检索结果-南通市图书馆

Application of Surface Water Quality Classification Models Using principal Components Analysis and Cluster Analysis

Journal of Geoscience and Environment Protection 2019年第6期7卷 26-41页

作者： Mohamed Ahmed Reda Hamed Civil Engineering Department Canadian International Colleague (CIC) El Sheikh Zayed Egypt

Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are mainly based on statistical methods, multivariate statistical techniques in particular. In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs). Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features. The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH3), and pH. However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from 1 to 3. However the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters. This study revealed that multivariate analysis techniques, as the extracted water quality dominant parameters and clustered information can be used in reducing the number of sampli

关键词： Surface Water principal Component Analysis Cluster Analysis

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Study of Work-Travel Related Behavior Using principal Component Analysis

引用

Open Journal of Statistics 2014年第11期4卷 889-901页

作者： Cira Souza Pitombo Monique Martins Gomes Department of Transportation Engineering University of S&atilde o Paulo S&atilde o Carlos Brazil

The main objective of this study is to analyze work travel-related behavior through a set of variables relative to socio-economic class, urban environment and travel characteristics. The principal Component Analysis was applied in a sample consisting of workers of the S?o Paulo Metropolitan Area, based on the origin-destination home interview survey, carried out in 1997, in order to: 1) examine the interdependence between travel patterns and a set of socioeconomic and urban environment variables;2) determine if the original database can be synthetized on components. The results enabled to observe relations between the individual’s socio-economic class and car usage, characteristics of urban environment and destination choices, as well as age and non-motorized travel mode choice. It is then concluded that the database can be adequately summarized in three components for subsequent analysis: 1) urban environment;2) socio-economic class;and 3) family structure.

关键词： Travel Behavior principal Component Analysis Exploratory Analysis

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Assessment of Environmental Carrying Capacity Using principal Component Analysis

引用

Journal of Geoscience and Environment Protection 2018年第3期6卷 54-65页

作者： Yinge Liu Junhui Zhang Shaoxiong Wang Yan Wang Aling Zhao Key Laboratory of Disaster Monitoring and Mechanism Simulating in Shaanxi Province College of Geography and Environment Baoji University of Arts and Sciences Baoji China

Regional environmental carrying capacity (ECC) is nonlinear and spatially specific. A hierarchy index system including resources, environmental and socio-economic elements was established using an analytic hierarchy process. principal component analysis (PCA) was used to estimate the regional size and differences of environmental carrying capacities. Main information of four principal components, i.e., carrying capacity of resources supply, carrying capacity of environmental quality, carrying capacity of social economy and carrying capacity of infrastructure construction, was extracted. The ECC evaluation value was divided into five levels of lowest carrying capacity, low carrying capacity, medium carrying capacity, high carrying capacity and highest carrying capacity, respectively. The results showed that on the whole ECC was at the medium carrying capacity level. ECC was generally highest in Guanzhong plain, followed by Loess Plateau, and was lowest in Qiba mountain. The carrying capacity of water resources and environmental quality was relatively low, and the infrastructure carrying capacity was highest among the four components. The temporal spatial variation of ECC was closely related to vulnerability of the natural resources and environment in the regions. Verification was proven that PCA was a useful tool when applied to evaluate ECC and reflect the spatial distribution of large-quantity ECC indices on a large regional scale. This study provides a basis for comprehensive understanding of resources, environment and management for regional balanced development.

关键词： Environmental Carrying Capacity principal Component Analysis Spatial Dif-ference Evaluation of Indicators

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Nonlinear principal and Canonical Directions from Continuous Extensions of Multidimensional Scaling

引用

Open Journal of Statistics 2014年第2期4卷 154-171页

作者： Carles M. Cuadras Department of Statistics University of Barcelona Barcelona Spain

A continuous random variable is expanded as a sum of a sequence of uncorrelated random variables. These variables are principal dimensions in continuous scaling on a distance function, as an extension of classic scaling on a distance matrix. For a particular distance, these dimensions are principal components. Then some properties are studied and an inequality is obtained. Diagonal expansions are considered from the same continuous scaling point of view, by means of the chi-square distance. The geometric dimension of a bivariate distribution is defined and illustrated with copulas. It is shown that the dimension can have the power of continuum.

