Analysis of heterojunction resonant cavity-enhanced Schottky photodiodes by using two-valley transport model

The article concerns heterojunction resonant cavity-enhanced (RCE) Schottky photodiodes with GaAs in the absorption layer. The quantum efficiency and linear pulse response have thoroughly been analysed. For the first time, the response of a heterojunction photodiode has been modelled by the phenomenological model for a two-valley semiconductor. The results obtained have shown that the satellite valleys, as well as the parasitic time constant, significantly influence the response and, accordingly, have to be taken into account when analysing and optimizing RCE photodetectors.     

深能级杂质对光导半导体开关非线性特性的影响

建立了非线性GaAs光导开关深能级杂质瞬态模型的基本方程,获得了与实验现象定性吻合的电流输出,给出了平均载流子随时间演化的情况.分析结果表明,在考虑了深能级杂质的俘获、发射和碰撞电离后,有可能对非线性光导开关中发生的一系列现象做出解释,进一步的仔细分析将对非线性光导开关的设计和制作提供理论指导.     

ASYMPTOTIC BEHAVIOR OF THE DRIFT-DIFFUSION SEMICONDUCTOR EQUATIONS

This paper is devoted to the long time behavior for the Drift-diffusion semi-conductor equations. It is proved that the dynamical system has a compact, connected and maximal attractor when the mobilities are constants and generation-recombination term is the Auger model; as well as the semigroup S(t) defined by the solutions map is differential. Moreover the upper bound of Hausdorff dimension for the attractor is given.     

On an approach to the study of the Jaynes–Cummings sum in quantum optics

A new approach to the study of the Jaynes–Cummings sum, which determines the atomic inversion in quantum model of a single two-level atom interacting with a single mode of the quantized radiation field, based on the number theory theorems on approximation of trigonometric sums is presented.      

神经网络在大型复杂武器装备质量综合评价排序中的应用

从大型复杂武器装备系统的具体特点出发,运用基于神经网络的评价方法,构建了大型复杂武器装备质量综合评价模型。实证分析的结果表明该方法得出的结果与实际情况相符。     

心理素质诊断模型的建立与应用

综合运用国内外多种测量量表对大学生的心理素质进行诊断与测试 ,并应用多元统计分析方法对其心理素质结构主因素进行定量分析 ,在此基础上建立了心理素质诊断模型 ,并运用该模型对实际问题进行了分析 .     

Compatible Lie Brackets and Integrable Equations of the Principal Chiral Model Type

We consider two classes of integrable nonlinear hyperbolic systems on Lie algebras. These systems generalize the principal chiral model. Each system is related to a pair of compatible Lie brackets and has a Lax representation, which is determined by the direct sum decomposition of the Lie algebra of Laurent series into the subalgebra of Taylor series and the complementary subalgebra corresponding to the pair. New examples of compatible Lie brackets are given.     

Global and exploding solutions in a model of self-gravitating systems

We study asymptotic properties of solutions to an extension to arbitrary dimensions of the astrophysical model proposed by Chavanis et al. to explain phenomena of gravitational collapse in clouds of self-gravitating particles. In particular, we show that in the two-dimensional case the solutions can be continued to global ones, while in three space dimensions large data of negative energy blow up in a finite time. Relations between isothermal, Streater's energy-transport and the present models are also studied.     

Randomly censored partially linear single-index models

This paper proposes a method for estimation of a class of partially linear single-index models with randomly censored samples. The method provides a flexible way for modelling the association between a response and a set of predictor variables when the response variable is randomly censored. It presents a technique for “dimension reduction” in semiparametric censored regression models and generalizes the existing accelerated failure-time models for survival analysis. The estimation procedure involves three stages: first, transform the censored data into synthetic data or pseudo-responses unbiasedly; second, obtain quasi-likelihood estimates of the regression coefficients in both linear and single-index components by an iteratively algorithm; finally, estimate the unknown nonparametric regression function using techniques for univariate censored nonparametric regression. The estimators for the regression coefficients are shown to be jointly root-n consistent and asymptotically normal. In addition, the estimator for the unknown regression function is a local linear kernel regression estimator and can be estimated with the same efficiency as all the parameters are known. Monte Carlo simulations are conducted to illustrate the proposed methodology.     

Confidence regions in a multivariate regression model with constraints II

The multivariate model, where not only parameters of the mean value of the observation matrix, but also some other parameters occur in constraints, is considered in the paper. Some basic inference is presented under the condition that the covariance matrix is either unknown, or partially unknown, or known. Supported by the grant of the Council of Czech Republic MSM 6 198 959 214.