International audience

International audience

International audience

International audience

International audience

International audience

International audience

International audience

The electrical appliances identification problem is gaining a rapidly growing interest these past few years due to the recent need of this information in the new smart grid configuration. In this work, we propose to construct an appliance identification system based on the use of Hidden Markov Models (HMM) to model transient and steady-state electrical current signals. For this purpose, we investigate the usefulness of different choices for the proposed identification system such as: the use of the transient and the steady-state current signals, the use of even and odd-order harmonics as features, and the optimal number of features to take into account. This work also discusses the choice of the Short-Time Fourier Series (STFS) coefficients as adapted features for the representation of transient and steady-state current signals.

In this paper, we investigate the binning problem of joint histogram estimation applied to mutual information based face recognition application. Classical approaches for histograms estimation tend to empirically fix the bin numbers. We evaluate in this work some state of the art rules for automatically choosing the bin numbers. The face recognition problem has been studied in the case of local and holistic methods. The choice’s performance has been evaluated using AT&T database with single sample in the training set. The results show that better accuracy recognition rates can be achieved with data driven bin number choices rather than fixed bin numbers. In the local method, the results show a higher robustness of the automatic vs fixed bin number choice when the regions become smaller.