室内惯性/视觉组合导航地面图像分割算法

导航技术是机器人实现自主移动的关键技术之一。针对惯性导航创建全局导航地图困难等问题,提出一种新的惯性/视觉组合导航室内全局地图创建方法。规定机器人只能在地面区域中移动,并利用室内俯视图像建立全局地图,提出一种俯视图像地面区域的自动分割算法。首先,利用主元分析算法对图像的局部颜色特征进行降维;其次,利用聚类算法对地面区域进行自动分割;最后,建立了室内俯视图像数据库并对算法的性能进行了验证。由于第四组图像中包含反光区域,算法的分割结果较差,平均正确分辨率为75%。算法在其他各组的平均正确分割率为85%左右。为提高算法的性能,可在应用本算法前利用反光区域检测算法对图像进行预处理。     

Rapid determination of RMSDs corresponding to macromolecular rigid body motions

Finding the root mean sum of squared deviations (RMSDs) between two coordinate vectors that correspond to the rigid body motion of a macromolecule is an important problem in structural bioinformatics, computational chemistry, and molecular modeling. Standard algorithms compute the RMSD with time proportional to the number of atoms in the molecule. Here, we present RigidRMSD, a new algorithm that determines a set of RMSDs corresponding to a set of rigid body motions of a macromolecule in constant time with respect to the number of atoms in the molecule. Our algorithm is particularly useful for rigid body modeling applications, such as rigid body docking, and also for high‐throughput analysis of rigid body modeling and simulation results. We also introduce a constant‐time rotation RMSD as a similarity measure for rigid molecules. A C++ implementation of our algorithm is available at http://nano‐d.inrialpes.fr/software/RigidRMSD . © 2014 Wiley Periodicals, Inc.     

团队中枢节点的效率模型

集团性是社会网络的显著特征.团队作为一个小规模的社会网络,也存在网络结构的非均匀性以及中枢节点.在阐述团队网络的集团性和中枢节点导致网络的两面性等结构特征的基础上,运用小世界模型的局部效率和集聚系数等指标,建立了团队中枢节点的效率模型,并通过网络结构的调整(如加键和断键重连)研究了网络结构对中枢节点的效率的影响.     

Linguistic modelling and information coarsening based on prototype theory and label semantics

The theory of prototypes provides a new semantic interpretation of vague concepts. In particular, the calculus derived from this interpretation results in the same calculus as label semantics proposed by Lawry. In the theory of prototypes, each basic linguistic label L has the form ‘about P’, where P is a set of prototypes of L and the neighborhood size of the underlying concept is described by the word ‘about’ which represents a probability density function δ on [0,+∞). In this paper we propose an approach to vague information coarsening based on the theory of prototypes. Moreover, we propose a framework for linguistic modelling within the theory of prototypes, in which the rules are concise and transparent. We then present a linguistic rule induction method from training data based on information coarsening and data clustering. Finally, we apply this linguistic modelling method to some benchmark time series prediction problems, which show that our linguistic modelling and information coarsening methods are potentially powerful tools for linguistic modelling and uncertain reasoning.     

蛋白质折叠类型的分类建模与识别

蛋白质的氨基酸序列如何决定空间结构是当今生命科学研究中的核心问题之一. 折叠类型反映了蛋白质核心结构的拓扑模式, 折叠识别是蛋白质序列-结构研究的重要内容. 我们以占Astral 1.65序列数据库中α, β和α/β三类蛋白质总量41.8%的36个无法独立建模的折叠类型为研究对象, 选取其中序列一致性小于25%的样本作为训练集, 以均方根偏差(RMSD)为指标分别进行系统聚类, 生成若干折叠子类, 并对各子类建立基于多结构比对算法(MUSTANG)结构比对的概形隐马尔科夫模型(profile-HMM). 将Astral 1.65中序列一致性小于95%的9505个样本作为检验集, 36个折叠类型的平均识别敏感性为90%, 特异性为99%, 马修斯相关系数(MCC)为0.95. 结果表明: 对于成员较多, 无法建立统一模型的折叠类型, 基于RMSD的系统分类建模均可实现较高准确率的识别, 为蛋白质折叠识别拓展了新的方法和思路, 为进一步研究奠定了基础.     

纹党参与白条党参红外光谱的SIMCA聚类鉴别方法研究

以纹党参和白条党参的红外光谱为聚类分析的对象,研究了红外光谱结合SIMCA聚类分析法对纹党参和白条党参进行识别与分类的可行性.选取400 ～2 000 cm~(-1)范围内的光谱,通过基线补偿(Offset)和散射校正(MSC)等预处理后,采用SIMCA聚类分析法建立识别模型.结果表明,所建模型对纹党参和白条党参的识别率分别达92%和96%,拒绝率均为100%.用盲样对所建模型进行了测试,测试结果全部正确.该法可实现对纹党参和白条党参的快速鉴别.     

中成药质量稳定性的原子吸收光谱鉴别分析

对全国各地用原子吸收光谱仪测定10批次大活络丸中12种无机元素及建立的指纹图谱,尝试用成分数据分析法、向量相似法和模糊聚类法建立全国无机元素的指纹图谱和中药质量控制标准方法,获得较满意的结果。研究表明,建立无机元素指纹图谱控制中成药中无机元素的质量,具有很好的实用性和可操作性,用于全国各地不同厂家生产的大活络丸无机元素的质量控制是切实可行的。     

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.     

Performance evaluation of CAESAR–QSAR output using PAHs as a case study

The set of 16 polycyclic aromatic hydrocarbon compounds was examined with the Internet available quantitative structure–activity relationship (QSAR) CAESAR models. For mutagenicity, carcinogenicity, developmental toxicity, and skin sensitization, the report includes the predicted classifications, the analysis of applicability domains, and the similarity sets, which consist of the similar compounds from the training sets. These results were further analyzed with chemometrical methods, that is, hierarchical clustering, principal component analysis, and self‐organizing maps, which were used for clustering and to define the cluster indicators. Such analysis assists the users in planning the application of QSAR models for hazard communication in regulatory compliance and in research of new active compounds. Copyright © 2013 John Wiley & Sons, Ltd.     

Consistency of regularized spectral clustering

Clustering is a widely used technique in machine learning, however, relatively little research in consistency of clustering algorithms has been done so far. In this paper we investigate the consistency of the regularized spectral clustering algorithm, which has been proposed recently. It provides a natural out-of-sample extension for spectral clustering. The presence of the regularization term makes our situation different from that in previous work. Our approach is mainly an elaborate analysis of a functional named the clustering objective. Moreover, we establish a convergence rate. The rate depends on the approximation property and the capacity of the reproducing kernel Hilbert space measured by covering numbers. Some new methods are exploited for the analysis since the underlying setting is much more complicated than usual. Some new methods are exploited for the analysis since the underlying setting is much more complicated than usual.