The Experts below are selected from a list of 6330 Experts worldwide ranked by ideXlab platform

Xiangguang Chen - One of the best experts on this subject based on the ideXlab platform.

  • online local learning based adaptive soft sensor and its application to an industrial fed batch chlortetracycline fermentation process
    Chemometrics and Intelligent Laboratory Systems, 2015
    Co-Authors: Huaiping Jin, Xiangguang Chen, Jianwen Yang, Li Wang
    Abstract:

    Abstract This work presents a new method for adaptive soft sensor development by further exploiting just-in-time modeling framework. In the presented method, referred to as online local learning based adaptive soft sensor (OLLASS), the samples used for local modeling are selected based on the mutual information (MI) weighted or neighbor sample based similarity measure. Then, two adaptive methods, namely self-validation and neighbor-validation, are developed to adaptively select the optimal local modeling size for scenarios without and with the neighbor output information, respectively. Further, a real-time performance improvement strategy is used to enhance the online modeling efficiency. Moreover, an online dual updating strategy is proposed to activate infrequent local model updating and model output offset updating in turn, which allows significantly reducing the online computational load by avoiding unnecessary local model reconstruction while at the same time maintaining high estimation accuracy by performing offset compensation. A maximal similarity Replacement Rule using MI weighted similarity measure is used for database updating. The superiority of the proposed OLLASS method over traditional soft sensors in terms of the estimation accuracy, adaptive capability and real-time performance is demonstrated through an industrial fed-batch chlortetracycline fermentation process.

  • adaptive soft sensor modeling framework based on just in time learning and kernel partial least squares regression for nonlinear multiphase batch processes
    Computers & Chemical Engineering, 2014
    Co-Authors: Xiangguang Chen, Jianwen Yang, Lei Wu
    Abstract:

    Abstract Batch processes are characterized by inherent nonlinearity, multiple phases and time-varying behavior that pose great challenges for accurate state estimation. A multiphase just-in-time (MJIT) learning based kernel partial least squares (KPLS) method is proposed for multiphase batch processes. Gaussian mixture model is estimated to identify different operating phases where various JIT-KPLS frameworks are built. By applying Bayesian inference strategy, the query data is classified into a particular phase with the maximal posterior probability, and thus the corresponding JIT-KPLS framework is chosen for online prediction. To further improve the predictive accuracy of the MJIT-KPLS algorithm, a hybrid similarity measure and an adaptive selection strategy are proposed for selecting local modeling samples. Moreover, maximal similarity Replacement Rule is proposed to update database. A procedure of input variable selection based on partial mutual information is also presented. The effectiveness of the MJIT-KPLS algorithm is demonstrated through application to industrial fed-batch chlortetracycline fermentation process.

Li Wang - One of the best experts on this subject based on the ideXlab platform.

  • online local learning based adaptive soft sensor and its application to an industrial fed batch chlortetracycline fermentation process
    Chemometrics and Intelligent Laboratory Systems, 2015
    Co-Authors: Huaiping Jin, Xiangguang Chen, Jianwen Yang, Li Wang
    Abstract:

    Abstract This work presents a new method for adaptive soft sensor development by further exploiting just-in-time modeling framework. In the presented method, referred to as online local learning based adaptive soft sensor (OLLASS), the samples used for local modeling are selected based on the mutual information (MI) weighted or neighbor sample based similarity measure. Then, two adaptive methods, namely self-validation and neighbor-validation, are developed to adaptively select the optimal local modeling size for scenarios without and with the neighbor output information, respectively. Further, a real-time performance improvement strategy is used to enhance the online modeling efficiency. Moreover, an online dual updating strategy is proposed to activate infrequent local model updating and model output offset updating in turn, which allows significantly reducing the online computational load by avoiding unnecessary local model reconstruction while at the same time maintaining high estimation accuracy by performing offset compensation. A maximal similarity Replacement Rule using MI weighted similarity measure is used for database updating. The superiority of the proposed OLLASS method over traditional soft sensors in terms of the estimation accuracy, adaptive capability and real-time performance is demonstrated through an industrial fed-batch chlortetracycline fermentation process.

