The operating data of high-speed train electric drive systems contain unknown disturbances and noise,which makes it challenging to identify incipient faults.In order to improve the incipient fault detection capability...The operating data of high-speed train electric drive systems contain unknown disturbances and noise,which makes it challenging to identify incipient faults.In order to improve the incipient fault detection capability of the electric drive system,a fault detection algorithm based on dynamic inner independent component analysis is proposed.In this paper,a mathematical proof of the dynamic inner independent component analysis algorithm is first given,and then the method is validated by means of an electric drive system simulation platform.The simulation results show that the dynamic fault detection method proposed in this paper can effectively monitor the operating status of the electric drive system without the need to establish a mathematical model of the system and expertise.Compared with the fault detection methods based on independent component analysis and principal component analysis,the proposed method decreases the fault detection time and reduces the false alarm rate and missing alarm rate.展开更多
This study takes City S,a mega-city in North China,as the research object.Based on a thorough review of relevant literature and theoretical foundations,it employs the Principal Component Analysis(PCA)method to constru...This study takes City S,a mega-city in North China,as the research object.Based on a thorough review of relevant literature and theoretical foundations,it employs the Principal Component Analysis(PCA)method to construct a multidimensional indicator system encompassing population,economy,society,and ecology.Using statistical data from 2013 to 2020,the study quantitatively analyzes the degree of influence exerted by various driving factors on urban landscape changes.The results show that natural factors,population factors,economic development factors,and social policy factors are the primary drivers of landscape change.Social development and ecological constraints also play a role in the adjustment of urban spatial structure to a certain extent.The study further reveals the comprehensive driving mechanism underlying urban landscape evolution and provides a theoretical basis and methodological support for urban land use optimization and landscape planning.PCA demonstrates strong applicability in identifying multifactor coupling mechanisms and can serve as a scientific reference for the formulation of urban sustainable development strategies.展开更多
The decrease of total cultivated area and the lower per capita available arable land resource are now serious problems in Shandong Province, a major agricultural province in China. These problems will become more seri...The decrease of total cultivated area and the lower per capita available arable land resource are now serious problems in Shandong Province, a major agricultural province in China. These problems will become more serious along with the further development of economy. In this paper, based on the statistical information at provincial and county levels, the changes of arable land in Shandong Province and their driving forces during the last 50 years are analyzed. The general changing trends of arable land and per capita available arable land are reducing, and the trends of decrease will continue when the economy is developing. The result of GIS spatial analysis shows that the change of the arable land use in Shandong Province has a regional difference. Eight variables having influences on cultivated land change are analyzed by principal component analysis. The results show that the dynamic development of economy, pressure of social system and progress of scientific techniques in agriculture are the main causes for cultivated land reduction. The principal factors which can be considered as driving forces for arable land change include per capita net living space, total population and per ha grain yield. By using regressive equation, along with analysis on population growth and economic development, cultivated areas in Shandong Province in 2005 and 2010 are predicted respectively. The predicted cultivated areas in Shandong will be 6435.47 thousand hain 2005 and 6336.23 thousand ha in 2010 respectively.展开更多
By selecting impact factors of driving force and formulating evaluation criteria of the impacts,the evaluation system of corresponding driving force impact of land use change was established.Taking Lu'an mining ar...By selecting impact factors of driving force and formulating evaluation criteria of the impacts,the evaluation system of corresponding driving force impact of land use change was established.Taking Lu'an mining area as an example,the specific impact factors of coal mine were comprehensively evaluated and analyzed in order to carry out qualitative and quantitative analysis for the driving force of mining-land use change.The principal component analysis shows that the social and economic development in mining area from 2000 to 2007 demonstrates continuous accelerate trends,and the impacts of its overall driving force to land use change are increased gradually.The socio-economic factors have more impacts to mining-land use change than those of the natural resources.The main driving force of mining-land use change also include population,technological progress and policy.展开更多
On the basis of overview of the study area,by analyzing the dynamic change of farmland in Ninglang County,we can find that the farmland area in this county tended to decrease from 1996 to 2008.According to the investi...On the basis of overview of the study area,by analyzing the dynamic change of farmland in Ninglang County,we can find that the farmland area in this county tended to decrease from 1996 to 2008.According to the investigation data concerning land change provided by Bureau of Land and Resources in Ninglang County and socio-economic data provided by Bureau of Statistics in Ninglang County,we select 11 indices,such as total population,GDP,total output value of county and so on,coupled with SPSS statistical method,we adopt principal component analysis method to analyze driving force factors of farmland use change in the high and cold areas in Northwest Yunnan.The results show that the two factors of economic development and population growth are the dominant driving factors impacting farmland use change,and the policy factors,such as"returning farmland to forests",are also the important driving factors impacting Ninglang County.展开更多
文摘The operating data of high-speed train electric drive systems contain unknown disturbances and noise,which makes it challenging to identify incipient faults.In order to improve the incipient fault detection capability of the electric drive system,a fault detection algorithm based on dynamic inner independent component analysis is proposed.In this paper,a mathematical proof of the dynamic inner independent component analysis algorithm is first given,and then the method is validated by means of an electric drive system simulation platform.The simulation results show that the dynamic fault detection method proposed in this paper can effectively monitor the operating status of the electric drive system without the need to establish a mathematical model of the system and expertise.Compared with the fault detection methods based on independent component analysis and principal component analysis,the proposed method decreases the fault detection time and reduces the false alarm rate and missing alarm rate.
