• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.5
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• pp.49-58
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• 2013

Appropriate random variables and probability density functions based on statistical analysis should be defined to execute reliability analysis. Most studies have focused on only normal distributions or assumed that the variables showing non-normal characteristics follow the normal distributions. In this study, the reliability problem with non-normal probability distribution was dealt with using the convolution method in the case that the integration domains of variables are limited to a finite range. The results were compared with the traditional method (linear transformation of normal distribution) and Monte Carlo simulation method to verify that the application was in good agreement with the characteristics of probability density functions with peak shapes. However it was observed that the reproducibility was slightly reduced down in the tail parts of density function.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.583-592
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• 2007

Given the moments of a symmetric random variable, its density and distribution functions can be accurately approximated by making use of the algorithm proposed in this paper. This algorithm is specially designed for approximating symmetric distributions and comprises of four phases. This approach is essentially based on the transformation of variable technique and moment-based density approximants expressed in terms of the product of an appropriate initial approximant and a polynomial adjustment. Probabilistic quantities such as percentage points and percentiles can also be accurately determined from approximation of the corresponding distribution functions. This algorithm is not only conceptually simple but also easy to implement. As illustrated by the first two numerical examples, the density functions so obtained are in good agreement with the exact values. Moreover, the proposed approximation algorithm can provide the more accurate quantities than direct approximation as shown in the last example.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.147-155
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• 2012

We propose some properties for a fuzzy correlation test by reduced-spread fuzzy variance for sample fuzzy data. First, we define the condition of fuzzy data for repeatedly observed data or that which includes error term data. By using the average of spreads for fuzzy numbers, we reduce the spread of fuzzy variance and define the agreement index for the degree of acceptance and rejection. Given a non-normal random fuzzy sample, we have bivariate normal distribution by apply Box-Cox power fuzzy transformation and test the fuzzy correlation for independence between the variables provided by the agreement index.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.127-136
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• 2011

Multiplicative noise model is the one of popular method for masking continuous variables. In this paper, we propose the transformation on the variable to which random noise was multiplied. An advantage of the masking method using proposed transformation is that the masking data users can obtain the unbiased values of mean and variance of original (unmasked) data. We also consider the data utility and correlation structure of variables when we apply the proposed multiplicative noise scheme. To investigate the properties of the method of masking based on multiplicative noise, a simulation study has been conducted using the 2008 Householder Income and Expenditure Survey data.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.258-261
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• 2002

This paper addresses the problem of merging two feedforward neural networks into one network. Merging is accomplished at the level of hidden layer. A new network selects its hidden layer's units from the two networks to be merged We uses information theoretic criterion (quadratic mutual information) in the selection process. The hidden unit's output and the target patterns are considers as random variables and the mutual information between them is calculated. The mutual information between hidden units are also considered to prevent the statistically dependent units from being selected. Because mutual information is invariant under linear transformation of the variables, it shows the property of the robust estimation.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.96.2-96
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• 2002

In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing scheme that represents the information from observations as a set of random variables in the form of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other, which can be utilized in linear p...

• Structural Engineering and Mechanics
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• v.67 no.5
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• pp.439-455
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• 2018

Within a probabilistic framework, this paper addresses the determination of the static structural response of beams and frames with partially restrained (semi-rigid) connections. The flexibility of the nodal connections is incorporated via an idealized linear-elastic behavior of the beam constraints through the use of rotational springs, which are here considered uncertain for taking into account the largely scattered results observed in experimental findings. The analysis is conducted via the Probabilistic Transformation Method, by modelling the spring stiffness terms (or equivalently, the fixity factors of the beam) as uniformly distributed random variables. The limit values of the Eurocode 3 fixity factors for steel semi-rigid connections are assumed. The exact probability density function of a few indicators of the structural response is derived and discussed in order to identify to what extent the uncertainty of the beam constraints affects the resulting beam response. Some design considerations arise which point out the paramount importance of probability-based approaches whenever a comprehensive experimental background regarding the stiffness of the beam connection is lacking, for example in steel frames with semi-rigid connections or in precast reinforced concrete framed structures. Indeed, it is demonstrated that resorting to deterministic approaches may lead to misleading (and in some cases non-conservative) outcomes from a design viewpoint.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.399-411
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• 2003

An approach determining a confidence interval of Nash's observed mean instantaneous unit hydrograph is developed. In the approach, both two parameters are treated as correlated gaussian random variables based on the theory of Box-Cox transformation and the regional similarity relation, so that linear statistical parameter estimation is possible. A parametric bootstrap method is adopted to give the confidence interval of the mean observed hydrograph. The proposed methodology is also applicable to estimate the parameters of Nash's model for un-gauged basins. An application to a watershed has shown that the proposed approach is adequate to assess the uncertainty of the Nash's hydrograph and to evaluate parameters for un-gauged basins.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.1
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• pp.42-54
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• 1998

For decreasing intersymbol interference (ISI) due to band-limited channels in digitalcommunication, the uses of equalization techniques are necessary. Among the useful adaptive equalization techniques, because of their ease of implementation and nonlinear capabilites, the neural networks have been used as an alternative for effectively dealing with the channel distortion. In this paepr, a complex-valued multilayer percepron is proposed as a nonlinear adaptive equalizer. After the important properties that a suitable complex-valued activation function must possess are discussed, a new complex-valued activation function is developed for the proposed schemes to deal with M-ary QAM signals of any constellation sizes. It has been further proven that by the nonlinear transformation of the proposed function, the correlation coefficient between the real and imaginary parts of input data decreases when they are jointly Gaussian random variables. Lastly, the effectiveness of the proposed scheme is demonstrated by simulations. The proposed scheme provides, compared with the linear equalizer using the least mean squares (LMS) algorith, an interesting improvement concerning Bit Error Rate (BER) when channel distortions are nonlinear.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1548-1555
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• 2016

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive - moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.