In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ...In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.展开更多
The financial crisis undoubtedly exerted a pressure on the companies operating in Poland. Thus, it is important to undertake researches that reveal the paths and strength of the transmission of financial crisis with r...The financial crisis undoubtedly exerted a pressure on the companies operating in Poland. Thus, it is important to undertake researches that reveal the paths and strength of the transmission of financial crisis with regard to the business entities. This paper presents partial results of the researches dedicated to the analysis of the impact of financial crisis on the financial situation of companies operating in Silesian Region in Poland. It analyses and discusses the general changes in the financial ratios that inform about the company＇s financial liquidity and the level of liquidity risk. As a research paper, it aims at justifying hypotheses about the changes of liquidity and liquidity risk in companies operating in Poland, Silesian Region within the period of 2006-2009. The tested hypotheses generally indicate the decrease of liquidity in the aftermath of crisis and a worse situation in the Silesian Region, as compared to the national level. The study is based on an application of a part of authors＇ self-developed method--the CFS Watch （Corporate Financial Situation Watch）, which consists of five analytical modules. In this study, one module is applied： the FLA Module （Financial Liquidity Analysis） with regard to financial liquidity and the level of liquidity risk. The research is based on the data collected by the Polish Central Statistical Office. The analysis of FLA Module is based on two samples of companies： companies operating in the Silesian Region （denoted as the MEPP sample）, and companies operating in Poland （denoted as the MAPP sample）. This allows developing a comparative analysis between regional and national dimension. The results of the study represent an interesting starting point for further comparative researches based on the analysis of the changes in the level of liquidity and liquidity risk of companies operating in different countries. It may form a base for finding similarities or differences in their financial situation in the aftermath of the financial crisis. The CFS Watch method in terms of the liquidity can be widely applied to make the results comparable.展开更多
基金Support by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019)
文摘In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
文摘The financial crisis undoubtedly exerted a pressure on the companies operating in Poland. Thus, it is important to undertake researches that reveal the paths and strength of the transmission of financial crisis with regard to the business entities. This paper presents partial results of the researches dedicated to the analysis of the impact of financial crisis on the financial situation of companies operating in Silesian Region in Poland. It analyses and discusses the general changes in the financial ratios that inform about the company＇s financial liquidity and the level of liquidity risk. As a research paper, it aims at justifying hypotheses about the changes of liquidity and liquidity risk in companies operating in Poland, Silesian Region within the period of 2006-2009. The tested hypotheses generally indicate the decrease of liquidity in the aftermath of crisis and a worse situation in the Silesian Region, as compared to the national level. The study is based on an application of a part of authors＇ self-developed method--the CFS Watch （Corporate Financial Situation Watch）, which consists of five analytical modules. In this study, one module is applied： the FLA Module （Financial Liquidity Analysis） with regard to financial liquidity and the level of liquidity risk. The research is based on the data collected by the Polish Central Statistical Office. The analysis of FLA Module is based on two samples of companies： companies operating in the Silesian Region （denoted as the MEPP sample）, and companies operating in Poland （denoted as the MAPP sample）. This allows developing a comparative analysis between regional and national dimension. The results of the study represent an interesting starting point for further comparative researches based on the analysis of the changes in the level of liquidity and liquidity risk of companies operating in different countries. It may form a base for finding similarities or differences in their financial situation in the aftermath of the financial crisis. The CFS Watch method in terms of the liquidity can be widely applied to make the results comparable.
