Spectroscopic ellipsometry of very thin tantalum pentoxide on Si

Variable angle spectroscopic ellipsometry of very thin T₂O₅ layers on Si and the previously published appropriate algorithm for data interpretation have been successfully applied in terms of accurate characterization of very thin T₂O₅/Si systems. The simulation procedure following a simple three and four layered model was used assuming an existence of inhomogeneous interfacial layers. Quantitative determination of the thicknesses and composition identification were achieved, both for the top T₂O₅ layer and for an interfacial layer. The constituents in the interfacial layer and its depth profiles were recognized.     

父代种群参与竞争遗传算法几乎必然收敛

熟知,标准遗传算法如不采用“杰出者记录策略”则必不收敛。本文发现：允许父代种群参与竞争是标准遗传算法几乎必然收敛的条件。特别地,我们运用鞅收敛定理证明：允许父代种群参与竞争型遗传算法能以概率1确保在限步内达到全局最优解,且收敛与种群规模无关。所获结果对该类遗传算法的应用奠定了可靠基础。     

非线性系统的高阶迭代学习控制算法

本文对一般非线性系统,提出了三种高阶迭代学习控制算法:(Ⅰ)u_(h 1)=sum from j=1 to r(P_ju_(k-j 1) Г_je_(h-j 1));(Ⅱ)u_(k 1)=sum from j=1 to r(P_ju_(k-j 1) F_je_(k-j 1));(Ⅲ)u_(k 1)=sum from j=1 to r{P_ju_(k-j 1) (Г_j F_jd/(dt))e_(k-j 1)},其中u_(k 1)=u_(k 1)(t)表示系统第k 1次运行时的输入;e_k=y_k-y_d;y_d是系统所期望的输出;y_k是系统第k次运行时的输出;P_j,Г_j,F_j(j=1,…,r)是常数阵;进而给出了比较弱的收敛性条件。     

Optimizing the performance of single-mode laser diode system using genetic algorithm

In this correspondence, micro-genetic algorithm (MGA) application results for optimizing the performance of electronic feedback of a laser diode are presented. The goal of optimization is to find the maximum bandwidth of the laser diode with electronic feedback used in fiber optic digital communication. A numerical analysis of the system theory of the single-mode laser diode to obtain numerical results of the gain, the pulse response, and the harmonic distortion for electronic feedback is also presented. The dependence of the system gain on the feedback gain and delay is examined. The pulse response is studied and it is shown that a transmission rate over 1 Gbyte/s can be achieved.     

欧拉游历间K1—变换下的算法复杂性

本文主要讨论了欧拉图方面的国际权威ＨｅｒｂｅｒｔＦｌｅｉｃｈｎｅｒ教授所著书［１］中的一个问题；对欧拉图Ｇ的任意两个欧拉游历要经过多少Ｋ－变换或Ｋ＾＊－变换才能从一个游历得到另一个游历？我们得到的结论是：对欧拉图Ｇ中的任两个欧拉游历Ｔ、Ｔ＇最多经过‖Ｅ（Ｇ）‖－‖Ｖ（Ｇ）‖－变换可以使Ｔ变换成Ｔ＇。且此结果不能再改进。进一步我们分别对Ｋ－变换和Ｋ＆－变换的算法复杂性进行了讨论。     

二元整系数多项式因式分解的一种理论和算法

我们发现可以把二元多项式盾成系数为一元多项式的一元多项式来进行分解，据此，本文建立了二元整系数多项式因式分解的一种理论，提出了一个完整的分解二元整系数多项式的算法。这个算法还能很自然地推广成分解多元整系数多项式的算法。     

关于二次规划问题分段线性同伦算法的改进

本文利用Ｃｈｏｌｅｓｋｙ分解，Ｇａｕｓｓ消去等技术和定义适当的同伦映射，将关于二次规划问题的分段线性同伦算法加以改进，改进后的算法，对于严格凸二次规划来说，计算效率与Ｇｏｌｄｆａｒｂ－Ｉｄｎａｎｉ的对偶法相当。     

Counterexamples in measuring the distance between binary trees

Examples are given to show that the closest partition distance measure need not agree with the nearest neighbor interchange distance for unordered labeled binary trees. Proposed algorithms for computing the closest partition distance are shown to be of exponential complexity and hence may not be useful in approximating the nearest neighbor interchange distance.     

Computational experience with the Chow—Yorke algorithm

The Chow—Yorke algorithm is a nonsimplicial homotopy type method for computing Brouwer fixed points that is globally convergent. It is efficient and accurate for fixed point problems. L.T. Watson, T.Y. Li, and C.Y. Wang have adapted the method for zero finding problems, the nonlinear complementarity problem, and nonlinear two-point boundary value problems. Here theoretical justification is given for applying the method to some mathematical programming problems, and computational results are presented.This work was partially supported by NSF Grant MCS 7821337.     

Development and optimisation of a novel genetic algorithm for studying model protein folding

Determination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. Location of the global minimum structure for a given sequence, however, is a difficult optimisation problem. In this paper, we describe the development and application of a genetic algorithm (GA) to find the lowest-energy conformations for the 2D HP lattice bead protein model. Optimisation of the parameters of our standard GA program reveals that the GA is most successful (at finding the lowest-energy conformations) for high rates of mating and mutation and relatively high elitism. We have also introduced a number of new genetic operators: a duplicate predator—which maintains population diversity by eliminating duplicate structures; brood selection—where two parent structures undergo crossover and give rise to a brood of (not just two) offspring; and a Monte Carlo based local search algorithm—to explore the neighbourhood of all members of the population. It is shown that these operators lead to significant improvements in the success and efficiency of the GA, both compared with our standard GA and with previously published GA studies for benchmark HP sequences with up to 50 beads.