• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.1
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• pp.91-98
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• 2016

In order to apply Kriging methods for geostatistics of spatial data, an estimation of spatial coherency functions is required priorly based on the spatial distance between measurement points. In the study, the typical coherency functions, such as semi-variogram, homeogram, and covariance function, were estimated using the national geoid model. The test area consisting of 2°×2° and the Unified Control Points (UCPs) within the area were chosen as sampling measurements of the geoid. Based on the distance between the control points, a total of 100 sampling points were grouped into distinct pairs and assigned into a bin. Empirical values, which were calculated with each of the spatial coherency functions, resulted out as a wave model of a semi-variogram for the best quality of fit. Both of homeogram and covariance functions were better fitted into the exponential model. In the future, the methods of various Kriging and the functions of estimated spatial coherency need to be studied to verify the prediction accuracy and to calculate the Mean Squared Prediction Error (MSPE).

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.116-123
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• 2013

This paper represents generation of time-varying underwater acoustic channels by performing scattering simulation with time-varying ocean surface and Kirchhoff approximation. In order to estimate the time-varying ocean surface, 1D Pierson-Moskowitz ocean power spectrum and Gaussian correlation function were used. The computed scattering coefficients are applied to the amplitudes of each impulse of BELLHOP simulation result. The scattering coefficients are then compared with measured doppler spectral density of signal components which were scattered from ocean surface and the correlation time used in the Gaussian correlation function was estimated by the comparison. Finally, bit-error-rate and channel correlation simulations were performed with the generated time-varying channel based on passive time-reversal communication scenario.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.869-879
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• 2023

Sparkle is one of the finalists in the Lightweight Cryptography Standardization Process conducted by NIST. It is a nonlinear permutation and serves as a core component for the authenticated encryption algorithm Schwaemm and the hash function Esch. In this paper, we provide specific forms of input and output differences for 6 rounds of Sparkle384 and 7 rounds of Sparkle512, and make formulas for the complexity of finding input pairs that satisfy these differentials. Due to the significantly lower complexity compared to similar tasks for random permutations with the same input and output sizes, they can be valid distinguishing attacks. The numbers(6 and 7) of attacked rounds are very close to the minimum numbers(7 and 8) of really used rounds.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.522-559
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• 2018

In this study, we reviewed and analyzed the influencing factors of technology transfer performance in the previous studies (52 domestic journals and theses) and classified the various influencing factors into 6 top factors and 13 sub-factors based on the theoretical background. The study results of previous articles were analyzed by meta-analysis method so as to calculate the overall average effect size of influencing factors of technology transfer performance. As the result, the overall effect size (ESr) calculated through meta-analysis applying random effect model is .269, which corresponds to the medium effect size. By comparing effect sizes of influencing factors, the four(4) key influencing factors were also identified, which are 'number of researchers', 'dedicated organization', 'possess technology', and 'external cooperation'. The technology transfer performance are divided into three types: the number of technology transfers, technology transfer income, and other technology transfer performances. The major influencing factors of each type are derived through meta-analysis at the sub-category level. As moderator variables, the paper type and the data type were analyzed but no significant results were obtained. Since this research is limited to the technology transfer, it is necessary to carry out the study related to the influencing factors on the technology commercialization as following study.