1,154 research outputs found
Phase I analysis of hidden operating status for wind turbine
Data-driven methods based on Supervisory Control and Data Acquisition (SCADA)
become a recent trend for wind turbine condition monitoring. However, SCADA
data are known to be of low quality due to low sampling frequency and complex
turbine working dynamics. In this work, we focus on the phase I analysis of
SCADA data to better understand turbines' operating status. As one of the most
important characterization, the power curve is used as a benchmark to represent
normal performance. A powerful distribution-free control chart is applied after
the power generation is adjusted by an accurate power curve model, which
explicitly takes into account the known factors that can affect turbines'
performance. Informative out-of-control segments have been revealed in real
field case studies. This phase I analysis can help improve wind turbine's
monitoring, reliability, and maintenance for a smarter wind energy system
De-Biased Two-Sample U-Statistics With Application To Conditional Distribution Testing
In some high-dimensional and semiparametric inference problems involving two
populations, the parameter of interest can be characterized by two-sample
U-statistics involving some nuisance parameters. In this work we first extend
the framework of one-step estimation with cross-fitting to two-sample
U-statistics, showing that using an orthogonalized influence function can
effectively remove the first order bias, resulting in asymptotically normal
estimates of the parameter of interest. As an example, we apply this method and
theory to the problem of testing two-sample conditional distributions, also
known as strong ignorability. When combined with a conformal-based rank-sum
test, we discover that the nuisance parameters can be divided into two
categories, where in one category the nuisance estimation accuracy does not
affect the testing validity, whereas in the other the nuisance estimation
accuracy must satisfy the usual requirement for the test to be valid. We
believe these findings provide further insights into and enhance the conformal
inference toolbox.Comment: 25 pages, 1 figure, 6 table
Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations
Low rank matrix approximation is an important tool in machine learning. Given
a data matrix, low rank approximation helps to find factors, patterns and
provides concise representations for the data. Research on low rank
approximation usually focus on real matrices. However, in many applications
data are binary (categorical) rather than continuous. This leads to the problem
of low rank approximation of binary matrix. Here we are given a
binary matrix and a small integer . The goal is to find two binary
matrices and of sizes and respectively, so
that the Frobenius norm of is minimized. There are two models of this
problem, depending on the definition of the dot product of binary vectors: The
model and the Boolean semiring model. Unlike low rank
approximation of real matrix which can be efficiently solved by Singular Value
Decomposition, approximation of binary matrix is -hard even for .
In this paper, we consider the problem of Column Subset Selection (CSS), in
which one low rank matrix must be formed by columns of the data matrix. We
characterize the approximation ratio of CSS for binary matrices. For
model, we show the approximation ratio of CSS is bounded by
and this bound is asymptotically tight. For
Boolean model, it turns out that CSS is no longer sufficient to obtain a bound.
We then develop a Generalized CSS (GCSS) procedure in which the columns of one
low rank matrix are generated from Boolean formulas operating bitwise on
columns of the data matrix. We show the approximation ratio of GCSS is bounded
by , and the exponential dependency on is inherent.Comment: 38 page
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