The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Yi Liu - One of the best experts on this subject based on the ideXlab platform.

  • Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers
    Chemometrics and Intelligent Laboratory Systems, 2018
    Co-Authors: Wenjian Zheng, Yi Liu, Zengliang Gao, Jianguo Yang
    Abstract:

    Abstract Increasing data-driven soft sensors have been adopted to online predict the quality indices in polymerization processes to improve the availability of measurements and efficiency. However, in industrial rubber mixing processes, most existing soft sensors for online prediction of the Mooney Viscosity only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is proposed to online predict the Mooney Viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into its prediction model. Unlike traditional just-in-time learning models only utilizing labeled data (e.g., just-in-time ELM and just-in-time support vector regression), the prediction performance of JSELM can be enhanced by taking advantage of the information in lots of unlabeled data. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. Compared with traditional soft sensor methods, the superiority of JSELM is validated via the Mooney Viscosity prediction in an industrial rubber mixer.

  • Online Semi-supervised Quality Prediction Model for Batch Mixing Process
    2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 2018
    Co-Authors: Mingtao Zhang, Bocheng Chen, Wu You, Deng Weiwei, Zhang Xuelei, Yi Liu
    Abstract:

    Current soft sensors for the Mooney Viscosity prediction in rubber mixing processes only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is presented to online predict the Mooney Viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into the JSELM model to enhance its prediction performance. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. The superiority of JSELM is validated via the industrial Mooney Viscosity prediction.

  • Industrial Mooney Viscosity prediction using fast semi-supervised empirical model
    Chemometrics and Intelligent Laboratory Systems, 2017
    Co-Authors: Wenjian Zheng, Xuejin Gao, Yi Liu, Limei Wang, Jianguo Yang, Zengliang Gao
    Abstract:

    Abstract In industrial rubber mixing processes, the quality index (i.e., Mooney Viscosity) cannot be online measured directly. Traditional data-driven empirical models for online prediction of the Mooney Viscosity have not utilized the information hidden in lots of unlabeled data (e.g., process input variables during each mixing batch). A simple semi-supervised nonlinear soft sensor method for the Mooney Viscosity prediction is developed. It integrates extreme learning machine (ELM) and the graph Laplacian regularization into a unified modeling framework. The useful information in unlabeled data can be explored and introduced into the prediction model. Furthermore, a bagging-based ensemble strategy is combined into semi-supervised ELM (SELM) to obtain more accurate predictions. The Mooney Viscosity prediction in an industrial internal mixer exhibits its promising prediction performance of the proposed method by incorporating the information in unlabeled data efficiently.

  • online prediction of Mooney Viscosity in industrial rubber mixing process via adaptive kernel learning method
    Conference on Decision and Control, 2009
    Co-Authors: Diancai Yang, Yi Liu, Yugang Fan, Haiqing Wang
    Abstract:

    Mooney Viscosity is an important while difficult-to-measure quality index with a long-term laboratory assay in nowadays internal rubber mixing processes. In this study, an adaptive kernel learning (AKL) algorithm suitable for nonlinear multi-input-multi-output process modeling is applied to online prediction of Mooney Viscosity. The developed AKL algorithm utilizes a sequentially sparse strategy to control the model complexity and adopts a two-stage recursive learning mechanism to update the network topology effectively. Consequently, the AKL model can trace different characteristics of the internal mixing process. The developed AKL modeling method has been successfully applied to online prediction of Mooney Viscosity in several rubber and tire manufactories in China. The industrial applications show that the AKL model exhibits good modeling ability and predicts Mooney Viscosity successfully. Furthermore, the comparison results indicate that AKL is superior to conventional recursive partial least squares method.

