• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.475-484
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• 2005

Let W(t) be a Wiener path, let $\xi\;:\;[0,\;{\infty})\;\to\;\mathbb{R}$ be a continuous and increasing function satisfying $\xi$(0) > 0, let $$T_{/xi}=inf\{t{\geq}0\;:\;W(t){\geq}\xi(t)\}$$ be the first-passage time of W over $\xi$, and let F denote the distribution function of $T_{\xi}$. Then the first passage problem has a unique continuous solution as following $$F(t)=u(t)+{\sum_{n=1}^\infty}\int_0^t\;H_n(t,s)u(s)ds$$, where $$u(t)=2\Psi(\xi(t)/\sqrt{t})\;and\;H_1(t,s)=d\Phi\;(\{\xi(t)-\xi(s)\}/\sqrt{t-s})/ds\;for\;0\;{\leq}\;s.

• Nonlinear Functional Analysis and Applications
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• v.26 no.2
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• pp.273-288
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• 2021

We proposed to give some new 𝜓-coupled fixed point theorems using simulation function coupled with other control functions in a complete partially ordered metric space which includes many related results. Further we prove the existence of solution of a fractional integral equation by using this fixed point theorem and explain it with the help of an example.

• Nonlinear Functional Analysis and Applications
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• v.27 no.3
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• pp.513-532
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• 2022

This paper deals with the stability of solutions to 𝜓-Hilfer fractional differential systems. We derive the fundamental solution for the system by using the generalized Laplace transform and the Mittag-Leffler function with two parameters. In addition, we obtained some necessary conditions on the stability of the solutions to linear fractional differential systems for homogeneous, non-homogeneous and non-autonomous cases. Numerical examples are also given to illustrate the behavior of solutions.

• East Asian mathematical journal
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• v.25 no.1
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• pp.55-68
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• 2009

In this paper we propose new large-update primal-dual inte-rior point algorithms for $P_*(\kappa)$ linear complementarity problems(LCPs). New search directions and proximity measures are proposed based on the kernel function$\psi(t)=\frac{t^{p+1}-1}{p+1}+\frac{e^{\frac{1}{t}}-e}{e}$,$p{\in}$[0,1]. We showed that if a strictly feasible starting point is available, then the algorithm has $O((1+2\kappa)(logn)^{2}n^{\frac{1}{p+1}}log\frac{n}{\varepsilon}$ complexity bound.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• Bulletin of the Korean Mathematical Society
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• v.51 no.4
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• pp.995-1003
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• 2014

A remarkably large number of integral formulas involving a variety of special functions have been developed by many authors. Also many integral formulas involving various Bessel functions have been presented. Very recently, Choi and Agarwal derived two generalized integral formulas associated with the Bessel function $J_{\nu}(z)$ of the first kind, which are expressed in terms of the generalized (Wright) hypergeometric functions. In the present sequel to Choi and Agarwal's work, here, in this paper, we establish two new integral formulas involving the generalized Bessel functions, which are also expressed in terms of the generalized (Wright) hypergeometric functions. Some interesting special cases of our two main results are presented. We also point out that the results presented here, being of general character, are easily reducible to yield many diverse new and known integral formulas involving simpler functions.

• Journal of Distribution Research
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• v.17 no.4
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• pp.29-53
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• 2012

As so many marketers get to use diverse sales promotion methods, manufacturer and retailer in a channel often use them too. In this context, diverse issues on sales promotion management arise. One of them is the issue of unplanned buying. Consumers' unplanned buying is clearly better off for the retailer but not for manufacturer. This asymmetric influence of unplanned buying should be dealt with prudently because of its possibility of provocation of channel conflict. However, there have been scarce studies on the sales promotion management strategy considering the unplanned buying and its asymmetric effect on retailer and manufacturer. In this paper, we try to find a better way for a manufacturer in a channel to promote performance through the retailer's sales promotion efforts when there is potential of unplanned buying effect. We investigate via game-theoretic modeling what is the optimal cost sharing level between the manufacturer and retailer when there is unplanned buying effect. We investigated following issues about the topic as follows: (1) What structure of cost sharing mechanism should the manufacturer and retailer in a channel choose when unplanned buying effect is strong (or weak)? (2) How much payoff could the manufacturer and retailer in a channel get when unplanned buying effect is strong (or weak)? We focus on the impact of unplanned buying effect on the optimal cost sharing mechanism for sales promotions between a manufacturer and a retailer in a same channel. So we consider two players in the game, a manufacturer and a retailer who are interacting in a same distribution channel. The model is of complete information game type. In the model, the manufacturer is the Stackelberg leader and the retailer is the follower. Variables in the model are as following table. Manufacturer's objective function in the basic game is as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. And retailer's is as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+p_u(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. The model is of four stages in two periods. Stages of the game are as follows. (Stage 1) Manufacturer sets wholesale price of the first period($w_1$) and cost sharing level of channel sales promotion(${\Psi}$). (Stage 2) Retailer sets retail price of the focal brand($p_1$), the unplanned buying item($p_u$), and sales promotion level(L). (Stage 3) Manufacturer sets wholesale price of the second period($w_2$). (Stage 4) Retailer sets retail price of the second period($p_2$). Since the model is a kind of dynamic games, we try to find a subgame perfect equilibrium to derive some theoretical and managerial implications. In order to obtain the subgame perfect equilibrium, we use the backward induction method. In using backward induction approach, we solve the problems backward from stage 4 to stage 1. By completely knowing follower's optimal reaction to the leader's potential actions, we can fold the game tree backward. Equilibrium of each variable in the basic game is as following table. We conducted more analysis of additional game about diverse cost level of manufacturer. Manufacturer's objective function in the additional game is same with that of the basic game as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. But retailer's objective function is different from that of the basic game as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+(p_u-c)(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. Equilibrium of each variable in this additional game is as following table. Major findings of the current study are as follows: (1) As the unplanned buying effect gets stronger, manufacturer and retailer had better increase the cost for sales promotion. (2) As the unplanned buying effect gets stronger, manufacturer had better decrease the cost sharing portion of total cost for sales promotion. (3) Manufacturer's profit is increasing function of the unplanned buying effect. (4) All results of (1),(2),(3) are alleviated by the increase of retailer's procurement cost to acquire unplanned buying items. The authors discuss the implications of those results for the marketers in manufacturers or retailers. The current study firstly suggests some managerial implications for the manufacturer how to share the sales promotion cost with the retailer in a channel to the high or low level of the consumers' unplanned buying potential.

