局部线性嵌入算法在图像处理中的应用

为了避免光照、姿态、附属物等因素对同一个图像矩阵的差异和识别的影响,采用了局部线性嵌入(LLE)算法,首先对图像库里的图像进行光照预处理,再进用LLE运算得到低维图像特征,并通过仿真实验获得了既能使图像识别避免光照等因素的影响,又能使图像识别率有了大幅度的提高,从而得到利用局部线性嵌入算法对图像进行处理的结论。     

基于超声-回弹法混凝土强度测定研究

介绍了超声-回弹法测定混凝土强度的原理,利用非线性最小二乘法求得了大庆地区混凝度强度测定的超声-回弹法公式.     

多方案非线性函数盈亏平衡分析的扩展模型研究

本文主要分析传统盈亏分析模型的应用情况与扩展方向,从经济意义和现实意义提出多方案(2≤n≤∞)非线性函数盈亏平衡分析扩展模型的研究必要性。通过采用流程图和模型数量求解过程,引入模型库来解决最优方案的最优值求解问题,最后提出多方案非线性盈亏平衡分析的其他可能扩展情况。     

基于批处理和核函数的非线性盲源分离算法

针对基于核函数的非线性盲源分离算法性能对核函数及其参数选择依赖性强这一问题 ,提出采用批处理方法代替聚类和核主成分分析方法来构造低维近似子空间的正交基,以 改进基于核函数的非线性盲源分离算法对核函数及其参数变化的稳健性,并对这种改进的非 线性盲源分离算法进行了完整的分析。通过仿真实验,对分离信号与源信号求相似度,可以 看到提出的基于批处理的非线性盲源分离算法能够取得更稳健、准确的分离效果。     

非线性节点探测系统的FPGA设计与实现

根据非线性半导 体PN结再辐射特性,设计了一种基于二次谐波接收的非线性节点探测系统。系统仿真 与实验表明：该系统装置使信号检测具有足够大的增益,并能较好地完成非线性节 点的探测。     

Nonlinear integration of London and Amsterdam stock markets in the 1700s

The financial markets in London and Amsterdam were some of the first to develop. Using threshold autoregressive models, we use data on two commonly traded stocks in these cities to show that the joint behaviour of the prices is consistent with the theory of arbitrage in the presence of transportation costs. The results suggest that prices converged more quickly as the price difference between the two markets increased. We also show that the threshold estimates are consistent between assets and across time. These results provide some of the earliest evidence of nonlinear mean reversion in asset prices in geographically separate financial markets.     

Nonlinear income effects in random utility models: revisiting the accuracy of the representative consumer approximation

This article investigates the implications of nonlinear income effects in Random Utility Models (RUM) for measuring general equilibrium welfare impacts. A popular approach in applied welfare analysis is to approximate the expected compensating variation (cv) for an amenity change as the cv of a representative consumer whose indirect utility is given by the expected maximum utility. However, this approach can be misleading in the case of nonmarginal changes as it implies that changes in income do not affect the consumer's choice. In this case the true expected cv can be obtained via simulation. Empirical applications to recreational demand find that the bias from the representative approach is small. This article re-evaluates the accuracy of the representative consumer approximation in the context of measuring the general equilibrium welfare impacts of large environmental changes. Our findings suggest that, though the representative consumer approximation could lead to biased point estimates of the expected cv, this bias is overwhelmed by the size of the confidence intervals that result from the empirical estimation of household preferences.     

Cost‐effective strategies to mitigate multiple pollutants in an agricultural catchment in North Central Victoria,Australia

Strategies to reduce phosphorus and sediment yields are identified for two Australian catchments using a nonlinear optimisation model. This provides novel insight into the cost‐effective management of dual pollutants of water courses in Australia. A strong degree of complementarity between the two pollutants is highlighted, given the adsorption of phosphorus to sediment that augments the value of gully and streambank management for mitigation. However, the relationship between the two pollutants is asymmetric. A 30 per cent reduction in phosphorus yield achieves a 75 per cent reduction in sediment yield in one catchment, while a 30 per cent reduction in sediment yield achieves only a 12 per cent reduction in phosphorus yield. Sediment abatement costs are low given the efficiency of gully and streambank management. A 30 per cent phosphorus reduction lowers profit by 3–7 per cent, while a 30 per cent sediment reduction lowers profit by around 1 per cent. Land‐use optimisation requires spatial heterogeneity in land‐use and gully/streambank management responses. Overall, this research demonstrates the need to determine whether one pollutant is more important than another, while recognising the potential that mitigation practices possess for the reduction of multiple emissions during their evaluation.     

OPTIMAL LOT SOLUTION TO CARDINALITY CONSTRAINED MEAN–VARIANCE FORMULATION FOR PORTFOLIO SELECTION

The pioneering work of the mean–variance formulation proposed by Markowitz in the 1950s has provided a scientific foundation for modern portfolio selection. Although the trade practice often confines portfolio selection with certain discrete features, the existing theory and solution methodologies of portfolio selection have been primarily developed for the continuous solution of the portfolio policy that could be far away from the real integer optimum. We consider in this paper an exact solution algorithm in obtaining an optimal lot solution to cardinality constrained mean–variance formulation for portfolio selection under concave transaction costs. Specifically, a convergent Lagrangian and contour-domain cut method is proposed for solving this class of discrete-feature constrained portfolio selection problems by exploiting some prominent features of the mean–variance formulation and the portfolio model under consideration. Computational results are reported using data from the Hong Kong stock market.