• Journal of Digital Convergence
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• v.11 no.11
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• pp.409-414
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• 2013

Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.8
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• pp.75-81
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• 2008

The authors present a more accurate analysis of the bit error rate (BER) performance for multicode code division multiple access (CDMA) systems equipped with a multipath interference canceler (MPIC) over multipath fading channels. At first, we consider the problem of previous researches. It is that the previous analyses do not match with simulations even in the case without MPIC. This mismatch also causes the difference between the analysis and simulations when an MPIC is applied. Therefore, what is needed is to verify the reason of this mismatch at the initial stage. Numerical results obtained from the proposed analytical method indicate that the performance mismatch between the previous analysis and simulations is caused by not fie Gaussian approximation which is generally used at the performance analysis of CDMA systems but the additional Gaussian approximation (AGA) for multipath interferences. Based on this effect, we propose a more accurate performance analysis not using AGA and its accuracy is verified by simulations. Furthermore, it is confirmed that the proposed analysis presents an analytical lower bound which can be achieved by MPIC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.425-430
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• 2014

This paper analyzed the change of subthreshold current for gate oxide thickness of double gate(DG) MOSFET. Poisson's equation had been used to analyze the potential distribution in channel, and Gaussian function had been used as carrier distribution. The potential distribution was obtained as the analytical function of channel dimension, using the boundary condition. The subthreshold current had been analyzed for gate oxide thickness, and projected range and standard projected deviation of Gaussian function. Since this analytical potential model was verified in the previous papers, we used this model to analyze the subthreshold current. Resultly, analytical model showed that subthreshold current was influenced by parameters of Gaussian function and gate oxide thickness of DGMOSFET.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.945-956
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• 2021

In this study, we considered the algorithm of Legendre and Legendre (2012), which derives canonical correspondence analysis from weighted principal component analysis. And, it was proved that the canonical correspondence analysis based on the weighted principal component analysis is exactly the same as Ter Braak's (1986) canonical correspondence analysis based on the Gaussian response model. Ter Braak (1986)'s canonical correspondence analysis derived from a Gaussian response curve that can explain the abundance of species in ecology well uses the basic assumption of the species packing model and then conducts generalized linear model and canonical correlation analysis. It is derived by way of binding. However, the algorithm of Legendre and Legendre (2012) is calculated in a method quite similar to Benzecri's correspondence analysis without such assumptions. Therefore, if canonical correspondence analysis based on weighted principal component analysis is used, it is possible to have some flexibility in using the results. In conclusion, this study shows that the two methods starting from different models have the same site scores, species scores, and species-environment correlations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1176-1182
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• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.2
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• pp.101-107
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• 2022

This study proposes the deconvolution method for the nanoindentation test results of geopolymer employing the Gaussian mixture model. Geopolymer has been studied extensively as an alternative construction material because it emits relatively lower CO₂ compared to ordinary Portland cement. Geopolymer is made of aluminosilicate and alkaline solution, and the Si/Al molar ratio affects its mechanical properties. Previous studies revealed that the Si/Al molar ratio of 1.8~2.0 results in the highest compressive strength, and the Si/Al molar ratio over 1.8 degrades the compressive strength of geopolymer severely; however the reason for the compressive strength degradation is still unclear. To understand the effect of the Si/Al molar ratio on the geopolymer structure, this study exploits the nanoindentation. The phase deconvolution of the indent modulus data is successful using the Gaussian mixture model, and it is observed that the Si/Al molar ratio alters the homogeneity of the geopolymer. Geopolymer becomes more homogeneous up to an Si/Al molar ratio of 1.8 at which geopolymer exhibits the highest compressive strength. The examination of this study is assumed to be adopted as evidence of strength degradation by the Si/Al ratio higher than the optimum value.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.115-127
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• 2023

Multinomial probit model is a popular model for multiclass classification and choice model. Markov chain Monte Carlo (MCMC) method is widely used for estimating multinomial probit model, but its computational cost is high. However, it is well known that variational Bayesian approximation is more computationally efficient than MCMC, because it uses subsets of samples. In this study, we describe multinomial probit model with Gaussian process classification and how to employ variational Bayesian approximation on the model. This study also compares the results of variational Bayesian multinomial probit model to the results of naive Bayes, K-nearest neighbors and support vector machine for the UCI mice protein expression level data.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.56-61
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• 2010

This paper describes optimization methods of acoustic models in HMM-based continuous speech recognition. Most of the conventional speech recognition systems use the same number of Gaussian mixture components for each HMM state. However, since the number of data samples available for each state is different from each other, it is possible to reduce the overall number of model parameters and the computational cost at the decoding step by optimizing the number of Gaussian mixture components. In this study, we introduced the Gaussian mixture weight term at the merging stage of Gaussian components in the minimum description length (MDL) based acoustic modeling optimization. Experimental results showed that the proposed method can obtain better ASR accuracy than the previous optimization method which does not consider the Gaussian mixture weight term.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.56-60
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• 2005

To calculate the probability of bit error of UWB communication systems, the exact expression of multiple access interference is essential. So far, in many researches, MAI has been modeled by the Gaussian Approximation, which leads to the huge errors. And there are some tries to obtain the exact model fot the MAI but they have some problems such as long calculation time. We introduce the simple expression to calculate the probability of error of an UWB-TH system with BPSK. The multiple access interference is explained by the characteristic function method combined with the Gaussian approximation. It allows us to easily and fast calculate the bit error rate of an UWB-TH system.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.1-6
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• 2016

The biased zero-error probability and its related algorithms require heavy computational burden related with some summation operations at each iteration time. In this paper, a recursive approach to the biased zero-error probability and related algorithms are proposed, and compared in the simulation environment of shallow water communication channels with ambient noise of biased Gaussian and impulsive noise. The proposed recursive method has significantly reduced computational burden regardless of sample size, contrast to the original MBZEP algorithm with computational complexity proportional to sample size. With this computational efficiency the proposed algorithm, compared with the block-processing method, shows the equivalent robustness to multipath fading, biased Gaussian and impulsive noise.