8,013 research outputs found
Feature Screening via Distance Correlation Learning
This paper is concerned with screening features in ultrahigh dimensional data
analysis, which has become increasingly important in diverse scientific fields.
We develop a sure independence screening procedure based on the distance
correlation (DC-SIS, for short). The DC-SIS can be implemented as easily as the
sure independence screening procedure based on the Pearson correlation (SIS,
for short) proposed by Fan and Lv (2008). However, the DC-SIS can significantly
improve the SIS. Fan and Lv (2008) established the sure screening property for
the SIS based on linear models, but the sure screening property is valid for
the DC-SIS under more general settings including linear models. Furthermore,
the implementation of the DC-SIS does not require model specification (e.g.,
linear model or generalized linear model) for responses or predictors. This is
a very appealing property in ultrahigh dimensional data analysis. Moreover, the
DC-SIS can be used directly to screen grouped predictor variables and for
multivariate response variables. We establish the sure screening property for
the DC-SIS, and conduct simulations to examine its finite sample performance.
Numerical comparison indicates that the DC-SIS performs much better than the
SIS in various models. We also illustrate the DC-SIS through a real data
example.Comment: 32 pages, 5 tables and 1 figure. Wei Zhong is the corresponding
autho
Integrated airfoil and blade design method for large wind turbines
This paper presents an integrated method for designing airfoil families of large wind turbine blades. For a given rotor diameter and tip speed ratio, the optimal airfoils are designed based on the local speed ratios. To achieve high power performance at low cost, the airfoils are designed with an objective of high Cp and small chord length. When the airfoils are obtained, the optimum flow angle and rotor solidity are calculated which forms the basic input to the blade design. The new airfoils are designed based on the previous in-house airfoil family which were optimized at a Reynolds number of 3 million. A novel shape perturbation function is introduced to optimize the geometry on the existing airfoils and thus simplify the design procedure. The viscos/inviscid code Xfoil is used as the aerodynamic tool for airfoil optimization where the Reynolds number is set at 16 million with a free-stream Mach number of 0.25 at the blade tip. Results show that these new airfoils achieve high power coefficient in a wide range of angles of attack (AOA) and they are extremely insensitive to surface roughness
Development of a high-fidelity noise prediction and propagation model for noise generated from wind turbines
Predicting networking couples for metabolic pathways of Arabidopsis
Given an enzyme-compound couple, how can we identify whether it belongs to a networking couple or
non-networking couple? This is very important for investigating the metabolic pathways. To address
this problem, a novel approach was developed that is featured by using the knowledge of gene ontology
(GO), chemical functional group (FunG), and pseudo amino acid composition (PseAA) to represent the
samples of enzyme-compound couples. Two basic identifiers were formulated: one is called "GOFunG",
and the other, "PseAA-FunG". The prediction was operated by fusing these two basic
identifiers into one. As a showcase, the metabolic pathways were investigated for Arabidopsis thaliana,
a small flowering plant widely used as a model organism for studies of the cellular and molecular
biology of flowering plants. The average overall success rate via the jackknife cross-validation tests for
the 72 metabolic pathways in the Arabidopsis system was over 95%, suggesting that the current
approach might become a very useful tool for studying metabolic pathways and many other problems in
the cellular networking related areas
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