考虑低穿特征的规模化光伏聚类分析方法

研究规模化光伏低电压穿越期间无功补偿等控制策略时,需要对暂态行为相似的光伏机组进行分群处理,以实现不同机组间的协调控制。首先归纳了规模化光伏在低穿期间的主流控制方式,指出光伏电站在故障期间有功电压耦合现象。考虑光伏与同步机暂态特性的差异性,提出一类考虑光伏低穿特征的聚类分析方法,该类方法克服了传统分群方法的静态局限,较好地反映了光伏暂态电压的集群现象,在等值系统和实际电网中验证了所提方法的有效性。

风电场集群接入系统后的聚类分析

提出了一种基于并网点暂态电压特性的聚类分群方法,即根据并网风电场受系统故障影响程度的不同,识别出动态行为相近的风电场群,进而实现同群风电场的协调控制。为验证所提方法的有效性,在高级可视化软件Powerworld建立了吉林西部电网仿真模型,并进行了聚类分析研究。仿真结果证明,该算法不仅结果相对稳定、符合电网实际情况,而且具有一定的脱网风电场动态识别功能。

基于聚类分析的双馈机组风电场动态等值模型的研究

针对大型双馈机组风电场,提出一种新的动态等效建模方法。该方法是一种基于双馈风力发电机暂态电压特性的聚类分群方法,即根据风电场内各双馈发电机受系统故障影响程度的不同,识别出电压的动态响应行为相近的风力发电机,并对分群后的双馈发电机及其电气接线系统进行等值聚合,实现了双馈机组风电场的动态等值多机表征。利用电力系统分析综合程序(PSASP 6.2),搭建了双馈机组风电场详细模型和等值模型,并与传统的单机等值模型进行了比较分析。结果表明,所建立的多机等值模型能够较准确地反映双馈机组风电场并网点的动态特性。

基于聚类算法的配电变压器经济运行分析研究

为确切反映配电变压器经济运行效果,提出了一种基于聚类分群算法的配电变压器经济运行特征分析方法。通过从配电变压器负载数据中提取出平均负载率、波动负载率和趋势负载率等3个判别配电变压器经济运行的特征指标,采用改进KMedoids算法对其进行聚类分群处理,得出运行特征分析结果和运行方式优化建议。仿真实验证明,该方法有效,对配电变压器运行状态分类效果明显,为提高配电变压器运行效率以及配电网节能降损提供数据支持。

基于ASW-FCM算法的风电场动态等效建模与仿真

针对直驱永磁风电机组风电场,提出一种新的动态等效建模方法。考虑风电机组间的尾流效应和风向变化计算有效输入风速,分析机组的运行特性,以反映机组运行特征的有效风速、转速、桨距角和输出功率为多分群指标。考虑机组间运行状况的差异性和关联性,设计自适应样本定权的模糊聚类算法(Adaptive Sample Weighting Fuzzy C-means,ASW-FCM)对风电场进行最优聚类分群。根据等效前后机组输出特性不变的原则建立聚类风电机群的等效模型。以某实际风电场系统作为算例进行建模仿真,验证所提等效建模方法的合理性和准确性。

基于旅客画像的航班出行选择预测方法研究与实现

根据预处理后的旅客出行数据,运用改进的K-means聚类算法进行用户分群,提取聚类特征得到旅客类型子集合,并构建用户画像.基于特征重构数据库,构建旅客类型、出行方式、出行时间段三个维度的交叉巢式Logit模型,捕捉选择方案间的相关性,预测旅客航班出行方式、出行时间段的交叉选择,并基于实际数据进行参数估值和检验.结果表明,旅客倾向于先根据个人及家庭的需求形成旅客类型,考虑选择何种出行方式,最后在旅客类型和出行方式的双重约束下选择航班的出发时间,为机场实施需求管理提供有效依据.

风电场动态等值建模研究综述

从风电场动态等值建模(风速等值、集电网络等值、分群聚类、等值参数计算4个步骤)的角度,探讨风电场动态等值建模研究成果。结果表明,风电场动态等值建模有2种方法:一是提高分群精度,获得唯一聚类结果,进行多机等值;二是降低分群精度,转而确定强相关的待辨识参数,辨识出等值机参数。最后,预测了风电场动态等值未来可能的研究方向。

Cluster Analysis of Morphologic Characteristic of Eight Geographical Populations of Rana Dybowskii

