基于回归模型的全卷积网络人群计数算法

针对当前采用密度图回归方法估计人群数量时人数被高估的问题,提出一种基于计数回归和密度图估计的全卷积网络框架,采用计数回归与密度图回归相结合的方式对人群密度图进行估计。为训练模型参数和有效避免过拟合现象的出现,设计一种更深更轻且参数数量很少的完全卷积网络(full convolutional network,FCN)作为人群密度图估计器。实验结果表明,提出算法对密度分布不均和尺度不一的人群图像都有很好的适用性和准确性,算法性能优于现有的人群计数算法。

基于Katz分布的计数数据自回归模型以及其在预测呼吸系统患病人数中的应用

泊松自回归模型假设到达过程为期望与方差相等的泊松分布,但事实上真正的数据生成过程中的到达过程的方差既可以高于期望也可以低于期望.本文提出了基于Katz到达过程(Katz arrivals)的计数数据自回归模型(INAR-Katz:integer valued autoregressive process with Katz arrivals).并采用蒙特卡罗模拟方法(Monte Carlo simulations)比较了INAR-Katz模型在矩估计以及极大似然估计下的估计准确程度.最后采用INAR-Katz模型对患呼吸系统疾病的急诊就诊人数进行建模,结果显示INAR-Katz模型优于普通泊松模型、PAR模型,具有很好的应用前景.

一种面向航空图像的自适应目标计数模型

目标计数旨在获取给定图像中包含的车辆、建筑物、人物等特定种类目标的数量,对城市规划、应急响应、国家安全等具有重要意义。当前目标计数任务主要依赖于低空摄像头所拍摄的图像,存在目标易被遮挡和计数空间范围小等突出问题。高清航空遥感图像的广泛使用使大范围目标计数成为可能。然而,面向航空图像的目标计数任务存在目标尺度差异大、分布密集、方向不确定等挑战,现有基于低空图像的目标检测计数模型和回归计数模型均无法适用于航空图像的目标计数。针对该问题,提出了一种面向航空图像的自适应目标计数模型。首先,利用几何自适应高斯卷积方法解决目标尺度变化问题;然后,利用基于结构相似性的图片损失判断方法解决目标密集区域计数稳定性较差的问题。实验结果表明,所提模型相较于基准模型取得了更好的目标计数精度。

Nonlinear Regression Fuzzy Analysis of Real Variable and Fuzzy Parameter

As far as the nonlinear regression method is concerned, the condition when both independent and dependent variable take the Fuzzy value, while the parameter, θ∈ΘR m the real value, have been discussed in . But for most of actual conditions, the independent variable generally takes the real value, while both parameter and dependent variable take the Fuzzy value. This paper propounded a method for the latter and its relevant Fuzzy regreession model. In addition the Fuzzy observation, matrix distribution and the rational estimation of modeling parameter have also been discussed. Furthermore, the Max min estimation of modeling parameter and its corresponding calculating sequence have also been offered to and the calculating example shows the method is feasible.

Parametric modeling of carbon nanotubes and estimating nonlocal constant using simulated vibration signals-ARMA and ANN based approach

Nonlocal continuum mechanics is a popular growing theory for investigating the dynamic behavior of Carbon nanotubes(CNTs).Estimating the nonlocal constant is a crucial step in mathematical modeling of CNTs vibration behavior based on this theory.Accordingly,in this study a vibration-based nonlocal parameter estimation technique,which can be competitive because of its lower instrumentation and data analysis costs,is proposed.To this end,the nonlocal models of the CNT by using the linear and nonlinear theories are established.Then,time response of the CNT to impulsive force is derived by solving the governing equations numerically.By using these time responses the parametric model of the CNT is constructed via the autoregressive moving average(ARMA)method.The appropriate ARMA parameters,which are chosen by an introduced feature reduction technique,are considered features to identify the value of the nonlocal constant.In this regard,a multi-layer perceptron(MLP)network has been trained to construct the complex relation between the ARMA parameters and the nonlocal constant.After training the MLP,based on the assumed linear and nonlinear models,the ability of the proposed method is evaluated and it is shown that the nonlocal parameter can be estimated with high accuracy in the presence/absence of nonlinearity.