Jianwen Yang - One of the best experts on this subject based on the ideXlab platform.

  • online local learning based adaptive soft sensor and its application to an industrial fed batch chlortetracycline fermentation process
    Chemometrics and Intelligent Laboratory Systems, 2015
    Co-Authors: Huaiping Jin, Xiangguang Chen, Jianwen Yang, Li Wang
    Abstract:

    Abstract This work presents a new method for adaptive soft sensor development by further exploiting just-in-time modeling framework. In the presented method, referred to as online local learning based adaptive soft sensor (OLLASS), the samples used for local modeling are selected based on the mutual information (MI) weighted or neighbor sample based similarity measure. Then, two adaptive methods, namely self-validation and neighbor-validation, are developed to adaptively select the optimal local modeling size for scenarios without and with the neighbor output information, respectively. Further, a real-time performance improvement strategy is used to enhance the online modeling efficiency. Moreover, an online dual updating strategy is proposed to activate infrequent local model updating and model output offset updating in turn, which allows significantly reducing the online computational load by avoiding unnecessary local model reconstruction while at the same time maintaining high estimation accuracy by performing offset compensation. A maximal similarity Replacement Rule using MI weighted similarity measure is used for database updating. The superiority of the proposed OLLASS method over traditional soft sensors in terms of the estimation accuracy, adaptive capability and real-time performance is demonstrated through an industrial fed-batch chlortetracycline fermentation process.

  • adaptive soft sensor modeling framework based on just in time learning and kernel partial least squares regression for nonlinear multiphase batch processes
    Computers & Chemical Engineering, 2014
    Co-Authors: Xiangguang Chen, Jianwen Yang, Lei Wu
    Abstract:

    Abstract Batch processes are characterized by inherent nonlinearity, multiple phases and time-varying behavior that pose great challenges for accurate state estimation. A multiphase just-in-time (MJIT) learning based kernel partial least squares (KPLS) method is proposed for multiphase batch processes. Gaussian mixture model is estimated to identify different operating phases where various JIT-KPLS frameworks are built. By applying Bayesian inference strategy, the query data is classified into a particular phase with the maximal posterior probability, and thus the corresponding JIT-KPLS framework is chosen for online prediction. To further improve the predictive accuracy of the MJIT-KPLS algorithm, a hybrid similarity measure and an adaptive selection strategy are proposed for selecting local modeling samples. Moreover, maximal similarity Replacement Rule is proposed to update database. A procedure of input variable selection based on partial mutual information is also presented. The effectiveness of the MJIT-KPLS algorithm is demonstrated through application to industrial fed-batch chlortetracycline fermentation process.

Huaiping Jin - One of the best experts on this subject based on the ideXlab platform.

  • online local learning based adaptive soft sensor and its application to an industrial fed batch chlortetracycline fermentation process
    Chemometrics and Intelligent Laboratory Systems, 2015
    Co-Authors: Huaiping Jin, Xiangguang Chen, Jianwen Yang, Li Wang
    Abstract:

    Abstract This work presents a new method for adaptive soft sensor development by further exploiting just-in-time modeling framework. In the presented method, referred to as online local learning based adaptive soft sensor (OLLASS), the samples used for local modeling are selected based on the mutual information (MI) weighted or neighbor sample based similarity measure. Then, two adaptive methods, namely self-validation and neighbor-validation, are developed to adaptively select the optimal local modeling size for scenarios without and with the neighbor output information, respectively. Further, a real-time performance improvement strategy is used to enhance the online modeling efficiency. Moreover, an online dual updating strategy is proposed to activate infrequent local model updating and model output offset updating in turn, which allows significantly reducing the online computational load by avoiding unnecessary local model reconstruction while at the same time maintaining high estimation accuracy by performing offset compensation. A maximal similarity Replacement Rule using MI weighted similarity measure is used for database updating. The superiority of the proposed OLLASS method over traditional soft sensors in terms of the estimation accuracy, adaptive capability and real-time performance is demonstrated through an industrial fed-batch chlortetracycline fermentation process.

