IEEE Transactions on Automation Science and Engineering, 4(3):465-469, July 2007 (article)
The final properties of sophisticated products can
be affected by many unapparent dependencies within the manufacturing
process, and the products integrity can often only be
checked in a final measurement. Troubleshooting can therefore
be very tedious if not impossible in large assembly lines.
In this paper we show that Feature Selection is an efficient tool for
serial-grouped lines to reveal causes for irregularities in product
attributes. We compare the performance of several methods for
Feature Selection on real-world problems in mass-production of
Note to Practitioners We present a data based procedure
to localize flaws in large production lines: using the results of
final quality inspections and information about which machines
processed which batches, we are able to identify machines which
cause low yield.
IEEE Transactions on Semiconductor Manufacturing, 19(4):475-486, February 2006 (article)
Fluctuations are inherent to any fabrication process.
Integrated circuits and micro-electro-mechanical systems are
particularly affected by these variations, and due to high quality
requirements the effect on the devices performance has to be
understood quantitatively. In recent years it has become possible
to model the performance of such complex systems on the basis
of design specifications, and model-based Sensitivity Analysis
has made its way into industrial engineering. We show how an
efficient Bayesian approach, using a Gaussian process prior, can
replace the commonly used brute-force Monte Carlo scheme,
making it possible to apply the analysis to computationally costly
models. We introduce a number of global, statistically justified
sensitivity measures for design analysis and optimization. Two
models of integrated systems serve us as case studies to introduce
the analysis and to assess its convergence properties. We show
that the Bayesian Monte Carlo scheme can save costly simulation
runs and can ensure a reliable accuracy of the analysis.
Journal of the European Ceramic Society, 26(15):3061-3065, November 2006 (article)
A common approach for the determination of Slow Crack Growth (SCG) parameters
are the static and dynamic loading method. Since materials with small Weibull
module show a large variability in strength, a correct statistical analysis of the
data is indispensable. In this work we propose the use of the Maximum Likelihood
method and a Baysian analysis, which, in contrast to the standard procedures, take
into account that failure strengths are Weibull distributed. The analysis provides
estimates for the SCG parameters, the Weibull module, and the corresponding confidence
intervals and overcomes the necessity of manual differentiation between inert
and fatigue strength data. We compare the methods to a Least Squares approach,
which can be considered the standard procedure. The results for dynamic loading
data from the glass sealing of MEMS devices show that the assumptions inherent
to the standard approach lead to significantly different estimates.
In ECML 2006, pages: 353-364, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)
Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore the behavior of the system, and to compute sensitivity measures as expectations over the input distribution. Monte Carlo methods are a simple approach to estimate these integrals, but they are infeasible when the models are computationally expensive. Using a Gaussian processes prior, expensive simulation runs can be saved. This Bayesian quadrature allows for an active selection of inputs where the simulation promises to be most valuable, and the number of simulation runs can be reduced further.
We present an active learning scheme for sensitivity analysis which is rigorously derived from the corresponding Bayesian expected loss. On three fully featured, high dimensional physical models of electro-mechanical sensors, we show that the learning rate in the active learning scheme is significantly better than for passive learning.
(136), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)
Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and classification models. Inference can be performed analytically only for the regression model with Gaussian noise. For all other likelihood models inference is intractable and various approximation techniques have been proposed. In recent years
expectation-propagation (EP) has been developed as a general method for approximate inference. This article provides a general summary of how expectation-propagation can be used for approximate
inference in Gaussian process models. Furthermore we present a case study describing its implementation for a new robust variant of
Gaussian process regression. To gain further insights into the quality of the EP approximation we present experiments in which we compare to results obtained by Markov chain Monte Carlo (MCMC) sampling.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems