrated for use in many cases, thus it is not very reasonable to apply it. Therefore, to establish a new design method considering the uncertainty of the ground, it was selected the Kwangyang port area of which the data have been collected recently thus are relatively more reliable as the subject region of the study in order to maximally reduce the uncertainty of test data. After performing the verification of the normality of the consolidation test data obtained from the selected region and the transformation of variables, a prediction formula was proposed through the regression model with the transformed variables and the proposed regression model with transformed variables was compared with existing empirical equations to verify the suitability of the proposed model formula. After analyzing, it was confirmed that the coefficient of determination was increased after the Box-Cox variable transformation, thus the explanatory power was being enhanced and through the root-mean-square-error method, it was confirmed that the proposed model formula showed the most closed value to the test value.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.155-164
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• 2002

In this study, in order to discuss the geometric correction methods of high-resolution IKONOS satellite image, the existing polynomial model and RFM which is able to rectify satellite image without auxiliary data are applied to IKONOS satellite image data. Then the accuracy of ground point versus number of GCPs and each order of RFM are assessed. A numerical instability is removed by application of Tikhonov regularization method. As the results of this study, the root mean square errors of RFM is decreased more than 2 pixels in comparison with the two dimensional polynomial model.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.993-1000
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• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.59-74
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• 2007

Computer simulation models are in general quite complex and time-consuming to run, and therefore, a simpler meta-model is usually constructed for further analysis. In this paper, JANUS, a war-game simulator, is used to describe a certain tank combat situation. Then, second-order response surface and artificial neural network meta-models are developed using the data from eight different experimental designs. Relative performances of the developed meta-models are compared in terms of the mean squared error of prediction. Computational results indicate that, for the given problem, the second-order response surface meta-model generally performs better than the neural network, and the orthogonal array-based Latin hypercube design(LHD) or LHD using maximin distance criterion may be recommended.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.708-715
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• 2014

In this paper, we suggest a method of realtime confidence interval estimation to detect abnormal states of sensor data. For realtime confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, where compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarming. As the suggested method is for realtime anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through realtime confidence interval estimation.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.14-21
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• 2021

In this paper, it is shown how the performance of the monopulse algorithm in the presence of an additive noise can be obtained analytically. In the previous study, analytic performance analysis based on the first-order Taylor series and the second-order Taylor series has been conducted. By adopting the third-order Taylor series, it is shown that the analytic performance based on the third-order Taylor series can be made closer to the performance of the original monopulse algorithm than the analytic performance based on the first-order Taylor series and the second-order Taylor series. The analytic MSE based on the third-order Taylor approximation reduces the analytic MSE error based on the second-order Taylor approximation by 89.5%. It also shows faster results in all cases than the Monte Carlo-based MSE. Through this study, it is possible to explicitly analyze the angle estimation ability of monopulse radar in an environment where noise jamming is applied.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.459-473
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• 2004

It has well known that an exhaustive search algorithm suggested by Breiman et. a1.(1984) has a trend to select the variable having relatively many possible splits as an splitting rule. We propose an algorithm to overcome this variable selection bias problem and then construct unbiased regression trees based on the algorithm. The proposed algorithm runs two steps of selecting a split variable and determining a split rule for binary split based on the split variable. Simulation studies were performed to compare the proposed algorithm with Breiman et a1.(1984)'s CART(Classification and Regression Tree) in terms of degree of variable selection bias, variable selection power, and MSE(Mean Squared Error). Also, we illustrate the proposed algorithm with real data sets.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.19-28
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• 2009

In case of land development projects constructed, to solve induced transportation volume needs analysis of traffic demand. Trip-generation of land development projects is exactly predicted by using traffic instigating-basic-unit in each facility of land developments. But in case of a phase of trip-distribution, because a range of destinations is very enormous and it needs enormous data to reflect all of its characters, whenever trip-distribution is predicted, the method which assumes the rate of trip-distribution is same both before completion of land development projects and after is often used. But because there is no exact criterion, the method suggested above is also affected by subjective opinion. Accordingly, this study look over using trip-distribution of specific areas's DB and suggests a size of zone to predict a distribution of land development projects exactly. Also production - constrained gravity model which uses the gap between a distribution of suggested ranges and induced land development project is suggested for more exact prediction of trip-distribution. Besides accuracy of prediction is scrutinized by using Mean Squared Error.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.206-214
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• 2018

This paper proposes an impact point estimation algorithm of the rifle using microphone sensors. The proposed algorithm resolves the time synchronization problem by expanding the existing ToA (Time of Arrival) method to TDoA (Time Difference of Arrival) method and verifies the performance of the algorithm through the actual shooting experiments. By comparing analysis of the actual and the estimated impact points by the algorithm, it is confirmed that the proposed algorithm has excellent performance by estimating the impact point accurately within the tolerance range.