Wallner Michael - One of the best experts on this subject based on the ideXlab platform.

  • Periodic P\'olya Urns, the Density Method, and Asymptotics of Young Tableaux
    2019
    Co-Authors: Banderier Cyril, Marchal Philippe, Wallner Michael
    Abstract:

    P\'olya urns are urns where at each unit of time a ball is drawn and replaced with some other balls according to its colour. We introduce a more general model: the Replacement Rule depends on the colour of the drawn ball and the value of the time ($\operatorname{mod} p$). We extend the work of Flajolet et al. on P\'olya urns: the generating function encoding the evolution of the urn is studied by methods of analytic combinatorics. We show that the initial partial differential equations lead to ordinary linear differential equations which are related to hypergeometric functions (giving the exact state of the urns at time n). When the time goes to infinity, we prove that these periodic P\'olya urns have asymptotic fluctuations which are described by a product of generalized gamma distributions. With the additional help of what we call the density method (a method which offers access to enumeration and random generation of poset structures), we prove that the law of the south-east corner of a triangular Young tableau follows asymptotically a product of generalized gamma distributions. This allows us to tackle some questions related to the continuous limit of large random Young tableaux and links with random surfaces.Comment: 56 pages, 11 figures, accepted in the Annals of Probabilit

  • Periodic Pólya urns and an application to Young tableaux
    Leibniz-Zentrum für Informatik, 2018
    Co-Authors: Banderier Cyril, Marchal Philippe, Wallner Michael
    Abstract:

    International audiencePólya urns are urns where at each unit of time a ball is drawn and is replaced with some other balls according to its colour. We introduce a more general model: The Replacement Rule depends on the colour of the drawn ball and the value of the time (mod p). We discuss some intriguing properties of the differential operators associated to the generating functions encoding the evolution of these urns. The initial partial differential equation indeed leads to ordinary linear differential equations and we prove that the moment generating functions are D-finite. For a subclass, we exhibit a closed form for the corresponding generating functions (giving the exact state of the urns at time n). When the time goes to infinity, we show that these periodic Pólya urns follow a rich variety of behaviours: their asymptotic fluctuations are described by a family of distributions, the generalized Gamma distributions, which can also be seen as powers of Gamma distributions. En passant, we establish some enumerative links with other combinatorial objects, and we give an application for a new result on the asymptotics of Young tableaux: This approach allows us to prove that the law of the lower right corner in a triangular Young tableau follows asymptotically a product of generalized Gamma distributions

  • Periodic P\'olya urns and an application to Young tableaux
    2018
    Co-Authors: Banderier Cyril, Marchal Philippe, Wallner Michael
    Abstract:

    P{\'o}lya urns are urns where at each unit of time a ball is drawn and is replaced with some other balls according to its colour. We introduce a more general model: The Replacement Rule depends on the colour of the drawn ball and the value of the time (mod p). We discuss some intriguing properties of the differential operators associated to the generating functions encoding the evolution of these urns. The initial partial differential equation indeed leads to ordinary linear differential equations and we prove that the moment generating functions are D-finite. For a subclass, we exhibit a closed form for the corresponding generating functions (giving the exact state of the urns at time n). When the time goes to infinity, we show that these periodic P{\'o}lya urns follow a rich variety of behaviours: their asymptotic fluctuations are described by a family of distributions, the generalized Gamma distributions, which can also be seen as powers of Gamma distributions. En passant, we establish some enumerative links with other combinatorial objects, and we give an application for a new result on the asymptotics of Young tableaux: This approach allows us to prove that the law of the lower right corner in a triangular Young tableau follows asymptotically a product of generalized Gamma distributions