This article focusses on the problems of feature extraction and the recognition of handwritten digits. A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to solve the first problem in a black box scheme without prior knowledge on the data. The classification task is performed by Support Vector Machines to enhance the generalization ability of LeNet5. In order to increase the recognition rate, new training samples are generated by affine transformations and elastic distortions. Experiments are performed on the well known MNIST database to validate the method and the results show that the system can outperfom both SVMs and LeNet5 while providing performances comparable to the best performance on this database. Moreover, an analysis of the errors is conducted to discuss possible means of enhancement and their limitations.

We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function.

This paper explores the incorporation of prior knowledge in support vector regresion by the addition of constraints. Equality and inequality constraints are studied with the corresponding types of prior knowledge that can be considered for the method. These include particular points with known values, prior knowledge on any derivative of the function either provided by a prior model or available only at some specific points and bounds on the function or any derivative in a given domain. Moreover, a new method for the simultaneous approximation of multiple outputs linked by some prior knowledge is proposed. This method also allows consideration of different types of prior knowledge on single outputs while training on multiple outputs. Synthetic examples show that incorporating a wide variety of prior knowledge becomes easy, as it leads to linear programs, and helps to improve the approximation in difficult cases. The benefits of the method are finally shown on a reallife application, the estimation of in-cylinder residual gas fraction in spark ignition engines, which is representative of numerous situations met in engineering.

For classification, support vector machines (SVMs) have recently been introduced and quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications. This paper gives a review of the current state of research regarding the incorporation of two general types of prior knowledge into SVMs for classification. The particular forms of prior knowledge considered here are presented in two main groups: class-invariance and knowledge on the data. The first one includes invariances to transformations, to permutations and in domains of input space, whereas the second one contains knowledge on unlabeled data, the imbalance of the training set or the quality of the data. The methods are then described and classified in the three categories that have been used in literature: sample methods based on the modification of the training data, kernel methods based on the modification of the kernel and optimization methods based on the modification of the problem formulation. A recent method, developed for support vector regression, considers prior knowledge on arbitrary regions of the input space. It is exposed here when applied to the classification case. A discussion is then conducted to regroup sample and optimization methods under a regularization framework.

A general framework and a systematic methodology for the cryptanalysis of a large class of chaotic cryptosystems are proposed. More precisely, it is tested, a priori, during the design stage, whether the parameters of a chaotic cryptosystem may play the role of the secret key or not. Robustness against brute force attacks is first considered. A connection between uniqueness in the parameters and identifiability is pointed out. Two approaches, the outputs equality approach and the input/output relation approach, are presented to test the identifiability of the system parameters. The second approach is constructive in the sense that not only it allows to conclude on the identifiability of the parameters but it also provides a systematic technique to retrieve the parameters in the context of a known plaintext attack. It is shown that cryptosystems involving polynomial nonlinearities, chaotic or not, are weak against this attack, called algebraic attack.

International audienceThis note deals with the state reconstruction of a class of discrete-time systems with time-varying parameters. While usually the parameters are assumed to be online available and exactly known, the special and realistic situation when the parameters are known with a finite accuracy is considered. The main objective of the note is to show that, despite of the resulting mismatch between the true system and the model, the state reconstruction error boundedness can be guaranteed and an explicit bound can be derived. The proof is based upon the concept of input-to-state stability

The internal temperature of Li-ion batteries for electric or hybrid vehicles is an important factor influencing their ageing. Generally not measured, it can be reconstructed from an external measurement and a model. This paper presents the simplified modelling of heat transfers in a battery module, leading to a Linear Parameter-Varying (LPV) model. Then, a polytopic observer is proposed to estimate the cell temperature and internal resistance, ensuring a tradeoff between the convergence speed and the noise of the estimated states. Experimental results show the good quality of the estimation and the diagnosis potential offered by internal resistance reconstruction.

Abstract. Hybrid system identification aims at both estimating the discrete state or mode for each data point, and the submodel governing the dynamics of the continuous state for each mode. The paper proposes a new method based on kernel regression and Support Vector Machines (SVM) to tackle this problem. The resulting algorithm is able to compute both the discrete state and the submodels in a single step, independently of the discrete state sequence that generated the data. In addition to previous works, nonlinear submodels are also considered, thus extending the class of systems on which the method can be applied from PieceWise Affine (PWA) and switched linear to PieceWise Smooth (PWS) and switched nonlinear systems with unknown nonlinearities. Piecewise systems with nonlinear boundaries between the modes are also considered with some preliminary results on this issue.