Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials
Chemical contents, the important quality indicators are crucial for the modeling of sintering process. However, the lack of these data can result in the biasedness of state estimation in sintering process. It, thus, greatly reduces the accuracy of modeling. Although there are some general imputation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2023-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2023/6647291 |
| _version_ | 1797409467013267456 |
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| author | Wei Liu Cailian Chen Junpeng Li Xinping Guan |
| author_facet | Wei Liu Cailian Chen Junpeng Li Xinping Guan |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Chemical contents, the important quality indicators are crucial for the modeling of sintering process. However, the lack of these data can result in the biasedness of state estimation in sintering process. It, thus, greatly reduces the accuracy of modeling. Although there are some general imputation methods to tackle the data lackness, they rarely consider the interoutputs correlation and the negative impacts caused by incorrect prefilling. In this article, a novel sparse multioutput Gaussian convolution process (MGCP) modeling framework is proposed for data imputation. MGCP can flexibly mine the relationships between the outputs by a convolution of a sharing latent function and different Gaussian kernels. Moreover, the penalization terms are designed to weaken the false relationship between these outputs. To generalize the MGCP to a long-period case, dynamic time warping term is introduced to keep the global similarity between the original and estimated time series. Compared with several existing methods, the proposed method shows great superiority in sintering raw material contents estimation with real-world data. |
| first_indexed | 2024-03-09T04:14:56Z |
| format | Article |
| id | doaj.art-423722937a804280821b9397b1a225b9 |
| institution | Directory Open Access Journal |
| issn | 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T04:14:56Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-423722937a804280821b9397b1a225b92023-12-03T13:55:11ZengHindawi-IETIET Signal Processing1751-96832023-01-01202310.1049/2023/6647291Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw MaterialsWei Liu0Cailian Chen1Junpeng Li2Xinping Guan3Department of AutomationDepartment of AutomationEngineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education of ChinaDepartment of AutomationChemical contents, the important quality indicators are crucial for the modeling of sintering process. However, the lack of these data can result in the biasedness of state estimation in sintering process. It, thus, greatly reduces the accuracy of modeling. Although there are some general imputation methods to tackle the data lackness, they rarely consider the interoutputs correlation and the negative impacts caused by incorrect prefilling. In this article, a novel sparse multioutput Gaussian convolution process (MGCP) modeling framework is proposed for data imputation. MGCP can flexibly mine the relationships between the outputs by a convolution of a sharing latent function and different Gaussian kernels. Moreover, the penalization terms are designed to weaken the false relationship between these outputs. To generalize the MGCP to a long-period case, dynamic time warping term is introduced to keep the global similarity between the original and estimated time series. Compared with several existing methods, the proposed method shows great superiority in sintering raw material contents estimation with real-world data.http://dx.doi.org/10.1049/2023/6647291 |
| spellingShingle | Wei Liu Cailian Chen Junpeng Li Xinping Guan Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials IET Signal Processing |
| title | Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials |
| title_full | Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials |
| title_fullStr | Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials |
| title_full_unstemmed | Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials |
| title_short | Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials |
| title_sort | regularized multioutput gaussian convolution process for chemical contents data imputation in sintering raw materials |
| url | http://dx.doi.org/10.1049/2023/6647291 |
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