• Tunnel and Underground Space
• /
• v.28 no.5
• /
• pp.426-441
• /
• 2018

Recently, the generalized Hoek-Brown (GHB) failure criterion has been actively employed in various rock engineering calculations, but the analytical form of the corresponding Mohr failure envelope is not available, making it difficult to extend the application of the GHB criterion. In order to overcome this disadvantage, this study proposes a new method to express the tangential friction angle as an explicit function of normal stress by invoking the polynomial best-fitting to the relationship between normal stress and tangent friction angle implied in the GHB failure function. If this normal stress - tangential friction angle relationship is best-fitted with linear or quadratic polynomial function, it is possible to find the analytical root for tangential friction angle. Subsequently, incorporating the root into the relationship between shear stress and tangential friction angle accomplishes the derivation of the approximate Mohr envelope for the GHB criterion. It is demonstrated that the derived approximate Mohr failure envelopes are very accurate in the entire range of GSI value.

• Geophysics and Geophysical Exploration
• /
• v.7 no.3
• /
• pp.191-196
• /
• 2004

Seismic waveform inversion can be solved by using the classical Gauss-Newton method, which needs to construct the huge Hessian by the directly computed Jacobian. The property of Hessian mainly depends upon a source and receiver aperture, a velocity model, an illumination Bone and a frequency content of source wavelet. In this paper, we try to invert the Marmousi seismic data by controlling the huge Hessian appearing in the Gauss-Newton method. Wemake the two kinds of he approximate Hessian. One is the banded Hessian and the other is the approximate Hessian with automatic gain function. One is that the 1st updated velocity model from the banded Hessian is nearly the same of the result from the full approximate Hessian. The other is that the stability using the automatic gain function is more improved than that without automatic gain control.

• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.181-192
• /
• 2016

We consider several methods to approximate option prices with correction terms to the Black-Scholes option price. These methods are able to compute option prices from various risk-neutral distributions using relatively small data and simple computation. In this paper, we compare the performance of Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method of using Normal inverse gaussian distribution, and an asymptotic method of using nonlinear regression through simulation experiments and real KOSPI200 option data. We assume the variance gamma model in the simulation experiment, which has a closed-form solution for the option price among the pure jump $L{\acute{e}}vy$ processes. As a result, we found that methods to approximate an option price directly from the approximate price formula are better than methods to approximate option prices through the approximate risk-neutral density function. The method to approximate option prices by nonlinear regression showed relatively better performance among those compared.

• Journal of the Korean Statistical Society
• /
• v.22 no.2
• /
• pp.325-337
• /
• 1993

This paper propose two test statistics which enable us to proceed the variable selection in Fisher's linear discriminant function for the case of heterogeneous discrimination with equal training sample size. Simultaneous confidence intervals associated with the test are also given. These are exact and approximate results. The latter is based upon an approximation of a linear sum of Wishart distributions with unequal scale matrices. Using simulated sampling experiments, powers of the two tests have been tabulated, and power comparisons have been made between them.

• Journal of the Chungcheong Mathematical Society
• /
• v.20 no.4
• /
• pp.543-550
• /
• 2007

In this paper, we define the ap-Denjoy integral and investigate some properties od the ap-Denjoy integral. In particular, we show that a function f : [a,b]${\rightarrow}\mathbb{R}$ is ap-Denjoy integrable on [a,b] if and only if there exists an $ACG_s$ function F on [a,b] such that $F^{\prime}_{ap}=f$ almost everywhere on [a,b].

• Communications for Statistical Applications and Methods
• /
• v.16 no.3
• /
• pp.541-547
• /
• 2009

We consider distribution, reliability and moment of ratio in two independent beta random variables X and Y, and reliability and $K^{th}$ moment of ratio are represented by a mathematical generalized hypergeometric function. We introduce an approximate maximum likelihood estimate(AML) of reliability and right-tail probability in the beta distribution.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10b
• /
• pp.33-39
• /
• 1995

One dimensional optimization problem is considered, we propose a method to find the global minimum of one-dimensional function with on gradient information but only the finite number of input-output samples. We construct a learning network which has a good learning capability and of which global maximum(or minimum) can be calculated with simple calculation. By teaching this network to approximate the given function with minimal samples, we can get the global minimum of the function. We verify this method using some typical esamples.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.48-52
• /
• 2001

In this paper presents an accurate AFM used that is free from the Z-directional distortion of a servo actuator is described. Two mathematical correction methods by the in-situ self-calibrationare employed in this AFM. One is the method by the integration, and the other is the method by inverse function of the calibration curve. The in situ self-calibration method by the integration, the derivative of the calibration curve function of the PZT actuator is calculated from the profile measurement data sets which are obtained by repeating measurements after a small Z-directional shift. Input displacement at each sampling point is approximately estimated first by using a straight calibration line. The derivative is integrated with reference to the approximate input to obtain the approximate calibration curve. Then the approximation of the input value of each sampling point is improved using the obtained calibration curve. Next the integral of the derivative is improved using the newly estimated input values. As a result of repeating these improving process, the calibration curve converges to the correct one, and the distortion of the AFM image can be corrected. In the in situ self-calibration through evaluating the inverse function of the calibration curve, the profile measurement data sets were used during the data processing technique. Principles and experimental results of the two methods are presented.

• Journal of the Chungcheong Mathematical Society
• /
• v.22 no.3
• /
• pp.467-480
• /
• 2009

In 1859, Bernhard Riemann, in his epoch-making memoir, extended the Euler zeta function $\zeta$(s) (s > 1; $s{\in}\mathbb{R}$) to the Riemann zeta function $\zeta$(s) ($\Re$(s) > 1; $s{\in}\mathbb{C}$) to investigate the pattern of the primes. Sine the time of Euler and then Riemann, the Riemann zeta function $\zeta$(s) has involved and appeared in a variety of mathematical research subjects as well as the function itself has been being broadly and deeply researched. Among those things, we choose to make a further investigation of the following subjects: Evaluation of $\zeta$(2k) ($k {\in}\mathbb{N}$); Approximate functional equations for $\zeta$(s); Series involving the Riemann zeta function.

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