两种聚类算法的比较与实现

基于统计的系统聚类分析是一种重要的数据挖掘算法。研究了一种多重系统聚类模型及其算法实现,把变量聚类和样本聚类相结合,并使用了两种方法赋值样本数据阵,使聚类结果更加直观。     

M0内核在通用文本型HMI中的应用

采用NUC100LE3AN为主控芯片,设计了通用文本型HMI(Human Machine Interface),实现了与大部分品牌PLC的通信连接。介绍了该装置的构架原理、功能及其软硬件设计思想。详细讨论了FLASH存储器空间分配、APROM区与LDROM相互跳转原理、以及应用程序区任务模块的组织形式等。最后通过与信捷XC1-32型PLC连接,进行了功能测试,结果表明设计满足了功能需求。     

Collision and intersection detection of two ruled surfaces using bracket method

Collision and intersection detection of surfaces is an important problem in computer graphics and robotic engineering. A key idea of our paper is to use the bracket method to derive the necessary and sufficient conditions for the collision of two ruled surfaces. Then the numerical intersection curve can be characterized. The cases for two bounded ruled surfaces are also discussed.     

Real time 3D simulation for nose surgery and automatic individual prosthesis design

This paper presents an intuitive nose surgery planning and simulation system, using 3D laser scan image and lateral X-ray image, to provide high quality prediction of the postoperative appearance, and design the patient specific prosthesis model automatically. After initial registration, the internal surface of soft tissue at the nose region was generated by the statistical data for soft tissue thickness adapted by the individual thickness information from the X-ray image. Then, the sketch contour of the 3D scan data on the lateral X-ray image was modified manually or adjusted automatically according to some aesthetic statistical data, to drive the simulation in real time by the state-of-the-art Laplacian surface deformation method. When satisfied with the 3D postoperative appearance, the deformation was mapped to the internal surface of soft tissue, and the change before and after simulation was utilized to generate the patient specific prosthesis model automatically. The surgeons who used the system confirmed that this planning system is attractive and has potential for daily clinical practice.     

化学因子分析法测定液体推进剂A

基于测定推进剂Ａ现有分析方法准确度低,神经网络法对仪器要求高等不足,提出了的一种新的分析方法。该方法利用已知酸的电位滴定数据建立多元线性回归数学模型,利用化学因子分解该模型,对未知样进行浓度测定。实际测定结果表明本方法的最大分析误差不超过０．１７％,标准偏差不超过１．９％,满足样品分析要求。     

健壮性学习算法

使用统计方法可以对汉英机器翻译的词性标注和句法语义分析阶段产生的歧义进行消歧,在估计过程中往往使用最大可能方法,但是并不是在所有的情况下取最大值都是正确的。为了从所有候选结果中取到正确的结果,本文使用健壮性学习算法。使用这个算法,当正确的候选结果评分不是最高时,仍能通过健壮性算法来调整正确结果的评分,使之最大,并且降低不正确候选的评分。而且,由于训练集与测试集存在不同,使训练集中的错误率最小不能保证测试集中的错误率也最小。因此当考虑训练语料库和测试语料库存在统计变化时,应该使用健壮性学习算法。     

动态地构建和维护基于小波的直方图*

利用小波变换的数学理论和方法,对基本的数据分布进行小波分解,在此基础上采用一种新的方法来动态地构建和维护基于小波的直方图,并给出相应的算法。实验证明基于这种方法的小波直方图能提供好的选择性估算和近似技术。     

溪洛渡水电站拱坝施工期坝基变形监测分析

为了解溪洛渡水电站拱坝坝基在施工期、蓄水初期及运行期不同阶段的变形情况和工作状态,确保大坝安全,同时为给工程建设及运行期管理提供科学依据,对坝基垂直位移和水平位移进行了监测,并对监测资料进行了初步分析.监测结果表明拱坝坝基沉降变形比较均匀,变化规律正常.     

Generalized Adjusted Rand Indices for cluster ensembles

In this paper, Adjusted Rand Index (ARI) is generalized to two new measures based on matrix comparison: (i) Adjusted Rand Index between a similarity matrix and a cluster partition (ARImp), to evaluate the consistency of a set of clustering solutions with their corresponding consensus matrix in a cluster ensemble, and (ii) Adjusted Rand Index between similarity matrices (ARImm), to evaluate the consistency between two similarity matrices. Desirable properties of ARI are preserved in the two new measures, and new properties are discussed. These properties include: (i) detection of uncorrelatedness; (ii) computation of ARImp/ARImm in a distributed environment; and (iii) characterization of the degree of uncertainty of a consensus matrix. All of these properties are investigated from both the perspectives of theoretical analysis and experimental validation. We have also performed a number of experiments to show the usefulness and effectiveness of the two proposed measures in practical applications.     

Identification of Breast Cancer Prognosis Markers via Integrative Analysis

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.