基于系统聚类算法和Fisher判别模型的古代玻璃成分分析与类别鉴定

古代玻璃体现了我国古代高度发达的工艺技术,新时代,玻璃成为了人们生产生活的必需品,研究古代玻璃有助于加深人们对玻璃的认识,唤醒我国人民对工艺研究的热情。本文对古代玻璃的实验数据进行分析,首先使用描述性统计分析和卡方检验分析研究玻璃文物样品表面有无风化化学成分含量的统计规律,得到玻璃表面风化与类型的关系;然后依据玻璃表面风化与类型的关系建立系统聚类算法模型,研究不同类别玻璃文物样品的化学成分之间的关联关系;最后依据化学成分之间关联关系建立Fisher判别模型,得到玻璃类别鉴定的Fisher判别公式,可以对未知类别的玻璃文物进行类别鉴定。本文对古代玻璃的成分分析与类别鉴定提供一定的借鉴意义。

一种基于系统聚类的图书馆读者细分模型

读者细分是图书馆管理中迫切需要解决的问题,"被动服务"的图书馆服务模式逐渐转向"读者驱动",所提供的个性化服务应该符合读者的实际需求,因此这种个性化服务对于目前单一的读者属性细分方式提出了更高的要求。本文采用系统聚类算法建立了图书馆读者细分模型,进而提出了四种不同的细分方式,并用图书馆流通数据对模型进行了测试,验证了该细分模型的实用性和有效性。

基于LSQR与系统距离聚类算法的专变用户非介入式负荷辨识

专变用户非介入式负荷辨识是实现以专变用户为主体的需求侧响应的重要环节。通过对专变用户负荷稳态过程与暂态过程的特征分析,选取有功功率、无功功率、电流有效值等作为稳态过程特征量,并选取暂态发生前、中、后三个阶段的特征量,如电流有效值、有功功率均值、无功功率均值、持续时间、电流有效值最大值等,构建全面的非介入式负荷辨识稳态过程与暂态过程负荷特征空间。以此为基础,利用最小二乘QR分解算法(Least Square QR,LSQR)进行稳态过程负荷分解获得各种负荷的运行情况。并基于系统距离聚类算法将常见专变用户负荷暂态事件进行分类,进一步辨识出哪一种或者哪一类负荷发生投切动作。最后,采集包括排水泵、搅拌机、水泥螺旋、污水泵、除尘器等工业负荷现场数据,对所提到的方法进行了仿真分析,结果表明所提的非介入式负荷辨识方法可获得较高的准确率。

水声目标信号的系统聚类分析

本文简要介绍了基于五种类间距离的系统聚类算法,利用它们实现水声目标的特征提取,并对一些实际水声目标数据进行了分析,最后,利用聚类分布图和分离系数对这些算法的有效性进行了比较评价。

煤与瓦斯突出预报数据关联性的聚类分析

合理地选择突出控制因素是进行突出预测的关键技术之一.首先给出了基于系统聚类的突出预报数据关联性分析方法,通过DB Index准则判断聚类模型的有效性.然后研究了煤与瓦斯突出控制因素的选择规则.最后以平顶山煤矿为例,分析了该矿突出预报数据间的关联性,得到了相应的变量聚类树,并选择了主要的突出控制因素.仿真结果验证了所提出的突出控制因素选择方法的合理性和有效性.图2,表3,参11.

图书馆读者细分模型的分析与建立

由于单一的读者属性细分方式很难满足个性化服务的需要,提出了4种不同的细分方式,利用系统聚类算法建立了图书馆读者细分模型,并通过大量图书馆流通数据的训练,验证了模型的有效性和实用性。

基于层次分析的养老服务床位需求与运营模式研究

本文主要针对养老服务床位运营的商业模式进行了相关研究,利用聚类分析的K-means算法与系统聚类算法,得出省级行政区的市场需求规模分类。再通过时间序列分析ARIMA算法,将影响床位数的各种因素预测至2023年,使数据具有鲁棒性。再通过多元回归分析,得出每一类未来所对应床位数供求关系曲线及函数。首先为了能够更准确的定位“商机”,从城市和乡村这个维度来进行分类,通过非线性回归模型,得出城市和乡村未来的供求曲线。发现城市的供求关系曲线更加陡哨,且供求不平衡程度更加显著,故城市市场的更具潜力。其次为了进一步挖掘城市中的“商机”,使用层次分析法(AHP),以公办养老院、民办养老院、公建民营养老院为一级指标,各自对应的普通床位、疾病床位、服务员工作为二级指标,以数据反映出的趋势为权重判断依据,构造成对比较矩阵,分析得出公办民营养老院中的疾病养老对养老服务床位的需求最大。最后再根据结果站在企业角度看第一类城市中的公办民营养老院中的疾病养老对养老服务床位的需求最大,企业应该多注重这些地区来增加自己的“商机”。

Power interconnected system clustering with advanced fuzzy C-mean algorithm

An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, modified similarity measure was considered to gather nodes having similar characteristics. The similarity measure was needed to contain locafi0nal prices as well as regional coherency. In order to consider the two properties simultaneously, distance measure of fuzzy C-mean algorithm had to be modified. Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm. The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system.

