Noise of low-coercivity single-layer perpendicular recording media

The noise of single layer CoCr media is investigated. A model is developed by treating the medium as a collection of columns. It appears that there is a strong correlation between magnetization direction of the columns. Neighbouring columns tend to have the same magnetization polarity, indicating exchange interaction. It is concluded that the media consist of domains. The domain width increases with the medium thickness. These domains can also result in a poor signal response at short wavelength.     

Increased extracellular calcium concentrations in the isolated rat heart: effects on mitochondrial contact site formation

In our previous study, mitochondrial creatine kinase (Mi-CK) activity is localised cytochemically in heart tissue. The activity was found to be exclusively localised in mitochondrial contact sites and the surface density of Mi-CK was increased upon myocardial stimulation (Biermans et al., 1989). As calcium is involved as an intracellular messenger of stimulation, we compared hearts noradrenaline-stimulated in vivo with isolated hearts perfused with buffers containing different calcium concentrations on the surface density Mi-CK.     

提高设计完善度的要素分析与过程控制

本文阐述了设计完善度的概念和内涵,通过对影响设计质量的要素进行分析,提出课题并进行过程控制.     

应用EMPCA算法对TE过程中随机及连续缺失数据进行补值的研究

基于传统的主元分析法,利用最大期望主元分析法EMPCA(Expectation-Maximization Principal Component Analysis)对TE(Tennessee Eastman)过程的随机缺失和连续缺失数据进行补值,并使用样本矩阵的平均相对误差及负载矩阵的误差平方和作为指标进行评价。结果表明,使用EMPCA算法补值能有效对随机缺失和连续缺失数据进行补值,效果明显优于均值补值,补值计算得到的负载矩阵误差也相对较小。     

基于机载激光雷达数据的森林结构参数反演

机载激光雷达(Light Detection And Ranging,LiDAR)技术对植被空间结构和地形的探测能力较强,在植被参数定量测量和反演方面具有显著优势。首先利用野外调查并结合高分辨率Geoeye-1影像数据,对黑河上游天涝池流域植被类型进行分类,提取研究区森林分布,然后结合0.5m×0.5m机载激光雷达(LiDAR)数据对森林结构参数(树高、冠幅、胸径和叶面积指数)进行反演,最后利用实际观测数据对反演结果进行验证。结果表明:机载激光雷达数据能够精确地反演森林结构参数,树高、冠幅、胸径和叶面积指数的实测值与估测值决定系数分别为0.98、0.84、0.57和0.73。本研究获得流域森林覆盖区域高精度树冠高度和叶面积指数空间分布图,同时分析了冠层高度和叶面积指数随高度的变化。本研究的结果为该流域分布式生态水文模型提供了重要的输入参数。     

Adaptive fuzzy clustering by fast search and find of density peaks

Clustering by fast search and find of density peaks (CFSFDP) is proposed to cluster the data by finding of density peaks. CFSFDP is based on two assumptions that: a cluster center is a high dense data point as compared to its surrounding neighbors, and it lies at a large distance from other cluster centers. Based on these assumptions, CFSFDP supports a heuristic approach, known as decision graph to manually select cluster centers. Manual selection of cluster centers is a big limitation of CFSFDP in intelligent data analysis. In this paper, we proposed a fuzzy-CFSFDP method for adaptively selecting the cluster centers, effectively. It uses the fuzzy rules, based on aforementioned assumption for the selection of cluster centers. We performed a number of experiments on nine synthetic clustering datasets and compared the resulting clusters with the state-of-the-art methods. Clustering results and the comparisons of synthetic data validate the robustness and effectiveness of proposed fuzzy-CFSFDP method.     

Interesting pattern mining in multi-relational data

Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for single-table databases, and are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-relational data. We propose a new syntax for multi-relational patterns as complete connected subsets of database entities. We show how this pattern syntax is generally applicable to multi-relational data, while it reduces to well-known tiles “ Geerts et al. (Proceedings of Discovery Science, pp 278–289, 2004)” when the data is a simple binary or attribute-value table. We propose RMiner, a simple yet practically efficient divide and conquer algorithm to mine such patterns which is an instantiation of an algorithmic framework for efficiently enumerating all fixed points of a suitable closure operator “Boley et al. (Theor Comput Sci 411(3):691–700, 2010)”. We show how the interestingness of patterns of the proposed syntax can conveniently be quantified using a general framework for quantifying subjective interestingness of patterns “De Bie (Data Min Knowl Discov 23(3):407–446, 2011b)”. Finally, we illustrate the usefulness and the general applicability of our approach by discussing results on real-world and synthetic databases.     

Self-Universum support vector machine

In this paper, for an improved twin support vector machine (TWSVM), we give it a theoretical explanation based on the concept of Universum and then name it Self-Universum support vector machine (SUSVM). For the binary classification problem, SUSVM takes the positive class and negative class as Universum separately to construct two classification problems with Universum; therefore, two nonparallel hyperplanes are derived. SUSVM has several improved advantages compared with TWSVMs. Furthermore, we improve SUSVM by formulating it as a pair of linear programming problems instead of quadratic programming problems (QPPs), which leads to the better generalization performance and less computational time. The effectiveness of the enhanced method is demonstrated by experimental results on several benchmark datasets.