  • CDC - Online prediction of Mooney Viscosity in industrial rubber mixing process via adaptive kernel learning method
    Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009
    Co-Authors: Diancai Yang, Yi Liu, Yugang Fan, Haiqing Wang
    Abstract:

    Mooney Viscosity is an important while difficult-to-measure quality index with a long-term laboratory assay in nowadays internal rubber mixing processes. In this study, an adaptive kernel learning (AKL) algorithm suitable for nonlinear multi-input-multi-output process modeling is applied to online prediction of Mooney Viscosity. The developed AKL algorithm utilizes a sequentially sparse strategy to control the model complexity and adopts a two-stage recursive learning mechanism to update the network topology effectively. Consequently, the AKL model can trace different characteristics of the internal mixing process. The developed AKL modeling method has been successfully applied to online prediction of Mooney Viscosity in several rubber and tire manufactories in China. The industrial applications show that the AKL model exhibits good modeling ability and predicts Mooney Viscosity successfully. Furthermore, the comparison results indicate that AKL is superior to conventional recursive partial least squares method.

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

  • Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers
    Chemometrics and Intelligent Laboratory Systems, 2018
    Co-Authors: Wenjian Zheng, Yi Liu, Zengliang Gao, Jianguo Yang
    Abstract:

    Abstract Increasing data-driven soft sensors have been adopted to online predict the quality indices in polymerization processes to improve the availability of measurements and efficiency. However, in industrial rubber mixing processes, most existing soft sensors for online prediction of the Mooney Viscosity only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is proposed to online predict the Mooney Viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into its prediction model. Unlike traditional just-in-time learning models only utilizing labeled data (e.g., just-in-time ELM and just-in-time support vector regression), the prediction performance of JSELM can be enhanced by taking advantage of the information in lots of unlabeled data. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. Compared with traditional soft sensor methods, the superiority of JSELM is validated via the Mooney Viscosity prediction in an industrial rubber mixer.

  • Industrial Mooney Viscosity prediction using fast semi-supervised empirical model
    Chemometrics and Intelligent Laboratory Systems, 2017
    Co-Authors: Wenjian Zheng, Xuejin Gao, Yi Liu, Limei Wang, Jianguo Yang, Zengliang Gao
    Abstract:

    Abstract In industrial rubber mixing processes, the quality index (i.e., Mooney Viscosity) cannot be online measured directly. Traditional data-driven empirical models for online prediction of the Mooney Viscosity have not utilized the information hidden in lots of unlabeled data (e.g., process input variables during each mixing batch). A simple semi-supervised nonlinear soft sensor method for the Mooney Viscosity prediction is developed. It integrates extreme learning machine (ELM) and the graph Laplacian regularization into a unified modeling framework. The useful information in unlabeled data can be explored and introduced into the prediction model. Furthermore, a bagging-based ensemble strategy is combined into semi-supervised ELM (SELM) to obtain more accurate predictions. The Mooney Viscosity prediction in an industrial internal mixer exhibits its promising prediction performance of the proposed method by incorporating the information in unlabeled data efficiently.

Cao Shuai-ying - One of the best experts on this subject based on the ideXlab platform.

  • Evaluation of comparative results of styrene-butadiene rubber Mooney Viscosity
    The Chemical Engineer, 2012
    Co-Authors: Cao Shuai-ying
    Abstract:

    According to GB/T 1232.1-2000,producing,processing,researching and testing of the synthetic rubber of 26 laboratories were organized to contrast the Mooney Viscosity data of styrene-butadiene rubber by robust statistical method.The laboratories taken part in this work reflected the testing level of the Mooney Viscosity in the synthetic rubber industry;the quality inspection institutions of the national and industry showed higher-level,and the laboratories of the scientific research development had an obvious advance on testing;laboratories of producing and processing industries can reach the testing requirements.Compared with the past,The testing level of all the laboratories had different improvement.The laboratories with the(|Z|) lower and laboratory accreditation were chose as qualified value for preparation of standard material of the Mooney Viscosity.

  • Preparation of Mooney Viscosity of butadiene rubber(BR) 9000
    Applied Chemical Industry, 2012
    Co-Authors: Cao Shuai-ying
    Abstract:

    The value of butadiene rubber was defined according with standard material value after preparated,passed by uniformity and stability measure by laboratories.The dispose of proceed of standard material value was used to judge of unusual numbers,inspect of distributed numbers,testy of equal precision,appraised of uncertainty.When remarkable level a=0.05 of standard material of Mooney Viscosity after tested by laboratory,the number was certainty,normal distribution and equal precision,the value of standard was(45.6±0.5)ML(1+4)100 ℃.Successful preparation of standard material should be supported to control of produce,trade of goods and arbitration of quality.