Radio-map Establishment based on Fuzzy Clustering for WLAN Hybrid KNN/ANN Indoor Positioning

A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect signifi cantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modifi cation of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT's coordinates are calculated byKNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because ofits great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.

Study of Fussy Clustering of Engineering Geological Environment with GIS

Based on previous evaluating methods, a new method which combines GIS with Fussy Clustering algorithm is proposed and applied in evaluating the engineering geological environment of the research area of XuZHou City in this paper. By analyzing the characteristics and formation of engineering geological environment,the major problems are discussed, including stability of basement rock, sandy soil liquefaction and cultural stratum.According to effecting factors of these problems, the stability of every engineering geological problem in the worked area is classified into different classes. Then, the Fussy Clustering method is used in assessing all conditions of engineering geological environment. Finally, the evaluation is fulfilled in the whole studied area. The calculating result shows the method is feasible.

Mining Knowledge from Result Comparison Between Spatial Clustering Themes

This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.

Two-Stage Resource Allocation Scheme for Three-Tier Ultra-Dense Network

In 5 G Ultra-dense Network(UDN), resource allocation is an efficient method to manage inter-small-cell interference. In this paper, a two-stage resource allocation scheme is proposed to supervise interference and resource allocation while establishing a realistic scenario of three-tier heterogeneous network architecture. The scheme consists of two stages: in stage I, a two-level sub-channel allocation algorithm and a power control method based on the logarithmic function are applied to allocate resource for Macrocell and Picocells, guaranteeing the minimum system capacity by considering the power limitation and interference coordination; in stage II, an interference management approach based on K-means clustering is introduced to divide Femtocells into different clusters. Then, a prior sub-channel allocation algorithm is employed for Femtocells in diverse clusters to mitigate the interference and promote system performance. Simulation results show that the proposed scheme contributes to the enhancement of system throughput and spectrum efficiency while ensuring the system energy efficiency.

Novel robust approach for constructing Mamdani-type fuzzy system based on PRM and subtractive clustering algorithm

A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

Design and Implementation of Office Management System in Small and Medium Sized Enterprises based on Data Mining Technology

This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.

基于数据挖掘技术探讨李声岳治疗变应性鼻炎用药规律

目的:通过中医传承辅助平台系统软件,探讨名老中医李声岳治疗变应性鼻炎用药规律。方法:收集名老中医李声岳门诊处方,采用关联规则、复杂系统熵聚类等无监督数据挖掘方法,获得各种药物的使用频次及药物之间的关联规则,分析李声岳治疗变应性鼻炎的用药规律。结果:对筛选出的196首处方进行分析,处方中涉及161味中药,高频率(大于100次)的有8味中药,24组核心组合,挖掘出12个新方组合。结论:李声岳治疗变应性鼻炎经验丰富,多用温肺益气,散邪通窍。中医传承辅助平台系统对于挖掘名老中医临床经验具有重要实用价值。

Distributed Clustering Algorithm for Energy Efficiency and Load-Balance in Large-Scale Multi-Agent Systems

To improve the energy efficiency and load-balance in large-scale multi-agent systems, a large-scale distributed cluster algorithm is proposed. At first, a parameter describing the spatial distribution of agents is designed to assess the information spreading capability of an agent. Besides, a competition resolution mechanism is proposed to tackle the competition problem in large-scale multiagent systems. Thus, the proposed algorithm can balance the load, adjust the system network locally and dynamically, reduce system energy consumption. Finally, simulations are presented to demonstrate the superiority of the proposed algorithm.

Intelligent diagnosis of the solder bumps defects using fuzzy C-means algorithm with the weighted coefficients

Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are hidden in the package. A nondestructive method using the transient active thermography has been proposed to inspect the defects of a solder bump, and we aim at developing an intelligent diagnosis system to eliminate the influence of emissivity unevenness and non-uniform heating on defects recognition in active infrared testing. An improved fuzzy c-means(FCM) algorithm based on the entropy weights is investigated in this paper. The captured thermograms are preprocessed to enhance the thermal contrast between the defective and good bumps. Hot spots corresponding to 16 solder bumps are segmented from the thermal images. The statistical features are calculated and selected appropriately to characterize the status of solder bumps in FCM clustering. The missing bump is identified in the FCM result, which is also validated by the principle component analysis. The intelligent diagnosis system using FCM algorithm with the entropy weights is effective for defects recognition in electronic packages.