Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process
In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-pr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Haibin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: A metric to gauge local distortion in metallic glasses and supercooled liquids - Wu, Chen ELSEVIER, 2014transfer abstract, a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:115 ; year:2022 ; pages:48-57 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.jprocont.2022.04.018 |
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ELV057990484 |
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| 520 | |a In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. | ||
| 520 | |a In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. | ||
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10.1016/j.jprocont.2022.04.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001797.pica (DE-627)ELV057990484 (ELSEVIER)S0959-1524(22)00074-9 DE-627 ger DE-627 rakwb eng 670 VZ 330 VZ Wu, Haibin verfasserin aut Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process 2022transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. Small data Elsevier Deep neural network Elsevier Process modeling Elsevier Autoencoder Elsevier Embedding Elsevier Lo, Yu-Han oth Zhou, Le oth Yao, Yuan oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:115 year:2022 pages:48-57 extent:10 https://doi.org/10.1016/j.jprocont.2022.04.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 115 2022 48-57 10 |
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10.1016/j.jprocont.2022.04.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001797.pica (DE-627)ELV057990484 (ELSEVIER)S0959-1524(22)00074-9 DE-627 ger DE-627 rakwb eng 670 VZ 330 VZ Wu, Haibin verfasserin aut Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process 2022transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. Small data Elsevier Deep neural network Elsevier Process modeling Elsevier Autoencoder Elsevier Embedding Elsevier Lo, Yu-Han oth Zhou, Le oth Yao, Yuan oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:115 year:2022 pages:48-57 extent:10 https://doi.org/10.1016/j.jprocont.2022.04.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 115 2022 48-57 10 |
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10.1016/j.jprocont.2022.04.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001797.pica (DE-627)ELV057990484 (ELSEVIER)S0959-1524(22)00074-9 DE-627 ger DE-627 rakwb eng 670 VZ 330 VZ Wu, Haibin verfasserin aut Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process 2022transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. Small data Elsevier Deep neural network Elsevier Process modeling Elsevier Autoencoder Elsevier Embedding Elsevier Lo, Yu-Han oth Zhou, Le oth Yao, Yuan oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:115 year:2022 pages:48-57 extent:10 https://doi.org/10.1016/j.jprocont.2022.04.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 115 2022 48-57 10 |
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10.1016/j.jprocont.2022.04.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001797.pica (DE-627)ELV057990484 (ELSEVIER)S0959-1524(22)00074-9 DE-627 ger DE-627 rakwb eng 670 VZ 330 VZ Wu, Haibin verfasserin aut Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process 2022transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. Small data Elsevier Deep neural network Elsevier Process modeling Elsevier Autoencoder Elsevier Embedding Elsevier Lo, Yu-Han oth Zhou, Le oth Yao, Yuan oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:115 year:2022 pages:48-57 extent:10 https://doi.org/10.1016/j.jprocont.2022.04.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 115 2022 48-57 10 |
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10.1016/j.jprocont.2022.04.018 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001797.pica (DE-627)ELV057990484 (ELSEVIER)S0959-1524(22)00074-9 DE-627 ger DE-627 rakwb eng 670 VZ 330 VZ Wu, Haibin verfasserin aut Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process 2022transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. Small data Elsevier Deep neural network Elsevier Process modeling Elsevier Autoencoder Elsevier Embedding Elsevier Lo, Yu-Han oth Zhou, Le oth Yao, Yuan oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:115 year:2022 pages:48-57 extent:10 https://doi.org/10.1016/j.jprocont.2022.04.018 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 115 2022 48-57 10 |
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Enthalten in A metric to gauge local distortion in metallic glasses and supercooled liquids Amsterdam [u.a.] volume:115 year:2022 pages:48-57 extent:10 |
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A metric to gauge local distortion in metallic glasses and supercooled liquids |
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A metric to gauge local distortion in metallic glasses and supercooled liquids |
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Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process |
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Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process |
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Wu, Haibin |
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A metric to gauge local distortion in metallic glasses and supercooled liquids |
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process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process |
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Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process |
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In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. |
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In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. |
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In the big data era, small data problems still exist in many industrial sectors. Taking the high-value process industries as an example, a large number of materials and processing methods are often tested at the design stage. However, only a small amount of data can be collected for each material-process combination, which poses a serious challenge to data-driven process modeling. There is a great necessity to integrate the small data measured in different tasks and build the process model by sharing the information. In this work, a deep embedding neural network is proposed to extract the qualitative task information for process modeling. Specifically, an autoencoder is used to learn embeddings which are combined with the quantitative process conditions as the inputs of a feed-forward neural network to produce the final predictions. The feasibility, including interpretability and prediction accuracy, of the developed method is illustrated with an extrusion process. |
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Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process |
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Lo, Yu-Han Zhou, Le Yao, Yuan |
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