Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

基于支持向量机的作战方案评估

作战方案评估是当今军事领域一个急需解决又具有相对难度的课题。针对目前的作战方案大多是由专家评估这一现实状况,提出了基于支持向量机的方案评估方法。以某次作战为研究背景,客观分析了影响作战方案实施的各因素,建立方案评估指标体系,将支持向量机的回归方法应用于作战方案评估模型的建立中。最后,通过实验验证了评估模型的可行性。     

PSO并行优化LSSVR非线性黑箱模型辨识

针对非线性黑箱系统辨识中存在不确定性、高阶次,采用常规辨识方法建立其精确数学模型十分困难等问题,提出一种基于自适应粒子群算法的最小二乘支持向量机回归(PSO-LSSVR)非线性系统辨识方法.该方法采用2组自适应粒子群算法并行计算模型,分别利用自适应粒子群算法对LSSVR中的参数进行自动选取和矩阵迭代求解,既克服了传统LSSVR参数难以确定的缺点,提高了辨识精度,同时避免了复杂矩阵求逆运算,加快了计算速度.将该方法应用于船舶操纵性模型非线性系统辨识,仿真结果表明,由该方法得到的LSSVR能够有效地对系统进行建模,仿真精度高,结构简单,具有一定的理论推广意义.     

Sparse polynomial division using a heap

In 1974, Johnson showed how to multiply and divide sparse polynomials using a binary heap. This paper introduces a new algorithm that uses a heap to divide with the same complexity as multiplication. It is a fraction-free method that also reduces the number of integer operations for divisions of polynomials with integer coefficients over the rationals. Heap-based algorithms use very little memory and do not generate garbage. They can run in the CPU cache and achieve high performance. We compare our C implementation of sparse polynomial multiplication and division with integer coefficients to the routines of the Magma, Maple, Pari, Singular and Trip computer algebra systems.     

Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method

A hybrid forecasting method is proposed which leverages from statistical and neural network techniques to perform multi-step ahead forecasting. The proposed method is based on the disaggregation of time series components, the prediction of each component individually and the reassembling of the extrapolations to obtain an estimation for the global data. The STL decomposition procedure from the literature [5] is implemented to obtain the seasonal, trend and irregular components of the time series, whilst Generalized Regression Neural Networks (GRNN) [12] are used to perform out-of sample extrapolations of the seasonal and residual components. The univariate Theta model is employed for the estimation of the directional component. The application of the GRNN is based on the dynamic calibration of the training process for each of the seasonal and irregular components individually. The proposed hybrid forecasting method is applied to 60 time series from the NN3 competition and 227 time series from the M1 Competition dataset, to obtain 18 out-of sample predictions. The results from the application demonstrate that the proposed method can outperform standard statistical techniques in the literature. One of the main contributions of the current research lies in the investigation of the strengths and weaknesses of the GRNN in extrapolating structural components of time series.     

基于相对熵的直方图差异与JPEG隐写的定量分析

针对图像隐写分析中常用的直方图特征,给出了一类基于相对熵的直方图差异计算方法,并提出了一种基于相对熵的JPEG隐写的定量分析方法.首先根据两假设检验中的最优检验——似然比检验,分析了相对熵在衡量2个直方图间的距离时的优越性,并给出了2种基于相对熵的直方图差异计算方法.然后,以新的直方图差异特征为基础,训练支持向量回归分析器,以估计隐写对DCT系数的更改比率.针对JSteg和改进的F5隐写的实验结果表明:与其他的直方图差异特征相比,根据所提出的基于相对熵的直方图差异特征所训练的定量隐写分析器具有更高的估计精度和稳定性.     

Hypothesis testing in rank-size rule regression

This note examines testing methods for Paretoness in the framework of rank-size rule regression. Rank-size rule regression describes a relationship found in the analysis of various topics such as city population, words in texts, scale of companies and so on. In terms of city population, it is basically an empirical rule that log?(_S(i))$\log ?(S_{(i)})$ is approximately a linear function of log?(i)$\log ?(i)$ where _S(i)$S_{(i)}$ is the number of population of i  th largest city in a country. This is closely related to the so-called Zipf’s law. It is known that this kind of empirical observation is found when the city population is a random variable following a Pareto distribution. Thus one may be willing to test if city size has a Pareto distribution or not. Rosen and Resnick [K.T. Rosen, M. Resnick, The size distribution of cities: an explanation of the Pareto law and primacy, Journal of Urban Economics 8 (1980), 165–186] and Soo [K.T. Soo, Zipf’s law for cities: a cross country investigation, Regional Science and Urban Economics (35) 2005, 239–263] regress log?(_S(i))$\log ?(S_{(i)})$ on log?(i)$\log ?(i)$ and log?²(i)${\log ?}^2(i)$ and test the null of Paretoness by standard t-test for the latter regressor. It is found that t-statistics take large values and the Paretoness is rejected in many countries. We study the statistical properties of the t-statistic and show that it explodes asymptotically, in fact, by simulation and thus the t-test does not provide a reasonable testing procedure. We propose an alternative test statistic which seems to be asymptotically normally distributed. We also propose a test with the null hypothesis that the city size distribution is Pareto with exponent unity, which is a modification of the F-test.     

Recursive reduced least squares support vector regression

Combining reduced technique with iterative strategy, we propose a recursive reduced least squares support vector regression. The proposed algorithm chooses the data which make more contribution to target function as support vectors, and it considers all the constraints generated by the whole training set. Thus it acquires less support vectors, the number of which can be arbitrarily predefined, to construct the model with the similar generalization performance. In comparison with other methods, our algorithm also gains excellent parsimoniousness. Numerical experiments on benchmark data sets confirm the validity and feasibility of the presented algorithm. In addition, this algorithm can be extended to classification.     

Commentary on Krishnaswamy et al. - Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate

Remote sensing is a powerful tool for characterizing, estimating or modelling species diversity. Differences in environmental properties of different habitats should lead to differences of spectral responses, which can be detected by satellite imagery. Hence, spectral distance may be related to species diversity. Based on previous studies, Krishnaswamy et al. [Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment.] used spectral distance to estimate species diversity. Since a noisy scatterplot of species versus spectral diversity is expected, the commonly used Ordinary Least Square regression may fail to detect trends which occur across other quantiles than the mean.Krishnaswamy et al. [Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment.] proposed a quantile-quantile plot method as an alternative to conventional regression based approaches which are inappropriate for dependent pair-wise dissimilarity or similarity data. By this commentary I demonstrate the utility of a quantile regression technique to complement the Krishnaswamy et al. [Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment.] graphical approach in terms of a predictive model.