Zengliang Gao - One of the best experts on this subject based on the ideXlab platform.

  • Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers
    Chemometrics and Intelligent Laboratory Systems, 2018
    Co-Authors: Wenjian Zheng, Yi Liu, Zengliang Gao, Jianguo Yang
    Abstract:

    Abstract Increasing data-driven soft sensors have been adopted to online predict the quality indices in polymerization processes to improve the availability of measurements and efficiency. However, in industrial rubber mixing processes, most existing soft sensors for online prediction of the Mooney Viscosity only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is proposed to online predict the Mooney Viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into its prediction model. Unlike traditional just-in-time learning models only utilizing labeled data (e.g., just-in-time ELM and just-in-time support vector regression), the prediction performance of JSELM can be enhanced by taking advantage of the information in lots of unlabeled data. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. Compared with traditional soft sensor methods, the superiority of JSELM is validated via the Mooney Viscosity prediction in an industrial rubber mixer.

  • Industrial Mooney Viscosity prediction using fast semi-supervised empirical model
    Chemometrics and Intelligent Laboratory Systems, 2017
    Co-Authors: Wenjian Zheng, Xuejin Gao, Yi Liu, Limei Wang, Jianguo Yang, Zengliang Gao
    Abstract:

    Abstract In industrial rubber mixing processes, the quality index (i.e., Mooney Viscosity) cannot be online measured directly. Traditional data-driven empirical models for online prediction of the Mooney Viscosity have not utilized the information hidden in lots of unlabeled data (e.g., process input variables during each mixing batch). A simple semi-supervised nonlinear soft sensor method for the Mooney Viscosity prediction is developed. It integrates extreme learning machine (ELM) and the graph Laplacian regularization into a unified modeling framework. The useful information in unlabeled data can be explored and introduced into the prediction model. Furthermore, a bagging-based ensemble strategy is combined into semi-supervised ELM (SELM) to obtain more accurate predictions. The Mooney Viscosity prediction in an industrial internal mixer exhibits its promising prediction performance of the proposed method by incorporating the information in unlabeled data efficiently.

Wenjian Zheng - One of the best experts on this subject based on the ideXlab platform.

  • Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers
    Chemometrics and Intelligent Laboratory Systems, 2018
    Co-Authors: Wenjian Zheng, Yi Liu, Zengliang Gao, Jianguo Yang
    Abstract:

    Abstract Increasing data-driven soft sensors have been adopted to online predict the quality indices in polymerization processes to improve the availability of measurements and efficiency. However, in industrial rubber mixing processes, most existing soft sensors for online prediction of the Mooney Viscosity only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is proposed to online predict the Mooney Viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into its prediction model. Unlike traditional just-in-time learning models only utilizing labeled data (e.g., just-in-time ELM and just-in-time support vector regression), the prediction performance of JSELM can be enhanced by taking advantage of the information in lots of unlabeled data. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. Compared with traditional soft sensor methods, the superiority of JSELM is validated via the Mooney Viscosity prediction in an industrial rubber mixer.

  • Industrial Mooney Viscosity prediction using fast semi-supervised empirical model
    Chemometrics and Intelligent Laboratory Systems, 2017
    Co-Authors: Wenjian Zheng, Xuejin Gao, Yi Liu, Limei Wang, Jianguo Yang, Zengliang Gao
    Abstract:

    Abstract In industrial rubber mixing processes, the quality index (i.e., Mooney Viscosity) cannot be online measured directly. Traditional data-driven empirical models for online prediction of the Mooney Viscosity have not utilized the information hidden in lots of unlabeled data (e.g., process input variables during each mixing batch). A simple semi-supervised nonlinear soft sensor method for the Mooney Viscosity prediction is developed. It integrates extreme learning machine (ELM) and the graph Laplacian regularization into a unified modeling framework. The useful information in unlabeled data can be explored and introduced into the prediction model. Furthermore, a bagging-based ensemble strategy is combined into semi-supervised ELM (SELM) to obtain more accurate predictions. The Mooney Viscosity prediction in an industrial internal mixer exhibits its promising prediction performance of the proposed method by incorporating the information in unlabeled data efficiently.