[ObJective] The research aimed to determine the geographic distribution map of system of Rana dybowskii. [Method] Four morphologic indices （body length, body weight, forelimb length, hindlimb length） of eight geographical populations of R.dybowskii which naturally distribute in Changhai Mountain and Xiaoxing＇an Mountain were measured. Measure results were variance analyzed and cluster analyzed. [Result] Variance analysis showed： the genetic branching among the Dongfanghong male population（ belongs to Wandashan） and Xiaoxing＇an Mountain male population and Changbai Mountain male population were significantly different （P〈0.05） ; the genetic branching between the Hebei female population （belongs to Xiaoxing＇an Mountain） and Changbai Mountain female population was significantly different （P〈0.05 ）. Cluster analysis showed ： male R.dybowskii can be divided into three groups ： the first group included Quanyang, Tianbei, Chaoyang and Ddkouqin, the second group included Tieli and Anshan, the third group included Dongfanghong; and the female R. dybowskii can be divided into three groups ： the first group included Quanyang and Chaoyang, the second group included Tianbei and Dakouqin, the third group included Hebei. [Condusion] The paper deduced that the Sanjiang Plain was the geographical origin center ofR. dybowskii which radiated to Changbai Mountain and Xiaoxing＇an Mountain along the adverse current of Songhua River basin, therefore, the current distribution pattern of R. dybowskii was formed.

大数据在新零售中的应用

随着数字化技术的发展,在大数据驱动变革的时代背景下,传统零售行业面临着Online&Offline品牌品类逐渐趋同的问题,转变营销模式势在必行。数字化技术已被逐渐应用在传统实体零售的每一个环节,实现用户数字化、门店数字化、渠道数字化、供应链数字化、营销数字化,如同线上零售电子系统一样,线下零售每一个环节都会产生数据,数据成为了新零售的内在核心驱动力。笔者以国内某大型百货零售企业为例,从数据基础、数据分析、实现系统、分析方法、模型搭建、分析实战与营销应用等几个方面全面解析大数据在新零售中的应用。

基于改进型K-means算法的笼式异步风电场等值研究

国外风电场采用分布式,我国大多是大规模集群风电接入电网。由于风的随机性、波动性等特点,在研究风电场并网对电网的影响时,提高风电场模型运行状态的精度就显得尤为重要了。文中对于笼式异步风电场提出一种以故障发生前的输出功率为分群指标,采用改进型K-means算法实现风电场多机等值建模的方法。针对同一机群风电机组,运用基于容量加权的等值方法计算相关等值参数。最后,以软件PSCAD/EMTDC为仿真平台,搭建了笼式异步风电场详细模型及其等值模型。仿真研究了在稳定运行及故障条件下风电场出口有功、无功动态变化轨线,验证了所提出的等值方法的快速性及有效性。

New two-dimensional fuzzy C-means clustering algorithm for image segmentation

To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.

Color image segmentation using mean shift and improved ant clustering

To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.

Factor analysis identifies subgroups of constipation

AIM:To determine whether distinct symptom groupings exist in a constipated population and whether such grouping might correlate with quantifiable pathophysiological measures of colonic dysfunction.METHODS:One hundred and ninety-one patients presenting to a Gastroenterology clinic with constipation and 32 constipated patients responding to a newspaper advertisement completed a 53-item,wide-ranging selfreport questionnaire.One hundred of these patients had colonic transit measured scintigraphically.Factor analysis determined whether constipation-related symptoms grouped into distinct aspects of symptomatology.Cluster analysis was used to determine whether indi-vidual patients naturally group into distinct subtypes.RESULTS:Cluster analysis yielded a 4 cluster solution with the presence or absence of pain and laxative unresponsiveness providing the main descriptors.Amongst all clusters there was a considerable proportion of patients with demonstrable delayed colon transit,irritable bowel syndrome positive criteria and regular stool frequency.The majority of patients with these characteristics also reported regular laxative use.CONCLUSION:Factor analysis identified four constipation subgroups,based on severity and laxative unresponsiveness,in a constipated population.However,clear stratification into clinically identifiable groups remains imprecise.

Different Criteria for the Optimal Number of Clusters and Selection of Variables with R

One of the most important problems of clustering is to define the number of classes. In fact, it is not easy to find an appropriate method to measure whether the cluster configuration is acceptable or not. In this paper we propose a possible and non-automatic solution considering different criteria of clustering and comparing their results. In this way robust structures of an analyzed dataset can be often caught （or established） and an optimal cluster configuration, which presents a meaningful association, may be defined. In particular, we also focus on the variables which may be used in cluster analysis. In fact, variables which contain little clustering information can cause misleading and not-robustness results. Therefore, three algorithms are employed in this study： K-means partitioning methods, Partitioning Around Medoids （PAM） and the Heuristic Identification of Noisy Variables （HINoV）. The results are compared with robust methods ones.

