• East Asian mathematical journal
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• v.28 no.1
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• pp.25-36
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• 2012

We introduced a Finsler space $F^n$ with an approximate infinite series (${\alpha},{\beta}$-metric $L({\alpha},{\beta})={\beta}\sum\limits_{k=0}^r\(\frac{\alpha}{\beta}\)^k$, where ${\alpha}<{\beta}$ and investigated it with respect to Berwald space ([12]) and Douglas space ([13]). The present paper is devoted to finding the condition that is projectively at on a Finsler space $F^n$ with an approximate infinite series (${\alpha},{\beta}$)-metric above.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.609-616
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• 2022

The performance of antenna reflectors crucially depends on the faceting error of the surface. The force density method (FDM) has been widely used for the form-finding analysis of the cable nets of reflectors. However, after performing form-finding of some cable nets, the effective reflective area will decrease. In addition, nonlinear deformations of the cable can not be achieved by using the FDM. Thus, an effective form-find methodology is proposed in this research. The whole parts of the cable networks are described by the absolute nodal coordinate formulation. The form-finding analysis of the reflector with standard configuration is performed to validate the proposed methodology. The influence of boundary condition changes on the configuration accuracy of the cable net is investigated.

• Bulletin of the Korean Mathematical Society
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• v.33 no.3
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• pp.377-383
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• 1996

We consider in this paper the following nonlinear regression model $$ (1.1) y_t = f(x_t, \theta_o) + \in_t, t = 1, \ldots, n, $$ where $y_t$ is the tth response, $x_t$ is m-vector imput variable, $\theta_o$ is a p-vector of unknown parameter belong to a parameter space $\Theta, f:R^m \times \Theta \ to R^1$ is a nonlinear known function, and $\in_t$ are independent unobservable random errors with finite second moment.

• Journal of the Korean Mathematical Society
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• v.32 no.3
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• pp.509-518
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• 1995

Let $M^n$ be a hypersurface in the Euclidean space $R^{n+1}$ with position vector field x and unit normal vector field G.

• Communications of the Korean Mathematical Society
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• v.12 no.2
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• pp.393-401
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• 1997

Let $A_1,A_2,\cdots,A_m$ and $B_1,B_2,\cdots,B_n$ be two sequences of events on a given probability space. Let $X_m$ and $Y_n$, respectively, be the number of those $A_i$ and $B_j$, which occur we establish new upper and lower bounds on the probability $P(X=r, Y=t)$ which improve upper bounds and classical lower bounds in terms of the bivariate binomial moment $S_{r,t},S_{r+1,t},S_{r,t+1}$ and $S_{r+1,t+1}$.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.50.1-50.1
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• 2021

The Gaia mission opens a new window to study the kinematics and dynamics of young stellar systems in detail. The kinematic properties of young stars provide vital constraints on the formation process of their host systems. Here, we present a kinematic study of the two associations Monoceros OB1 (Mon OB1) and R1 (Mon R1). Member candidates are first selected from the published list of member candidates, a compilation of OB star catalogues, and the classification of young stellar objects with the AllWISE data. According to the conventional wisdom, we selected a total of 728 members with similar proper motions at almost the same distance. Mon OB1 and Mon R1 have high levels of substructures that are also kinematically distinct. We identify six stellar groups in these associations, of which five show a pattern of expansion. In addition, the signature of rotation is found in two stellar groups of Mon OB1. Star formation history is inferred from a color-magnitude diagram. As a result, star formation in Mon OB1 has been sustained for several million years, while Mon R1 formed at almost the same epoch as the recent star formation in Mon OB1. Some old members in the outskirt of Mon OB1 have outward motions, which rules out the previously proposed outside-in star formation scenario. Star-forming regions including Mon OB1 and Mon R1 are found along a large arc-like gas structure. Hence, the formation of these two associations may originate from the hierarchical star formation along filaments in a turbulent molecular cloud.

• The Bulletin of The Korean Astronomical Society
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• v.31 no.2
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• pp.57.2-57.2
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• 2006

• Bulletin of the Korean Mathematical Society
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• v.23 no.2
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• pp.123-132
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• 1986

Let X be a real Banach space with norm vertical bar . vertical bar and let I denote the identity operator. Then an operator A.contnd.X*X with domain D(A) and range R(A) is said to be accretive if vertical bar x$_{1}$-x$_{2}$ vertical bar.leq.vertical bar x$_{1}$-x$_{2}$+r(y$_{1}$-y$_{2}$) vertical bar for all y$_{i}$.mem.Ax$_{i}$, i=1, 2, and r>0. An accretive operator A.contnd.X*X is m-accretive if R(I+rA)=X for all r>0.r>0.

• The Bulletin of The Korean Astronomical Society
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• v.33 no.2
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• pp.73.1-73.1
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• 2008

• Journal of Korea Technology Innovation Society
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• v.11 no.1
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• pp.72-90
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• 2008

What has been the contribution of industrial innovation to economic growth? Typically, the issue has been approached with growth-accounting methods augmented to include a "stock of knowledge". An independent estimate of the rate of return to R&D is found in order to impute patents granted to the accumulation of knowledge. Griliches(1973) then uses a regression approach to assess the effect of an R&D variable on the computed TFP growth rate. The regression coefficient on the R&D variable would provide an estimate of the social rate of return to R&D. The related studies tend to show high social rates of return to R&D, typically in a range of 20 to 40 % per year. We need to provide multiple equation dynamic system for productivity and innovation in Korean economy in state space form. A wide range of time series models, including the classical linear regression model, can be written and estimated as special cases of a state space specification. State space models have been applied in the econometrics literature to model unobserved variables like productivity. Estimation produces the following results. Considering the goodness of fit, we can see that the evidence is strongly in favor of the range $0.120{\sim}0.135$ for the elasticity of TFP to R&D stock in the period between 1970's and the early 2000's.