• Journal of the Korean Mathematical Society
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• v.38 no.3
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• pp.657-667
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• 2001

We propose a statistical interpolation approximate solution for a nonlinear stochastic integral equation of a stock price process. The proposed method has the order O(h$^2$) of local error under the weaker conditions of $\mu$ and $\sigma$ than those of Milstein' scheme.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.455-471
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• 2000

A new method is developed for estimating the mean of a population which has a linear trend. The proposed estimator is based on the balanced systematic sampling method and the concept of interpolation. The efficiency of the proposed method is compared with that of existing methods.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.175-185
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• 2002

A method is proposed for efficiently estimating the mean of a population which has a linear trend. The proposed estimator is based on the centered modified systematic sampling method and the concept of interpolation. Using the expected mean square error criterion, it is shown that the proposed method is more efficient than conventional methods in most real cases.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.227-233
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• 2019

In this paper, we propose a deep neural network-based sound interpolation method for realizing virtual reality sound. Through this method, sound between two points is generated by using acoustic signals obtained from two points. Sound interpolation can be performed by statistical methods such as arithmetic mean or geometric mean, but this is insufficient to reflect actual nonlinear acoustic characteristics. In order to solve this problem, in this study, the sound interpolation is performed by training the deep neural network based on the acoustic signals of the two points and the target point, and the experimental results show that the deep neural network-based sound interpolation method is superior to the statistical methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.49-58
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• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.446-451
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• 2017

The statistical context-sensitive spelling correction technique in this thesis is based upon Shannon's noisy channel model. The interpolation method is used for the improvement of the correction method proposed in the paper, and the general interpolation method is to fill the middle value of the probability by (N-1)-gram and (N-2)-gram. This method is based upon the same statistical corpus. In the proposed method, interpolation is performed using the frequency information between the statistical corpus and the correction document. The advantages of using frequency of correction documents are twofold. First, the probability of the coined word existing only in the correction document can be obtained. Second, even if there are two correction candidates with ambiguous probability values, the ambiguity is solved by correcting them by referring to the correction document. The method proposed in this thesis showed better precision and recall than the existing correction model.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.113-121
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• 1992

Statistical interpolation which uses past experience about the behaviour of the atmosphere(correlation function) to interpolate the observations irregularly distributed in space and time to regular grids is discussed. Correlation functions are computed in the Far East Asian region during the winter periods of December 1977 to February 1980. Results show from the computation of correlation functions that there exists a large difference in the autocorrelation functions by slowly decreasing geopotential height and temperature, and rapidly decreasing wind and mixing ratio with increasing data-correlation functions between geopotential height and wind are well corresponed to persistence of wintertime synoptic features.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.93-96
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• 2000

In this paper, we present an improved novel interpolator that performs high quality interpolation on both synthetic and real world images. Its structure, which is based on a four directional linear predictor with equiripple windowed samples and phase matching equalizer, provides edge-directional data interpolation so that sharp and artifacts-free images are obtained at a reasonable computational cost.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.17-24
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• 2001

A method is proposed for efficiently estimating the mean of a population which has a linear trend. The proposed estimator is based on the centered modified systematic sampling method and the concept or interpolation. Using the expected mean square error criterion, it is shown that the proposed method is more efficient than conventional methods in most real cases.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.4
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• pp.343-350
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• 2006

For integrating and utilizing the statistical data, which is summarized by arbitrary areal unit such as demographics, with stellite imagery or other GIS data, areal unit of both data should be accorded. Dasymetric mapping is proposed as a useful method fur disaggregating the aggregated statistical data to finer areal unit or generating surface model from object data such as polygonal area. This research evaluate the effectiveness of dasymetric mapping by 1) summarizing the yellow page information by administrative district, 2) modeling the business density using dasymetric mapping, and 3) comparing the business densities of raw data and that of spatial interpolation result.