针对小规模数据集的多模型融合算法研究

目前,对小规模数据集进行预测时,主要使用传统机器学习算法,但传统单一模型预测效果不能达到预期准确率,且无法兼顾多项评价指标。因此,文中以小规模数据集为研究对象,融合决策树、逻辑回归、支持向量机三类模型,提出了一种多模型融合算法,并分析了其在小规模数据集上的应用效果。首先,简述了决策树、逻辑回归和支持向量机的算法原理;其次,使用决策树、逻辑回归和支持向量机作为基学习器并完成单独训练,将各模型输出结果用于下一阶段模型输入,同时使用最大似然估计迭代优化参数,从而完成多模型融合过程;最后,对数据集进行分析和处理,通过实验与单一模型进行指标对比。实验结果表明,多模型融合算法在预测精确率、召回率、准确率等方面有明显提升。     

基于J2EE的物流管理系统的设计与实现

叙述了基于J2EE技术的物流管理系统的设计流程,详细介绍了系统的逻辑和功能结构,以及在系统的设计与实现中所采用的具体原理和方法,并给出了部分核心源代码．     

Effect-Size Indices for Dichotomized Outcomes in Meta-Analysis.

It is very common to find meta-analyses in which some of the studies compare 2 groups on continuous dependent variables and others compare groups on dichotomized variables. Integrating all of them in a meta-analysis requires an effect-size index in the same metric that can be applied to both types of outcomes. In this article, the performance in terms of bias and sampling variance of 7 different effect-size indices for estimating the population standardized mean difference from a 2 × 2 table is examined by Monte Carlo simulation, assuming normal and nonnormal distributions. The results show good performance for 2 indices, one based on the probit transformation and the other based on the logistic distribution. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

定位2运输路线安排问题的两阶段启发式算法

重点研究了集成化物流中一类特殊的定位一运输路线安排问题(LRP)的解决方法．LRP问题包括设施定位和运输路线优化两方面决策，属于NP-hard难题．由于问题的复杂性，提出基于假设前提的LRP模型及其两阶段启发式求解算法．该方法分两步实现：首先，采用基于最小包络聚类分析的启发式方法确定被选择的潜在设施及由每一个选中的设施所要提供服务的客户群；其次，运用带有控制开关的遗传算法求解每一确定客户类中的优化运输路线．提出利用两阶段启发式算法求解LRP问题，此方法实现容易、运算简单，一定程度上避免了遗传算法中的“局部最优现象”．仿真实验证明了该算法求解单目标LRP的有效性和准确性．     

基于序优化的分布式光伏配电网储能系统容量评估

我国分布式光伏发电产业近年来实现快速增长,对所在配电网的运行提出严峻挑战。由于分布式光伏发电具有随机性、波动性与不确实性,需要一定容量的储能系统来提供灵活的调节能力,以实现配电网的安全可靠运行。因此,研究针对分布式光伏配电网储能系统的容量评估具有重要意义。建立考虑分布式光伏并网的配电网运行模拟模型,利用多场景生成技术与序优化求解方法,模拟不同储能系统容量规划下的分布式光伏并网运行情况,从而得到最优配电网储能系统配套容量。结果表明,利用序优化方法可以实现快速高效求解,得到适应于配电网运行需求的储能系统容量配置方案。     

Dynamics of Indigenous Microbial Populations of Butter Head Lettuce Grown in Mulch and on Bare Soil

ABSTRACT:  The native microflora of lettuce cultivated in mulch and on bare soil and its evolution during storage at optimal condition were evaluated. Inner, mid, and outer leaves of the lettuce heads were analyzed separately and the evolution of the microbial populations were fitted to Gompertz and logistic models. The cultivation method (bare soil and mulch) introduced differences in the initial counts, evolution, and tolerance to refrigeration temperatures for some of the microbial populations under study. Most microbial populations from mulch lettuce presented a decline or little growth under refrigerated storage. However, populations from bare soil lettuce presented some growth phase during storage. Lactic acid bacteria from bare soil lettuce presented significant growth after 8 d of storage while LAB from mulch grown lettuce did not. Concurrently with the LAB growth, there was a decline in the coliform counts in bare soil grown lettuce. At the end of storage, the inner and mid leaves of mulch lettuce presented lower counts of psychrotrophic bacteria, LAB, and yeast and molds.     

Supporting ordinal four-state classification decisions using neural networks

Many accounting and finance problems require ordinal multi-state classification decisions, (e.g., control risk, bond rating, financial distress, etc.), yet few decision support systems are available to aid decision makers in such tasks. In this study, we develop a Neural Network based decision support system (NN-DSS) to classify firms in four ordinal states of financial condition namely healthy, dividend reduction, debt default and bankrupt. The classification results of the NN-DSS model are compared with those of a Naïve model, a Multiple Discriminant Analysis (MDA) model, and an Ordinal Logistic Regression (OLGR) model. Four different evaluation criteria are used to compare the models, namely, simple classification accuracy, distance-weighted classification accuracy, expected cost of misclassification (ECM) and ranked probability score. Our study shows that NN-DSS models perform significantly better than the Naïve, MDA, and OLGR models on the ECM criteria, and provide better results than MDA and OLGR on other criteria, although not always significantly better. The effect of the proportion of firms of each state in the training set is also studied. A balanced training set leads to more uniform (less skewed) classification across all four states, whereas an unbalanced training set biases the classification results in favor of the state with the largest number of observations.     

Multivariate statistical log log-facies classification on a shallow marine reservoir

Sedimentary facies are important in reservoir characterization because flow properties are commonly assigned using facies-specific correlations. Multivariate statistical methods provide a powerful vehicle to extract facies responses from different well logs, to predict facies in uncored wells and evaluate uncertainty.Previous study shows a difficulty in bin selection to accurately reproduce the samples' conditional probability distribution. In this paper, a new method uses empirical beta distributions to model the distribution of petrophysical properties conditional to facies. Petrophysical property distributions are assumed conditionally independent, simplifying the use of Bayes rule.Three multivariate statistical methods (beta-Bayesian, multinomial logistic regression, and discriminant analysis) are examined in this paper using log and facies data from a western African sandstone reservoir. Three derived probability logs compare the prediction performance of the statistical methods as well as illustrate influences of log combinations and sample size. Two-way analysis of variance compares prediction accuracy of the models. For a given dataset, there are no significant differences (with 90% confidence) in predictions by the three methods. Additional logs improve prediction accuracy from 30 to above 80%. Final prediction accuracy is 82 to 90% for these three algorithms. Including 20 to 25% of the complete core and facies data in model construction provides accurate predictions; models were validated against the data not used in model construction. The fitted classification models can generate three-dimensional log-derived facies distributions for geologic modeling and reservoir simulation. The three methods are straightforward, efficient, and have quantifiable prediction errors.     

公共物流区空间规划探析——以北京公共物流区空间规划研究为例

基于当前物流业面临良好发展机遇与条件分析,本文以北京为研究对象,学习借鉴国内外物流园区的发展经验,分析北京发展公共物流区的影响因素,提出了以物流基地(一级)、综合物流区(二级)、专业物流区(三级)三个层级为核心的物流园区空间规划,并对公共物流区的规划规模、控制强度等提出了控制建议。     

城市道路堵塞问题浅析与初步对策

从我国多数城市的痼疾交通堵塞问题入手,结合北京和上海城市道路发展实例加以分析,试图找出造成城市交通堵塞问题的根本原因,经过分析得出几个初步解决问题的构想,并对于城市道路发展展开大胆预想,从多角度、多层次为我国城市道路堵塞问题开出药方。