This article proposes a novel stable clustering design method for hierarchical satellite network in order to increase its stability,reduce the overhead of storage and exert effective control of the delay performances ...This article proposes a novel stable clustering design method for hierarchical satellite network in order to increase its stability,reduce the overhead of storage and exert effective control of the delay performances based on a 5-dimensional vector model. According to the function of stability measureinent and owing to the limitation of minimal average routing table length, the hierarchical satellite network is grouped into separate stable connected clusters to improve destruction resistance and reconstruction ability in the future integrated network. In each cluster, redundant communication links with little contribution to network stability and slight influences on delay variation are deleted to satisfy the requirements for stability and connectivity by means of optimal link resources, and, also, the idea of logical weight is introduced to select the optimal satellites used to communicate with neighboring cluster satellites. Finally, the feasibility and effectiveness of the proposed method are verified by comparing it with the simulated performances of other two typical hierarchical satellite networks, double layer satellite constellation（DLSC） and satellite over satellite（SOS）.展开更多
Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the S...Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.展开更多
For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying speed.The corresponding literatures for the cit...For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying speed.The corresponding literatures for the cities in China have not considered the indicators of economy and industry in detail.In this paper,based on multiple indicators of economy and industry,the urban hierarchical structure of 285 cities above the prefecture level in China is investigated.The indicators from the economy,industry,infrastructure,medical care,population,education,culture,and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure.The factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in China.According to the comprehensive scores,285 cities above the prefecture level are clustered into 15 levels by using K-means clustering algorithm.Then,the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are proposed.The results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing countries.The methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.展开更多
This study aims to construct a new method for collecting colours from Guangzhou historical buildings using the Natural Color Sytem(NCS)colour system for on-site comparison.It seeks to detect and classify the primary a...This study aims to construct a new method for collecting colours from Guangzhou historical buildings using the Natural Color Sytem(NCS)colour system for on-site comparison.It seeks to detect and classify the primary and secondary colours of the buildings through image attributes,and to analyse the group co-occurrence and clustering algorithm characteristics of the colours of the historical buildings,as well as the colour hierarchy based on the image clustering algorithm.The study further evaluates the colour ratings of the historical buildings through questionnaire validation,explores the relationship between colour attributes(hue,lightness,and chroma),and classifies the colour evaluation criteria into five tiers.It was found that the symbiotic clustering diagrams of the colours of the historic buildings in Guangzhou exhibited distinct colour rating relationships,with the clustering colour hierarchy showing a high correlation with the results of the colour ratings assessed by questionnaire validation.A close relationship between the computer-generated output and the perceptual judgement is demonstrated,further proving the reliability and accuracy of the computer model.The results of the study provide comparable data and practical tools for colour planning and management of urban historic building facades.展开更多
