The Experts below are selected from a list of 16638 Experts worldwide ranked by ideXlab platform

S. De Waele - One of the best experts on this subject based on the ideXlab platform.

  • Monitoring and detection with time series Models
    IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Ca, 2001
    Co-Authors: P.m.t. Broersen, S. De Waele
    Abstract:

    Many Types of random data can be considered as more or less stationary. Stationary stochastic data are characterized optimally by the parameters of a time series Model, if Model Type and Model order are known in advance. Recently, a new development in time series analysis gives the possibility to select automatically, with statistical criteria, the Model Type and the Model order for data with unknown characteristics. Hence, the statistically significant features of measured data can be determined without a priori knowledge. This creates the possibility to use estimated and selected Models for the automatic monitoring of stochastic data and for the detection of changes. The paper describes variations that can be detected. It shows that considering a measured signal as a stationary stochastic process is already sufficient a priori information to use a powerful statistical framework for the accurate description of observations and for the automatic detection of changes.

  • Efficient estimation of autocorrelation functions of random data with time series Models
    Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2001
    Co-Authors: P.m.t. Broersen, S. De Waele
    Abstract:

    Sample covariances, estimated as mean-lagged products of random data, are poor and inaccurate fundaments for the non-parametric spectral estimation with tapered and windowed periodograms. However, the autocovariance can be estimated efficiently with a parametric method as transformation of an estimated time series Model, if the Model Type and Model order are known a-priori. A recent development in time-series analysis gives the possibility to automatically select the Model Type and the Model order for data with unknown characteristics. After the computation of hundreds of candidate Models of different orders and Types, a statistical criterion can select a single time series Model. The accuracy of this identification from many candidates is sufficient to approach the performance that can be obtained with parametric estimation if the Type and the order of the time series Model would be known a priori. Hence, the accuracy (mean square error) of parametric covariance estimates is typically the same or better than what can be achieved by non-parametric mean-lagged-product estimates.

D Gorissen - One of the best experts on this subject based on the ideXlab platform.

  • Pareto-based multi-output Model Type selection
    2020
    Co-Authors: D Gorissen, Karel Crombecq, Ivo Couckuyt, Tom Dhaene
    Abstract:

    In engineering design the use of approximation Models (= surrogate Models) has become standard practice for design space exploration, sensitivity analysis, Visualization and optimization. Popular surrogate Model Types include neural networks, support vector machines, Kriging Models, and splines. An engineering simulation typically involves multiple response variables that must be approximated. With many approximation methods available, the question of which method to use for which response consistently arises among engineers and domain experts. Traditionally, the different responses are Modeled separately by independent Models, possibly involving a comparison among Model Types. Instead, this paper proposes a multi-objective approach can benefit the domain expert since it enables automatic Model Type selection for each output on the fly without resorting to multiple runs. In effect the optimal Model complexity and Model Type for each output is determined automatically. In addition a multi-objective approach gives information about output correlation and facilitates the generation of diverse ensembles. The merit of this approach is illustrated with a Modeling problem from aerospace.

  • Winter Simulation Conference - Automatic surrogate Model Type selection during the optimization of expensive black-box problems
    Proceedings of the 2011 Winter Simulation Conference (WSC), 2011
    Co-Authors: Ivo Couckuyt, Tom Dhaene, Filip De Turck, D Gorissen
    Abstract:

    The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate Type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate Model Type selection framework that is able to dynamically select the best Model Type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization.

  • Automatic surrogate Model Type selection during the optimization of expensive black-box problems
    Proceedings of the 2011 Winter Simulation Conference (WSC), 2011
    Co-Authors: Ivo Couckuyt, Tom Dhaene, Filip De Turck, D Gorissen
    Abstract:

    The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate Type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate Model Type selection framework that is able to dynamically select the best Model Type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization.

  • Evolutionary Model Type Selection for Global Surrogate Modeling
    Journal of Machine Learning Research, 2009
    Co-Authors: D Gorissen, Tom Dhaene, Filip De Turck
    Abstract:

    Due to the scale and computational complexity of currently used simulation codes, global surrogate (metaModels) Models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation Models while minimizing the computational cost and maximizing Model accuracy. Many surrogate Model Types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no Type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the Model Type selection problem. We describe an adaptive global surrogate Modeling environment with adaptive sampling, driven by speciated evolution. Different Model Types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal Model Type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each Model Type.

