The Experts below are selected from a list of 88722 Experts worldwide ranked by ideXlab platform
Carlos Magno Couto Jacinto - One of the best experts on this subject based on the ideXlab platform.
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nsga ii Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
Valeria Vitelli - One of the best experts on this subject based on the ideXlab platform.
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nsga ii Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
Chao Weng - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - A Comparison of Lattice-free Discriminative Training Criteria for Purely Sequence-Trained Neural Network Acoustic Models
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Chao WengAbstract:In this work, three lattice-free (LF) discriminative training criteria for purely sequence-Trained Neural Network acoustic models are compared on LVCSR tasks, namely maximum mutual information (MMI), boosted maximum mutual information (bMMI) and state-level minimum Bayes risk (sMBR). We demonstrate that, analogous to LF-MMI, a Neural Network acoustic model can also be Trained from scratch using LF-bMMI or LF-sMBR criteria respectively without the need of cross-entropy pre-training. Furthermore, experimental results on Switchboard-300hrs and Switchboard+Fisher-2100hrs datasets show that models Trained with LF-bMMI consistently outperform those Trained with plain LF-MMI and achieve a relative word error rate (WER) reduction of ∼5% over competitive temporal convolution projected LSTM (TDNN-LSTMP) LF-MMI baselines.
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A Comparison of Lattice-free Discriminative Training Criteria for Purely Sequence-Trained Neural Network Acoustic Models
arXiv: Learning, 2018Co-Authors: Chao WengAbstract:In this work, three lattice-free (LF) discriminative training criteria for purely sequence-Trained Neural Network acoustic models are compared on LVCSR tasks, namely maximum mutual information (MMI), boosted maximum mutual information (bMMI) and state-level minimum Bayes risk (sMBR). We demonstrate that, analogous to LF-MMI, a Neural Network acoustic model can also be Trained from scratch using LF-bMMI or LF-sMBR criteria respectively without the need of cross-entropy pre-training. Furthermore, experimental results on Switchboard-300hrs and Switchboard+Fisher-2100hrs datasets show that models Trained with LF-bMMI consistently outperform those Trained with plain LF-MMI and achieve a relative word error rate (WER) reduction of 5% over competitive temporal convolution projected LSTM (TDNN-LSTMP) LF-MMI baselines.
Enrico Zio - One of the best experts on this subject based on the ideXlab platform.
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nsga ii Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
Enrique López Droguett - One of the best experts on this subject based on the ideXlab platform.
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nsga ii Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-Trained Neural Network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a Neural Network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-Trained Neural Network is shown capable of providing PIs with both high coverage and small width.
