The homogeneity of groups of 16-dimensional wind direction roses (obtained by hierarchical clustering in a previous report) is discussed through the application of Andrews’ Curves. Principal Component Analysis (PCA) ...The homogeneity of groups of 16-dimensional wind direction roses (obtained by hierarchical clustering in a previous report) is discussed through the application of Andrews’ Curves. Principal Component Analysis (PCA) is employed to reduce dimensionality and to provide an ordering of the variables to compute Andrews’ Curves. Our results suggest that Andrews’ Curves greatly facilitate the visualization of homogeneity as well as reveal information that allows improving the clusters’ arrangement. A combined analysis employing Andrews’ Curves and Calinkski and Harabasz’ approach (a method for determining the optimal number of groups) helps to assess the strength of the group structure of the data as well as to detect anomalies such as misclassified objects or atypical values. Furthermore, it allows finding out that the 24 original seasonal hourly roses (representing the “day”) become better represented by 6 groups (rather than by 5 as proposed in the previous report). The new group arrangement was consistent with the dendogram for another cut-off distance. As a result the wind occurrences are now represented by a more detailed and smooth pattern: there is a decrease in northern wind between midday and twilight while eastern winds become more important towards the evening. The methodology proposed is a subject to be considered to become part of an automated system.展开更多
The flowers of ZhaoFen and RouFurong may contain essential oils with natural aromatic ingredients. In the present work, the chemical compositions of essential oil of Paeonia suffruticosa Andrews from the flowers of Zh...The flowers of ZhaoFen and RouFurong may contain essential oils with natural aromatic ingredients. In the present work, the chemical compositions of essential oil of Paeonia suffruticosa Andrews from the flowers of ZhaoFen and RouFurong grown only in China were investigated by GC-MS analysis. The results indicate that there are 27 constituents in ZhaoFen and 29 constituents in RouFurong, which account for 96.04% and 95.90% of the oils of ZhaoFen and RouFurong, respectively. The major components of the essential oils are character-rized by oxygenated terpenols, and their content is, respectively, 85.06% and 83.47%. The essential oil of Paeonia suffruticosa Andrews was reported for the first time on the aerial parts.展开更多
This paper proposes an intelligent process fault diagnosis system through integrating the techniques of Andrews plot and convolutional neural network.The proposed fault diagnosis method extracts features from the on-l...This paper proposes an intelligent process fault diagnosis system through integrating the techniques of Andrews plot and convolutional neural network.The proposed fault diagnosis method extracts features from the on-line process measurements using Andrews function.To address the uncertainty of setting the proper dimension of extracted features in Andrews function,a convolutional neural network is used to further extract diagnostic information from the Andrews function outputs.The outputs of the convolutional neural network are then fed to a single hidden layer neural network to obtain the final fault diagnosis result.The proposed fault diagnosis system is compared with a conventional neural network based fault diagnosis system and integrating Andrews function with neural network and manual selection of features in Andrews function outputs.Applications to a simulated CSTR process show that the proposed fault diagnosis system gives much better performance than the conventional neural network based fault diagnosis system and manual selection of features in Andrews function outputs.It reveals that the use of Andrews function and convolutional neural network can improve the diagnosis performance.展开更多
文摘The homogeneity of groups of 16-dimensional wind direction roses (obtained by hierarchical clustering in a previous report) is discussed through the application of Andrews’ Curves. Principal Component Analysis (PCA) is employed to reduce dimensionality and to provide an ordering of the variables to compute Andrews’ Curves. Our results suggest that Andrews’ Curves greatly facilitate the visualization of homogeneity as well as reveal information that allows improving the clusters’ arrangement. A combined analysis employing Andrews’ Curves and Calinkski and Harabasz’ approach (a method for determining the optimal number of groups) helps to assess the strength of the group structure of the data as well as to detect anomalies such as misclassified objects or atypical values. Furthermore, it allows finding out that the 24 original seasonal hourly roses (representing the “day”) become better represented by 6 groups (rather than by 5 as proposed in the previous report). The new group arrangement was consistent with the dendogram for another cut-off distance. As a result the wind occurrences are now represented by a more detailed and smooth pattern: there is a decrease in northern wind between midday and twilight while eastern winds become more important towards the evening. The methodology proposed is a subject to be considered to become part of an automated system.
文摘The flowers of ZhaoFen and RouFurong may contain essential oils with natural aromatic ingredients. In the present work, the chemical compositions of essential oil of Paeonia suffruticosa Andrews from the flowers of ZhaoFen and RouFurong grown only in China were investigated by GC-MS analysis. The results indicate that there are 27 constituents in ZhaoFen and 29 constituents in RouFurong, which account for 96.04% and 95.90% of the oils of ZhaoFen and RouFurong, respectively. The major components of the essential oils are character-rized by oxygenated terpenols, and their content is, respectively, 85.06% and 83.47%. The essential oil of Paeonia suffruticosa Andrews was reported for the first time on the aerial parts.
基金supports from the European Commission (Project No.:PIRSES-GA-2013-612230)National Natural Science Foundation of China (project No.:61673236)are gratefully acknowledged.
文摘This paper proposes an intelligent process fault diagnosis system through integrating the techniques of Andrews plot and convolutional neural network.The proposed fault diagnosis method extracts features from the on-line process measurements using Andrews function.To address the uncertainty of setting the proper dimension of extracted features in Andrews function,a convolutional neural network is used to further extract diagnostic information from the Andrews function outputs.The outputs of the convolutional neural network are then fed to a single hidden layer neural network to obtain the final fault diagnosis result.The proposed fault diagnosis system is compared with a conventional neural network based fault diagnosis system and integrating Andrews function with neural network and manual selection of features in Andrews function outputs.Applications to a simulated CSTR process show that the proposed fault diagnosis system gives much better performance than the conventional neural network based fault diagnosis system and manual selection of features in Andrews function outputs.It reveals that the use of Andrews function and convolutional neural network can improve the diagnosis performance.
