膨胀土判别与分类的Bayes判别分析法

基于Bayes判别分析法以建立膨胀土判别与胀缩等级分类的Bayes判别分析模型. 模型选用液限,胀缩总率,塑性指数,天然含水率,自由膨胀率等5个指标作为判别因子;将膨胀土的胀缩性分为极强、强、中、弱4个等级并作为Bayes判别分析的4个正态总体;以训练样本建立Bayes线性判别函数,以该函数计算待判样品的Bayes判别函数值,以最大值对应的总体作为样品的归属;以刀切法对判别准则进行评价. 研究表明,该模型以刀切法计算的误判率为13.3%.     

基于漫反射法的钢构件塑性变形与损伤检测

从结构损伤识别的需要出发研究能够准确反映钢构件承载过程中发生的塑性变形和损伤过程的量化测试方法. 用漫反射法测量了钢构件在变形和损伤过程中表面粗糙度变化的图像,依据记录下来的表面图像进一步处理得到构件表面粗糙度的平均灰度值的变化,据此寻找对构件塑性变形和损伤状态相关并且敏感的参数指标. 研究结果表明,基于漫反射法和图像处理得到的钢构件承载过程中表面粗糙度的平均灰度值的变化可以判断金属试样是否发生了塑性变形,并可定量描述试样在塑性变形后期的损伤程度.     

用于中能重离子反应测量的塑料闪烁探测器阵列

描述了用于中能(10-100MeV/u)重离子核反应前角度(5°≤θ≤20°)测量的36单元phoswich闪烁探测器阵列.每个phoswich探测器单元由1mm厚的快塑料闪烁体NE102A和100mm厚的慢塑料闪烁体NE115组成,用传统的快、慢门控制的QDC积分方法,对Z=1-20范围的碎片得到了好的粒子鉴别,其粒子分辨能力Z/△Z～45.     

Parameter and state estimation algorithm for single-input single-output linear systems using the canonical state space models

This paper presents a new parameter and state estimation algorithm for single-input single-output systems based on canonical state space models from the given input–output data. Difficulties of identification for state space models lie in that there exist unknown noise terms in the formation vector and unknown state variables. By means of the hierarchical identification principle, those noise terms in the information vector are replaced with the estimated residuals and a new least squares algorithm is proposed for parameter estimation and the system states are computed by using the estimated parameters. Finally, an example is provided.     

An efficient and robust numerical algorithm for estimating parameters in Turing systems

We present a new algorithm for estimating parameters in reaction–diffusion systems that display pattern formation via the mechanism of diffusion-driven instability. A Modified Discrete Optimal Control Algorithm (MDOCA) is illustrated with the Schnakenberg and Gierer–Meinhardt reaction–diffusion systems using PDE constrained optimization techniques. The MDOCA algorithm is a modification of a standard variable step gradient algorithm that yields a huge saving in computational cost. The results of numerical experiments demonstrate that the algorithm accurately estimated key parameters associated with stationary target functions generated from the models themselves. Furthermore, the robustness of the algorithm was verified by performing experiments with target functions perturbed with various levels of additive noise. The MDOCA algorithm could have important applications in the mathematical modeling of realistic Turing systems when experimental data are available.