• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.3-11
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• 2011

Evaluation of cube roots in characteristic three finite fields is required for Tate (or modified Tate) pairing computation. The Hamming weights (the number of nonzero coefficients) in the polynomial representations of $x^{1/3}$ and $x^{2/3}$ determine the efficiency of cube roots computation, where $F_{3^m}$is represented as $F_3[x]/(f)$ and $f(x)=x^m+ax^k+b{\in}F_3[x]$ (a, $b{\in}F_3$) is an irreducible trinomial. O. Ahmadi et al. determined the Hamming weights of $x^{1/3}$ and $x^{2/3}$ for all irreducible trinomials. In this paper, we present formulas for cube roots in $F_{3^m}$ using the shifted polynomial basis(SPB). Moreover, we provide the suitable shifted polynomial basis bring no further modular reduction process.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1433-1441
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• 2016

Small area model provides reliable and accurate estimations when the sample size is not sufficient. Our dataset has an inherent nonlinear pattern which signicantly affects our inference. In this case, we could consider semiparametric models such as truncated polynomial basis function and radial basis function. In this paper, we study four Bayesian semiparametric models for small areas to handle this point. Four small area models are based on two kinds of basis function and different knots positions. To evaluate the different estimates, four comparison measurements have been employed as criteria. In these comparison measurements, the truncated polynomial basis function with equal quantile knots has shown the best result. In Bayesian calculation, we use Gibbs sampler to solve the numerical problems.

• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.341-344
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• 2001

본 논문에서는 타원곡선 알고리즘에 기반한 공개키암호시스템 구현을 다룬다. 공개키의 길이는 193비트를 갖고 기약다항식은 p(x)=x/sup 193＋x/sup 15＋1을 사용하였다. 타원곡선은 polynomial basis 로 표현하였으며 SEC 2 파라메터를 기준으로 하였다 암호시스템은 polynomial basis 유한체 연산기로 구성되며 특히, digit-serial 구조로 스마트카드와 같이 제한된 면적에서 구현이 가능하도록 하였다. 시스템의 회로는 VHDL, SYNOPSYS 시뮬레이션 및 회로합성을 이용하여 XILINX FPGA로 회로를 구현하였다. 본 시스템 은 Diffie-Hellman 키교환에 적용하여 동작을 검증하였다.

• Nuclear Engineering and Technology
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• v.31 no.3
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• pp.303-313
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• 1999

Various interpolation methods have been compared for reconstruction of LMR pin power distributions in hexagonal geometry. Interpolation functions are derived for several combinations of nodal quantities and various sets of basis functions, and tested against fine mesh calculations. The test results indicate that the interpolation functions based on the sixth degree polynomial are quite accurate, yielding maximum interpolation errors in power densities less than 0.5%, and maximum reconstruction errors less than 2% for driver assemblies and less than 4% for blanket assemblies. The main contribution to the total reconstruction error is made tv the nodal solution errors and the comer point flux errors. For the polynomial interpolations, the basis monomial set needs to be selected such that the highest powers of x and y are as close as possible. It is also found that polynomials higher than the seventh degree are not adequate because of the oscillatory behavior.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1863_1864
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• 2009

본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 입력개수, 입력변수, 클러스터의 개수를 PSO알고리즘(Particle Swarm Optimization)을 사용하여 최적화 시켰다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.8 s.185
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• pp.89-99
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• 2006

In this paper a new shape reconstruction method that allows us to construct surface models from very large sets of points is presented. In this method the global domain of interest is divided into smaller domains where the problem can be solved locally. These local solutions of subdivided domains are blended together according to weighting coefficients to obtain a global solution using partition of unity function. The suggested approach gives us considerable flexibility in the choice of local shape functions which depend on the local shape complexity and desired accuracy. At each domain, a quadratic polynomial function is created that fits the points in the domain. If the approximation is not accurate enough, other higher order functions including cubic polynomial function and RBF(Radial Basis Function) are used. This adaptive selection of local shape functions offers robust and efficient solution to a great variety of shape reconstruction problems.

• Korean Journal of Mathematics
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• v.27 no.4
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• pp.899-927
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• 2019

In this paper we establish some results depending on the comparative growth properties of composite entire and meromorphic functions using relative (p, q)-th order and relative (p, q)-th lower order where p, q are any two positive integers and that of a special type of differential polynomial generated by one of the factors.

• Korean Journal of Mathematics
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• v.28 no.3
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• pp.639-647
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• 2020

A simple type of Cohen's transformation consists of a polynomial and a linear fractional transformation. We study the effectiveness of Cohen transformation to find N-polynomials over finite fields.

• ETRI Journal
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• v.39 no.4
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• pp.570-581
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• 2017

Multiplication in finite fields is used in many applications, especially in cryptography. It is a basic and the most computationally intensive operation from among all such operations. Several systolic multipliers are proposed in the literature that offer low hardware complexity or high speed. In this paper, a bit-parallel polynomial basis systolic multiplier for generic irreducible polynomials is proposed based on a modified interleaved multiplication method. The hardware complexity and delay of the proposed multiplier are estimated, and a comparison with the corresponding multipliers available in the literature is presented. Of the corresponding multipliers, the proposed multiplier achieves a reduction in the hardware complexity of up to 20% when compared to the best multiplier for m = 163. The synthesis results of application-specific integrated circuit and field-programmable gate array implementations of the proposed multiplier are also presented. From the synthesis results, it is inferred that the proposed multiplier achieves low power consumption and low area complexitywhen compared to the best of the corresponding multipliers.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.