关键词： Statistical Distances Orthogonal Expansions principal Directions of Random Variables Diagonal Expansions Copulas Uncountable Dimensionality

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Seismic Data Quality Control and Interpolation Using principal Component Analysis

引用

International Journal of Geosciences 2019年第10期10卷 950-966页

作者： Qingmou Li Sonya A. Dehler Natural Resources Canada Canadian Hazards Information Service (CHIS) Ottawa ON Canada Natural Resources Canada Geological Survey of Canada (Calgary) Calgary Alberta Canada.

Commonly, seismic data processing procedures, such as stacking and prestack migration, require the ability to detect bad traces/shots and restore or replace them by interpolation, particularly when the seismic observations are noisy or there are malfunctioned components in the recording system. However, currently available trace/shot interpolation methods in the spatial or Fourier domain must deal with requirements such as evenly sampled traces/shots, infinite bandwidth of the signals, and linear seismic events. In this paper, we present a novel method, termed the E-S (eigenspace seismic) method, using principal component analysis (PCA) of the seismic signal to address the issue of reliable detection or interpolation of bad traces/shots. The E-S method assumes the existence of a correlation between the observed seismic entities, such as trace or shot gathers, making it possible to estimate one of these entities from all others for interpolation or seismic quality control. It first transforms a trace (or shot) gather into an eigenspace using PCA. Then in the eigenspace, it treats every trace as a point with its loading scores of PCA as its coordinates. Simple linear, bilinear, or cubic spline 1 dimensional (1D) interpolation is used to determine PCA loading scores for any arbitrary coordinate in the eigenspace, which are then used to construct an interpolated trace for the desired position in physical space. This E-S method works with either regular or irregular sampling and, unlike various other published methods, it is well-suited for band-limited seismic records with curvilinear reflection events. We developed related algorithms and applied these to processed synthetic and offshore seismic survey data with or without simulated noises to demonstrate their performance. By comparing the interpolated and observed seismic traces, we find that the E-S method can effectively assess the quality of the trace, and restore poor quality data by interpolation. The successful processing of synthetic and real data using the E

关键词： Seismic Quality Control Seismic Interpolation Eigenspace principal Component Analysis

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Fast Tensor principal Component Analysis via Proximal Alternating Direction Method with Vectorized Technique

引用

Applied Mathematics 2017年第1期8卷 77-86页

作者： Haiyan Fan Gangyao Kuang Linbo Qiao Department of Electronic Science and Engineering National University of Defense Technology Changsha China Department of Computer National University of Defense Technology Changsha China

This paper studies the problem of tensor principal component analysis (PCA). Usually the tensor PCA is viewed as a low-rank matrix completion problem via matrix factorization technique, and nuclear norm is used as a convex approximation of the rank operator under mild condition. However, most nuclear norm minimization approaches are based on SVD operations. Given a matrix , the time complexity of SVD operation is O(mn2), which brings prohibitive computational complexity in large-scale problems. In this paper, an efficient and scalable algorithm for tensor principal component analysis is proposed which is called Linearized Alternating Direction Method with Vectorized technique for Tensor principal Component Analysis (LADMVTPCA). Different from traditional matrix factorization methods, LADMVTPCA utilizes the vectorized technique to formulate the tensor as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. In the experiment part, synthetic tensor data with different orders are used to empirically evaluate the proposed algorithm LADMVTPCA. Results have shown that LADMVTPCA outperforms matrix factorization based method.

