时域自适应精细算法求解对流热传导问题

应用时域自适应精细算法求解对流传热问题.通过展开技术,可更准确地描述变量随时间的变化,同时将时空耦合的初边值问题转化为一系列的空间边值问题,并采用有限元方法递推求解.自适应技术可弥补时间步长不同时可能造成的计算精度损失.并进行了数值检验.     

SELF-ADAPTIVE STRATEGY FOR ONE-DIMENSIONAL FINITE ELEMENT METHOD BASED ON ELEMENT ENERGY PROJECTION METHOD

Based on the newly-developed element energy projection (EEP) method for computation of super-convergent results in one-dimensional finite element method (FEM), the task of self-adaptive FEM analysis was converted into the task of adaptive piecewise polynomial interpolation. As a result, a satisfactory FEM mesh can be obtained, and further FEM analysis on this mesh would immediately produce an FEM solution which usually satisfies the user specified error tolerance. Even though the error tolerance was not completely satisfied, one or two steps of further local refinements would be sufficient. This strategy was found to be very simple, rapid, cheap and efficient. Taking the elliptical ordinary differential equation of second order as the model problem, the fundamental idea, implementation strategy and detailed algorithm are described. Representative numerical examples are given to show the effectiveness and reliability of the proposed approach.     

Novel high-voltage self-adaptive power device based on interface charge＊

This paper presents a novel high-voltage lateral double diffused metal-oxide semiconductor （LDMOS） with self- adaptive interface charge （SAC） layer and its physical model of the vertical interface electric field. The SAC can be self-adaptive to collect high concentration dynamic inversion holes, which effectively enhance the electric field of dielectric buried layer （EI） and increase breakdown voltage （BV）. The BV and EI of SAC LDMOS increase to 612 V and 600 V/tim from 204 V and 90.7 V/ttm of the conventional silicon-on-insulator, respectively. Moreover, enhancement factors of r/which present the enhanced ability of interface charge on EI are defined and analysed.     

Decomposition Method with a Variable Parameter for a Class of Monotone Variational Inequality Problems

In this paper, we focus on a useful modification of the decomposition method by He et al. (Ref. 1). Experience on applications has shown that the number of iterations of the original method depends significantly on the penalty parameter. The main contribution of our method is that we allow the penalty parameter to vary automatically according to some self-adaptive rules. As our numerical simulations indicate, the modified method is more flexible and efficient in practice. A detailed convergence analysis of our method is also included.     

A self-adaptive trust region method for the extended linear complementarity problems

By using some NCP functions, we reformulate the extended linear complementarity problem as a nonsmooth equation. Then we propose a self-adaptive trust region algorithm for solving this nonsmooth equation. The novelty of this method is that the trust region radius is controlled by the objective function value which can be adjusted automatically according to the algorithm. The global convergence is obtained under mild conditions and the local superlinear convergence rate is also established under strict complementarity conditions. This work is supported by National Natural Science Foundation of China (No. 10671126) and Shanghai Leading Academic Discipline Project (S30501).     

基于自适应BP神经网络的结构损伤检测

描述基于人工神经网络的结构损伤检测的基本步骤以及该方法在实际5层钢框架结构损伤检测上的应用．提出了一种改进的BP神经网络方法，它能够解决传统BP神经网络在实际应用中存在的两个问题：收敛速度慢并存在局部极小．其基本思想是引入动态自适应算子加速传统BP算法的梯度下降速度，从而提高运算速度，通过自调节保证学习过程中每一时刻具有较大的sigmoid函数值，从而可以避免局部极小．数值仿真结果表明基于该自适应神经网络的结构损伤检测方法具有强的鲁棒性，而且与传统的BP神经网络相比，不仅提高了计算速度，并且具有很高的精度．最后，实例的应用也证明了该方法的有效性．     

自适应卡尔曼滤波在惯性测量组合误差补偿中的应用

惯性元件误差是捷联惯导系统的主要误差源，必须在导航过程中加以补偿。根据机动目标跟踪理论和惯性测量组合动态模型，分别建立状态方程和观测方程，利用机动频率自适应的算法进行卡尔曼滤波，以此达到惯性测量组合动态误差和随机误差补偿的目的。仿真结果说明该方法可行有效，优于传统的误差补偿算法，能较好地提高系统导航精度。     

基于MMSE先验信噪比估计的语音增强

针对传统最小均方误差（MMSE）沿爵增强方法无法准确处理快速语音的变化问题，提出了一种堆于MMSE估计先验信噪比的语音增强方法，利用该笔法估计每帧语音俯号的每一个频率分量上的平滑系数αn,k。进行平均反馈估计，从而得到更加接近于真实情况的先验信噪比，进而使用MMSE方法进行语音的增强，提高了对于快速变化语肯的增强效果，实验结果农明，本方法比传统MMSE语青增强方法在增强性能上提高了近2dB。     

一类自适应广义交替方向乘子法

广义交替方向乘子法是求解凸优化问题的有效算法.当实际问题中子问题难以求解时,可以采用在子问题中添加邻近项的方法处理,邻近矩阵正定时,算法收敛,然而这也会使迭代步长较小.最新研究表明,邻近矩阵可以有一定的不正定性.本文在基于不定邻近项的广义交替方向乘子法框架下,提出一种自适应的广义交替方向乘子法,动态地选择邻近矩阵,增大迭代步长.在一些较弱的假设下,证明了算法的全局收敛性.我们进行一些初等数值实验,验证了算法的有效性.     

A self-adaptive stochastic resonance system design and study in chaotic interference

The us of stochastic resonance （SR） can effectively achieve the detection of weak signal in white noise and colored noise. However, SR in chaotic interference is seldom involved. In view of the requirements for the detection of weak signal in the actual project and the relationship between the signal, chaotic interference, and nonlinear system in the bistable system, a self-adaptive SR system based on genetic algorithm is designed in this paper. It regards the output signal-to-noise ratio （SNR） as a fitness function and the system parameters are jointly encoded to gain optimal bistable system parameters, then the input signal is processed in the SR system with the optimal system parameters. Experimental results show that the system can keep the best state of SR under the condition of low input SNR, which ensures the effective detection and process of weak signal in low input SNR.