文摘This study takes City S,a mega-city in North China,as the research object.Based on a thorough review of relevant literature and theoretical foundations,it employs the Principal Component Analysis(PCA)method to construct a multidimensional indicator system encompassing population,economy,society,and ecology.Using statistical data from 2013 to 2020,the study quantitatively analyzes the degree of influence exerted by various driving factors on urban landscape changes.The results show that natural factors,population factors,economic development factors,and social policy factors are the primary drivers of landscape change.Social development and ecological constraints also play a role in the adjustment of urban spatial structure to a certain extent.The study further reveals the comprehensive driving mechanism underlying urban landscape evolution and provides a theoretical basis and methodological support for urban land use optimization and landscape planning.PCA demonstrates strong applicability in identifying multifactor coupling mechanisms and can serve as a scientific reference for the formulation of urban sustainable development strategies.
基金The National Natural Science Foundation of China, No.49971004
文摘The decrease of total cultivated area and the lower per capita available arable land resource are now serious problems in Shandong Province, a major agricultural province in China. These problems will become more serious along with the further development of economy. In this paper, based on the statistical information at provincial and county levels, the changes of arable land in Shandong Province and their driving forces during the last 50 years are analyzed. The general changing trends of arable land and per capita available arable land are reducing, and the trends of decrease will continue when the economy is developing. The result of GIS spatial analysis shows that the change of the arable land use in Shandong Province has a regional difference. Eight variables having influences on cultivated land change are analyzed by principal component analysis. The results show that the dynamic development of economy, pressure of social system and progress of scientific techniques in agriculture are the main causes for cultivated land reduction. The principal factors which can be considered as driving forces for arable land change include per capita net living space, total population and per ha grain yield. By using regressive equation, along with analysis on population growth and economic development, cultivated areas in Shandong Province in 2005 and 2010 are predicted respectively. The predicted cultivated areas in Shandong will be 6435.47 thousand hain 2005 and 6336.23 thousand ha in 2010 respectively.
基金Project(MTKJ2010-377)supported by the Sci-tech Plan Project of China National Coal AssociationProject(B2006-18)supported by the Doctor Fund of Henan Polytechnic University
文摘By selecting impact factors of driving force and formulating evaluation criteria of the impacts,the evaluation system of corresponding driving force impact of land use change was established.Taking Lu'an mining area as an example,the specific impact factors of coal mine were comprehensively evaluated and analyzed in order to carry out qualitative and quantitative analysis for the driving force of mining-land use change.The principal component analysis shows that the social and economic development in mining area from 2000 to 2007 demonstrates continuous accelerate trends,and the impacts of its overall driving force to land use change are increased gradually.The socio-economic factors have more impacts to mining-land use change than those of the natural resources.The main driving force of mining-land use change also include population,technological progress and policy.
基金Supported by The National Natural Science Foundation of China(40861014)
文摘On the basis of overview of the study area,by analyzing the dynamic change of farmland in Ninglang County,we can find that the farmland area in this county tended to decrease from 1996 to 2008.According to the investigation data concerning land change provided by Bureau of Land and Resources in Ninglang County and socio-economic data provided by Bureau of Statistics in Ninglang County,we select 11 indices,such as total population,GDP,total output value of county and so on,coupled with SPSS statistical method,we adopt principal component analysis method to analyze driving force factors of farmland use change in the high and cold areas in Northwest Yunnan.The results show that the two factors of economic development and population growth are the dominant driving factors impacting farmland use change,and the policy factors,such as"returning farmland to forests",are also the important driving factors impacting Ninglang County.
