应急资源配送中Dijkstra改进算法的研究

路径规划问题是应急资源配送中的核心问题,最短路径算法在路径规划过程中起着决定性的作用,在众多路径规划算法中最经典且最具代表性的就是Dijkstra算法。以传统的Dijkstra算法分析为基础,从存储结构和算法过程两个方面进行一定程度的改进,目的是在节点数和边数较多的情况下,提高网络模型的处理效率。以真实道路交通数据为基础进行相关实验,结果证明,改进后的Dijkstra算法可以有效减少节点的计算量,提高算法的运行效率。     

含位形权重的磁流变液孤立链流变剪应力模型

为降低采用标准孤立链理论模型计算高体积分数磁流变液流变剪应力时的计算误差,提出一种通过距离权重系数评价磁链间相互作用的流变模型.编写Monte Carlo仿真程序计算颗粒位置与磁矩方向矢量、同时输出与系统颗粒间距有关的距离权重系数.按标准和距离权重这两种链结构模型计算磁流变液剪切应力数值,并和4种磁流变液(30.58%~44.79%)的流变仪剪应力测试数据进行比较,结果显示,权重模型在磁场全范围内具有更高的计算精度,并且和流变试验实际结果基本符合,证实了磁链微观结构间的相互作用力是影响磁流变液流变特性的主要因素之一.     

高温合金轴向超声振动钻削减摩机理

针对高温合金钻削困难、钻削过程中摩擦力大等问题,采用超声振动技术加工.首先,分析了超声振动减摩机理,构建了平均摩擦力与振动参数之间的关系,并得出了平均摩擦力与振幅之间的变化趋势.建立轴向超声振动钻削系统,利用该系统对高温合金进行钻削加工.结果表明:振动振幅对孔壁表面质量的影响符合超声振动钻削的减摩特性.利用优化后的振幅超声振动钻削,与普通钻削相比,切屑形态更加规整,孔壁表面粗糙度值更小,表面质量更好.     

A Proof of the Duality of the DINA Model and the DINO Model

The Deterministic Input Noisy Output “AND” gate (DINA) model and the Deterministic Input Noisy Output “OR” gate (DINO) model are two popular cognitive diagnosis models (CDMs) for educational assessment. They represent different views on how the mastery of cognitive skills and the probability of a correct item response are related. Recently, however, Liu, Xu, and Ying demonstrated that the DINO model and the DINA model share a “dual” relation. This means that one model can be expressed in terms of the other, and which of the two models is fitted to a given data set is essentially irrelevant because the results are identical. In this article, a proof of the duality of the DINA model and the DINO model is presented that is tailored to the form and parameterization of general CDMs that have become the new theoretical standard in cognitively diagnostic modeling.     

早春类短命植物生物量研究（Ⅳ）--荷青花生物量及其模型构建

基于荷青花在药用、观赏以及森林生态系统养分循环等方面存在的价值,利用全株收获法采样,经分选、烘干、称重和数据统计后,获得了荷青花生物量及分配特征,并以其易测生长指标为自变量构建了总生物量的一元和多元线性回归方程.结果表明:荷青花群丛在营养生长停止期,各生长指标及生物量分配在个体间有较大差异,但平均丛高和根冠比变化较小;地下部分所支持的丛幅冠、丛株数和花果数等存在一定的供给限制,且这种限制与地下生物量间存在着显著或极显著的正相关关系.虽然荷青花生物量随各生长指标的变化存在不同程度的差异,但根冠比的变化差异均不显著,这在一定程度上印证了荷青花存在权衡有限光合资源分配的能力.荷青花生物量模型以多元线性回归的拟合结果较好,但考虑自变量过多带来的系统误差,生物量预估模型可选用以丛株数和花果数为自变量的拟合方程.     

Generalized Multivariate Gumbel Distributions——Dependence,Aging Properties and Applications

This paper introduces a generalized multivariate Gumbel (GMG) distribution using a survival copula. Various dependence properties of the GMG distribution and some analytical properties of the generators of the GMG distribution are studied. Furthermore, the authors also investigate the dependence behavior of the residual lifetime vector of the GMG distribution. As an illustration, the GMG distribution is applied to fit a real data set.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

A two-dimensional improvement for Farr-Gao algorithm

Farr-Gao algorithm is a state-of-the-art algorithm for reduced Gröbner bases of vanishing ideals of finite points, which has been implemented in Maple as a build-in command. This paper presents a two-dimensional improvement for it that employs a preprocessing strategy for computing reduced Gröbner bases associated with tower subsets of given point sets. Experimental results show that the preprocessed Farr-Gao algorithm is more efficient than the classical one.     

The complexity and on-line algorithm for automated storage and retrieval system with stacker cranes on one rail

This paper considers an on-line scheduling and routing problem concerning the automated storage and retrieval system from tobacco industry. In this problem, stacker cranes run on one common rail between two racks. Multiple input/output-points are located at the bottom of the racks. The stacker cranes transport bins between the input/output-points and cells on the racks to complete requests generated over time. Each request should be accomplished within its response time. The objective is to minimize the time by which all the generated requests are completed. Under a given physical layout, the authors study the complexity of the problem and design on-line algorithms for both one-stacker-crane model and two-stacker-crane model. The algorithms are validated by instances and numerical simulations.