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The Experts below are selected from a list of 2778117 Experts worldwide ranked by ideXlab platform

James Zhao - One of the best experts on this subject based on the ideXlab platform.

  • Random sampling of contingency tables via probabilistic divide-and-conquer
    Computational Statistics, 2019
    Co-Authors: Stephen Desalvo, James Zhao
    Abstract:

    We present a new approach for random sampling of contingency tables of any size and constraints based on a recently introduced probabilistic divide-and-conquer (PDC) technique. Our first application is a recursive PDC: it samples the least significant bit of each entry in the table, motivated by the fact that the bits of a geometric random variable are independent. The second application is via PDC deterministic second half, where one divides the sample space into two pieces, one of which is deterministic conditional on the other; this approach is highlighted via an exact sampling algorithm in the $$2\times n$$ 2 × n case. Finally, we also present a generalization to the sampling algorithm where each entry of the table has a specified marginal distribution.

Jose J Lopez - One of the best experts on this subject based on the ideXlab platform.

  • modelling and control of coiling entry temperature using interval type 2 fuzzy logic systems
    Ironmaking & Steelmaking, 2010
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    The set-up of the cooling water applied to the strip as it traverses the runout table in order to achieve the coiler entry temperature was made by an intelligent model implemented using interval type-2 fuzzy logic systems. The model uses as inputs the targets for coiling entry temperature, strip thickness, finish mill exit temperature and finishing mill exit speed. The experiments of this application were carried out for three different types of coil in a real hot strip mill. The results proved the feasibility of the system developed for coiler entry temperature prediction. Comparison with the online type-1 fuzzy logic based model shows that the proposed interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.

  • application of interval type 2 fuzzy logic systems for control of the coiling entry temperature in a hot strip mill
    Hybrid Artificial Intelligence Systems, 2009
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    An interval type-2 fuzzy logic system is used to setup the cooling water applied to the strip as it traverses the run out table in order to achieve the coiler entry temperature target. The interval type-2 fuzzy setup model uses as inputs the target coiling entry temperature, the target strip thickness, the predicted finish mill exit temperature and the target finishing mill exit speed. The experimental results of the application of the interval type-2 fuzzy logic system for coiler entry temperature prediction in a real hot strip mill were carried out for three different types of coils. They proved the feasibility of the systems developed here for coiler entry temperature prediction. Comparison with an on-line type-1 fuzzy logic based model shows that the interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.

Stephen Desalvo - One of the best experts on this subject based on the ideXlab platform.

  • Random sampling of contingency tables via probabilistic divide-and-conquer
    Computational Statistics, 2019
    Co-Authors: Stephen Desalvo, James Zhao
    Abstract:

    We present a new approach for random sampling of contingency tables of any size and constraints based on a recently introduced probabilistic divide-and-conquer (PDC) technique. Our first application is a recursive PDC: it samples the least significant bit of each entry in the table, motivated by the fact that the bits of a geometric random variable are independent. The second application is via PDC deterministic second half, where one divides the sample space into two pieces, one of which is deterministic conditional on the other; this approach is highlighted via an exact sampling algorithm in the $$2\times n$$ 2 × n case. Finally, we also present a generalization to the sampling algorithm where each entry of the table has a specified marginal distribution.

  • random sampling of contingency tables via probabilistic divide and conquer
    arXiv: Statistics Theory, 2015
    Co-Authors: Stephen Desalvo, James Y Zhao
    Abstract:

    We present a new approach for random sampling of contingency tables of any size and constraints based on a recently introduced $\textit{probabilistic divide-and-conquer}$ technique. A simple exact sampling algorithm is presented for $2\times n$ tables, as well as a generalization where each entry of the table has a specified marginal distribution.

Gerardo M Mendez - One of the best experts on this subject based on the ideXlab platform.

  • modelling and control of coiling entry temperature using interval type 2 fuzzy logic systems
    Ironmaking & Steelmaking, 2010
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    The set-up of the cooling water applied to the strip as it traverses the runout table in order to achieve the coiler entry temperature was made by an intelligent model implemented using interval type-2 fuzzy logic systems. The model uses as inputs the targets for coiling entry temperature, strip thickness, finish mill exit temperature and finishing mill exit speed. The experiments of this application were carried out for three different types of coil in a real hot strip mill. The results proved the feasibility of the system developed for coiler entry temperature prediction. Comparison with the online type-1 fuzzy logic based model shows that the proposed interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.

  • application of interval type 2 fuzzy logic systems for control of the coiling entry temperature in a hot strip mill
    Hybrid Artificial Intelligence Systems, 2009
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    An interval type-2 fuzzy logic system is used to setup the cooling water applied to the strip as it traverses the run out table in order to achieve the coiler entry temperature target. The interval type-2 fuzzy setup model uses as inputs the target coiling entry temperature, the target strip thickness, the predicted finish mill exit temperature and the target finishing mill exit speed. The experimental results of the application of the interval type-2 fuzzy logic system for coiler entry temperature prediction in a real hot strip mill were carried out for three different types of coils. They proved the feasibility of the systems developed here for coiler entry temperature prediction. Comparison with an on-line type-1 fuzzy logic based model shows that the interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.

Rafael Colas - One of the best experts on this subject based on the ideXlab platform.

  • modelling and control of coiling entry temperature using interval type 2 fuzzy logic systems
    Ironmaking & Steelmaking, 2010
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    The set-up of the cooling water applied to the strip as it traverses the runout table in order to achieve the coiler entry temperature was made by an intelligent model implemented using interval type-2 fuzzy logic systems. The model uses as inputs the targets for coiling entry temperature, strip thickness, finish mill exit temperature and finishing mill exit speed. The experiments of this application were carried out for three different types of coil in a real hot strip mill. The results proved the feasibility of the system developed for coiler entry temperature prediction. Comparison with the online type-1 fuzzy logic based model shows that the proposed interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.

  • application of interval type 2 fuzzy logic systems for control of the coiling entry temperature in a hot strip mill
    Hybrid Artificial Intelligence Systems, 2009
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    An interval type-2 fuzzy logic system is used to setup the cooling water applied to the strip as it traverses the run out table in order to achieve the coiler entry temperature target. The interval type-2 fuzzy setup model uses as inputs the target coiling entry temperature, the target strip thickness, the predicted finish mill exit temperature and the target finishing mill exit speed. The experimental results of the application of the interval type-2 fuzzy logic system for coiler entry temperature prediction in a real hot strip mill were carried out for three different types of coils. They proved the feasibility of the systems developed here for coiler entry temperature prediction. Comparison with an on-line type-1 fuzzy logic based model shows that the interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.