Diagnostics in generalized nonlinear models based on maximum L_q-likelihood estimation

In order to detect whether the data conforms to the given model, it is necessary to diagnose the data in the statistical way. The diagnostic problem in generalized nonlinear models based on the maximum Lq-likelihood estimation is considered. Three diagnostic statistics are used to detect whether the outliers exist in the data set. Simulation results show that when the sample size is small, the values of diagnostic statistics based on the maximum Lq-likelihood estimation are greater than the values based on the maximum likelihood estimation. As the sample size increases, the difference between the values of the diagnostic statistics based on two estimation methods diminishes gradually. It means that the outliers can be distinguished easier through the maximum Lq-likelihood method than those through the maximum likelihood estimation method.

Using Statistical Learning Algorithms in Regional Landslide Susceptibility Zonation with Limited Landslide Field Data

Regional Landslide Susceptibility Zonation(LSZ) is always challenged by the available amount of field data, especially in southwestern China where large mountainous areas and limited field information coincide. Statistical learning algorithms are believed to be superior to traditional statistical algorithms for their data adaptability. The aim of the paper is to evaluate how statistical learning algorithms perform on regional LSZ with limited field data. The focus is on three statistical learning algorithms, Logistic Regression(LR), Artificial Neural Networks(ANN) and Support Vector Machine(SVM). Hanzhong city, a landslide prone area in southwestern China is taken as a study case. Nine environmental factors are selected as inputs. The accuracies of the resulting LSZ maps are evaluated through landslide density analysis(LDA), receiver operating characteristic(ROC) curves and Kappa index statistics. The dependence of the algorithm on the size of field samples is examined by varying the sizes of the training set. The SVM has proven to be the most accurate and the most stable algorithm at small training set sizes and on all known landslide sizes. The accuracy of SVM shows a steadilyincreasing trend and reaches a high level at a small size of the training set, while accuracies of LR and ANN algorithms show distinct fluctuations. The geomorphological interpretations confirm the strength of SVM on all landslide sizes. Our results show that the strengths of SVM in generalization capability and model robustness make it an appropriate and efficient tool for regional LSZ with limited landslide field samples.

Quantile Trends in Temperature Extremes in China

A number of recent studies have examined trends in extreme temperature indices using a linear regression model based on ordinary least-squares. In this study, quantile regression was, for the first time, applied to examine the trends not only in the mean but also in all parts of the distribution of several extreme temperature indices in China for the period 1960–2008. For China as a whole, the slopes in almost all the quantiles of the distribution showed a notable increase in the numbers of warm days and warm nights, and a significant decrease in the number of cool nights. These changes became much faster as the quantile increased. However, although the number of cool days exhibited a significant decrease in the mean trend estimated by classical linear regression, there was no obvious trend in the upper and lower quantiles. This finding suggests that examining the trends in different parts of the distribution of the time-series is of great importance. The spatial distribution of the trend in the 90 th quantile indicated that there was a pronounced increase in the numbers of warm days and warm nights, and a decrease in the number of cool nights for most of China, but especially in the northern and western parts of China, while there was no significant change for the number of cool days at almost all the stations.

Optimal control of cobalt crust seabedmining parameters based on simulated annealing genetic algorithm

Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.

Coefficient of Partial Correlation and Its Calculation

This thesis offers the general concept of coefficient of partial correlation.Starting with regres-sion analysis,the paper,by using samples,infers the general formula of expressing coefficient of partial correlation by way of simple correlation coefficient.

On the L_p Convergence Rate of Kernel Estimates for the Nonparametric Regression Function

Let (X,Y) be an R^d×R^1 valued random vector (X_1,Y_1),…, (X_n,Y_n) be a random sample drawn from (X,Y), and let E|Y|<∞. The regression function m(x)=E(Y|X=x) for x∈R^d is estimated by where, and h_n is a positive number depending upon n only, nad K is a given nonnegative function on R^d. In the paper, we study the L_p convergence rate of kernel estimate m_n(x) of m(x) in suitable condition, and improve and extend the results of Wei Lansheng.

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Comparative Study of Response Surface Designs with Errors-in-Variables Model

This paper investigates the scaled prediction variances in the errors-in-variables model and compares the performance with those in classic model of response surface designs for three factors.The ordinary least squares estimators of regression coefficients are derived from a second-order response surface model with errors in variables.Three performance criteria are proposed.The first is the difference between the empirical mean of maximum value of scaled prediction variance with errors and the maximum value of scaled prediction variance without errors.The second is the mean squared deviation from the mean of simulated maximum scaled prediction variance with errors.The last performance measure is the mean squared scaled prediction variance change with and without errors.In the simulations,1 000 random samples were performed following three factors with 20 experimental runs for central composite designs and 15 for Box-Behnken design.The independent variables are coded variables in these designs.Comparative results show that for the low level errors in variables,central composite face-centered design is optimal;otherwise,Box-Behnken design has a relatively better performance.

