Clustering electricity consumers using high‐dimensional regression mixture models

A massive amount of data about individual electrical consumptions are now provided with new metering technologies and smart grids. These new data are especially useful for load profiling and load modeling at different scales of the electrical network. A new methodology based on mixture of high‐dimensional regression models is used to perform clustering of individual customers. It leads to uncovering clusters corresponding to different regression models. Temporal information is incorporated in order to prepare the next step, the fit of a forecasting model in each cluster. Only the electrical signal is involved, slicing the electrical signal into consecutive curves to consider it as a discrete time series of curves. Interpretation of the models is given on a real smart meter dataset of Irish customers.     

基于聚类算法的提升管内颗粒聚团演化行为研究

本文基于K-means聚类算法对提升管内瞬时颗粒分布图进行图像分割,捕捉颗粒聚团的内部结构及演化过程,并结合聚团区域灰度直方图和分布图研究了有核聚团、颗粒云团以及提升管不同区域聚团演化特性.研究发现当固含率较高时,聚团内部形成致密的核心,且这些聚团相对比较稳定,它们由聚团核心及其周围的颗粒云组成,而在固含率较低时,聚团...     

水蒸气在高分子膜中吸附和传递行为的研究进展

水蒸气作为一种可凝聚性气体,导致它在高分子膜中的渗透行为比其它非凝聚性气体复杂。简要综述了水蒸气分子在高分子膜中的溶解和扩散行为,在不同的水蒸气活度下,考察了水蒸气在高分子膜中的溶解系数、扩散系数和渗透系数。分析了水分子与膜之间的相互作用,及水分子的成簇机理。     

基于轨迹聚类的天光光谱特征分析

天光背景扣除是LAMOST 1D光谱数据处理中重要的环节,其扣除好坏直接影响光谱产品质量,因此构造理想的超级天光光谱模型具有重要的意义。通常超级天光是由与目标天体同时观测的天光光纤光谱构造而成,同一区域的天光背景可能随着不同的观测时刻有着规律性的变化特征（如月相变化）,如果能充分分析并利用这些特征,可有效校正超级天光模型,从而提高减天光效果。轨迹聚类方法是一种分析目标随时、空变化特征的有效工具,针对LAMOST天光光谱中可能存在的变化规律,给出一种基于轨迹聚类的天光光谱特征分析方法。主要分以下三部分：首先是天光光谱的时序化描述。LAMOST pipeline采用且提供了每个观测天体的即时超级天光光谱,为了获取特定天区背景天光的光变特征,需选择天光光纤光谱以及扣除目标天体光谱的背景光谱,以5°视场（LAMOST望远镜视场）为单位,按观测日期MJD均匀分组,从而对特定区域的天光光谱进行了时序化表征;其次给出基于密度的天光光谱数据聚类算法STK-means。为解决随机参数导致收敛及聚类效果不理想的问题,在分析天光光谱时序数据特征的基础上,给出基于密度的相似性度量公式,并作为传统k-means聚类的初始参数选择依据,从而给出基于密度的天光光谱数据聚类算法STK-means;最后进行实验分析。实验验证了该方法的正确性和有效性以及不同初始参数K值的选择对聚类结果的影响。在此基础上,利用STK-means聚类方法,对LAMOST第一期巡天中一个完备小天区的天光光谱时序数据进行了轨迹特征分析,结果表明,除个别光谱质量较差或常说异常外,该特定区域的天光背景以农历每月十五、十六为中心向两边呈对称分布,反映了该区域观测过程中受月相的影响变化情况,该特征经量化后可为校正超级天光模型提供一种有效途径。同时,由于时序化描述过程中均匀采样的要求,该方法可适用于反银心、盘、晕等高天体数密度区域,而对于高银纬低数密度区域则需要更长时间的巡天观测。此外,该方法还可有效发现特定区域的离群(异常)天光光谱,为天文学家进一步分析提供珍稀样本。     

三维NiO/CoMoO4纳米线/片复合材料的制备及其超级电容特性

采用两步水热法和高温退火法成功制备了三维氧化镍/钼酸钴(NiO/CoMoO_4)复合电极材料。利用XRD、扫描电镜、透射电镜和电化学方法分别对其结构、表面形貌和电化学性能进行了表征和研究。结果表明,NiO/CoMoO_4呈独特的纳米线/片状结构而不同于NiO的针状形貌,其结构为活性物质提供了更大的活性位点。在电流密度为0. 3A/g时,复合物的比电容高达2253F/g,远远高于同电流密度下纯NiO电极材料的比电容,循环2000圈后,电容的保持率为92%,NiO和CoMoO_4的协同效应增强了其超级电容特性。     

A low-temperature synthesis-induced defect formation strategy for stable hierarchical porous metal-organic frameworks

A stable hierarchical porous metal-organic framework PCN-56 with abundant Lewis acid sites was synthesized by the low-temperature synthesis-induced defect formation method. It exhibits good adsorption capacity for dyes and high loading capacity toward macromolecular drugs.     

Combinatorial optimization and hierarchical classifications

This paper is devoted to some selected topics relating Combinatorial Optimization and Hierarchical Classification. It is oriented toward extensions of the standard classification schemes (the hierarchies): pyramids, quasi-hierarchies, circular clustering, rigid clustering and others. Bijection theorems between these models and dissimilarity models allow to state some clustering problems as optimization problems. Within the galaxy of optimization we have especially discussed the following: NP-completeness results and search for polynomial instances; problems solved in a polynomial time (e.g. subdominant theory); design, analysis and applications of algorithms. In contrast with the orientation to new clustering problems, the last part discusses some standard algorithmic approaches.Received: July 2004, Revised: September 2004, MSC classification: 62H30, 91C20, 05C65, 90C27, 68R01     

Cluster-C, an algorithm for the large-scale clustering of protein sequences based on the extraction of maximal cliques

Although the characterization of proteins cannot solely rely upon sequence similarity, it has been widely proved that all-vs-all massive sequence comparisons may be an effective approach and a good basis for the prediction of biochemical functions or for the delineation of common shared properties. The program Cluster-C presented here enables a stand-alone and efficient construction of protein families within whole proteomes. The algorithm, which is based on the detection of cliques, ensures a high level of connectivity within the clusters. As opposed to the single transitive linkage method, Cluster-C allows a large number of sequences to be classified in such a way that the multidomain proteins do not produce a chain-grouping effect resulting in meaningless clusters. Moreover, some proteins can be present in several different but relevant clusters, which is of help in the determination of their functional domains. In the present analysis we used the Z-value, an evaluation of the significance of the similarity score, as the criterion for connecting sequences (the user can freely define the threshold of the similarity criterion). The clusters built with a rather low threshold (Z= 14) include more than 97% of the sequences and are consistent with known protein families and PROSITE patterns.     

An Ideal Assortative Network and Synchronization

This paper proposes a novel complex network with assortative property based on multi-center networks. The average path length and clustering coefficient of the network are calculated, and the impact on the network topology is investigated. A simple dynamic system established on the proposed network is used to analyze how the assortative property of the network affects synchronization.     

汉语自然口语中声调识别的研究

汉语是一种带声调的语言，声调信息在汉语识别中具有非常重要的意义.传统的声调识别一般只研究朗读式语音中相对标准的声调，很少对声调调型比较复杂的自然口语进行专门的处理.针对汉语自然口语的特点，在声调建模单元的选择时提出了真实上下文的模型.同时，为了对声调模式进行精细建模，采用了一种层次聚类的方法来获得更多的声调模式.实验结果证明了方法的有效性. 关键词： 声调识别 自然口语 真实上下文模型 聚类