Predictive Modeling With Multiresolution Pyramid VAE and Industrial Soft Sensor Applications
In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development. In this work, a multiresolution pyramid variation...
Saved in:
| Published in: | IEEE transactions on cybernetics Vol. 53; no. 8; pp. 4867 - 4879 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 2168-2267, 2168-2275, 2168-2275 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development. In this work, a multiresolution pyramid variational autoencoder (MR-PVAE) predictive model is proposed to solve this problem based on the deep feature extraction and feature pyramid augmentation. First, a multirate data filter is designed through a resolution searching strategy to turn the original process data into a multiresolution dataset. Then, the pyramid variational autoencoder (PVAE) is proposed to extract deep nonlinear features from the data with different resolutions. In PVAE, the augmented feature pyramid is constructed layer by layer to fuse extracted features from low resolution to the high. As a consequence, the extracted features with various resolutions are gathered to form the regression model, where the process information contained in data with discrepant sampling rates can be fully utilized. Due to the layer-by-layer enhanced features, the prediction accuracy of the soft sensing model are gradually improved. Meanwhile, an optimized training strategy is established to select the optimal feature pyramid for prediction. A numerical experiment and an industrial soft sensing case are given to validate the effectiveness and superiority of the proposed MR-PVAE model. |
|---|---|
| AbstractList | In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development. In this work, a multiresolution pyramid variational autoencoder (MR-PVAE) predictive model is proposed to solve this problem based on the deep feature extraction and feature pyramid augmentation. First, a multirate data filter is designed through a resolution searching strategy to turn the original process data into a multiresolution dataset. Then, the pyramid variational autoencoder (PVAE) is proposed to extract deep nonlinear features from the data with different resolutions. In PVAE, the augmented feature pyramid is constructed layer by layer to fuse extracted features from low resolution to the high. As a consequence, the extracted features with various resolutions are gathered to form the regression model, where the process information contained in data with discrepant sampling rates can be fully utilized. Due to the layer-by-layer enhanced features, the prediction accuracy of the soft sensing model are gradually improved. Meanwhile, an optimized training strategy is established to select the optimal feature pyramid for prediction. A numerical experiment and an industrial soft sensing case are given to validate the effectiveness and superiority of the proposed MR-PVAE model. In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development. In this work, a multiresolution pyramid variational autoencoder (MR-PVAE) predictive model is proposed to solve this problem based on the deep feature extraction and feature pyramid augmentation. First, a multirate data filter is designed through a resolution searching strategy to turn the original process data into a multiresolution dataset. Then, the pyramid variational autoencoder (PVAE) is proposed to extract deep nonlinear features from the data with different resolutions. In PVAE, the augmented feature pyramid is constructed layer by layer to fuse extracted features from low resolution to the high. As a consequence, the extracted features with various resolutions are gathered to form the regression model, where the process information contained in data with discrepant sampling rates can be fully utilized. Due to the layer-by-layer enhanced features, the prediction accuracy of the soft sensing model are gradually improved. Meanwhile, an optimized training strategy is established to select the optimal feature pyramid for prediction. A numerical experiment and an