• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.5
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• pp.317-322
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• 2014

Data security and integrity is a critical issue in data transmission over wired/wireless links. A large amount of data is encrypted before transmission, by block cipher using mode of operation. RBF mode is a block cipher mode of operation which uses random characteristics. In this paper, we analyze the safety against known plaintext attack and chosen plaintext attack of RBF mode compared to the traditional modes. According to the analysis, RBF mode is known to be secure while the traditional modes are not secure against them.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.61-70
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• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.405-424
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• 2013

This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB's (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.73-77
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• 2019

Data mining techniques can also be used to increase the efficiency of production in the tax sector, which requires professional skills. As tax-related computerization was carried out, large amounts of data were accumulated, creating a good environment for data mining. In this paper, we have developed a system that can help tax accountant who have existing professional abilities by using data mining techniques on accumulated tax related data. The data mining technique used is random forest and improved by using f1-score. Using the implemented system, data accumulated over two years was learned, showing high accuracy at prediction.

• Smart Structures and Systems
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• v.20 no.1
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• pp.35-42
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• 2017

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.295-300
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• 2002

In this paper, we apply the 3-dimension algorithm to the random interleaver and s-random interleaver and analyze the performance of the turbo code system with random interleaver (or s-random interleaver). In general, the performance of interleaver is determined by minimum distance between neighbor data, thus we could improve the performance of interleaver by increasing the distance of the nearest data. The interleaver using 3-dimension algorithm has longer minimum distance and average distance compared to existing random-interleaver (s-random interleaver) because the output data is generated randomly from 3-dimension storage. To verify and compare the performance of our proposed system, the computer simulations have been performed in turbo code system under gaussian noise environment.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.247-259
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• 2003

In Korea, quota sampling is mainly adopted in telephone surveys, instead of random sampling which requires call-back procedure and random selection of respondent within households. The contact mode based on the se $x^{*}$age quotas is economically more advantageous and less time-consuming. However, it lacks theoretical ground for valid statistical inference, so that it is hardly accepted in academic circles despite of widely spread practice. Subsequently, survey theoreticians argued that random sampling-based telephone surveys should be tried. In response, Research & Research (R&R), a private research company in Seoul, executed atelephone survey by random sampling mode for the prediction of 2002 Ulsan City Mayor Election. The aim of this case study is to find out various effects of the call-back rule with random selection of respondents by statistically re-analyzing R&R’s Ulsan Survey Data.s by statistically re-analyzing R&R’s Ulsan Survey Data.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.53-62
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• 1994

When a comparison is made with respect to the unknown best treatment, Hsu (1984, 1985) proposed the so called multiple comparisons procedures with the best in the analysis of variance model. Applying Hsu's results to the analysis of covariance model, simultaneous confidence intervals for multiple comparisons with the best in a balanced one-way layout with a random covariate are developed and are applied to a real data example.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.