• Bulletin of the Korean Mathematical Society
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• v.48 no.3
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• pp.655-672
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• 2011

Let $C^r$[0,t] be the function space of the vector-valued continuous paths x : [0,t] ${\rightarrow}$ $R^r$ and define $X_t$ : $C^r$[0,t] ${\rightarrow}$ $R^{(n+1)r}$ and $Y_t$ : $C^r$[0,t] ${\rightarrow}$ $R^{nr}$ by $X_t(x)$ = (x($t_0$), x($t_1$), ..., x($t_{n-1}$), x($t_n$)) and $Y_t$(x) = (x($t_0$), x($t_1$), ..., x($t_{n-1}$)), respectively, where 0 = $t_0$ < $t_1$ < ... < $t_n$ = t. In the present paper, with the conditioning functions $X_t$ and $Y_t$, we introduce two simple formulas for the conditional expectations over $C^r$[0,t], an analogue of the r-dimensional Wiener space. We establish evaluation formulas for the analogues of the analytic Wiener and Feynman integrals for the function $G(x)=\exp{{\int}_0^t{\theta}(s,x(s))d{\eta}(s)}{\psi}(x(t))$, where ${\theta}(s,{\cdot})$ and are the Fourier-Stieltjes transforms of the complex Borel measures on ${\mathbb{R}}^r$. Using the simple formulas, we evaluate the analogues of the conditional analytic Wiener and Feynman integrals of the functional G.

• Journal of Korean Ophthalmic Optics Society
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• v.12 no.3
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• pp.77-81
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• 2007

We sputtered $TiN_x$ (titanium nitride) thin films on silicon substrates using ultra high vacuum RF magnetron sputtering method, and measured spectra of ellipsometry angles ${\Delta}$ and ${\Psi}$ in the photon-energy range of 1.5-5.0 eV using a variable angle spectroscopic ellipsometer. The optical constants, refractive index and extinction coefficient of the $TiN_x$ films were determined via the dispersion parameters extracted from the curve-fitting process based on Drude+Lorentz oscillator dispersion function. The reliability of determined optical constants were verified through the comparison of between simulated reflectance and reflectance spectra measured using a spectrophotometer.

• Polymer(Korea)
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• v.25 no.3
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• pp.367-374
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• 2001

The electromagnetic interference (EMI) shielding effectiveness (SE) of poly(vinylidene fluoride) (PVDF) composites was investigated using carbon nanofiber fillers prepared by catalytic chemical vapor deposition of various carbon-containing gases over Ni and Ni-Cu catalysts. The electrical conductivity of carbon nanofiber which was regarded as the key property of filler for the application of EMI shielding ranged from 4.2 to 22.4 S/cm at a pressure of 10000 psi. The electrical conductivity of carbon nanofiber/PVDF composites ranged from 0.22 to 2.46 S/cm and the EMI SE of those was in the range of 2∼13 dB. The electrical conductivity of carbon nanofibers increased with the increase in heat treatment temperature and time, while the electrical conductivity of the composites increased rapidly at the initial heat treatment and then approached a certain value with the further increase of heat treatment. The SE of the composites showed a maximum at the medium heat treatment and was proportional to the electrical conductivity of the composites. It was concluded that the specific surface area of carbon nanofibers decreased with the continual heat treatment and the specific surface area of filler was an important factor for the SE of the composites.