The Experts below are selected from a list of 19917 Experts worldwide ranked by ideXlab platform

Hatim Y. Yamin - One of the best experts on this subject based on the ideXlab platform.

P. Beauseroy - One of the best experts on this subject based on the ideXlab platform.

  • Mutual information-based feature extraction on the time-frequency plane
    IEEE Transactions on Signal Processing, 2002
    Co-Authors: E. Grall-maes, P. Beauseroy
    Abstract:

    A method is proposed for automatic extraction of effective features for class separability. It applies to nonstationary processes described only by sample sets of stochastic signals. The extraction is based on time-frequency representations (TFRs) that are potentially suited to the characterization of nonstationarities. The features are defined by parameterized mappings applied to a TFR. These mappings select a region of the time-frequency plane by using a two-dimensional (2-D) parameterized weighting function and provide a standard characteristic in the restricted representation obtained. The features are automatically drawn from the TFR by tuning the weighting function parameters. The extraction is driven to maximize the information brought by the features about the class membership. It uses a mutual information criterion, based on Estimated Probability distributions. The framework is developed for the extraction of a single feature and extended to several features. A classification scheme adapted to the extracted features is proposed. Finally, some experimental results are given to demonstrate the efficacy of the method.

Kaoru Sezaki - One of the best experts on this subject based on the ideXlab platform.

  • Estimation of achievable power capacity from plug-in electric vehicles for V2G frequency regulation: Case studies for market participation
    IEEE Transactions on Smart Grid, 2011
    Co-Authors: Sekyung Han, Soo Hee Han, Kaoru Sezaki
    Abstract:

    It is essential to estimate how much power can be delivered from vehicles to grid, called achievable power capacity (APC), for practical vehicle-to-grid (V2G) services. We propose a method of estimating the APC in a probabilistic manner. Its Probability distribution is obtained from the normal approximation to the binomial distribution, and hence represented with two parameters, i.e., mean and covariance. Based on the Probability distribution of the APC, we calculate the power capacity that V2G regulation providers (or V2G aggregators) are contracted to provide grid operators with, called the contracted power capacity (CPC). Four possible contract types between a grid operator and a V2G regulation provider are suggested and, for each contract type, a profit function is developed from the APC and the penalty imposed to the V2G aggregator. The CPCs for four contract types are chosen to maximize the corresponding profit functions. Finally, simulations are provided to illustrate the accuracy of the Estimated Probability distribution of APC and the effectiveness of the profit functions.

  • Stochastic analysis on the energy constraint of V2G frequency regulation
    2010 IEEE Vehicle Power and Propulsion Conference, 2010
    Co-Authors: Kaoru Sezaki
    Abstract:

    Energy constraint for V2G frequency regulation is illustrated in terms of state-of-charge (SOC) of the pertaining vehicle battery. Actual regulation signal is investigated, and energy deviation caused by a single regulation signal is obtained. With the derived energy deviation model, a Probability distribution of successful regulation is Estimated. Random walk theory is employed for stochastic analysis of the distribution. For the derived Probability distribution, an approximation to the normal distribution is made to perform practical calculation on a digital computer. Estimated Probability distribution is averaged over the unit contract time, usually an hour, to yield a weight function that represents the energy constraint. Finally, simulations are provided to with various parameters.

E. Grall-maes - One of the best experts on this subject based on the ideXlab platform.

  • Mutual information-based feature extraction on the time-frequency plane
    IEEE Transactions on Signal Processing, 2002
    Co-Authors: E. Grall-maes, P. Beauseroy
    Abstract:

    A method is proposed for automatic extraction of effective features for class separability. It applies to nonstationary processes described only by sample sets of stochastic signals. The extraction is based on time-frequency representations (TFRs) that are potentially suited to the characterization of nonstationarities. The features are defined by parameterized mappings applied to a TFR. These mappings select a region of the time-frequency plane by using a two-dimensional (2-D) parameterized weighting function and provide a standard characteristic in the restricted representation obtained. The features are automatically drawn from the TFR by tuning the weighting function parameters. The extraction is driven to maximize the information brought by the features about the class membership. It uses a mutual information criterion, based on Estimated Probability distributions. The framework is developed for the extraction of a single feature and extended to several features. A classification scheme adapted to the extracted features is proposed. Finally, some experimental results are given to demonstrate the efficacy of the method.

Maria Prandini - One of the best experts on this subject based on the ideXlab platform.

  • Randomized algorithms for the synthesis of cautious adaptive controllers
    Systems & Control Letters, 2003
    Co-Authors: Marco C. Campi, Maria Prandini
    Abstract:

    Abstract We introduce a new methodology for the design of cautious adaptive controllers based on the following two-step procedure: (i) a Probability measure describing the likelihood of different models is updated on-line based on observations, and (ii) a controller with certain robust control specifications is tuned to the updated Probability by means of randomized algorithms. The robust control specifications are assigned as average specifications with respect to the Estimated Probability measure, and randomized algorithms are used to make the controller tuning computationally tractable. This paper provides a general overview of the proposed new methodology. Still, many issues remain open and represent interesting topics for future research.

  • Randomized algorithms for the synthesis of cautious adaptive controllers
    Systems and Control Letters, 2003
    Co-Authors: Marco C. Campi, Maria Prandini
    Abstract:

    We introduce a new methodology for the design of cautious adaptive controllers based on the following two-step procedure: (i) a Probability measure describing the likelihood of different models is updated on-line based on observations, and (ii) a controller with certain robust control specifications is tuned to the updated Probability by means of randomized algorithms. The robust control specifications are assigned as average specifications with respect to the Estimated Probability measure, and randomized algorithms are used to make the controller tuning computationally tractable. This paper provides a general overview of the proposed new methodology. Still, many issues remain open and represent interesting topics for future research. © 2003 Elsevier Science B.V. All rights reserved.