While model-based process monitoring and system identiication share the common goal of describing the behavior of a physical system based on a mathematical model, the assumptions made by system identiication are typically too strong to apply it directly to monitoring problems. This article describes SQUID, a new system identiication method that uses refutation rather than search to identify a m...

Given measured data we propose a model consisting of a linear, time-invariant system aaected by norm-bounded perturbation. In this paper we examine the special case of (real) parameter uncertainty. The plants belonging to the resulting uncertain family form a convex set. A robust control method based on strict-positive realness results can be applied. This method allows the computation of a con...

In this article, we propose a robust person identification algorithm in video that is able to detect and identify faces. The method we have developed combines approaches for detection and recognition using an efficient correlation technique and auto associative neural network. The experiments confirm the robustness of our approach against occlusion and illumination changes.

Previous approaches to the task of native language identification (Koppel et al., 2005) have been limited to small, within-corpus evaluations. Because these are restrictive and unreliable, we apply cross-corpus evaluation to the task. We demonstrate the efficacy of lexical features, which had previously been avoided due to the within-corpus topic confounds, and provide a detailed evaluation of ...

The problem of identification of uncertain systems arises whenever we must choose a model from a model class that only approximately describes the underlying system. Identification schemes here need to overcome the distortions in the data due to undermodeling error in addition to random stochastic noise. We introduce the notion of robust consistency, which requires estimating the optimal approx...

Right after its invention in the late nineties, Frequency Domain Decomposition (FDD) identification technique became very popular operational modal analysis community due to simplicity and robustness. The underlying idea of this consists computing singular value decomposition Power Spectral Densities (PSDs) estimated from measured vibration responses with periodogram (also known as “Welch’s”) a...

Identification for robust control must deliver not only a nominal model, but also a reliable estimate of the uncertainty associated with the model. This paper addresses recent approaches to robust identification, that aim at dealing with contributions from the two main uncertainty sources: unmodeled dynamics and noise affecting the data. In particular, the following methods are considered: non-...