scholarly journals Principal Component Analysis of the Potential for Increased Rail Competitiveness in East-Central Europe

2019 ◽  
Vol 11 (15) ◽  
pp. 4181 ◽  
Author(s):  
Szabolcs Duleba ◽  
Bálint Farkas
Keyword(s):  
Principal Component Analysis ◽  
Latent Variable ◽  
Structural Changes ◽  
Principal Component ◽  
Component Analysis ◽  
Factor Space ◽  
Market Liberalization ◽  
East Central Europe ◽  
East Central ◽  
Rail Freight

Increased rail competitiveness has been the objective of many countries around the world, including member states of the EU. Although railway market liberalization has always been accompanied by high expectations of increased efficiency and competitiveness, the overall impact of such decisions can be considered controversial. This paper aims to contribute to the scientific debate by conducting a factor analysis of some East-Central European countries from the aspect of rail freight competitiveness. Since many highly correlated factors influence competitiveness, its mathematical–statistical representation and analysis is difficult due to the high number of dimensions of the factor space. Moreover, competitiveness cannot be measured directly only as a latent variable which is a feature of Principal Component Analysis (PCA). The introduced PCA, model by way of reducing the number of dimensions, can highlight the relations among the attributes and determine the most crucial issues capable of increasing rail competitiveness in the given countries and also of clustering those national railway markets. Recommendations for structural changes in national rail freight markets of the region are also supplied. Our results show that international rail competitiveness depends rather on market efficiency than on market liberalization due to the fact that the Global Competitiveness Index and Export/Import attributes did not significantly correlate with market concentration. As for the larger domestic rail freight sectors, small freight forwarders—spawned by liberalization—are shown to play a significant role in increasing competitiveness.

scholarly journals Multi-trait Analysis of Agroclimate Variations During the Growing Season in East-Central Poland (1971-2005)

2015 ◽  
Vol 29 (2) ◽  
pp. 213-219 ◽  
Author(s):  
Elżbieta Radzka ◽  
Katarzyna Rymuza
Keyword(s):  
Principal Component Analysis ◽  
Air Temperature ◽  
Meteorological Data ◽  
Principal Component ◽  
Growing Season ◽  
Component Analysis ◽  
The South ◽  
East Central ◽  
Central Poland ◽  
The North

Abstract The work is based on meteorological data recorded by nine stations of the Institute of Meteorology and Water Management located in east-central Poland from 1971 to 2005. The region encompasses the North Podlasian Lowland and the South Podlasian Lowland. Average values of selected agroclimate indicators for the growing season were determined. Moreover, principal component analysis was conducted to indicate elements that exerted the greatest influence on the agroclimate. Also, cluster analysis was carried out to select stations with similar agroclimate. Ward method was used for clustering and the Euclidean distance was applied. Principal component analysis revealed that the agroclimate of east-central Poland was predominantly affected by climatic water balance, number of days of active plant growth, length of the farming period, and the average air temperature during the growing season (Apr-Sept). Based on the analysis, the region of east-central Poland was divided into two groups (areas) with different agroclimatic conditions. The first area comprized the following stations: Szepietowo and Białowieża located in the North Podlasian Lowland and Biała Podlaska situated in the northern part of the South Podlasian Lowland. This area was characterized by shorter farming periods and a lower average air temperature during the growing season. The other group included the remaining stations located in the western part of both the Lowlands which was warmer and where greater water deficits were recorded.

Structural performance assessment considering both observed and latent environmental and operational conditions: A Gaussian process and probability principal component analysis method

2022 ◽  
pp. 147592172110620
Author(s):  
Yi-Chen Zhu ◽  
Wen Xiong ◽  
Xiao-Dong Song
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Process ◽  
Performance Assessment ◽  
Structural Changes ◽  
Principal Component ◽  
Component Analysis ◽  
Assessment Model ◽  
Monitoring Data ◽  
Model Parameters ◽  
Structural Responses

Structural faults like damage and degradations will cause changes in structure response data. Performance assessment can be conducted by investigating such changes. In real implementations however, structural responses are affected by environmental and operational variations (EOVs) as well. Such variation should be well captured by the assessment model when detecting structural changes. It should be noted that not all EOVs can be measured by the monitoring system. When both observed and latent EOVs have significant effects on the monitored structural responses, these two effects should be considered properly. Furthermore, uncertainties can be significant for the monitoring data since loads and EOVs cannot be directly controlled under working conditions. To address these problems, this work proposes a performance assessment method considering both observed and latent EOVs. A Gaussian process is used to model the functional behaviour between structural response and observed EOVs whilst principal component analysis is used to eliminate the effect of latent EOVs. These two methods are combined using a Bayesian formulation and the effect of both observed and latent EOVs are modelled. The associated model parameters are inferred through probability density functions to account for the uncertainties. A synthetic data example is presented to validate the proposed method. It is also applied to the monitoring data of a long-span cable-stayed bridge with different damage scenarios considered, illustrating its capability of real implementations in structural health monitoring.

scholarly journals Finding Structures of Interest in a Large Data Set Using Factor Analysis

2016 ◽  
Vol 26 (2) ◽  
Author(s):  
Peter Filzmoser
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Statistical Method ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Factor Space ◽  
Data Set ◽  
Large Data Set

In this paper we introduce a statistical method which can be used in combination with principal component analysis or factor analysis. Certain variables of a large data set which are of interest can be selected in order to calculate loadings and scores of these variables. We describe how the remaining variables of the data set can be presented in the previously extracted factor space. Furthermore, a possibility for the representation of the results is shown which is helpful for the interpretation.

Sign in / Sign up

Export Citation Format

Share Document