Effect of sumatriptan on gastric emptying:A crossover study using the BreathID system

AIM:To determine the effect of oral sumatriptan on gastric emptying using a continuous 13 C breath test(BreathID system).METHODS:Ten healthy male volunteers participated in this randomized,2-way crossover study.The subjects fasted overnight and were randomly assigned to receive a test meal(200 kcal/200 mL) 30 min after pre-medication with sumatriptan 50 mg(sumatriptan condition),or the test meal alone(control condition).Gastric emptying was monitored for 4 h after administration of the test meal by the 13 C-acetic acid breath test performed continually using the BreathID system.Then,using Oridion Research Software(β version),the time taken for emptying of 50% of the labeled meal(T 1/2) similar to the scintigraphy lag time for 10% emptying of the labeled meal(T lag),the gastric emptying coefficient(GEC),and the regression-estimated constants(β and κ) were calculated.The statistical significance of any differences in the parameters were analyzed using Wilcoxon's signed-rank test.RESULTS:In the sumatriptan condition,significant differences compared with the control condition were found in T 1/2 [median 131.84 min(range,103.13-168.70) vs 120.27 min(89.61-138.25);P = 0.0016],T lag [median 80.085 min(59.23-125.89) vs 61.11 min(39.86-87.05);P = 0.0125],and β [median 2.3374(1.6407-3.8209) vs 2.0847(1.4755-2.9269);P = 0.0284].There were no significant differences in the GEC or κ between the 2 conditions.CONCLUSION:This study showed that oral sumatriptan significantly delayed gastric emptying of a liquid meal.

PSYCHOLOGICAL EVALUATION OF COLOR DESIGN

In this paper, a method of predicting psychological values of color design was performed by using random color patterns. The results were analyzed in terms of the Fourier transform of the color patterns, and it was found that the psychological values of the random color patterns depended not only on the zero frequency component but also on the dynamic components of the Fourier transform of the patterns. The application of the estimation method was discussed.

Admissibilty for Nonhomogeneous Linear Estimates on Multivariate Random Regression Coefficients and Parameters

In this paper,we consider the admissibility for nonhomogeneous linear estimates on regression coefficients and parameters in multivariate random effect linear model and give eight definitions of different forms for admissibility. We not only prove that they can be divided into three identical subclasses,but also gain three kinds of necessary and sufficient conditions.

New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis

There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.

Corporate Tax Differences under the New Accounting Standards

The tax differences is the enterprise according to the provisions of accounting standards to calculate the total profit and tax calculated according to the enterprise income tax＇s tax base taxable income differences. Enterprise accounting standards and tax for the same subject for different purposes, leading to both the principle is different, as well as for the same matter measurement and confirmation of different, resulting in tax differences. In this paper, from the aspect of theory to the study of tax differences, from tax differences that the tax law and accounting purposes different proceed with, on the basis of theoretical analysis, design a regression model from empirical research on tax differences. According to the 2009,2010 and201 lthree annual sample data regression results, discussed in the new＂ accounting standards for business enterprises＂ of corporate tax difference change tendency, and according to the tax law and accounting standards caused by the different effects of tax differences for the main project correlation analysis, so as to verify the theoretical expectations.

Pointwise Convergence of a Nonparametric Estimator of Regression in a Measurable Space Used in Contingent Valuation Method

The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual＇s Willingness To Pay according to geographical location. Within this framework, an estimator （of type Nadaraya-Watson） is proposed for the regression of the variable related to geolocation. The specific characteristics of the location variable lead us to a more general regression model than the traditional models. Results are established for convergence of our estimator.

地方自有财力对交通设施类PPP项目实施的影响

本文利用PPP项目库中1341个交通设施类PPP项目信息和中国县域、城市年鉴的截面数据,以地方的财政、经济、金融、交通等状况作为自变量,采用多种计量回归方法进行比较分析,探究了地方政府自有财力对一地交通设施建设采用PPP模式的影响。结果表明,我国交通设施PPP项目实施与地方自有财力具有明显关联,但这种关系却是负向的:自有财力薄弱的地方政府,很少采用PPP模式建设交通基础设施。进一步的回归模型表明,自有财力薄弱的政府更倾向于在PPP项目中采用政府付费的回报模式。对这一结论,本文给出了几点基于中国国情和现状的解释,并提出了相关政策建议。