A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion proces...A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion processing of borehole geophysical data locally. As having barely more types of data than unknowns in a depth, a set of marginally over-determined inverse problems has to be solved along a borehole, which is a rather noise sensitive procedure. For the reduction of noise effect, the amount of overdetermination must be increased. To fulfill this requirement, we suggest the use of our interval inversion method, which inverts simultaneously all data from a greater depth interval to estimate petrophysical parameters of reservoirs to the same interval. A series expansion based discretization scheme ensures much more data against unknowns that significantly reduces the estimation error of model parameters. The knowledge of reservoir boundaries is also required for reserve calculation. Well logs contain information about layer-thicknesses, but they cannot be extracted by the local inversion approach. We showed earlier that the depth coordinates of layerboundaries can be determined within the interval inversion procedure. The weakness of method is that the output of inversion is highly influenced by arbitrary assumptions made for layer-thicknesses when creating a starting model (i.e. number of layers, search domain of thicknesses). In this study, we apply an automated procedure for the determination of rock interfaces. We perform multidimensional hierarchical cluster analysis on well-logging data before inversion that separates the measuring points of different layers on a lithological basis. As a result, the vertical distribution of clusters furnishes the coordinates of layer-boundaries, which are then used as initial model parameters for the interval inversion procedure. The improved inversion method gives a fast, automatic and objective estimation to layer-boundaries and petrophysical parameters, which is demonstrated by a hydrocarbon field example.展开更多
为维护网络运行安全,保证网络信息安全存储,提出基于多源数据挖掘的网络安全态势评估系统。首先建立以应用层、控制层、数据转发层为核心的3层网络安全态势系统架构,为保证应用层与网络设备之间信息有效传输,利用OSGi(Open Service Gate...为维护网络运行安全,保证网络信息安全存储,提出基于多源数据挖掘的网络安全态势评估系统。首先建立以应用层、控制层、数据转发层为核心的3层网络安全态势系统架构,为保证应用层与网络设备之间信息有效传输,利用OSGi(Open Service Gateway Initiative)设计模式对控制层的ONOS(Open Network Operating System)控制器实施5层平行建构,以保障网络安全态势的决策响应。利用流量探测模块内多探测器的部署,实现网络多源数据的深度挖掘;引入LEACH(Low Energy Adaptive Clustering Hierarchy)算法,在网络簇首实现多源数据融合。通过安全态势评估模块对网络入侵因子威胁等级进行分析后,结合权系数理论对网络态势威胁因子进行威胁度赋值,并结合网络层次划分法对运行网络服务层、主机层、网络层安全态势实施分层评估。实验表明,所提方法对网络数据运行状态分析能力较高,面对多类型网络威胁因子的攻击行为能做到精准识别,为网络安全运行提供重要保障。展开更多
针对带时间窗的多车型电动车辆路径问题(heterogeneous electric vehicle routing problem with time windows,HEVRPTW),综合考虑客户需求差异、车辆异构特性和充电约束等因素,构建以总行驶成本最小化为目标的混合整数规划模型,并提出...针对带时间窗的多车型电动车辆路径问题(heterogeneous electric vehicle routing problem with time windows,HEVRPTW),综合考虑客户需求差异、车辆异构特性和充电约束等因素,构建以总行驶成本最小化为目标的混合整数规划模型,并提出结合层次聚类机制的混合变邻域搜索算法(hybrid variable neighborhood search,HVNS)进行求解。该算法采用层次聚类机制对客户节点进行空间划分,并结合贪婪算法生成初始解;在局部搜索阶段,整合单点插入、两点交换、两段交换及2–opt等多种邻域操作算子,并引入充电站优化策略优化路径选择。基于标准测试案例通过与Gurobi求解器和遗传算法(genetic algorithm,GA)进行仿真对比实验,并对电池容量、充电时间、时间窗宽度、车辆数量等关键参数进行敏感性分析。结果表明:HVNS能在更短时间内获得与Gurobi相近的优质解,验证了模型的正确性及其在不同规模问题求解中的优越性能;与GA相比,HVNS在求解质量上实现了10%~20%的提升,同时在稳定性和收敛性方面更优;通过参数优化确定了最佳配置方案(电池容量为150 kWh、充电时间为45 min、时间窗宽度为90 min、车辆数量为8辆),实现了总行驶成本最小化与客户满意度最大化的平衡。研究结果验证了HVNS是求解HEVRPTW的有效方法,本研究为物流企业电动车辆路径优化提供了科学的决策支持工具。展开更多
基金National Natural Science Foundation of China(60532030)
文摘This article proposes a novel stable clustering design method for hierarchical satellite network in order to increase its stability,reduce the overhead of storage and exert effective control of the delay performances based on a 5-dimensional vector model. According to the function of stability measureinent and owing to the limitation of minimal average routing table length, the hierarchical satellite network is grouped into separate stable connected clusters to improve destruction resistance and reconstruction ability in the future integrated network. In each cluster, redundant communication links with little contribution to network stability and slight influences on delay variation are deleted to satisfy the requirements for stability and connectivity by means of optimal link resources, and, also, the idea of logical weight is introduced to select the optimal satellites used to communicate with neighboring cluster satellites. Finally, the feasibility and effectiveness of the proposed method are verified by comparing it with the simulated performances of other two typical hierarchical satellite networks, double layer satellite constellation（DLSC） and satellite over satellite（SOS）.
文摘Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.
基金supported by National Key Research and Development Program of China(Grant No.2018YFC0704903).
