• Journal of the Korean Mathematical Society
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• v.53 no.3
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• pp.709-723
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• 2016

Let C[0, t] denote a generalized Wiener space, the space of real-valued continuous functions on the interval [0, t] and define a random vector $Z_n:C[0,t]{\rightarrow}{\mathbb{R}}^n$ by $Zn(x)=(\int_{0}^{t_1}h(s)dx(s),{\cdots},\int_{0}^{t_n}h(s)dx(s))$, where 0 < $t_1$ < ${\cdots}$ < $t_n$ < t is a partition of [0, t] and $h{\in}L_2[0,t]$ with $h{\neq}0$ a.e. In this paper we will introduce a simple formula for a generalized conditional Wiener integral on C[0, t] with the conditioning function $Z_n$ and then evaluate the generalized analytic conditional Wiener and Feynman integrals of the cylinder function $F(x)=f(\int_{0}^{t}e(s)dx(s))$ for $x{\in}C[0,t]$, where $f{\in}L_p(\mathbb{R})(1{\leq}p{\leq}{\infty})$ and e is a unit element in $L_2[0,t]$. Finally we express the generalized analytic conditional Feynman integral of F as two kinds of limits of non-conditional generalized Wiener integrals of polygonal functions and of cylinder functions using a change of scale transformation for which a normal density is the kernel. The choice of a complete orthonormal subset of $L_2[0,t]$ used in the transformation is independent of e and the conditioning function $Z_n$ does not contain the present positions of the generalized Wiener paths.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.259-266
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• 2002

The main issue in software development is the ability of software project effort and cost estimation in the early phase of software life cycle. The regression models for project effort and cost estimation are presented by function point that is a software sire. The data sets used to conduct previous studies are of ten small and not too recent. Applying these models to 789 project data developed from 1990 ; the models only explain fewer than 0.53 $R^2$(Coefficient of determination) of the data variation. Homogeneous group in accordance with project delivery rate (PDR) divides the data sets. Then this paper presents general effort estimation models using project delivery rate. The presented model has a random distribution of residuals and explains more than 0.93 $R^2$ of data variation in most of PDR ranges.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.23 no.3
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• pp.108-114
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• 1997

The skin permeation study has two meanings in cosmetics. One is how to promote the skin permeation of active meterials for improving their bioavailabilities and the other is how to decrease it of irritants for reducing their skin side effects. In this study, we selected methyl paraben, one of the preservatives, as a model irritant and tried to reduce the skin irritation by the decrease of skin permeation. Furthermore, the relationship between skin permeation and skin primary irritation was discussed. For in vitro skin permeation experiments, Franz type diffusion cells and the excised skin of female hairless mouse from 8 weeks old were used. The donor compartment was charged with oil only or O/W emulsion containing 0.3% MP. We selected 19 oils, including esters, triglycerides, plant oils, hydrocarbons, and alchols, which are broadly used in cosmetics. We evaluated with female guinea pig. The skin permeahility of MP from the oils showed following order: ester oils > triglycerides > plant oils > hydrocarbons > alcohols. We considered that this result was based on the different effect of each oil on the barrier function of stratum corneum. In O/W emulsion containing each oil, the skin permeability of MP decreased as the oil/water partition coefficient of MP increased. The skin primary irritation increased as the skin permeability of MP increased. In conclusion, we suggest that the skin irritation could be reduced by the decrease of skin permeability of MP, which may be obtained by the good selection of oils in cosmetic preparations.

• Journal of the Korean Chemical Society
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• v.10 no.2
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• pp.91-97
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• 1966

The partition function for liquid water is developed according to the transient state theory of significant liquid structure proposed by Pak, Ahn and Chang. This theory assumes that the molecules may possess solid-like, transient and gas-like degrees of freedom in liquid state. Although liquid water has several special properties, for example, minimum molar volume at 4^{\circ}C$, the general theory of liquid can be applied successfully. The theoretically calculated values for thermodynamic properties at the liquid temperature range and for the critical properties are in good agreement with the observed values.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2504-2526
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• 2016

Wireless sensors are always deployed in brutal environments, but as we know, the nodes are powered only by non-replaceable batteries with limited energy. Sending, receiving and transporting information require the supply of energy. The essential problem of wireless sensor network (WSN) is to save energy consumption and prolong network lifetime. This paper presents a new communication protocol for WSN called Dynamical Threshold Control Algorithm with three-parameter Particle Swarm Optimization and Ant Colony Optimization based on residual energy (DPA). We first use the state of WSN to partition the region adaptively. Moreover, a three-parameter of particle swarm optimization (PSO) algorithm is proposed and a new fitness function is obtained. The optimal path among the CHs and Base Station (BS) is obtained by the ant colony optimization (ACO) algorithm based on residual energy. Dynamical threshold control algorithm (DTCA) is introduced when we re-select the CHs. Compared to the results obtained by using APSO, ANT and I-LEACH protocols, our DPA protocol tremendously prolongs the lifecycle of network. We observe 48.3%, 43.0%, and 24.9% more percentages of rounds respectively performed by DPA over APSO, ANT and I-LEACH.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.11 s.188
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• pp.93-102
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• 2006

