Projection Pursuit for Exploratory Supervised Classification

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data . We introduce new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.Data mining, Exploratory multivariate data analysis, Gene expression data, Discriminant analysis

explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology

The datasets being produced by high-throughput biological experiments, such as microarrays, have forced biologists to turn to sophisticated statistical analysis and visualization tools in order to understand their data. We address the particular need for an open-source exploratory data analysis tool that applies numerical methods in coordination with interactive graphics to the analysis of experimental data. The software package, known as explorase, provides a graphical user interface (GUI) on top of the R platform for statistical computing and the GGobi software for multivariate interactive graphics. The GUI is designed for use by biologists, many of whom are unfamiliar with the R language. It displays metadata about experimental design and biological entities in tables that are sortable and filterable. There are menu shortcuts to the analysis methods implemented in R, including graphical interfaces to linear modeling tools. The GUI is linked to data plots in GGobi through a brush tool that simultaneously colors rows in the entity information table and points in the GGobi plots.

Discrete tonal noise of NACA0015 airfoil at low reynolds number

This paper is a pilot study of the effect of external forcing and passive control on the generation of airfoil whistle noise. Interaction between instability travelling inside laminar boundary layer with the airfoil trailing edge produces discrete tonal noise. This phenomenon commonly found at low-to-moderate Reynolds numbers. The characteristics and behavior of tonal emissions at low Reynolds number differs from that at higher Reynolds number. Therefore, the purpose of this work is to study the discrete tonal noise generated by laminar boundary layer instability at low Reynolds number as well as at a variation of angle of attack. Experimental testing on NACA0015 was done in the anechoic wind tunnel to measure the sound spectrum at Reynolds number of Re~104 and angle of attack of 0°≤α≤5°. This work is intended to provide additional information to the tonal behavior of NACA series airfoil. Flow separation without reattachment occurs on the suction side within the selected Reynolds number and angle of attack. No tonal sound was found if fs falls below 40dB. At low Reynolds number, airfoil discrete tone consists of high intensity fs accompanied by more pronounced fn as freestream velocity increases. Airfoil tonal noise gradually decreases as angle of attack increases from α=0^° before disappearing beyond α=5°. Moreover, previously proposed empirical models to predict fs were found to have limitation in predicting tonal frequency at low Reynolds number at a variation of angle of attack. In addition, general observation shows fn has a velocity dependency of ~U0.8 while f_s is prone to exhibit ladder structure behavior with velocity dependency of ~U1.3