Earticle

Workers in nuclear power plants are likely to be exposed to radiation from variousgeometrical sources. In order to evaluate the exposure level, the point-kernel method can be utilized. In order to perform a dose assessment based on this method, the radiation source should be dividedinto point sources, and the number of divisions should be set by the evaluator. However, for the generalpublic, there may be difficulties in selecting the appropriate number of divisions and performing anevaluation. Therefore, the purpose of this study is to develop an algorithm for dose assessment forarbitrary shaped sources based on the point-kernel method. For this purpose, the point-kernel methodwas analyzed and the main factors for the dose assessment were selected. Subsequently, based onthe analyzed methodology, a dose assessment algorithm for arbitrary shaped sources was developed. Lastly, the developed algorithm was verified using Microshield. The dose assessment procedure of thedeveloped algorithm consisted of 1) boundary space setting step, 2) source grid division step, 3) the setof point sources generation step, and 4) dose assessment step. In the boundary space setting step, theboundaries of the space occupied by the sources are set. In the grid division step, the boundary space isdivided into several grids. In the set of point sources generation step, the coordinates of the point sourcesare set by considering the proportion of sources occupying each grid. Finally, in the dose assessmentstep, the results of the dose assessments for each point source are summed up to derive the dose rate. In order to verify the developed algorithm, the exposure scenario was established based on the standardexposure scenario presented by the American National Standards Institute. The results of the evaluationwith the developed algorithm and Microshield were compare. The results of the evaluation with thedeveloped algorithm showed a range of 1.99×10-1~9.74×10-1 μSv hr-1, depending on the distance and theerror between the results of the developed algorithm and Microshield was about 0.48~6.93%. The errorwas attributed to the difference in the number of point sources and point source distribution between thedeveloped algorithm and the Microshield. The results of this study can be utilized for external exposureradiation dose assessments based on the point-kernel method.

Kernel type estimations of discontinuity point at an unknown location in regression function or its derivatives have been developed. It is known that the discontinuity point estimator based on $Gasser-M\ddot{u}ller$ regression estimator with a one-sided kernel function which has a zero value at the point 0 makes a poor asymptotic behavior. Further, the asymptotic variance of $Gasser-M\ddot{u}ller$ regression estimator in the random design case is 1.5 times larger that the one in the corresponding fixed design case, while those two are identical for the local polynomial regression estimator. Although $Gasser-M\ddot{u}ller$ regression estimator with a one-sided kernel function which has a non-zero value at the point 0 for the modification is used, computer simulation show that this phenomenon is also appeared in the discontinuity point estimation.

In this paper we propose new large-update primal-dual interior point algorithms for $P_{\neq}({\kappa})$ linear complementarity problems(LCPs). New search directions and proximity measures are proposed based on a specific class of kernel functions, ${\psi}(t)={\frac{t^{p+1}-1}{p+1}}+{\frac{t^{-q}-1}{q}}$, q>0, $p{\in}[0,\;1]$, which are the generalized form of the ones in [3] and [12]. It is the first to use this class of kernel functions in the complexity analysis of interior point method(IPM) for $P_*({\kappa})$LCPs. We showed that if a strictly feasible starting point is available, then new large-update primal-dual interior point algorithms for $P_*({\kappa})$ LCPs have the best known complexity $O((1+2{\kappa}){\sqrt{2n}}(log2n)log{\frac{n}{\varepsilon}})$ when p=1 and $q=\frac{1}{2}(log2n)-1$.

Change point testing problem is considered. Kernel density estimators are used for constructing proposed change point test statistics. The proposed method can be used to the hypothesis testing of not only parameter change but also distributional change. Bootstrap method is applied to get the sampling distribution of proposed test statistic. Small sample Monte Carlo Simulation were also conducted in order to show the performance of proposed method.

In this paper, we construct a complex reproducing kernel space for singular multi-point BVPs, and skillfully obtain reproducing kernel expressions. Then, we transform the problem into an equivalent operator equation, and give a numerical algorithm to provide the approximate solution. The uniform convergence of this algorithm is proved, and complexity analysis is done. Lastly, we show the validity and feasibility of the numerical algorithm by two numerical examples.

In this paper, we construct a complex reproducing kernel space for singular multi-point BVPs, and skillfully obtain reproducing kernel expressions. Then, we transform the problem into an equivalent operator equation, and give a numerical algorithm to provide the approximate solution. The uniform convergence of this algorithm is proved, and complexity analysis is done. Lastly, we show the validity and feasibility of the numerical algorithm by two numerical examples.

It is well known that each kernel function defines primal-dual interior-point method (IPM). Most of polynomial-time interior-point algorithms for linear optimization (LO) are based on the logarithmic kernel function ([9]). In this paper we define new eligible kernel function and propose a new search direction and proximity function based on this function for LO problems. We show that the new algorithm has $\mathcal{O}(({\log}\;p)^{\frac{5}{2}}\sqrt{n}{\log}\;n\;{\log}\frac{n}{\epsilon})$ and $\mathcal{O}(q^{\frac{3}{2}}({\log}\;p)^3\sqrt{n}{\log}\;\frac{n}{\epsilon})$ iteration complexity for large- and small-update methods, respectively. These are currently the best known complexity results for such methods.

In this paper, we present a new method for solving a nonlinear two-point boundary value problem with finitely many singularities. Its exact solution is represented in the form of series in the reproducing kernel space. In the mean time, the n-term approximation $u_n(x)$ to the exact solution u(x) is obtained and is proved to converge to the exact solution. Some numerical examples are studied to demonstrate the accuracy of the present method. Results obtained by the method are compared with the exact solution of each example and are found to be in good agreement with each other.

In this paper we generalize large-update primal-dual interior point methods for linear optimization problems in [2] to the $P_*(\kappa)$ linear complementarity problems based on a new kernel function which includes the kernel function in [2] as a special case. The kernel function is neither self-regular nor eligible. Furthermore, we improve the complexity result in [2] from $O(\sqrt[]{n}(\log\;n)^2\;\log\;\frac{n{\mu}o}{\epsilon})$ to $O\sqrt[]{n}(\log\;n)\log(\log\;n)\log\;\frac{m{\mu}o}{\epsilon}$.

새로운 자유격자 관사를 이용한 점별 계산법을 제안한다 이동 최소 자승법을 이용한 기저의 생성과 기저의 근사적 미분을 동시에 구해내는 자유격자 근사를 유도하여, 직접 점별 계산법을 고안하였다. 기존의 자유 격자 법에서는 기저의 직접 미분을 사용하므로 높은 계산 비용이 필요하지만, 이 논문에서 제안된 방법은 기저의 생성과 동시에 기저의 근사적 미분을 구하게 된다. 또한 기존의 방법에서 필요하였던, 창 함수(window function)의 미분가능성을 연속성으로 대치할 수 있으므로, 주어진 문제에 따라 다양한 창 함수를 이용할 수 있다. 기저의 재생성과 interpolation의 수렴성을 소개하고, 수치 예제로서, Poisson 문제를 통해 이 방법의 유효함을 보인다.