  • HAIS - Pareto-Based Multi-output Model Type Selection
    Lecture Notes in Computer Science, 2009
    Co-Authors: D Gorissen, Karel Crombecq, Ivo Couckuyt, Tom Dhaene
    Abstract:

    In engineering design the use of approximation Models (= surrogate Models) has become standard practice for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate Model Types include neural networks, support vector machines, Kriging Models, and splines. An engineering simulation typically involves multiple response variables that must be approximated. With many approximation methods available, the question of which method to use for which response consistently arises among engineers and domain experts. Traditionally, the different responses are Modeled separately by independent Models, possibly involving a comparison among Model Types. Instead, this paper proposes a multi-objective approach can benefit the domain expert since it enables automatic Model Type selection for each output on the fly without resorting to multiple runs. In effect the optimal Model complexity and Model Type for each output is determined automatically. In addition a multi-objective approach gives information about output correlation and facilitates the generation of diverse ensembles. The merit of this approach is illustrated with a Modeling problem from aerospace.

Tom Dhaene - One of the best experts on this subject based on the ideXlab platform.

  • Pareto-based multi-output Model Type selection
    2020
    Co-Authors: D Gorissen, Karel Crombecq, Ivo Couckuyt, Tom Dhaene
    Abstract:

    In engineering design the use of approximation Models (= surrogate Models) has become standard practice for design space exploration, sensitivity analysis, Visualization and optimization. Popular surrogate Model Types include neural networks, support vector machines, Kriging Models, and splines. An engineering simulation typically involves multiple response variables that must be approximated. With many approximation methods available, the question of which method to use for which response consistently arises among engineers and domain experts. Traditionally, the different responses are Modeled separately by independent Models, possibly involving a comparison among Model Types. Instead, this paper proposes a multi-objective approach can benefit the domain expert since it enables automatic Model Type selection for each output on the fly without resorting to multiple runs. In effect the optimal Model complexity and Model Type for each output is determined automatically. In addition a multi-objective approach gives information about output correlation and facilitates the generation of diverse ensembles. The merit of this approach is illustrated with a Modeling problem from aerospace.

  • Winter Simulation Conference - Automatic surrogate Model Type selection during the optimization of expensive black-box problems
    Proceedings of the 2011 Winter Simulation Conference (WSC), 2011
    Co-Authors: Ivo Couckuyt, Tom Dhaene, Filip De Turck, D Gorissen
    Abstract:

    The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate Type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate Model Type selection framework that is able to dynamically select the best Model Type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization.

  • Automatic surrogate Model Type selection during the optimization of expensive black-box problems
    Proceedings of the 2011 Winter Simulation Conference (WSC), 2011
    Co-Authors: Ivo Couckuyt, Tom Dhaene, Filip De Turck, D Gorissen
    Abstract:

    The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate Type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate Model Type selection framework that is able to dynamically select the best Model Type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization.

  • Evolutionary Model Type Selection for Global Surrogate Modeling
    Journal of Machine Learning Research, 2009
    Co-Authors: D Gorissen, Tom Dhaene, Filip De Turck
    Abstract:

    Due to the scale and computational complexity of currently used simulation codes, global surrogate (metaModels) Models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation Models while minimizing the computational cost and maximizing Model accuracy. Many surrogate Model Types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no Type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the Model Type selection problem. We describe an adaptive global surrogate Modeling environment with adaptive sampling, driven by speciated evolution. Different Model Types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal Model Type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each Model Type.

  • HAIS - Pareto-Based Multi-output Model Type Selection
    Lecture Notes in Computer Science, 2009
    Co-Authors: D Gorissen, Karel Crombecq, Ivo Couckuyt, Tom Dhaene
    Abstract:

    In engineering design the use of approximation Models (= surrogate Models) has become standard practice for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate Model Types include neural networks, support vector machines, Kriging Models, and splines. An engineering simulation typically involves multiple response variables that must be approximated. With many approximation methods available, the question of which method to use for which response consistently arises among engineers and domain experts. Traditionally, the different responses are Modeled separately by independent Models, possibly involving a comparison among Model Types. Instead, this paper proposes a multi-objective approach can benefit the domain expert since it enables automatic Model Type selection for each output on the fly without resorting to multiple runs. In effect the optimal Model complexity and Model Type for each output is determined automatically. In addition a multi-objective approach gives information about output correlation and facilitates the generation of diverse ensembles. The merit of this approach is illustrated with a Modeling problem from aerospace.