关键词： Tensor principal Component Analysis Proximal Alternating Direction Method Vectorized Technique

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Integration of principal components analysis and cellular automata for spatial decisionmaking and urban simulation

引用

Science China Earth Sciences 2002年第6期45卷 521-529页

作者：黎夏叶嘉安 Guangzhou Institute of Geography Guangzhou 510070 China Centre of Urban Planning and Environmental Management The University of Hong Kong Hong Kong SAR China

This paper discusses the issues about the correlation of spatial variables during spatial decisionmaking using multicriteria evaluation (MCE) and cellular automata (CA). The correlation of spatial variables can cause the malfunction of MCE. In urban simulation, spatial factors often exhibit a high degree of correlation which is considered as an undesirable property for MCE. This study uses principal components analysis (PCA) to remove data redundancy among a large set of spatial variables and determine 'ideal points' for land development. PCA is integrated with cellular automata and geographical information systems (GIS) for the simulation of idealized urban forms for planning purposes.

关键词： principal components analysis, cellular automata, geographical information systems, urban simulation.

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Development of a Parametric Clavicle Finite Element Model Using principal Component Analysis and Mesh Morphing Algorithm

引用

Chinese Journal of Biomedical Engineering(English Edition) 2017年第3期26卷 93-104页

作者： SHI Xiang-nan HE Yu-hao School of Transportation and Vehicle Engineering Shandong University of Technology University of Michigan Transportation Research Institute Department of Mechanical Engineering University of Michigan

The susceptibility of the clavicle to injury underscores its role as an important loading path during both frontal impact and side impact crashes, since clavicle is loaded directly by shoulder belt in frontal impact and through shoulder in lateral impact crashes. This study aims to developing a parametric clavicle finite element model capable of simulating impact responses with different parameters. A statistical clavicle geometry model was developed based on computed tomography(CT) scans from 89 subjects using principal component analysis(PCA) and multivariate regression analysis. Using this statistical clavicle geometry model and mesh morphing method developed in this study, a series of detailed 3D clavicle geometries were predicted by age, gender, and length of the bone, all of which showed strong effects on clavicle geometry. After assigning material properties and validation process, this parametric model developed in this study can be used to investigate the effects of age, gender and length at the same time.

关键词： clavicle principal component analysis mesh morphing parametric model

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Application of principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall

引用

Open Journal of Statistics 2014年第9期4卷 678-686页

作者： Sitti Sahriman Anik Djuraidah Aji Hamim Wigena Department of Statistics Bogor Agricultural University Bogor Indonesia

Statistical downscaling (SD) analyzes relationship between local-scale response and global-scale predictors. The SD model can be used to forecast rainfall (local-scale) using global-scale precipitation from global circulation model output (GCM). The objectives of this research were to determine the time lag of GCM data and build SD model using PCR method with time lag of the GCM precipitation data. The observations of rainfall data in Indramayu were taken from 1979 to 2007 showing similar patterns with GCM data on 1st grid to 64th grid after time shift (time lag). The time lag was determined using the cross-correlation function. However, GCM data of 64 grids showed multicollinearity problem. This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model. Dummy variables were determined based on partial least squares regression (PLSR). The PCR model with dummy variables improved the rainfall prediction. The SD model with lag-GCM predictors was also better than SD model without lag-GCM.

关键词： Cross Correlation Function Global Circulation Model Partial Least Square Regression principal Component Regression Statistical Downscaling

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Polarimetric Meteorological Satellite Data Processing Software Classification Based on principal Component Analysis and Improved K-Means Algorithm

引用

Journal of Geoscience and Environment Protection 2017年第7期5卷 39-48页

作者： Manyun Lin Xiangang Zhao Cunqun Fan Lizi Xie Lan Wei Peng Guo National Satellite Meteorological Centre Beijing China

With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.

关键词： principal Component Analysis Improved K-Mean Algorithm Meteorological Data Processing Feature Analysis Similarity Algorithm

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：