基于少量典型样点土壤属性空间分布推测模型中的土壤属性参数敏感性分析

针对基于少量典型样点土壤属性空间分布推测模型中的土壤属性参数敏感性问题,以坡位渐变信息结合典型土壤样点的加权平均模型为例,利用东北地形平缓小流域的土壤表层有机质含量样点集,使用阶乘设计、箱线图分析、扰动分析法和本文新设计的MR指数评价该模型的参数敏感性。结果表明,该模型中土壤属性参数敏感性较大,其大小与典型样点空间分布有关。敏感性主要由应用该模型时采用的坡位分类体系的不确定性引起。该文的分析方法可用于对基于少量典型样点的土壤属性空间分布推测模型进行参数敏感性综合分析。

徐州地区疟疾发病率的区间划分、预测及下降阶段推测模型

运用灰色模型对徐州地区1956—1986年的疟疾发病率进行了区间划分,并对1987年及1988年的疟疾发病率进行灰色GM(1,1)预测。预测精度分别为94.83％及82.44％。为对间日疟发病率下降规律进行探索,运用灰色Verhulst模型对疟疾发病率进行拟合,在此基础上提出了间日疟发病率下降的推测模型。

基于神经网络的公路工程造价推测模型

本文介绍了工程造价推测模型的发展,叙述了神经网络系统的原理,基于此建立公路工程造价推测模型。实例说明,此模型具有快速、准确的优点。

二次成本函数下的推测变差模型及其复杂性分析

以寡头市场中两个企业进行产量竞争为基础,构造基于二次成本函数下推测变差的动力学模型并分析推测变差平衡点的稳定性.通过数值仿真的方法研究企业调整速度变化所产生的诸如分岔、混沌以及奇异吸引子等复杂动力学行为.得出随着调整速度的增加,系统将会从稳定状态转变成分岔状态甚至混沌,并且初值条件极小的变化都将导致产量产生剧烈的波动.此时企业需要将调整速度控制在一定范围内来调整自身产量才能最大化自身利润.一旦系统处于混沌状态,市场将会变得混乱.因此通过状态反馈和参数调整控制的方法对混沌进行控制使其形成新的稳定结构.

基于链路重构-解构的端到端网络链路时延推测研究

基于网络时延推测的2个假设、时延推测模型和路径时延数据采集方法,提出了一种基于链路重构—解构的端到端网络链路时延推测方法,应用伪似然估计将原整体问题分解为若干独立子问题分别求解,利用链路重构—解构确定可求解的推测单元,控制平均采样精度和减少推测单元链路数,从而显著降低计算复杂度。通过基于模型的计算和基于NS2的仿真实验研究,验证了推测方法的准确性和有效性。

Prophet推测多线程系统设计与实现

推测多线程技术通过推测执行的方式开发应用程序的线程级并行性,以提高程序执行性能。该技术一般通过执行模型来检测运行时可能的线程推测错误情况,并采取合适的机制恢复程序正确运行。描述的Prophet是一种基于硬件实现的推测多线程执行模型。重点描述了Prophet执行模型针对执行模型设计的关键问题的解决方案,包括Prophet的线程状态控制和多版本的Cache系统,Prophet的多版本Cache系统提供了推测数据缓存功能,并使用基于总线监听的Cache协议实现了数据依赖违规检测。还给出了使用Olden基准程序对Prophet执行模型进行功能和性能测试的结果,并分析说明了Prophet系统可以有效地开发应用程序的线程级并行性。

对影响我国优秀女子铁饼运动员成绩主要身体素质及评定方法的研究

通过对我国19名优秀女子铁饼运动员12项一般身体素质、专项身体素质的研究,运用相关理论和逐步回归分析方法,建立我国优秀女子铁饼运动员身体素质,专项成绩发展的检测模型,并制定出各项素质评定标准及实际应用的方法,为我国优秀女子铁饼运动员的训练提供参考。

改进的基于属性不变量生成和数学归纳法的时序逻辑优化算法

为了在时序逻辑综合中使电路面积和关键路径延迟同时得到快速优化,提出一种改进的基于假设后验证的时序优化算法.在位并行随机模拟提取候选属性不变量之前,利用寄存器共享来降低初始候选不变量数目,以减少SAT程序的频繁调用;然后利用推测化简模型和改进的数学归纳法将基本条件和归纳步骤合并处理,有效地降低了电路规模和关键路径延迟,同时提高了算法运行速度.实验数据表明,文中算法使寄存器和节点规模平均下降41%和48%,关键路径延迟减小30%;与同类方法相比,该算法运行时间平均下降17%.

云南省近期疟疾流行因素灰色预测初探

本文应用灰色系统理论与方法探讨疟原虫的预测。对云南省疟疾流行地区,1986—1990年的1902158名发热病人的两种疟原虫检出率构成的离散数列,建立灰色系统GM(1,1)预测模型。预测精度分别为98.45%—98.46%和92.85%—83.03%,并对1991年和1992年作灰色GM(1,1)的推测模型,其中1991年理论值和实际值基本吻合,1992年尚待进一步验证。

Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R^2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.

中国车险市场的卡特尔问题研究

车险费率改革促进了我国保险业的快速发展。随着改革的逐步深入,车险市场的竞争问题日益突出。车险费率的过度竞争导致保险公司间纷纷签订自律协议,结成车险市场卡特尔同盟。以理论经济学为分析基础,以产业组织理论中的推测变差模型为分析工具,对车险市场卡特尔的形成原因、稳定性、绩效等三个方面进行了分析。最后,针对我国车险市场现实状况、结合即将实施的《反垄断法》相关条例,给出了规范性的政策建议。

A Probabilistic Rating Prediction and Explanation Inference Model for Recommender Systems

Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming to predict a user's rating for those items which were not rated yet by the user. However, with the increasing number of items and users, thedata is sparse.It is difficult to detectlatent closely relation among the items or users for predicting the user behaviors. In this paper,we enhance the rating prediction approach leading to substantial improvement of prediction accuracy by categorizing according to the genres of movies. Then the probabilities that users are interested in the genres are computed to integrate the prediction of each genre cluster. A novel probabilistic approach based on the sentiment analysis of the user reviews is also proposed to give intuitional explanations of why an item is recommended.To test the novel recommendation approach, a new corpus of user reviews on movies obtained from the Internet Movies Database(IMDB) has been generated. Experimental results show that the proposed framework is effective and achieves a better prediction performance.

