Physico‐Mechanical Properties and the TVOC Emission Factor of Gypsum Particleboards Manufactured with Pinus Massoniana and Eucalyptus Sp.

The effect of wood species on the TVOC emission factor and the physico‐mechanical properties of GPBs is investigated. Of the two wood species, the water absorption was higher for the GPBs made using Eucalyptus sp. than for those using Pinus massoniana. The Eucalyptus sp. GPBs pressed at room temperature, 40 and 60 °C all demonstrated higher moisture absorption than commercial GPBs. The TVOC emission factor decreased with increasing press temperature, especially for Eucalyptus sp. but remained under ‘excellent’ grade as defined by the KACA. From these results, GPB with higher content of wood particles should be considered for the replacement of wood‐based panels such as particleboard and medium density fiberboard (MDF).

     

微细通道尺寸对甲醇水蒸汽重整反应性能影响

针对微细通道反应器内甲醇水蒸汽重整制氢反应,建立了二维稳态多组分传输反应模型.分析了通道几何尺寸的变化对产物的组成以及通道内部温度分布的影响.结果表明,通道长高比的增加能增强通道壁面与流体的换热性能,提高甲醇转化率和产物中氢含量,但同时也会造成产物中CO含量的增加,影响到质子交换膜燃料电池的正常工作.     

A non-parametric method to determine basic probability assignment for classification problems

As an important tool for knowledge representation and decision-making under uncertainty, Dempster-Shafer evidence theory (D-S theory) has been used in many fields. The application of D-S theory is critically dependent on the availability of the basic probability assignment (BPA). The determination of BPA is still an open issue. A non-parametric method to obtain BPA is proposed in this paper. This method can handle multi-attribute datasets in classification problems. Each attribute value of the dataset sample is treated as a stochastic quantity. Its non-parametric probability density function (PDF) is calculated using the training data, which can be regarded as the probability model for the corresponding attribute. The BPA function is then constructed based on the relationship between the test sample and the probability models. The missing attribute values in datasets are treated as ignorance in the framework of the evidence theory. This method does not have the assumption of any particular distribution. As a result, it can be flexibly used in many engineering applications. The obtained BPA can avoid high conflict between evidence, which is desired in data fusion. Several benchmark classification problems are used to demonstrate the proposed method and to compare against existing methods. The constructed classifier based on the proposed method compares well to the state-of-the-art algorithms.     

Video background modeling: recent approaches,issues and our proposed techniques

Effective and efficient background subtraction is important to a number of computer vision tasks. We introduce several new techniques to address key challenges for background modeling using a Gaussian mixture model (GMM) for moving objects detection in a video acquired by a static camera. The novel features of our proposed model are that it automatically learns dynamics of a scene and adapts its parameters accordingly, suppresses ghosts in the foreground mask using a SURF features matching algorithm, and introduces a new spatio-temporal filter to further refine the foreground detection results. Detection of abrupt illumination changes in the scene is dealt with by a model shifting-based scheme to reuse already learned models and spatio-temporal history of foreground blobs is used to detect and handle paused objects. The proposed model is rigorously tested and compared with several previous models and has shown significant performance improvements.     

注塑模微机CAD系统

本文介绍了一个微机上运行的注塑模ＣＡＤ系统，此系统主要解决了四个难点，浇注系统设计，冷却系统分析，型腔，型芯的曲面造型设计，装配图和零件工作图的设计绘制，经厂家实际运用证明，该系统使用方便，结果正确，能提高设计效率，降低生产成本。     

MMCKDE:基于数据流的m-混合聚类核概率密度估计

数据流挖掘应用对时间、空间有着较高的要求,因而传统的密度估计方法,如核密度估计法、压缩集密度估计法等并不适用于数据流密度估计.提出一种新颖的面向在线数据流的m-混合聚类核密度估计(m-mixed clustering kernel density estimation,MMCKDE)方法,该方法通过创建MMCKDE节点,用固定个数的混合聚类核获得聚类信息,以代替其他密度估计方法中的所有核.针对数据量不断增加的情况,通过计算Kullback Leibler(KL)距离进行核合并,可进一步以更紧凑的形式表示概率密度估计信息.较之于其他一些方法只能估计整段数据流的密度,MMCKDE方法最终获得的模型不仅适用于整段数据流,还适用于任意时间段上的密度估计.MMCKDE算法同SOMKE算法在不同基准数据集及真实数据集上进行密度估计精度和运行时间的比较.实验结果表明,MMCKDE算法具有更好的性能.     

基于RNN-RBM语言模型的语音识别研究

近年来深度学习兴起,其在语言模型领域有着不错的成效,如受限玻尔兹曼机(restricted Boltzmann machine,RBM)语言模型等.不同于N-gram语言模型,这些根植于神经网络的语言模型可以将词序列映射到连续空间来评估下一词出现的概率,以解决数据稀疏的问题.此外,也有学者使用递归神经网络来建构语言模型,期望由递归的方式充分利用所有上文信息来预测下一词,进而有效处理长距离语言约束.根据递归受限玻尔兹曼机神经网络(recurrent neural network-restricted Boltzmann machine,RNN-RBM)的基础来捕捉长距离信息;另外,也探讨了根据语言中语句的特性来动态地调整语言模型.实验结果显示,使用RNN-RBM语言模型对于大词汇连续语音识别的效能有相当程度的提升.     

基于梯度功能的陶瓷水煤浆喷嘴的设计

在分析水煤浆喷嘴工作特性和磨损机理的基础上,提出了梯度功能陶瓷水煤浆喷嘴的设计模型,’并对单向梯度功能陶瓷喷嘴的残余应力进行了有限元分析。结果显示,梯度功能陶瓷喷嘴内部的残余压应力能缓解喷嘴工作时的热应力,提高陶瓷喷嘴的抗热冲蚀磨损能力。     

ADO.NET在网站建设中的应用

ASP．NET是微软公司的最新一种网页编程代码,它已逐渐成为网站建设者首选的网页编程语言。ASP．NET中连接数据库和操作数据库的就是ADO．NET,ADO．NET是与数据源交互的．NET技术。本文将通过分析ADO．NET,研究其在网站建设中的应用。     

浅谈防范工程项目不平衡报价

在目前建筑工程招投标采购中,投标单位在总价不变的前提下。常采取不平衡报价来获取更多效益。通过完善招标文件,限制不平衡报价等方法,能有效地防范工程项目不平衡报价。