2,159 research outputs found
Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process
Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach
Digital Beamforming and Traffic Monitoring Using the new FSAR System of DLR
In November 2006 the first X-band test flight of DLR’s new FSAR system has been performed successfully and in February 2007 the first flight campaign has been conducted for acquiring experimental multi-channel data of controlled ground moving targets. In the paper the performed experiments and the used setup of the FSAR X-band section are described and preliminary results in the field of ground moving target indication and digital beamforming are presented
Reconstructing the free-energy landscape of Met-enkephalin using dihedral Principal Component Analysis and Well-tempered Metadynamics
Well-Tempered Metadynamics (WTmetaD) is an efficient method to enhance the
reconstruction of the free-energy surface of proteins. WTmetaD guarantees a
faster convergence in the long time limit in comparison with the standard
metadynamics. It still suffers however from the same limitation, i.e. the non
trivial choice of pertinent collective variables (CVs). To circumvent this
problem, we couple WTmetaD with a set of CVs generated from a dihedral
Principal Component Analysis (dPCA) on the Ramachadran dihedral angles
describing the backbone structure of the protein. The dPCA provides a generic
method to extract relevant CVs built from internal coordinates. We illustrate
the robustness of this method in the case of the small and very diffusive
Metenkephalin pentapeptide, and highlight a criterion to limit the number of
CVs necessary to biased the metadynamics simulation. The free-energy landscape
(FEL) of Met-enkephalin built on CVs generated from dPCA is found rugged
compared with the FEL built on CVs extracted from PCA of the Cartesian
coordinates of the atoms.Comment: 17 pages, 9 figures (4 in color
DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets
Principal component analysis (PCA) has well-documented merits for data
extraction and dimensionality reduction. PCA deals with a single dataset at a
time, and it is challenged when it comes to analyzing multiple datasets. Yet in
certain setups, one wishes to extract the most significant information of one
dataset relative to other datasets. Specifically, the interest may be on
identifying, namely extracting features that are specific to a single target
dataset but not the others. This paper develops a novel approach for such
so-termed discriminative data analysis, and establishes its optimality in the
least-squares (LS) sense under suitable data modeling assumptions. The
criterion reveals linear combinations of variables by maximizing the ratio of
the variance of the target data to that of the remainders. The novel approach
solves a generalized eigenvalue problem by performing SVD just once. Numerical
tests using synthetic and real datasets showcase the merits of the proposed
approach relative to its competing alternatives.Comment: 5 pages, 2 figure
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