• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.100-107
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• 1999

This paper describes a probabilistic remaining life assessment program for the creep crack growth. The probabilistic life assessment program is developed to increase the reliability of life assessment. The probabilistic life assessment involves some uncertainties, such as, initial crack size, material properties, and loading condition, and a triangle distribution function is used for random variable generation. The resulting information provides the engineer with an assessment of the probability of structural failure as a function of operating time given the uncertainties in the input data. This study forms basis of the probabilistic life assessment technique and will be extended to other damage mechanisms.

• Journal of Powder Materials
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• v.10 no.3
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• pp.201-208
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• 2003

Distributions of diamond particles protruding on the surface of worn diamond segments in circular saw has been investigated. Scanning electron microscope was used to examine the worn ,surface and radial saw blade wear and grinding ratio was measured. The number of protruded diamond particle was approximately 50% of the total number of particles, and that was independent of diamond particle concentration and table speed. It was also noted that the inter-particle distance did not follow a symmetric function like Gaussian distribution function, instead it fitted well with a probability density function based on gamma function. The distribution of inter-particle spacing, therefore, was analyzed using a gamma function model.

• Speech Sciences
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• v.7 no.4
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• pp.11-26
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• 2000

In this paper, we propose the HMM with the MIN module. Because initial and re-estimated variance vectors are important elements for performance in HMM recognition systems, we propose a method which compensates for the mismatched statistical feature of training and test data. The MIN module function is a differentiable function similar to the sigmoid function. Unlike a continuous density function, it does not include variance vectors of the data set. The proposed hybrid HMM/MIN module is a unified network in which the observation probability in the HMM is replaced by the MIN module neural network. The parameters in the unified network are re-estimated by the gradient descent method for the Maximum Likelihood (ML) criterion. In estimating parameters, the variance vector is not estimated because there is no variance element in the MIN module function. The experiment was performed to compare the performance of the proposed HMM and the conventional HMM. The experiment measured an isolated number for speaker independent recognition.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.3-5
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• 1996

The Preisach model needs density function or Everett function for the sample material to calculate the hysteresis characteristics. To obtain these functions, many experimental data obtained from the first order transition curves are required. However, it is not simple task to measure the curves. In this paper, a simple generalized technique to get the Everett function using saturation hysteresis loop and two first order transition curves is proposed. These three data makes three equations for the proposed Everett function model and we can get three variables by those equations. From the simulation, we got acceptable results.

• Journal of Applied and Pure Mathematics
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• v.4 no.3_4
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• pp.185-209
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• 2022

Here we exhibit multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝ^N, N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the generalized symmetrical sigmoid function. The approximations are point-wise and uniform. The related feed-forward neural network is with one hidden layer.

• Journal of Applied and Pure Mathematics
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• v.5 no.1_2
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• pp.69-93
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• 2023

Here we give multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝ^N, N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a parametrized Gudermannian sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.157-167
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• 2010

Density is the most important congestion indicator among the three fundamental flow variables, flow, speed and density. Measuring density in the field has two different ways, direct and indirect. Taking photos with wide views is one of direct ways, which is not widely used because of its cost and lacking of proper positions. Another direct density measuring method using two point detectors has been introduced with the concept of instantaneous density, average density and measurement interval. The relationship between accuracy and measurement interval has been investigated using the SIMULATION data produced by Paramics Application Programming Interface function. We analyze the affect of segment density accuracy by sensing gap each road condition such as sensing segment length, lane and LOS after gathering data by Paramics Application Programming Interface.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1950-1956
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• 1997

An engineering goal in vibration and noise professionals is to develope quiet machines at the preliminary design stage, and various numerical techniques such as FEM, SEA or BEM are one of the schemes toward the goal. In this paper, the research has been focused on the sensitivity effect of mesh sizes for FEM application so that the optimum size of the mesh that leads to engineering solution within acceptable computing time could be generated. In order to evaluate the mesh size effect, three important parameters have been examined : natural frequencies, number of modes and driving point mobility. First, several lower modes including the fundamental frequency of a 2-D plate structure have been calculated as mesh size changes. Since theoretical values of natural frequencies for a simple structure are known, the deviation between the numerical and theoretical values is obtained as a function of mesh size. The result shows that the error is no longer decreased if the mesh size becomes a quarter wavelength or smaller than that. Second, the mesh size effect is also investigated for the number of modes. For the frequency band up to 1.4 kHz, the structure should have 38 modes in total. As the mesh size reaches to the quarter wavelength, the total count in modes approaches to the same values. Third, a mobility function at the driving point is compared between SEA and FEM result. In SEA application, the mobility function is determined by the modal density and the mass of the structure. It is independent of excitation frequencies. When the mobility function is calculated from a wavelength to one-tenth of it, the mobility becomes constant if the mesh becomes a quarter wavelength or smaller. We can conclude that dynamic parameters, such as eigenvalues, mode count, and mobility function, can be correctly estimated, while saving the computing burden, if a quarter wavelength (.lambda./4) mesh is used. Therefore, (.lambda./4) mesh is recommended in structural vibration analysis.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.102-109
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• 2016

This paper proposes a noise suppression method through SNR (Singal-to Noise Ratio) estimation in the two microphone array environment of close spacing. The conventional method uses a noise suppression method for a gain function obtained through the SNR estimation based on coherence function from full band. However, this method cause performance decreased by the noise damage that affects all the feature vector component. So, we propose a noise suppression method that allocates a frequency domain signal into N constant multi frequency band and each frequency band gets a gain function through SNR estimation based on coherence function. Performance evaluation of the proposed method is shown by comparison with PESQ (Perceptual Evaluation of Speech Quality) value which is an objective quality evaluation method provided by the ITU-T (International Telecommunications Union Telecommunication).

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.891-904
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• 2006

The estimation of the probability precipitation is essential for the design of hydrologic projects. The techniques to calculate the probability precipitation can be determined by the point frequency analysis and the regional frequency analysis. The regional frequency analysis includes index-flood technique and L-moment technique. In the regional frequency analysis, even if the rainfall data passed homogeneity, suitable distributions can be different at each point. However, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to parametric point frequency analysis because of suppositions about probability distributions. Therefore, this paper applies kernel density function to precipitation data so that homogeneity is defined. In this paper, The data from 16 rainfall observatories were collected and managed by the Korea Meteorological Administration to achieve the point frequency analysis and the regional frequency analysis. The point frequency analysis applies parametric technique and nonparametric technique, and the regional frequency analysis applies index-flood techniques and L-moment techniques. Also, the probability precipitation was calculated by the regional frequency analysis using variable kernel density function.