Influences of sea ice on eastern Bering Sea phytoplankton

The influence of sea ice on the species composition and cell density of phytoplankton was investigated in the eastern Bering Sea in spring 2008. Diatoms, particularly pennate diatoms, dominated the phytoplankton community. The dominant species were Grammonema islandica （Grunow in Van Heurck） Hasle, Fragilariopsis cylindrus （Grunow） Krieger, F. oceanica （Cleve） Hasle, Navicula vanhoeffenii Gran, Thalassiosira antarctica Comber, T. gravida Cleve, T. nordenskioeldii Cleve, and T. rotula Meunier. Phytoplankton cell densities varied from 0.08× 10^4 to 428.8× 10^4 cells/L, with an average of 30.3× 10^4 cells/L. Using cluster analysis, phytoplankton were grouped into three assemblages defined by ice-forming conditions： open wate.r, ice edge, and sea ice assemblages. In spring, when the sea ice melts, the phytoplankton dispersed from the sea ice to the ice edge and even into open waters. Thus, these phytoplankton in the sea ice may serve as a “seed bank” for phytoplankton population succession in the subarctic ecosystem. Moreover, historical studies combined with these results suggest that the sizes of diatom species have become smaller, shifting from microplankton to nannoplankton-dominated communities.

Research on Image Segmentation Algorithm based on Fuzzy C-mean Clustering

This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.

Clustering： from Clusters to Knowledge

Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.

Spatiotemporal patterns of the fish assemblages downstream of the Gezhouba Dam on the Yangtze River

An explicit demonstration of the changes in fish assemblages is required to reveal the influence of damming on fish species.However,information from which to draw general conclusions regarding changes in fish assemblages is insufficient because of the limitations of available approaches.We used a combination of acoustic surveys,gillnet sampling,and geostatistical simulations to document the spatiotemporal variations in the fish assemblages downstream of the Gezhouba Dam,before and after the third impoundment of Three Gorges Reservoir(TGR).To conduct a hydroacoustic identification of individual species,we matched the size distributions of the fishes captured by gillnet with those of the acoustic surveys.An optimum threshold of target strength of 50 dB re 1 m 2 was defined,and acoustic surveys were purposefully extended to the selected fish assemblages(i.e.,endemic Coreius species) that was acquired by the size and species selectivity of the gillnet sampling.The relative proportion of fish species in acoustic surveys was allocated based on the composition(%) of the harvest in the gillnet surveys.Geostatistical simulations were likewise used to generate spatial patterns of fish distribution,and to determine the absolute abundance of the selected fish assemblages.We observed both the species composition and the spatial distribution of the selected fish assemblages changed significantly after implementation of new flow regulation in the TGR,wherein an immediate sharp population decline in the Coreius occurred.Our results strongly suggested that the new flow regulation in the TGR impoundment adversely affected downstream fish species,particularly the endemic Coreius species.To determine the factors responsible for the decline,we associated the variation in the fish assemblage patterns with changes in the environment and determined that substrate erosion resulting from trapping practices in the TGR likely played a key role.

Cluster Partition Function and Invariants of 3-Manifolds

The author reviews some recent developments in Chern-Simons theory on a hyperbolic 3-manifold M with complex gauge group G. The author focuses on the case of G = SL(N, C) and M being a knot complement: M = S^3\ K. The main result presented in this note is the cluster partition function, a computational tool that uses cluster algebra techniques to evaluate the Chern-Simons path integral for G = SL(N, C). He also reviews various applications and open questions regarding the cluster partition function and some of its relation with string theory.

Symptom clustering in chronic gastritis based on spectral clustering

OBJECTIVE: Apply spectral clustering to analyze the patterns of symptoms in patients with chronic gastritis(CG).METHODS: Based on 919 CG subjects, we applied mutual information feature selection to choose the positively correlated symptoms with each pattern.Then, we used the Shi and Malik spectral clustering algorithm to select the top 20 correlated symptoms.RESULTS: We ascertained the results of six patterns.There were three categories for the pattern of accumulation of damp heat in the spleen-stomach(0.00332). There were six categories for the pattern of dampness obstructing the spleen-stomach(0.02466). There were two categories for the pattern of spleen-stomach Qi deficiency(0.013 89).There were three categories for the pattern of spleen-stomach deficiency cold(0.009 15). There were five categories for the pattern of liver-Qistagnation(0.01910).There were four categories for the pattern of stagnant heat in the liver-stomach(0.00585).CONCLUSION: Most of the spectral clustering results of the symptoms of CG patterns were in accordance with clinical experience and Traditional Chinese Medicine theory. Most categories suggested the nature and/or location of the disease.