• Journal of Korean Society of Coastal and Ocean Engineers
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• v.10 no.4
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• pp.204-210
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• 1998

This paper presents the development of the probability density function applicable for wave heights (peak-to-trough excursions) in finite water depth including shallow water depth. The probability distribution applicable to wave heights of a non-Gaussian random process is derived based on the concept of the maximum entropy method. When wave heights are limited by breaking wave heights (or water depth) and only first and second moments of wave heights are given, the probability density function developed is closed form and expressed in terms of wave parameters such as $H_m$(mean wave height), $H_{rms}$(root-mean-square wave height), $H_b$(breaking wave height). When higher than third moment of wave heights are given, it is necessary to solve the system of nonlinear integral equations numerically using Newton-Raphson method to obtain the parameters of probability density function which is maximizing the entropy function. The probability density function thusly derived agrees very well with the histogram of wave heights in finite water depth obtained during storm. The probability density function of wave heights developed using maximum entropy method appears to be useful in estimating extreme values and statistical properties of wave heights for the design of coastal structures.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.633-641
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• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.3
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• pp.335-342
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• 1988

In this paper, we proposed a new algorithm for EMG low frequency analysis. Through the power spectrum analysis of Gaussian's, Gamma's and Erlang's PDF(probability density function) based on the proposed algorithm, the proper PDF of IPI (inter pulse interval) representing the firing rate of muscle was suggested. In order to verify the proposed algorithm EMG signals of masseter and biceps muscle were detected by surface electrode and its power spectrum analysis was performed. The experimental results are compared with the computer simulaiton. As a result, the masseter muscle's IPI was fitted by Gamma PDF, having a 10Hz fundamental frequency including n(1+\ulcornerfp high harmnic frequency on 10% MVC(maximum voluntary contaraction). And the biceps muscle's IPI was fitted by Gaussian PDF, also it have a 14Hz fundamental frequency.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.357-365
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• 2003

The major problems with the conventional neural network, especially Back Propagation Neural Network, arise from the necessity of many training data for neural network learning and ambiguity in the relation of neural network structure to the convergence of solution. In this paper, the PNN is used as a pattern classifier to detect the damage of structure to avoid those drawbacks of the conventional neural network. In the PNN-based pattern classification problems, the probability density function for patterns is usually assumed by Gaussian distribution. But, in this paper, several probability density functions are investigated in order to select the most approriate one for structural damage assessment.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.301-309
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• 2003

The non linear control algorithm with actuator saturation for a randomly excited oscillator has been widely explored and has shown promising results, but the probabilistic analysis of the algorithm has been rarely made due to its non-linear nature and the fact that the analytical solution of probability density function (PDF) for controlled responses does not exist. In this paper, a method for the probabilistic analysis on the non linear control algorithm with actuator saturation is proposed based on the equivalent non linear system method. Numerical examples are given to verify the approximation solution of PDF comparing to a statistically obtained PDF using a Gaussian white noise and a Kanai - Tagimi filtered Gaussian white noise.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.26-29
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• 2002

Radial Basis Function (RBF) networks is known as efficient method in classification problems and function approximation. The basis function of RBF networks is usual adopted normal distribution like the Gaussian function. The output of the Gaussian function has the maximum at the center and decrease as increase the distance from the center. For learning of neural network, the method treating the limited area of input space is sometimes more useful than the method treating the whole of input space. The q-normal distribution is the set of probability density function include the Gaussian function. In this paper, we introduce the RBF networks with the basis function of q-normal distribution and actually approximate a function using the RBF networks.

• Wind and Structures
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• v.4 no.1
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• pp.73-84
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• 2001

Analysis of pressures measured on the roof of the full-scale Texas Tech building and a 1/50 scale model of a typical house showed that the pressure fluctuations on cladding fastener and cladding-truss connection tributary areas have similar characteristics. The probability density functions of pressure fluctuations on these areas are negatively skewed from Gaussian, with pressure peak factors less than -5.5. The fluctuating pressure energy is mostly contained at full-scale frequencies of up to about 0.6 Hz. Pressure coefficients, $C_p$ and local pressure factors, $K_l$ given in the Australian wind load standard AS1170.2 are generally satisfactory, except for some small cladding fastener tributary areas near the edges.

• Structural Engineering and Mechanics
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• v.77 no.3
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• pp.355-368
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• 2021

The time-varying mean (TVM) component of non-stationary wind speeds is commonly extracted utilizing empirical mode decomposition (EMD) in practice, whereas the accuracy of the extracted TVM is difficult to be quantified. To deal with this problem, this paper proposes an approach to identify and extract the optimal TVM from several TVM results obtained by the EMD. It is suggested that the optimal TVM of a 10-min time history of wind speeds should meet both the following conditions: (1) the probability density function (PDF) of fluctuating wind component agrees well with the modified Gaussian function (MGF). At this stage, a coefficient p is newly defined as an evaluation index to quantify the correlation between PDF and MGF. The smaller the p is, the better the derived TVM is; (2) the number of local maxima of obtained optimal TVM within a 10-min time interval is less than 6. The proposed approach is validated by a numerical example, and it is also adopted to extract the optimal TVM from the field measurement records of wind speeds collected during a sandstorm event.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9C
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• pp.853-858
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• 2006

TMHMM(Tied Mixture Hidden Markov Model) is an important approach to reduce the number of free parameters in speech recognition. However, this model suffers from a degradation in recognition accuracy due to its GPDF (Gaussian Probability Density Function) clustering error. This paper proposes a clustering algorithm, called HCNN(Homogeneous Centroid Neural network), to cluster acoustic feature vectors in TMHMM. Moreover, the HCNN uses the heterogeneous distance measure to allocate more code vectors in the heterogeneous areas where probability densities of different states overlap each other. When applied to Korean digit isolated word recognition, the HCNN reduces the error rate by 9.39% over CNN clustering, and 14.63% over the traditional K-means clustering.

• ETRI Journal
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• v.33 no.6
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• pp.949-952
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• 2011

In this letter, a simplified suboptimum receiver based on soft-limiting for the detection of binary antipodal signals in non-Gaussian noise modeled as a generalized normal-Laplace (GNL) distribution combined with Gaussian noise is presented. The suboptimum receiver has low computational complexity. Furthermore, when the number of diversity branches is small, its performance is very close to that of the Neyman-Pearson optimum receiver based on the probability density function obtained by the Fourier inversion of the characteristic function of the GNL-plus-Gaussian distribution.