• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.385-388
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• 2013

In this paper, we show the weakness of the image matching method by using MPEG-7 EHD(Edge Histogram Descriptor) and suggest how to improve this weakness by using image normalization. EHD algorithm is an image matching technique that collects edge's slope of distribution and same distribution. However, the EHD error rate is high because EHD is sensitive for changes of object distortion and rotation that will be matched. We improve matching performance by accurately extract edge information in image by using normalization. We compare and analyze the normalized EHD algorithm by using distortion and rotation and it shows robustness for changes of the size and rotation.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.1-11
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• 2008

For a large data set the high computational cost of estimating the parameters of normal mixtures with the conventional EM algorithm is crucially impedimental in applying the algorithm to the areas requiring high speed computation such as real-time speech recognition. Simulations show that the binned EM algorithm, being compared to the standard one, significantly reduces the cost of computation without loss in accuracy of the final estimates.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.22 no.4
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• pp.345-352
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• 2012

1980년대 초 Sherwood교수가 한국에 와 있으면 당시 탄광의 호흡성먼지의 농도를 측정한 자료를 활용하여 로그-정규분포에 대한 분석을 한 자료이다. 탄광의 건조상태에서 농도는 물을 뿌리면서 하는 작업에 비해 매우 높은 수준을 보인다. 건조한 탄광에서 기하평균농도는 $26.1mg/m^{3}$이었고 물을 뿌린 탄광에서는 $4.1mg/m^{3}$이었다. 이는 매우 높은 수준이었음을 알 수 있다. 각 탄갱에서의 농도는 로그정규분포를 하였고 석탄을 접하는 탄갱에서의 농도는 $1.65mg/m^{3}$에서 $35mg/m^{3}$까지 다양하였다. 호흡성분진의 농도는 석탄을 접하는 탄갱에서 암석을 접하는 탄갱보다 높았는데 이는 분진의 원인이 석탄 때문이었다.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.7
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• pp.394-400
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• 2002

Networks of Workstations(NOWs), that has features of flexibility and scalability, recently has emerged as an inexpensive alternative to massively parallel multicomputers. However it is not easier to perform deadlock-free multicast than regular topologies like mash or hypercube. Single phase differential multicast(SPDM) is a modified multicast algorithm with less burden of the root node. By applying quality of serviece(QoS), a specific node can have differentiated service and artificial change of message flow pattern is also available. As the results of performance evaluation experiments, SPDM has lower latency and lower packet concentration rate of the root node than the case of SPAM, and has ability to control network load distribution among switch nodes by controlling the assignment rate among nodes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.943-951
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• 2012

In this paper, we propose a novel spectrum sensing scheme based on the order statistic for cooperative cognitive radio network in non-Gaussian noise environments. Specifically, we model the ambient noise as the bivariate isotropic symmetric ${\alpha}$-stable random variable, and then, propose a cooperative spectrum sensing scheme based on the order of observations and the generalized likelihood ratio test. From numerical results, it is confirmed that the proposed scheme offers a substantial performance improvement over the conventional scheme in non-Gaussian noise environments.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.18 no.2
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• pp.112-123
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• 2006

Double-peak normal distribution function was suggested as the probability density function of the non-tidal components (NTC) data in Korean coastal zone. Frequency distribution analysis of the NTC data was carried out using hourly tidal elevation data of the ten tidal gauging stations, i.e., Incheon, Gunsan, Mokpo, Jeju, Yeosu, Masan, Gadeokdo, Busan, Pohang, and Sokcho which were served through the Internet Homepage by the National Ocean Research Institute. NTC data is defined as the difference between the measured tidal elevation data and the astronomical tidal elevation data using 64 tidal constituents information. Based on the RMS error and R2 value comparison analysis, it was found that this suggested function as the probability density function of the NTC data was found to be more appropriate than the normal distribution function. The parameters of the double-peak function were estimated optimally using Levenberg-Marquardt method which was modified from the Newton method. The standard deviation and skewness coefficient were highly correlated with the non-tidal constants of the tidal gauging stations except Mokpo, Jeju and Sokcho stations.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.321-338
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• 2009

The objective of this paper is to compare spatial prediction capabilities of univariate kriging algorithms for generating GIS-based thematic maps from ground survey data with asymmetric distributions. Four univariate kriging algorithms including traditional ordinary kriging, three non-linear transform-based kriging algorithms such as log-normal kriging, multi-Gaussian kriging and indicator kriging are applied for spatial interpolation of geochemical As and Pb elements. Cross validation based on a leave-one-out approach is applied and then prediction errors are computed. The impact of the sampling density of the ground survey data on the prediction errors are also investigated. Through the case study, indicator kriging showed the smallest prediction errors and superior prediction capabilities of very low and very high values. Other non-linear transform based kriging algorithms yielded better prediction capabilities than traditional ordinary kriging. Log-normal kriging which has been widely applied, however, produced biased estimation results (overall, overestimation). It is expected that such quantitative comparison results would be effectively used for the selection of an optimal kriging algorithm for spatial interpolation of ground survey data with asymmetric distributions.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.840-845
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• 2004

The reliability analysis for nonnormal distributions using the three level DOE(design of experiments) method was developed by Seo and Kwak in 2002. Although this method estimates only up to the first four moments(mean, standard deviation, skewness, and kurtosis) of the system response function, the result and the type of probability distribution determined by using the Pearson system are shown very good. However the accuracy is low in case of nonlinear performance function and sometimes, the level calculated is outside of the region in which the random variable is defined. In this article we suggest a modified three level DOE method to overcome these weaknesses and to obtain optimum choice for 3 levels and weights to handle nonnormal distributions. Furthermore we extend it to finding the optimum choice for 5 levels and weights to increase the accuracy in case of nonlinear performance function. A systematic procedure for reliability analysis is then proposed by using the Pearson system.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.169-174
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• 2013

Speech recognition, the problem of performance degradation is the difference between the model training and recognition environments. Silence features normalized using the method as a way to reduce the inconsistency of such an environment. Silence features normalized way of existing in the low signal-to-noise ratio. Increase the energy level of the silence interval for voice and non-voice classification accuracy due to the falling. There is a problem in the recognition performance is degraded. This paper proposed a robust speech detection method in noisy environments using a silence feature normalization and voice energy maximize. In the high signal-to-noise ratio for the proposed method was used to maximize the characteristics receive less characterized the effects of noise by the voice energy. Cepstral feature distribution of voice / non-voice characteristics in the low signal-to-noise ratio and improves the recognition performance. Result of the recognition experiment, recognition performance improved compared to the conventional method.