文摘For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying speed.The corresponding literatures for the cities in China have not considered the indicators of economy and industry in detail.In this paper,based on multiple indicators of economy and industry,the urban hierarchical structure of 285 cities above the prefecture level in China is investigated.The indicators from the economy,industry,infrastructure,medical care,population,education,culture,and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure.The factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in China.According to the comprehensive scores,285 cities above the prefecture level are clustered into 15 levels by using K-means clustering algorithm.Then,the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are proposed.The results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing countries.The methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.
文摘This study aims to construct a new method for collecting colours from Guangzhou historical buildings using the Natural Color Sytem(NCS)colour system for on-site comparison.It seeks to detect and classify the primary and secondary colours of the buildings through image attributes,and to analyse the group co-occurrence and clustering algorithm characteristics of the colours of the historical buildings,as well as the colour hierarchy based on the image clustering algorithm.The study further evaluates the colour ratings of the historical buildings through questionnaire validation,explores the relationship between colour attributes(hue,lightness,and chroma),and classifies the colour evaluation criteria into five tiers.It was found that the symbiotic clustering diagrams of the colours of the historic buildings in Guangzhou exhibited distinct colour rating relationships,with the clustering colour hierarchy showing a high correlation with the results of the colour ratings assessed by questionnaire validation.A close relationship between the computer-generated output and the perceptual judgement is demonstrated,further proving the reliability and accuracy of the computer model.The results of the study provide comparable data and practical tools for colour planning and management of urban historic building facades.
文摘A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion processing of borehole geophysical data locally. As having barely more types of data than unknowns in a depth, a set of marginally over-determined inverse problems has to be solved along a borehole, which is a rather noise sensitive procedure. For the reduction of noise effect, the amount of overdetermination must be increased. To fulfill this requirement, we suggest the use of our interval inversion method, which inverts simultaneously all data from a greater depth interval to estimate petrophysical parameters of reservoirs to the same interval. A series expansion based discretization scheme ensures much more data against unknowns that significantly reduces the estimation error of model parameters. The knowledge of reservoir boundaries is also required for reserve calculation. Well logs contain information about layer-thicknesses, but they cannot be extracted by the local inversion approach. We showed earlier that the depth coordinates of layerboundaries can be determined within the interval inversion procedure. The weakness of method is that the output of inversion is highly influenced by arbitrary assumptions made for layer-thicknesses when creating a starting model (i.e. number of layers, search domain of thicknesses). In this study, we apply an automated procedure for the determination of rock interfaces. We perform multidimensional hierarchical cluster analysis on well-logging data before inversion that separates the measuring points of different layers on a lithological basis. As a result, the vertical distribution of clusters furnishes the coordinates of layer-boundaries, which are then used as initial model parameters for the interval inversion procedure. The improved inversion method gives a fast, automatic and objective estimation to layer-boundaries and petrophysical parameters, which is demonstrated by a hydrocarbon field example.
文摘针对带时间窗的多车型电动车辆路径问题(heterogeneous electric vehicle routing problem with time windows,HEVRPTW),综合考虑客户需求差异、车辆异构特性和充电约束等因素,构建以总行驶成本最小化为目标的混合整数规划模型,并提出结合层次聚类机制的混合变邻域搜索算法(hybrid variable neighborhood search,HVNS)进行求解。该算法采用层次聚类机制对客户节点进行空间划分,并结合贪婪算法生成初始解;在局部搜索阶段,整合单点插入、两点交换、两段交换及2–opt等多种邻域操作算子,并引入充电站优化策略优化路径选择。基于标准测试案例通过与Gurobi求解器和遗传算法(genetic algorithm,GA)进行仿真对比实验,并对电池容量、充电时间、时间窗宽度、车辆数量等关键参数进行敏感性分析。结果表明:HVNS能在更短时间内获得与Gurobi相近的优质解,验证了模型的正确性及其在不同规模问题求解中的优越性能;与GA相比,HVNS在求解质量上实现了10%~20%的提升,同时在稳定性和收敛性方面更优;通过参数优化确定了最佳配置方案(电池容量为150 kWh、充电时间为45 min、时间窗宽度为90 min、车辆数量为8辆),实现了总行驶成本最小化与客户满意度最大化的平衡。研究结果验证了HVNS是求解HEVRPTW的有效方法,本研究为物流企业电动车辆路径优化提供了科学的决策支持工具。