industrial soft sensing case are given to validate the effectiveness and superiority of the proposed MR-PVAE model.In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development. In this work, a multiresolution pyramid variational autoencoder (MR-PVAE) predictive model is proposed to solve this problem based on the deep feature extraction and feature pyramid augmentation. First, a multirate data filter is designed through a resolution searching strategy to turn the original process data into a multiresolution dataset. Then, the pyramid variational autoencoder (PVAE) is proposed to extract deep nonlinear features from the data with different resolutions. In PVAE, the augmented feature pyramid is constructed layer by layer to fuse extracted features from low resolution to the high. As a consequence, the extracted features with various resolutions are gathered to form the regression model, where the process information contained in data with discrepant sampling rates can be fully utilized. Due to the layer-by-layer enhanced features, the prediction accuracy of the soft sensing model are gradually improved. Meanwhile, an optimized training strategy is established to select the optimal feature pyramid for prediction. A numerical experiment and an industrial soft sensing case are given to validate the effectiveness and superiority of the proposed MR-PVAE model. |
| Author | Yao, Le Ge, Zhiqiang Shen, Bingbing |
| Author_xml | – sequence: 1 givenname: Bingbing orcidid: 0000-0002-8832-9147 surname: Shen fullname: Shen, Bingbing email: shenbingbing@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Le orcidid: 0000-0002-0881-213X surname: Yao fullname: Yao, Le email: yaole_frank@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China – sequence: 3 givenname: Zhiqiang orcidid: 0000-0002-2071-4380 surname: Ge fullname: Ge, Zhiqiang email: gezhiqiang@zju.edu.cn organization: State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35175925$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kV1LHDEUhoMoftUfUAol0BtvdpuPmWRyuV38AqWCVikUQmZyRiPZZJtkCv57Z93VCy-amxPC8xxOznuAtkMMgNBnSqaUEvX9dv77x5QRxqacVlxQvoX2GRXNhDFZb7_fhdxDRzk_kfE045NqdtEer6msFav30Z_rBNZ1xf0DfBUteBce8L0rj_hq8MUlyNEPxcWAr5-TWTiL72Yn2ASLL4IdcknOeHwT-4JvIOSY8Gy59K4zKyV_Qju98RmONvUQ_To9uZ2fTy5_nl3MZ5eTjleqTGyvRG2osAx4X5G24gwEsE4K01SyN9IY20JbEWip4rJpTcOsqDpRCyJMy_khOl73Xab4d4Bc9MLlDrw3AeKQNROcKE4FFSP67QP6FIcUxuk0a7iqaaMkGamvG2poF2D1MrmFSc_6bW8jINdAl2LOCXrdufL66ZKM85oSvQpJr0LSq5D0JqTRpB_Mt-b_c76sHQcA77ySVJCa8RfzZZvu |
| CODEN | ITCEB8 |
| CitedBy_id | crossref_primary_10_1109_TII_2024_3371990 crossref_primary_10_1088_1361_6501_addc87 crossref_primary_10_1109_TIM_2024_3394496 crossref_primary_10_1109_TIM_2025_3565026 crossref_primary_10_1016_j_jtice_2025_106289 crossref_primary_10_1109_JSEN_2024_3384262 crossref_primary_10_1109_TII_2024_3475419 crossref_primary_10_1109_TIM_2024_3443350 crossref_primary_10_1109_TIM_2025_3551122 crossref_primary_10_1109_TCYB_2025_3580633 crossref_primary_10_1016_j_jprocont_2025_103488 crossref_primary_10_3390_pr12091807 crossref_primary_10_1109_TASE_2025_3531850 crossref_primary_10_1109_TCYB_2024_3431636 crossref_primary_10_1109_TII_2023_3295428 crossref_primary_10_1109_JAS_2023_123396 crossref_primary_10_1109_ACCESS_2024_3409899 crossref_primary_10_1016_j_neunet_2024_106482 crossref_primary_10_1016_j_compind_2025_104364 crossref_primary_10_1109_TIM_2023_3323006 crossref_primary_10_1109_TIM_2025_3551426 crossref_primary_10_1109_TIM_2023_3264046 crossref_primary_10_1109_TII_2024_3495779 crossref_primary_10_1109_TASE_2025_3548053 crossref_primary_10_1109_TII_2024_3507937 crossref_primary_10_3390_bioengineering11080803 crossref_primary_10_1016_j_psep_2025_107400 crossref_primary_10_1016_j_jprocont_2024_103355 crossref_primary_10_1109_TNNLS_2024_3360030 crossref_primary_10_1109_TSMC_2024_3495020 crossref_primary_10_1016_j_jprocont_2025_103497 |
