三维空间孔道的精确校核

二维空间孔道的传统校核方法是把三维问题化成二维问题,再简化或近似处理,这样的方法在高精度场合往往不能满足设计要求;还有的是对孔表面网格化、布尔运算等,算法复杂,难于实现。本文从计算几何理论上探讨了三维空间孔的精确校核方法,简单、易行、可靠。     

Gr（C）纤维增强镁基复合材料的界面结构

采用真空—压力浸渗工艺制备Ｇｒ（Ｃ）／Ｍｇ复合材料，Ｇｒ／Ｍｇ材料界面上无化学反应迹象，有析出物γ相（Ｍｇ１７Ａｌ１２），该析出物形状、大小与制备工艺条件，主要是纤维预制件温度有关。Ｃ／Ｍｇ复合材料界面Ａｌ含量较基体高出２倍多，文中分析了可能的原因     

基质组成对矾土－尖晶石浇注料性能的影响

采用磷酸盐微粉复合结合剂，以矾土熟料为骨料，通过强化基质，研制了矾土－尖晶石浇注料。基质中除一部分预合成尖晶石外，再加入适量的高纯镁砂细粉及Ａｌ２Ｏ３细粉可以改善材料的性能，加入少量的ＳｉＯ２微粉可提高施工性能，促进烧结，原料的纯度对材料性能的影响较大。     

TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting tweet messages for each user. Recently, collaborative filtering techniques for recommendations based on friend relationships in social networks have been widely investigated. However, since such techniques do not work well when friend relationships are not sufficient, we need to take advantage of as much other information as possible to improve the performance of recommendations.In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic modeling based on latent Dirichlet allocation which recommends top-K users to follow and top-K tweets to read for a user. Our model can capture the realistic process of posting tweet messages by generalizing an LDA model as well as the process of connecting to friends by utilizing matrix factorization. We next develop an inference algorithm based on the variational EM algorithm for learning model parameters. Based on the estimated model parameters, we also present effective personalized recommendation algorithms to find the users to follow as well as the interesting tweet messages to read. The performance study with real-life data sets confirms the effectiveness of the proposed model and the accuracy of our personalized recommendations.     

心内膜三维几何模型交互式网格切割算法研究

针对心内膜三维标测系统中心内膜模型网格切割模块,提出一种新的交互式网格切割算法。系统在载入心内膜三维几何模型后初始化切割平板模型,由用户交互移动切割平板和旋转切割平板的法线。借助矩阵变换,针对心内膜三维几何模型实现能反映用户切割意图和保持网格细节特征的网格切割,而且切割平板可以消除锯齿效应。实验对比结果证明,该算法对于心内膜三维几何模型可以实现令用户满意的实时无锯齿交互式网格切割。     

稀疏卷积非负矩阵分解的语音增强算法

非平稳噪声和低信噪比条件下提高增强语音质量一直以来都是语音增强研究的难题。近年来,卷积非负矩阵分解在语音增强算法中成功应用,本文进一步考虑语音信号在时频域的稀疏性,提出了稀疏卷积非负矩阵分解(Sparse Convolutive Nonnegative Matrix Factorization, SCNMF)的语音增强算法。该算法包括训练和增强两个阶段。训练阶段通过SCNMF算法分别对纯净语音和噪声的频谱进行训练,得到纯净语音和噪声字典,并将其作为增强阶段的先验信息。增强阶段首先通过SCNMF算法对带噪语音的频谱进行分解,然后利用纯净语音和噪声联合字典对语音编码矩阵进行估计,重构增强语音。本文通过实验仿真分析了稀疏因子对增强语音质量的影响。实验结果表明,在非平稳噪声和低信噪比条件下,本文算法增强效果均优于多带谱减、非负矩阵分解、卷积非负矩阵分解等传统的算法。     

模糊聚类在股票投资中的应用

聚类分析即将一组事物根据其性质上亲疏远近的程度进行分类,把性质相近的个体归为一类,使得同一类中的个体具有高度的同质性,不同类之间的个体具有高度的异质性。模糊聚类分析是现今模糊理论应用最广泛和最富成果的技术之一。阐述模糊聚类的理论,以部分石油股票为例,抽取影响石油股票收益因素的数据,利用最大最小法建立相似矩阵,用传递闭包法作出聚类分析,并进行总结。     

一种应用PCA的人脸图像集识别方法

提出一种适用于同类别的图像子集的类别判断方法。同类别的图像子集经过PCA主特征提取后,选择较大的p个特征值对应的线性无关的特征向量,组成特征矩阵,则同类别的图像子集可以转化成一个特征矩阵,图像子集与特征矩阵一一对应,进而整个图像库能够用矩阵集合来表示。定义一种矩阵间的距离及最小二乘距离,通过计算待测图像子集对应特征矩阵与图像库中不同类别对应特征矩阵之间的距离或最小二乘距离,判断待测试图像子集所属类别。     

Adaptive Linear Dynamic Matrix Control applied to an integrating process

Dynamic Matrix Control (DMC) has proven to be a powerful tool for optimal regulation of chemical processes under constrained conditions. It is based on a linear convolution model derived from step-response measurements. A model predictive control algorithm optimises closed-loop performance for a nominal operating point. However, as the process moves away from this point, control usually becomes sub-optimal due to process non-linearity. As seen in this work, the DMC algorithm can be made adaptive, to establish a new local model, by recursive estimation of the local step response parameters from normal plant variations. However, when used for control of plants containing integrating process units, steady-state offsets occur for sustained changes. Thus, a novel Adaptive Linear Dynamic Matrix Control (ALDMC) algorithm has been developed and used to control a 2-input/2-output system with an integrating behaviour. Comparisons of model control and plant control with and without these features demonstrated the importance of integral compensation for integrating processes, and model adaptation in the case of plant/model mismatch. Some cross-compensation of integration by the adaptive feature was also noted. An holistic technique is demonstrated which simultaneously recognises residual integration disturbances and matrix parameter variations, whereas previous techniques which recognise only one of these will fail in the presence of the other.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.