P.m.t. Broersen - One of the best experts on this subject based on the ideXlab platform.

  • Monitoring and detection with time series Models
    IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Ca, 2001
    Co-Authors: P.m.t. Broersen, S. De Waele
    Abstract:

    Many Types of random data can be considered as more or less stationary. Stationary stochastic data are characterized optimally by the parameters of a time series Model, if Model Type and Model order are known in advance. Recently, a new development in time series analysis gives the possibility to select automatically, with statistical criteria, the Model Type and the Model order for data with unknown characteristics. Hence, the statistically significant features of measured data can be determined without a priori knowledge. This creates the possibility to use estimated and selected Models for the automatic monitoring of stochastic data and for the detection of changes. The paper describes variations that can be detected. It shows that considering a measured signal as a stationary stochastic process is already sufficient a priori information to use a powerful statistical framework for the accurate description of observations and for the automatic detection of changes.

  • Efficient estimation of autocorrelation functions of random data with time series Models
    Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2001
    Co-Authors: P.m.t. Broersen, S. De Waele
    Abstract:

    Sample covariances, estimated as mean-lagged products of random data, are poor and inaccurate fundaments for the non-parametric spectral estimation with tapered and windowed periodograms. However, the autocovariance can be estimated efficiently with a parametric method as transformation of an estimated time series Model, if the Model Type and Model order are known a-priori. A recent development in time-series analysis gives the possibility to automatically select the Model Type and the Model order for data with unknown characteristics. After the computation of hundreds of candidate Models of different orders and Types, a statistical criterion can select a single time series Model. The accuracy of this identification from many candidates is sufficient to approach the performance that can be obtained with parametric estimation if the Type and the order of the time series Model would be known a priori. Hence, the accuracy (mean square error) of parametric covariance estimates is typically the same or better than what can be achieved by non-parametric mean-lagged-product estimates.

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

  • ITSC - A Hierarchical Algorithm for Vehicle Model Type Recognition on Time-Sequence Road Images
    2006 IEEE Intelligent Transportation Systems Conference, 2006
    Co-Authors: Mingxie Zheng, T. Gotoh, M. Shiohara
    Abstract:

    This paper describes a vision-based algorithm for recognizing vehicle Model Types from time-sequence road images. Many Types of vehicle Models are offered commercially, and some of them resemble in shape. This prevents us to discriminate their Model Types from the others easily. To solve these problems, we propose a hierarchical recognition method with learning process, in which the resembling Model groups are first generated and the effective features to discriminate the Models in the each group are then selected using the subspace method in learning. In the recognition process, the front area of a vehicle is first detected from each frame of the input time-sequence images, then a hierarchical recognition which consists of a group and a category discrimination is performed. Finally, the results of frame recognition are integrated to realize stable recognition. The experimental results using time-sequence road images shows the proposed method is effective: the recognition rate for the registered Model Types is more than 99%, and the rejection rate for unregistered vehicle Type is more than 92%

  • A Hierarchical Algorithm for Vehicle Model Type Recognition on Time-Sequence Road Images
    2006 IEEE Intelligent Transportation Systems Conference, 2006
    Co-Authors: Mingxie Zheng, T. Gotoh, M. Shiohara
    Abstract:

    This paper describes a vision-based algorithm for recognizing vehicle Model Types from time-sequence road images. Many Types of vehicle Models are offered commercially, and some of them resemble in shape. This prevents us to discriminate their Model Types from the others easily. To solve these problems, we propose a hierarchical recognition method with learning process, in which the resembling Model groups are first generated and the effective features to discriminate the Models in the each group are then selected using the subspace method in learning. In the recognition process, the front area of a vehicle is first detected from each frame of the input time-sequence images, then a hierarchical recognition which consists of a group and a category discrimination is performed. Finally, the results of frame recognition are integrated to realize stable recognition. The experimental results using time-sequence road images shows the proposed method is effective: the recognition rate for the registered Model Types is more than 99%, and the rejection rate for unregistered vehicle Type is more than 92%