| Cites_doi | 10.1016/j.conengprac.2019.104198 10.1109/ACCESS.2021.3058305 10.1109/TII.2019.2951622 10.1016/0169-7439(95)00043-7 10.1016/j.jprocont.2014.01.012 10.1038/nature14539 10.1016/j.jprocont.2017.02.010 10.1109/CVPR.2017.106 10.1109/TII.2021.3053128 10.1002/9781118445112.stat03287 10.1109/TIE.2016.2622234 10.1109/TIE.2019.2927197 10.1109/TIE.2017.2682012 10.1109/TII.2018.2889750 10.1016/j.measurement.2020.108200 10.1109/TASE.2019.2896205 10.1002/aic.14299 10.1145/3065386 10.1016/j.chemolab.2015.12.011 10.1016/j.jprocont.2019.11.007 10.1155/2016/3159805 10.1016/j.ifacol.2015.09.062 10.1016/j.ces.2020.115509 10.1109/TCYB.2016.2625419 10.1016/j.isatra.2019.07.001 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TCYB.2022.3143613 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Aerospace Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed Aerospace Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 2168-2275 |
| EndPage | 4879 |
| ExternalDocumentID | 35175925 10_1109_TCYB_2022_3143613 9716052 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: China Postdoctoral Science Foundation grantid: 2021T140597; 2019M662050 funderid: 10.13039/501100002858 – fundername: National Natural Science Foundation of China (NSFC) grantid: 62003300; 62103362; 92167106 funderid: 10.13039/501100001809 |
| GroupedDBID | 0R~ 4.4 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM RIG 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c349t-df965a16d2e3f40b432e6e2c76a847fa7aadbeb40eb19378ba82d64c65606ab33 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 50 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000761342500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2168-2267 2168-2275 |
| IngestDate | Sun Nov 09 12:31:20 EST 2025 Sun Nov 09 08:50:27 EST 2025 Mon Jul 21 05:54:58 EDT 2025 Sat Nov 29 02:02:36 EST 2025 Tue Nov 18 22:39:02 EST 2025 Wed Aug 27 02:25:58 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c349t-df965a16d2e3f40b432e6e2c76a847fa7aadbeb40eb19378ba82d64c65606ab33 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-0881-213X 0000-0002-8832-9147 0000-0002-2071-4380 |
| PMID | 35175925 |
| PQID | 2839518970 |
| PQPubID | 85422 |
| PageCount | 13 |
| ParticipantIDs | ieee_primary_9716052 crossref_primary_10_1109_TCYB_2022_3143613 pubmed_primary_35175925 crossref_citationtrail_10_1109_TCYB_2022_3143613 proquest_journals_2839518970 proquest_miscellaneous_2630931616 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-08-01 |
| PublicationDateYYYYMMDD | 2023-08-01 |
| PublicationDate_xml | – month: 08 year: 2023 text: 2023-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Piscataway |
| PublicationTitle | IEEE transactions on cybernetics |
| PublicationTitleAbbrev | TCYB |
| PublicationTitleAlternate | IEEE Trans Cybern |
| PublicationYear | 2023 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref12 ref15 ref14 ref31 ref30 ref32 ref2 liu (ref28) 2020 ref1 ref17 ref19 lecun (ref6) 2015; 521 ref18 wang (ref24) 2021; 70 ref23 ref26 ref25 ref20 ref22 ref21 kingma (ref10) 2013 ref27 sun (ref13) 2020 ref29 ref8 ref9 bishop (ref7) 2006 ref4 ref3 chen (ref16) 2020 ref5 doersch (ref11) 2016 |
| References_xml | – ident: ref14 doi: 10.1016/j.conengprac.2019.104198 – ident: ref29 doi: 10.1109/ACCESS.2021.3058305 – ident: ref15 doi: 10.1109/TII.2019.2951622 – ident: ref22 doi: 10.1016/0169-7439(95)00043-7 – ident: ref5 doi: 10.1016/j.jprocont.2014.01.012 – volume: 70 year: 2021 ident: ref24 article-title: Dual neural extended Kalman filtering approach for multirate sensor data fusion publication-title: IEEE Trans Instrum Meas – volume: 521 start-page: 436 year: 2015 ident: ref6 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: ref23 doi: 10.1016/j.jprocont.2017.02.010 – year: 2020 ident: ref13 article-title: Gated stacked target-related autoencoder: A novel deep feature extraction and layerwise ensemble method for industrial soft sensor application publication-title: IEEE Trans Cybern – ident: ref30 doi: 10.1109/CVPR.2017.106 – ident: ref2 doi: 10.1109/TII.2021.3053128 – year: 2020 ident: ref16 article-title: Discriminative mixture variational autoencoder for semisupervised classification publication-title: IEEE Trans Cybern – ident: ref3 doi: 10.1002/9781118445112.stat03287 – ident: ref20 doi: 