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In this paper, we design a diagnostic technique for a partially observed labeled Petri net where the faults of the system are modeled by unobservable transitions. The fault detection and isolation uses an on-line count vector estimation associated with the firing of unobservable transitions exploiting the observation of firing occurrences of some observable transitions. The support of the approach is an algebraic description of the process under the form of a polyhedron developed on a receding horizon. We show that a diagnostic can be made despite that different transitions can share the same label and that the unobservable part of the Petri net can contain circuits.

In this paper, we design an efficient diagnosis technique for partially observed discrete event systems modeled by labeled Petri nets. The fault detection is based on analytical redundancy relationships derived from the nominal model. The decomposition of the Tun‐induced subnet to connected subgraphs allows determining the subgraphs that may contain faults. To appreciate the fault localization, a set of analytical redundancy relationships is etablished for each fault transition based on the fault model. The proposed diagnosis approach is independent of the length of the observed sequence and independent of the number of unobservable transitions. The detected faults with the proposed approach are faults which led to a change in the number of tokens in the net.

In this paper, we present a new framework to analyse and process 3D animations (defined by sequences of triangular meshes sharing the same connectivity at any frame). Our idea is to develop a spatio-temporal wavelet filtering for such data, leading to a relevant multiresolution decomposition. In geometry processing, the most efficient spatial wavelets are based on a semiregular sampling. Since the 3D animations generally have an irregular sampling, one of our contribution is a remeshing technique, transforming an animation in a sequence of semiregular meshes, which presents regularity in time but also in space. This new sampling has the advantage to improve the quality of the multiresolution decomposition in the spatial dimension. To show the contribution of such a spatio-temporal filtering in animation processing, we present some experimental results in data compression.