• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1065-1074
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• 2007

Among various methods to compare reference signatures with an input signature, the segment-to-segment matching method has more advantages than global and point-to-point methods. However, the segment-to-segment matching method has the problem of having lower recognition rate according to the variation of partitioning points. To resolve this drawback, this paper proposes a signature verification method by considering linear discriminant analysis as well as segment-to-segment matching method. For the final decision step, we adopt statistical based Bayesian classifier technique to effectively combine two individual systems. Under the various experiments, the proposed method shows better performance than segment-to-segment based matching method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.11-19
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• 1994

The matching probability P(ο/$\lambda$), of the signal sequence(ο) observed for a finite time interval with a HMM (Hidden Markov Model $\lambda$) indicates the probability that signal comes from the given model. By utilizing the fact that the probability represents matching score of the observed signal with the model we can recognize an unknown signal pattern by comparing the magnitudes of the matching probabilities with respect to the known models. Because the algorithm however uses floating point variables during the computing process hardware implementation of the algorithm requires floating point units. This paper proposes an integer algorithm which uses positive integer numbers rather than float point ones to compute the matching probability so that we can economically realize the algorithm into hardware. The algorithm makes the model parameters integer numbers by multiplying positive constants and prevents from divergence of data through the normalization of variables at each step. The final equation of matching probability is composed of constant terms and a variable term which contains logarithm operations. A scheme to make the log conversion table smaller is also presented. To analyze the qualitive characteristics of the proposed algorithm we attatch simulation result performed on two groups of 10 hypothetic models respectively and inspect the statistical properties with repect to the model order the magnitude of scaling constants and the effect of the observation length.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.73-78
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• 2004

By modeling the block matching algorithm as a function of the correlation of image blocks, we derive search patterns for fast block matching motion estimation. The proposed approach provides an analytical support lot the diamond-shape search pattern, which is widely used in fast block matching algorithms. We also propose a new fast motion estimation algorithm using adaptive search patterns and statistical properties of the object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the block differences. By changing the search pattern adaptively, we improve motion prediction accuracy while reducing required computational complexity compared to other fast block matching algorithms.

• Korea Trade Review
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• v.45 no.3
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• pp.1-19
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• 2020

Recently, traditional trade in the global trade market has stagnated in the aftermath of the US-China trade war and the coronavirus (COVID-19) pandemic. However, the global e-commerce market is growing rapidly, presenting a new opportunity for exports. To examine the effect of an online matching and logistics system on reverse overseas direct purchase and the mediating effect of reliability, this study conducted a questionnaire survey on 320 employees in a Korean trade company from March 10 to April 30, 2018. The study model's goodness of fit was tested, and an analysis was performed using the AMOS statistical package. The online matching and logistics system were found to have a positive effect on reverse overseas direct purchase. Furthermore, results revealed that while a country's reliability mediated online matching and reverse overseas direct purchase, it did not mediate the logistics system. These results mean that online matching is affected by a country's reliability in overseas consumers' buying decision process. This study provides implications for the future directions of export companies and national policies to promote reverse overseas direct purchase. Future research including more countries and companies would be able to make further contributions toward the development of the Korean cross-border e-commerce industry.

• Knowledge Management Research
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• v.24 no.1
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• pp.245-271
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• 2023

The change to the data economy requires a new analysis beyond ordinary research in the management field. Data matching refers to a technique or processing method that combines data sets collected from different samples with the same population. In this study, statistical matching was performed using random hotdeck and Mahalanobis distance functions using 2020 Survey of Korea Venture Firms and 2020 Korea Innovation Survey datas. Among the variables used for statistical matching simulation, the industry and the number of workers were set to be completely consistent, and region, business power, listed market, and sales were set as common variables. Simulation verification was confirmed by mean test and kernel density. As a result of the analysis, it was confirmed that statistical matching was appropriate because there was a difference in the average test, but a similar pattern was shown in the kernel density. This result attempted to expand the spectrum of the research method by experimenting with a data matching research methodology that has not been sufficiently attempted in the management field, and suggests implications in terms of data utilization and diversity.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.235-242
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• 2002

In this paper, we develop noninformative priors that are used for estimating the ratio of failure rates under Freund's bivariate exponential distribution. A class of priors is found by matching the coverage probabilities of one-sided Baysian credible interval with the corresponding frequentist coverage probabilities. Also the propriety of posterior under the noninformative priors is proved and the frequentist coverage probabilities are investigated for small samples via simulation study.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.1-9
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• 2003

In this article, we consider a Bayesian estimation method for the geometric mean of $textsc{k}$ exponential parameters, Using the Tibshirani's orthogonal parameterization, we suggest an invariant prior distribution of the $textsc{k}$ parameters. It is seen that the prior, probability matching prior, is better than the uniform prior in the sense of correct frequentist coverage probability of the posterior quantile. Then a weighted Monte Carlo method is developed to approximate the posterior distribution of the mean. The method is easily implemented and provides posterior mean and HPD(Highest Posterior Density) interval for the geometric mean. A simulation study is given to illustrates the efficiency of the method.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.489-500
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• 2007

In this paper, we consider Bayesian approach to the Fieller-Creasy problem using noninformative priors. Specifically we extend the results of Yin and Ghosh (2000) to the unbalanced case. We develop some noninformative priors such as the first and second order matching priors and reference priors. Also we prove the posterior propriety under the derived noninformative priors. We compare these priors in light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.357-364
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• 1997

The early detection of breast cancer is clearly a key ingredient for reducing breast cancer mortality. Microcalcification is the only visible feature of the DCIS's(ductal carcinoma in situ) which consist 15 ~ 20% of screening-detected breast cancer. Therefore, the analysis of the shapes and distributions of microcalcifications is very significant for the early detection. The automatic detection procedures have b(:on the concern of digital image processing for many years. We proposed here one efficient method which is essentially statistical pattern classification accelerated by one representative feature, correlation coefficient. We compared the results by this additional feature with results by a simple gray level thresholding. The average detection rate was increased from 48% by gray level feature only to 83% by the proposed method The performances were evaluated with TP rates and FP counts, and also with Bayes errors.

• The Journal of the Korean dental association
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• v.58 no.3
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• pp.152-168
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• 2020

The propensity score matching method is a statistical method used to reduce selection bias in observational studies and to show effects similar to random allocation. There are many observational studies in dentistry research, and differences in baseline covariates between the control and case groups affect the outcome. In order to reduce the bias due to confounding variables, the propensity scores are used by equating groups based on the baseline covariates. This method is effective, especially when there are many covariates or the sample size is small. In this paper, the propensity score matching method was explained in a simple way with a dental example by using R software. This simulated data were obtained from one of retrospective study. The control group and the case group were matched according to the propensity score and compared before and after treatment. The propensity score matching method could be an alternative to compensate for the disadvantage of the observation study by reducing the bias based on the covariates with the propensity score.