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Cédric Join

Bio: Cédric Join is an academic researcher from University of Lorraine. The author has contributed to research in topics: Nonlinear system & Fault detection and isolation. The author has an hindex of 32, co-authored 178 publications receiving 4562 citations. Previous affiliations of Cédric Join include Nancy-Université & Concordia University Wisconsin.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a phenomenological model is used for nonlinear black box identification, where a model is replacing a precise mathematical description, and simulations are provided for two examples: the classic ball and beam system and a large scale linear system.

47 citations

Proceedings ArticleDOI
10 Dec 2013
TL;DR: Experimental results are compared to those obtained via other control techniques, showing at least on-par performance with this very straightforward approach, which is moreover quite easy to implement.
Abstract: The newly introduced model-free control is applied to the stabilization of an active magnetic bearing, which is a most important industrial device. Experimental results are compared to those obtained via other control techniques, showing at least on-par performance with this very straightforward approach, which is moreover quite easy to implement.

47 citations

Proceedings ArticleDOI
23 Jun 2010
TL;DR: A mathematical explanation of the ubiquity of PID controllers in the industry is provided by comparing their sampling with the one of “intelligent” PID controllers, which were recently introduced.
Abstract: The ubiquity of PID controllers in the industry has remained mysterious until now. We provide here a mathematical explanation of this strange phenomenon by comparing their sampling with the the one of “intelligent” PID controllers, which were recently introduced. Some computer simulations nevertheless confirm the superiority of the new intelligent feedback design.

47 citations

Journal ArticleDOI
TL;DR: In this article, a model-free water level control method is proposed for a run-of-the-river power plant with a cascade of cascaded power plants, and the set-point is followed even in severe operating conditions.

43 citations

Journal ArticleDOI
28 Feb 2005
TL;DR: In this article, a systematic method that ensures the synthesis of asymptotic nonlinear filters for residual generation is proposed for fault detection and isolation in a nonlinear framework is addressed.
Abstract: The problem of fault detection and isolation in a nonlinear framework is addressed. A systematic method that ensures the synthesis of asymptotic nonlinear filters is proposed for residual generation. In order to show the effectiveness of the proposed approach, each theoretical step is illustrated by means of simulations. The example of a three-tank system is then experimentally considered to verify the validity of our approach.

42 citations


Cited by
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Journal ArticleDOI
TL;DR: A bibliographical review on reconfigurable fault-tolerant control systems (FTCS) is presented, with emphasis on the reconfiguring/restructurable controller design techniques.

2,455 citations

Book ChapterDOI
15 Feb 2011

1,876 citations

01 Nov 1981
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

1,829 citations