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.

A mathematical proof of the existence of trends in financial time series

Abstract: We are settling a longstanding quarrel in quantitative finance by proving the existence of trends in financial time series thanks to a theorem due to P. Cartier and Y. Perrin, which is expressed in the language of nonstandard analysis (Integration over finite sets, F. & M. Diener (Eds): Nonstandard Analysis in Practice, Springer, 1995, pp. 195--204). Those trends, which might coexist with some altered random walk paradigm and efficient market hypothesis, seem nevertheless difficult to reconcile with the celebrated Black-Scholes model. They are estimated via recent techniques stemming from control and signal theory. Several quite convincing computer simulations on the forecast of various financial quantities are depicted. We conclude by discussing the role of probability theory.

A new control strategy for shape memory alloys actuators

Abstract: Shape memory alloys (SMA) are more and more integrated in engineering applications. These materials with their shape memory effect permit to simplify mechanism and to reduce the size of actuators. SMA parts can easily be activated by Joule effect but their modelling and consequently their control remains difficult, it is principally due to their hysteretic thermomechanical behaviour. Another difficulty is that the characteristics of the material are time-varying, especially during cyclic loadings. So, most of successful control strategy applied to SMA actuator are particularly heavy and used the Preisach model or neural networks to model the hysteretic behaviour of these material but this kind of models are difficult to identify and to use in real time. That is why this study deals with an application of the new framework of model-free control and restricted model control applied to a SMA spring based actuator. This control strategy is based on new results on fast derivative s estimation of noisy signals, its main advantages are: its simplicity, its robustness and the fact that it is easy to compute. Experimental results and comparisons with PID control are exposed that demonstrate the efficiency of this new control strategy despite thermal perturbations.

Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations

Abstract: The model-based control of building heating systems for energy saving encounters severe physical, mathematical and calibration difficulties in the numerous attempts that has been published until now. This topic is addressed here via a new model-free control setting, where the need of any mathematical description disappears. Several convincing computer simulations are presented. Comparisons with classic PI controllers and flatness-based predictive control are provided.

Linear systems

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: