Inference and learning in hybrid probabilistic network

This paper proposed a novel hybrid probabilistic network, which is a good tradeoff between the model complexity and learnability in practice. It relaxes the conditional independence assumptions of Naive Bayes while still permitting efficient inference and learning. Experimental studies on a set of natural domains prove its clear advantages with respect to the generalization ability.     

NUMERICAL SIMULATION OF STRATIFIED FLOW WITH BOTH VERTICAL TEMPERATURE GRADIENT AND VERTICAL SALINITY GRADIENT

Density stratified flow with both the vertical temperature gradient and the verti-cal salinity gradient is simulated numerically, in which turbulent terms are calculated by a sim-plified turbulence stress/flux algebraic model. The feature of stratification and turbulence is de-scribed correctly by the model. The computational results agree well with the experimental data.However, the k-e turbulence model is not so successful.     

Instability due to Viscosity Stratification Downstream of a Centerline Injector

A common injector geometry upstream of a static mixer is the centerline injector. A flow instability can arise due to viscosity differences between the injected core‐flow and the outer co‐flow. This instability can adversely affect the effectiveness of the mixing operation. An experimental investigation of miscible viscosity‐stratified flow in a circular geometry was performed using Laser Induced Fluorescence (LIF) and Particle Image Velocimetry (PIV). The experimental results for the stable region agree with the analytical results. The unstable region exhibits different modes depending on the viscosity ratio, volume flux ratio, and Reynolds number. The modes include wavy core‐flow with fissures and wavy core‐flow with core breakup. The time‐averaged experiment velocity profiles for the unstable core indicate a broadening of the jet at the centerline, which is consistent with the LIF visualization.     

利用射线路径反演层状介质速度的方法

本文根据欧拉方程对于连续层状介质进行了方程推导，得出一个波传播的射线路径公式，利用中 公式导出了ＶＳＰ资料处理中的速度反演公式。     

独立学院大学计算机基础课程教学的几点思考

针对当前独立学院大学计算机基础课程教学中面临的一些问题,结合教学实践,提出了分层教学模式,以期更好地为独立学院培养计算机技能上手快,后劲足的应用型人才:对不同层次的对象采取不同的教学内容和方式;面向专业,将计算机基础教学内容模块化,结合专业划分必修与选修模块,在内容深度和广度上更好地为专业服务;教学内容面向实用,兼顾体系,突出重点,精讲精练.     

无线传感器网络中一次性数字签名算法设计

一次性数字签名算法是构造密码协议的基础算法,可用于认证无线传感器网络(WSN)中传输数据的真实性和完整性。针对WSN的应用需求,给出一种高效的一次性数字签名算法。与现有一次性签名算法相比,该算法生成签名简单快捷,只需执行一次模指数乘法运算;签名长度短,只需传输一个群元素;适用范围广,能根据待签名消息的长度自动适配对应的公钥参数,该特性非常适合WSN中数据传输量较小的应用;同时该算法还能在标准模型下得到形式化的安全证明,其抗伪造攻击安全性可归约为双线性映射群中计算的Diffie-Hellman数学难题。     

求解缓变波导中反问题的一种步进方法

对于声学和电磁学中的大尺度反问题,基于直接方法(例如有限元或者无网格方法)的数值计算往往会导致一个巨大的线性系统,因而在计算速度和存储上并不现实.基于求解Helmholtz方程正问题的Dirichlet-to-Neuman(DtN)算子步进方法,提出了一种用于求解波在大尺度缓变波导中传播的反问题的反向基本解算子步进方法(IFOMM).     

基于“应用”目标下的高职数学课程改革对策研究

在分析高职院校数学课程教学现状及问题的基础上,提出以"应用"为目标,从教学宗旨、教学方法、教学方案、教学科研能力、多元化考核模式等方面,对高职数学教学改革的对策进行探讨。     

Global structure constrained local shape prior estimation for medical image segmentation

Organ shape plays an important role in clinical diagnosis, surgical planning and treatment evaluation. Shape modeling is a critical factor affecting the performance of deformable model based segmentation methods for organ shape extraction. In most existing works, shape modeling is completed in the original shape space, with the presence of outliers. In addition, the specificity of the patient was not taken into account. This paper proposes a novel target-oriented shape prior model to deal with these two problems in a unified framework. The proposed method measures the intrinsic similarity between the target shape and the training shapes on an embedded manifold by manifold learning techniques. With this approach, shapes in the training set can be selected according to their intrinsic similarity to the target image. With more accurate shape guidance, an optimized search is performed by a deformable model to minimize an energy functional for image segmentation, which is efficiently achieved by using dynamic programming. Our method has been validated on 2D prostate localization and 3D prostate segmentation in MRI scans. Compared to other existing methods, our proposed method exhibits better performance in both studies.     

在癌症分类中基于分层抽样的神经网络集成算法

在基因表达谱数据的分析中,针对有效合理地选择特征基因集的问题,本文将分层抽样技术引入特征基因选择,提高特征基因集的分类能力。以神经网络作为分量分类器,神经网络集成进行分类预测。并在结肠癌数据集上进行实验,实验结果表明该方法能有效地降低特征基因集选择的复杂性,提高对于未知样本的分类预测效果。