Marangoni Benard convection, which is mainly driven by the thermocapillary （Marangoni） effect, occurs in a thin liquid layer heated uniformly from the bottom. The wavenumber of supercritical convection is studied ex...Marangoni Benard convection, which is mainly driven by the thermocapillary （Marangoni） effect, occurs in a thin liquid layer heated uniformly from the bottom. The wavenumber of supercritical convection is studied experimentally in a 160×160-mm^2 cavity that ＆ heated from the bottom block. The convection pattern ＆ visualized by an infrared thermography camera. It is shown that the onset of the Benard cell is consistent with theoretical analysis. The wavenumber decreases obviously with increasing temperature, except for a slight increase near the onset. The wavenumber gradually approaches the minimum when the supercritical number e is larger than 10. Finally, a formula is devised to describe the wavenumber selection in supercritical convection.展开更多
Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,...Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality.However,for cluster analysis,this must be done in an unsupervised manner without the benefit of data labels.This paper presents a novel feature selection approach for cluster analysis,utilizing clusterability metrics to remove features that least contribute to a dataset’s tendency to cluster.Two versions are presented and evaluated:The Hopkins clusterability filter which utilizes the Hopkins test for spatial randomness and the Dip clusterability filter which utilizes the Dip test for unimodality.These new techniques,along with a range of existing filter and wrapper feature selection techniques were evaluated on eleven real-world spectroscopy datasets using internal and external clustering indices.Our newly proposed Hopkins clusterability filter performed the best of the six filter techniques evaluated.However,it was observed that results varied greatly for different techniques depending on the specifics of the dataset and the number of features selected,with significant instability observed for most techniques at low numbers of features.It was identified that the genetic algorithm wrapper technique avoided this instability,performed consistently across all datasets and resulted in better results on average than utilizing the all the features in the spectra.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 11502271 and 11372328the Strategic Priority Research Program on Space Science of the Chinese Academy of Sciences under Grant Nos XDA04020405 and XDA04020202-05the China Manned Space Engineering Program
文摘Marangoni Benard convection, which is mainly driven by the thermocapillary （Marangoni） effect, occurs in a thin liquid layer heated uniformly from the bottom. The wavenumber of supercritical convection is studied experimentally in a 160×160-mm^2 cavity that ＆ heated from the bottom block. The convection pattern ＆ visualized by an infrared thermography camera. It is shown that the onset of the Benard cell is consistent with theoretical analysis. The wavenumber decreases obviously with increasing temperature, except for a slight increase near the onset. The wavenumber gradually approaches the minimum when the supercritical number e is larger than 10. Finally, a formula is devised to describe the wavenumber selection in supercritical convection.
文摘Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality.However,for cluster analysis,this must be done in an unsupervised manner without the benefit of data labels.This paper presents a novel feature selection approach for cluster analysis,utilizing clusterability metrics to remove features that least contribute to a dataset’s tendency to cluster.Two versions are presented and evaluated:The Hopkins clusterability filter which utilizes the Hopkins test for spatial randomness and the Dip clusterability filter which utilizes the Dip test for unimodality.These new techniques,along with a range of existing filter and wrapper feature selection techniques were evaluated on eleven real-world spectroscopy datasets using internal and external clustering indices.Our newly proposed Hopkins clusterability filter performed the best of the six filter techniques evaluated.However,it was observed that results varied greatly for different techniques depending on the specifics of the dataset and the number of features selected,with significant instability observed for most techniques at low numbers of features.It was identified that the genetic algorithm wrapper technique avoided this instability,performed consistently across all datasets and resulted in better results on average than utilizing the all the features in the spectra.
