Flammability ranking of foliage species by factor analysis of physical and chemical pyric properties

In this paper, factor analysis is introduced to evaluate the flammability of 55 foliage species that may be used in China for construction of the fuel break network of forest strips with lower flammability. Six pyric parameters, i.e. air dry moisture content, absolute dry moisture content, ignition point, ash content, caloric value and extractive content, are measured and used as variables for factor analysis. The covariance analysis shows that four principal factors can be extracted to reflect the flammability in different physical and chemical senses. In terms of the contributions of the four factors to the variances and the physical significance of the relevant parameters, the four factors are, respectively, termed as ‘flaming factor,’ ‘air dry factor,’ ‘ash factor’ and ‘absolute dry factor.’ The stability of the factor analysis method is examined by a different number of samples considered, and the variation degrees of the orderings indicate that the method has high reliability to measure the total flammability of foliage species. The results of the flammability evaluation are verified by comparison with the recommended tree species in the Chinese technology standard. Copyright © 2008 John Wiley & Sons, Ltd.     

The Growth of a Single Cell/Dendrite in a Directional Solidification Process

Following the original model for a single cell/dendrite growth by Hunt,[5] systematic numerical simulations are carried out with focus on cell shape evolution and tip radius selection in a directional solidification process conducted in a capillary tube. Computations indicate that the anisotropy of solid/liquid interfacial energy and the diameter of a capillary tube have significant influences on the selection of a tip shape. The anisotropy in dilute SCN-salol alloys has been determined through the equilibrium droplet shape method and its value is 1.09 pct. The calculated cell/dendrite shapes with this value of anisotropy agree with experimental observations where a single cell/dendrite of SCN-salol alloys grows in a capillary tube.     

Three-node, 40 Gbit/s OTDM network experiment using electro-opticswitches

The authors demonstrate, for the first time, a three node optically time division multiplexed network with a maximum capacity of 40 Gbit/s. Electro-optical switches were used for both `drop and insert' and demultiplexing, and electronic clock recovery provided synchronisation throughout the network     

浅谈液化石油气火灾的特点及消防措施

以某煤气公司液化石油气火灾事故为例,详细分析了液化石油气火灾的特点。并针对防火、安全管理、应急预案、扑救措施等问题提出了可借鉴的措施。     

用面向对象编程实现问题求解自动化

本文从面向对象方法基本原理和面向对象编程的技术特征出发，阐明所建应用软件系统中的对象，类及相应类层次结构和类组合结构，使系统具有很强的表现真空世界复杂系统结构的能力，系统通过消息传递在程序执行中实现对操作的调用机制，使之在面向用户问题选择和执行求解策略方面有很强的适应性，这一点对于强调人－机交互和解题协作的智能决策支持系统设计至关重要，文章给出了用Ｃ＋＋实现的模型对象系统及消息传递机制。     

高职计算机文化分专业分层次教学的实践与思考

指出了高职计算机文化课程教学面临的诸多问题,这些问题的存在严重制约了课程教学效果的提高.为此,在深入企业调研和对教学对象调查的基础上,结合各专业类别特点及其对计算机基本操作技能的不同需求,选取不同专业的班级作为研究对象,采用“横向分专业,纵向分层次”的教学方法进行了实践教学.实践结果表明,分专业分层次教学方法能有效解决高职“计算机文化”课程教学中存在的主要问题.     

基于D-FNN的混沌铁磁谐振系统非线性补偿控制方法

针对电网混沌铁磁谐振系统产生的混沌现象,提出基于动态模糊神经网络的混沌铁磁谐振系统非线性补偿控制方法。该方法采用动态模糊神经网络来逼近系统的非线性部分,消除了过电压的混沌现象,将系统稳定到目标位置,实现了对系统的非线性补偿控制。Matlab仿真结果表明,基于动态模糊神经网络的非线性补偿控制方法控制结果正确,响应快速。     

图像处理中模糊算法问题的分析

图像处理是计算机常用技术之一,模糊算法的运用有助于提高图像处理的效果。传统图像处理存在诸多弊端,影响了用户获取信息的便捷性。本文分析了图像处理的基本操作内容,并阐述了模糊算法在图像处理流程中的运用,以进一步提升原始图像处理的效果。     

Adsorption characteristics of hexadecyl ammonium with different numbers of carbon chains in montmorillonite and the structure of the prepared composites

Journal of Porous Materials - The dynamics and thermodynamics of adsorption of hexadecyl ammonium with different numbers of carbon chains in montmorillonite (Mt) with different layer charge density...     

Multi-Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification

基于多尺度空洞卷积神经网络的高光谱图像分类

郑姗姗¹,刘文¹,单锐¹,赵静一²,江国乾³,张智⁴

（1. 燕山大学理学院,河北 秦皇岛 066004;2. 燕山大学机械工程学院,河北 秦皇岛 066004;3. 燕山大学电气工程学院,河北 秦皇岛 066004;4. 北京航天研究所,北京 100094）

创新点说明：

1）将图像分割方法——空洞卷积用于卷积神经网络进行高光谱图像分类,提取更加广泛、抽象的图像特征。

2）构建基于多尺度空洞卷积神经网络的高光谱图像分类方法。搭建多尺度聚合结构,在每个通道中使用快捷连接和空洞卷积结构,有效提取图像特征,避免信息丢失。

研究目的：

针对图像信息丢失问题,得到高精度的高光谱图像分类方法。

研究方法：

在Indian Pines和Pavia University数据集上,与4个已有的高光谱图像分类方法进行对比实验,比较OA, AA和Kappa值。

研究结果：

多尺度空洞卷积神经网络在Indian Pines和Pavia University数据集上OA值分别达到了99.58%,99.92%。AA值分别达到了99.57%,99.90%。Kappa分别达到了99.52%,99.89%。

结论：

1）在卷积神经网络中引入空洞卷积,可以有效避免图像信息丢失。

2）多尺度空洞卷积神经网络能提取更佳的判别性特征,实现高分类性能。

关键词：多尺度聚合;空洞卷积;高光谱图像分类;快捷连接