关键词： Logistics   Biomass supply   Bioenergy  

摘要： Many linear programming models have been developed to study the logistics and determine the best setup for bioenergy chains. Most use network structures built from nodes with one or more depots and arcs connecting these depots. Each depot is a source of a certain biomass type. Nodes can also represent a biomass storage point or a production facility (e.g., power plant) where the biomass is used. Arcs represent transport routes between depots. To combine GIS spatial studies with linear programming models, it is necessary to design a network from a digital map. In this research a mathematical calculation method is developed to select the actual points on the map for the locations of a bioenergy plant that will then be considered as biomass destinations in a network model. The base data for this model is city's locations and the bioenergy they required, given in GIS maps (shape files), although also it can be the points where the biomass is produced. The limits of the studied area should be defined in advance, for example, a country, a province or a region. Criteria selection for plant location is two: all the energy produced by the plant should be used;the cost to transport the energy produced should be minimal. With these criteria, the cities are grouped in sets, which should be supplied by a power energy plant. Each plant supplying a given subset of cities will be located in the center of gravity specified by the coordinates and the energy required by each city in the subset. The algorithm provides the locations of points where plants that transform biomass into bioenergy for a group of cities are placed. The points to locate plants are then taken as destination nodes in the network when the logistics models are implemented. In the next step, the network is analyzed by linear programming techniques to supply the optimal location for the power plants or factories depending on the available biomass sources. In this paper a practical example applied to Spanish rural re

关键词： 车牌定位   数学形态学   结构元素   边缘检测  

摘要： 在汽车牌照识别系统中,车牌定位是整个识别模块实现的前提,目前车牌定位的方法多种多样,各有所长,但存在着计算量大或定位准确率不高等问题。边缘检测是常用的车牌定位方法,边缘检测的质量决定了车牌图像的最终定位结果。一般人们习惯于用基于梯度和基于模板的算子提取边缘,但这类算子都不能很好地滤除噪声,因而给噪声图像边缘检测带来了困难。根据数学形态学原理与方法,提出一种扩展数学形态学车牌图像边缘检测算子,并结合水平和垂直投影进行车牌定位。实验结果表明,该算法不仅能成功提取车牌图像边缘,而且能很好地滤除噪声,从而实现准确车牌定位。

关键词： 数学教学   算法   多样化   主体  

摘要： 算法多样化是新课标倡导的重要数学教学理念,它可以促进全体学生主动参与,体现学生的主体地位,发挥每个学生的潜能。在数学教学中教师要引导学生参与计算原理和方法的探索过程,让学生善于发现问题、思考问题、探索规律。

关键词： 数学高考   2010年   试题评析   算法   概率   计算机科学   必修教材   新课标  

摘要： 算法不仅是数学及其应用的组成部分，也是计算机科学的重要基础．算法初步作为新课标新增于必修教材的唯一全新并单独成章的内容，势必作为高考的一个新考点，也倍受命题者的青睐．

关键词： 虹膜识别   数学形态学   虹膜定位  

摘要： 基于虹膜识别的身份特征认证以其极高的可靠性而倍受人们的关注。在虹膜识别算法中,虹膜定位是极为关键和耗时的部分,运用数学形态学的方法实现对虹膜的快速定位。实验结果表明,本算法的定位速度快、准确率较高。

关键词： 电压质量   静态无功优化   动态无功优化   耦合性  

摘要： 较为全面的研究了国内外学术界对配电网无功动态优化调度问题的研究状况;概括了动态无功优化与静态无功优化的具体区别;通过对动态无功优化模型求解算法的分析,总结了该项目研究的四种典型算法,即动态规划法、非线性混合整数优化法、其他数学计算方法和人工智能算法;通过综合比对,发现第三种计算方法数学模型清晰简单,有利于对配电网动态无功优化算法的进一步研究。

关键词： 2010年   程序框图   数学高考   试题评析   算法   数学教材   新课标   知识点  

摘要： 算法与程序框图是新课标高中数学教材的新增内容，在施行新课标高考省市的试卷中，算法与程序框图成了必考知识点，2010年也不例外．

关键词： 社会生活   案例分析   算法   数学学习   教学   现代信息技术   基础知识   数学课程  

摘要： 随着现代信息技术的飞速发展，算法的基础知识、方法、思想也逐渐融人社会生活的许多方面，于是“算法初步”作为数学课程新增内容之一，被编入《教学3》（必修）教材中．

关键词： 聚类集成   数学形态学   结构元素  

摘要： 提出了基于数学形态学的聚类集成算法CEOMM。它利用不同的结构元素的探针作用,对不同的结构元素探测出来的簇核心图进行集成,在集成所得到的簇核心基础上聚类。实验结果表明,算法CEOMM对有复杂类形状的数据集进行聚类时,效果比传统聚类算法更好,且能确定聚类数。而且由于采用了不同的结构元素进行探测,对于由不同形状的类构成的数据集其聚类效果很理想。

关键词： mathematical programming   mathematical model   cutting plane algorithm   earnings management   false financial statements  

摘要： The primary goal of this study was to propose an algorithm using mathematical programming to detect earnings management practices. In order to evaluate the ability of this proposed algorithm, the traditional statistical models are used as a benchmark vis-a-vis their time series counterparts. As emerging techniques in the area of mathematical programming yield better results, application of suitable models is expected to result in highly performed forecasts. The motivation behind this paper is to develop an algorithm which will succeed in detecting companies that appeal to financial manipulation. The methodology is based on cutting plane formulation using mathematical programming. A sample of 126 Turkish manufacturing firms described over 10 financial ratios and indexes are used for detecting factors associated with false financial statements. The results indicate that the proposed three-phase cutting plane algorithm outperforms the traditional statistical techniques which are widely used for false financial statement detections. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of false financial statements and highlight the importance of financial ratios/indexes such as Days' Sales in Receivables Index (DSRI), Gross Margin Index (GMI), Working Capital Accruals to Total Assets (TATA) and Days to Inventory Index (DINV). Copyright (C) 2009 John Wiley & Sons, Ltd.