10.1109/TIE.2016.2622234 – ident: ref12 doi: 10.1109/TIE.2019.2927197 – ident: ref31 doi: 10.1109/TIE.2017.2682012 – year: 2016 ident: ref11 article-title: Tutorial on variational autoencoders publication-title: arXiv 1606 05908 – ident: ref18 doi: 10.1109/TII.2018.2889750 – ident: ref25 doi: 10.1016/j.measurement.2020.108200 – ident: ref26 doi: 10.1109/TASE.2019.2896205 – ident: ref4 doi: 10.1002/aic.14299 – year: 2020 ident: ref28 article-title: Multiresolution convolutional autoencoders publication-title: arXiv 2004 04946 – year: 2013 ident: ref10 article-title: Auto-encoding variational bayes publication-title: arXiv 1312 6114 – year: 2006 ident: ref7 publication-title: Pattern Recognition and Machine Learning – ident: ref32 doi: 10.1145/3065386 – ident: ref1 doi: 10.1016/j.chemolab.2015.12.011 – ident: ref27 doi: 10.1016/j.jprocont.2019.11.007 – ident: ref17 doi: 10.1155/2016/3159805 – ident: ref21 doi: 10.1016/j.ifacol.2015.09.062 – ident: ref19 doi: 10.1016/j.ces.2020.115509 – ident: ref8 doi: 10.1109/TCYB.2016.2625419 – ident: ref9 doi: 10.1016/j.isatra.2019.07.001 |
| SSID | ssj0000816898 |
| Score | 2.5280013 |
| Snippet | In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 4867 |
| SubjectTerms | Data mining Data models Deep feature extraction Feature extraction feature pyramid augmentation Industrial applications Measuring instruments multirate data filter multiresolution pyramid variational autoencoder (MR-PVAE) Numerical models Numerical prediction Prediction models Predictive models Process variables Regression models Sampling soft sensor Soft sensors Spatial resolution |
| Title | Predictive Modeling With Multiresolution Pyramid VAE and Industrial Soft Sensor Applications |
| URI | https://ieeexplore.ieee.org/document/9716052 https://www.ncbi.nlm.nih.gov/pubmed/35175925 https://www.proquest.com/docview/2839518970 https://www.proquest.com/docview/2630931616 |
| Volume | 53 |
| WOSCitedRecordID | wos000761342500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2168-2275 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816898 issn: 2168-2267 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS9xAEB-slOKLrR_VtCor9EGL6eV2c7vZx1OUPsmB1p5QCJvshh5oTnJ3gv99ZzZ7qQ-t4NtCNh9kZmfmt7MzP4AvtuLUg0TEvDQyTnk1iAvRF7FBZ1w4K5KKt2QT6vIyG4_1aAVOuloY55w_fOa-0dDn8u20XNBWWY_aHSUDNLhvlJJtrVa3n-IJJDz1LcdBjFGFCknMfqJ712e3pwgGOUeMmgp0YWvwTgzQdWriyH7mkTzFyv-jTe91Lt6_7ns_wHqILtmwVYcNWHH1JmyE9TtjR6HJ9PEW_Bo1lKIhY8eID42q0tnPyfw38yW5CMKDTrLRU2PuJ5bdDM-ZqS37S_bBrtCGsysEwtOGDZ9lwrfhx8X59dn3ODAtxKVI9Ty2lZYD05eWO1GlSZEK7qTjpZIGvVdllDG2cEWaoGXHeCYrTMatTEvq3CNNIcRHWK2ntdsFplNptEVcpAyCL2ONqmzfIcgTorAYIESQLP92XoY25MSGcZd7OJLonGSVk6zyIKsIvna3PLQ9OF6avEWC6CYGGUSwtxRpHlbpLMfQCgPMTKskgsPuMq4vSpqY2k0XOEdSrhjjYhnBTqsK3bOXGvTp3-_8DGtETt8eF9yD1XmzcPvwtnycT2bNASrxODvwSvwHL-jqPA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dT9swED8hmIAXtvKZjW1G4mGbCKR26tSPHQJ1Gqsq0TGQkCIndkQllk5pO4n_njvHzXhgk_ZmKc6Hcue7-_l89wM4NAWnHiQi5LmWYcyLTpiJtgg1OuPMGhEVvCabSAaD7vW1Gi7BUVMLY611h8_sMQ1dLt9M8jltlZ1Qu6OogwZ3hZizfLVWs6PiKCQc-S3HQYhxReLTmO1InYxObz4jHOQcUWos0Imtw6rooPNUxJL9xCc5kpW_x5vO75y__L8vfgUbPr5kvVohWrBky01o-RU8ZR98m-mPW3A7rChJQ-aOESMa1aWzH-PZHXNFuQjDvVay4UOlf44Nu-qdMV0a9ofug12iFWeXCIUnFes9yYVvw_fzs9FpP_RcC2EuYjULTaFkR7el4VYUcZTFgltpeZ5Ijf6r0InWJrNZHKFtx4imm-kuNzLOqXeP1JkQO7BcTkq7B0zFUiuDyCjRCL-00Ulh2hZhnhCZwRAhgGjxt9PcNyInPoz71AGSSKUkq5RklXpZBfCpueVX3YXjX5O3SBDNRC-DAPYXIk39Op2mGFxhiNlVSRTAQXMZVxilTXRpJ3OcIylbjJGxDGC3VoXm2QsNev38O9_DWn_07SK9-DL4-gbWiaq-Pjy4D8uzam7fwov892w8rd45VX4EBTjsnQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Predictive+Modeling+With+Multiresolution+Pyramid+VAE+and+Industrial+Soft+Sensor+Applications&rft.jtitle=IEEE+transactions+on+cybernetics&rft.au=Shen%2C+Bingbing&rft.au=Yao%2C+Le&rft.au=Ge%2C+Zhiqiang&rft.date=2023-08-01&rft.issn=2168-2275&rft.eissn=2168-2275&rft.volume=53&rft.issue=8&rft.spage=4867&rft_id=info:doi/10.1109%2FTCYB.2022.3143613&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2267&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2267&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2267&client=summon |