Prediction of Planing Craft Motion Based on Grey System Theory

In order to minimize the harm caused by the instability of a planing craft, a motion prediction model is essential. This paper analyzed the feasibility of using an MGM（1,N） model in grey system theory to predict planing craft motion and carried out the numerical simulation experiment. According to the characteristics of planing craft motion, a recurrence formula was proposed of the parameter matrix of an MGMfl,N） model. Using this formula, data can be updated in real-time without increasing computational complexity significantly. The results of numerical simulation show that using an MGM（1,N） model to predict planing motion is feasible and useful for prediction. So the method proposed in this study can reflect the planing craft motion mechanism successfully, and has rational and effective functions of forecasting and analyzing trends.

Time-series gas prediction model using LS-SVR within a Bayesian framework

The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.

模糊坡位信息在精细土壤属性空间推测中的应用

坡位的空间渐变特征影响着小流域及坡面尺度上的土壤、水文、地貌等现象和过程,因此对精细尺度下的地理建模(如土壤空间信息推理)有重要作用。虽然目前已有多种模糊坡位信息定量提取方法,但所得到的模糊坡位信息还缺乏实际应用。本文以精细尺度下的土壤属性空间分布推测为例,对此展开探索。应用模型假设:(1)在小流域内,地形因素主导着土壤属性空间分布的变化;(2)典型坡位上对应分布着典型的土壤属性值,土壤属性与坡位之间存在协同变化关系。据此建立以模糊坡位信息对各类典型坡位上土壤样点属性值的加权平均模型,推测土壤属性的空间分布。模型应用于黑龙江省嫩江流域一个地形平缓的小区(面积约60 km2),通过一个以坡位典型位置作为原型的模糊坡位定量方法提取5类坡位(山脊、坡肩、背坡、坡脚、沟谷)的空间渐变信息,对土壤表层有机质含量的空间分布进行推测。推测结果通过研究区70个土壤采样点进行评价,以推测结果与评价样点集之间的相关系数、平均绝对误差、均方根误差作为定量评价指标,与使用常用地形属性的多元线性回归模型推测结果进行对比。评价结果表明,仅使用极少建模点的加权平均模型的推测结果优于多元线性回归模型的推测结果。

A Conjecture on Veronese Generating Submanifolds

In this paper,the higher dimensional conjecture on Veronese generating subman-ifolds proposed by Prof. SUN Zhen-zu is generalized to Pseudo-Euclidean Space L1(m+1), it is proved that in the higher dimentional Lorentz Space L1(m+1), the generating submanifolds of an n dimentional submanifold of Pseudo-Riemannian unit sphere S1m is an n+1 dimentional minimal submanifold of S1(m+1) in L1(m+2) and is of 1-type in L1(m+2).

Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization

To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.

Empirical likelihood-based inferences for the area under the ROC curve with covariates

In the receiver operating characteristic (ROC) analysis,the area under the ROC curve (AUC) is a popular summary index of discriminatory accuracy of a diagnostic test.Incorporating covariates into ROC analysis can improve the diagnostic accuracy of the test.Regression model for the AUC is a tool to evaluate the effects of the covariates on the diagnostic accuracy.In this paper,empirical likelihood (EL) method is proposed for the AUC regression model.For the regression parameter vector,it can be shown that the asymptotic distribution of its EL ratio statistic is a weighted sum of independent chi-square distributions.Confidence regions are constructed for the parameter vector based on the newly developed empirical likelihood theorem,as well as for the covariate-specific AUC.Simulation studies were conducted to compare the relative performance of the proposed EL-based methods with the existing method in AUC regression.Finally,the proposed methods are illustrated with a real data set.

ANALYSIS OF HEIGHT CURVES BY STOCHASTIC DIFFERENTIAL EQUATIONS

Height diameter models are classically analyzed by fixed or mixed linear and non-linear regression models. In order to possess the among-plot variability, we propose the method- ology of stochastic differential equations that is derived from the standard deterministic ordinary differential equation by adding the process variability to the growth dynamic. Age-diameter varying height model was deduced using a two-dimensional stochastic Gompertz shape process. Another focus of the article is the investigation of normal cop- ula procedure, when the tree diameter and height are governed by univariate stochastic Gompertz shape processes. The advantage of the stochastic differential equation method- ology is that it analyzes a residual variability, corresponding to measurements error, and an individual variability to represent heterogeneity between subjects more complex than commonly used fixed effect models. An analysis of 900 Scots pine （Pinus sylvestris） trees provided the data for this study.

Post-J test inference in non-nested linear regression models

This paper considers the post-J test inference in non-nested linear regression models. Post-J test inference means that the inference problem is considered by taking the first stage J test into account. We first propose a post-J test estimator and derive its asymptotic distribution. We then consider the test problem of the unknown parameters, and a Wald statistic based on the post-J test estimator is proposed. A simulation study shows that the proposed Wald statistic works perfectly as well as the two-stage test from the view of the empirical size and power in large-sample cases, and when the sample size is small, it is even better. As a result,the new Wald statistic can be used directly to test the hypotheses on the unknown parameters in non-nested linear regression models.