In this study an advanced domain decomposition method is suggested in order to construct surface models from very large amount of points. In this method the spatial domain of interest that is occupied by the input set of points is divided in repetitive manner. First, the space is divided into smaller domains where the problem can be solved independently. Then each subdomain is again divided into much smaller domains where the problem can be solved locally. These local solutions of subdivided domains are blended together to obtain a solution of each subdomain using partition of unity function. Then the solutions of subdomains are merged together in order to construct whole surface model. The suggested methods are conceptually very simple and easy to implement. Since RDDM(Repetitive Domain Decomposition Method) is effective in the computation time and memory consumption, the present study is capable of providing a fast and accurate reconstructions of complex shapes from large amount of point data containing millions of points. The effectiveness and validity of the suggested methods are demonstrated by performing numerical experiments for the various types of point data.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.9-9
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• 2003

Let $\Omega$ be a bounded, open, and polygonal domain in $R^2$ with re-entrant corners. We consider the following Partial Differential Equations: $$(I-\nabla\nabla\cdot+\nabla^{\bot}\nabla\times)u\;=\;f\;in\;\Omega$$, $$n\cdotu\;0\;0\;on\;{\Gamma}_{N}$$, $${\nabla}{\times}u\;=\;0\;on\;{\Gamma}_{N}$$, $$\tau{\cdot}u\;=\;0\;on\;{\Gamma}_{D}$$, $$\nabla{\cdot}u\;=\;0\;on\;{\Gamma}_{D}$$ where the symbol $\nabla\cdot$ and $\nabla$ stand for the divergence and gradient operators, respectively; $f{\in}L^2(\Omega)^2$ is a given vector function, $\partial\Omega=\Gamma_{D}\cup\Gamma_{N}$ is the partition of the boundary of $\Omega$; nis the outward unit vector normal to the boundary and $\tau$represents the unit vector tangent to the boundary oriented counterclockwise. For simplicity, assume that both $\Gamma_{D}$ and $\Gamma_{N}$ are nonempty. Denote the curl operator in $R^2$ by $$\nabla\times\;=\;(-{\partial}_2,{\partial}_1$$ and its formal adjoint by $${\nabla}^{\bot}\;=\;({-{\partial}_1}^{{\partial}_2}$$ Consider a weak formulation(WF): Find $u\;\in\;V$ such that $$a(u,v):=(u,v)+(\nabla{\cdot}u,\nabla{\cdot}v)+(\nabla{\times}u,\nabla{\times}V)=(f,v),\;A\;v{\in}V$$. (2) We assume there is only one singular corner. There are many methods to deal with the domain singularities. We introduce them shortly and we suggest a new Finite Element Methods by using Singular representation for the solution.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.444-449
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• 1998

A modal transducer in two-dimensional structure can be implemented by varying the distributed transducer's gain spatially. In this paper, a method based on finite element method is developed for optimizing spatial gain distribution of PVDF transducer to create the modal transducer for specific modes. Using this concept, one can design the modal transducer in two-dimensional structure having arbitrary geometry and boundary conditions. As a practical means for implementing this continuous gain distribution without repoling die PVDF film, the gain distribution is approximated by optimizing gain-weights of interface circuit. The whole spatial area of the PVDF film is divided into several electrode segments and the signals from each segment are properly weighted and summed by interface circuit. This corresponds to the approximation of a continuous function using discrete values. The electrode partition is optimized using the genetic algorithm. Gain-weights are optimized using the simplex search method. A modal sensor for first to fourth modes of aluminum plate is designed using PVDF film with gain-weighted interface circuit. Various lamination angles of PVDF film are taken into consideration to utilize the anisotropy of the PVDF film. Performance of the optimized' PVDF sensor is demonstrated by numerical simulations..

• Journal of Microbiology and Biotechnology
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• v.12 no.3
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• pp.522-525
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• 2002

A very rapid and efficient separation technique for cellular rhizobial cyclosophoraoses was developed based on fractional precipitation and partition chromatography. Cyclosophoraoses are known to function in the osmotic regulation and root nodule formation of legumes during the nitrogen fixation process. Cyclosophoraoses are produced as unbranched cyclic (1longrightarrow12)-${\beta}$-D-glucans in Agrobacterium or Rhizobium species. Recent research has shown that cyclosophoraoses can form inclusion complexation with various unstable or insoluble guest chemicals, thereby implying great potential for industrial application. Typical separation of pure cellular cyclosophoraoses has been so far carried out by several time-consuming steps, including size exclusion, anion exchange, and desalting liquid chromatographies, with a relatively poor recovery. However, the proposed method demonstrated that the successive application of fractional ethanol precipitation and one step of silica gel-based flash column chromatography was enough to simultaneously purify neutral or anionic forms of cyclosophoraoses. This novel technique is very rapid and provides a high recovery.