A Fuzzy PID-Controlled Iterative Calderon's Method for Binary Distribution in Electrical Capacitance Tomography
Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical two-phase flows can be roughly treated as binary-valued material distributions, in this article, a fuzzy PID-controlled iterative algorithm is propo...
        Saved in:
      
    
          | Published in | IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 11 | 
|---|---|
| Main Authors | , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        New York
          IEEE
    
        2021
     The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 0018-9456 1557-9662  | 
| DOI | 10.1109/TIM.2021.3052249 | 
Cover
| Abstract | Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical two-phase flows can be roughly treated as binary-valued material distributions, in this article, a fuzzy PID-controlled iterative algorithm is proposed for image reconstruction in cases of binary distributions. A closed-loop control system includes a fuzzy PID controller, Calderon's method, and fast calculation of the Dirichlet-to-Neumann map. Capacitances measured in an electrode array of the ECT sensor are compared with the feedback, and the difference is input to the controller. Fuzzy rules are used to automatically adjust the three parameters of the controller, i.e., <inline-formula> <tex-math notation="LaTeX">K_{P} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">K_{I} </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">K_{D} </tex-math></inline-formula>. The controller passes the difference to Calderon's method for reconstructing permittivity distribution. Reconstructed distribution is used to calculate a boundary map for feedback, by fast calculation of the Dirichlet-to-Neumann map, and serves as an updated reference for measured capacitances. A smooth segmentation method is also introduced to deal with the binary distribution and release the fluctuation in the tuning of the PID controller. Numerical simulations were done to verify the performance of the proposed iterative Calderon's method for binary distributions. Experiments on real phantoms were also carried out using an ECT system to evaluate the proposed method. Several distributions were set up with solid particles and air. The results show that the proposed method can produce images with clear edges and shapes of binary distributions. | 
    
|---|---|
| AbstractList | Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical two-phase flows can be roughly treated as binary-valued material distributions, in this article, a fuzzy PID-controlled iterative algorithm is proposed for image reconstruction in cases of binary distributions. A closed-loop control system includes a fuzzy PID controller, Calderon's method, and fast calculation of the Dirichlet-to-Neumann map. Capacitances measured in an electrode array of the ECT sensor are compared with the feedback, and the difference is input to the controller. Fuzzy rules are used to automatically adjust the three parameters of the controller, i.e., <inline-formula> <tex-math notation="LaTeX">K_{P} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">K_{I} </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">K_{D} </tex-math></inline-formula>. The controller passes the difference to Calderon's method for reconstructing permittivity distribution. Reconstructed distribution is used to calculate a boundary map for feedback, by fast calculation of the Dirichlet-to-Neumann map, and serves as an updated reference for measured capacitances. A smooth segmentation method is also introduced to deal with the binary distribution and release the fluctuation in the tuning of the PID controller. Numerical simulations were done to verify the performance of the proposed iterative Calderon's method for binary distributions. Experiments on real phantoms were also carried out using an ECT system to evaluate the proposed method. Several distributions were set up with solid particles and air. The results show that the proposed method can produce images with clear edges and shapes of binary distributions. Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical two-phase flows can be roughly treated as binary-valued material distributions, in this article, a fuzzy PID-controlled iterative algorithm is proposed for image reconstruction in cases of binary distributions. A closed-loop control system includes a fuzzy PID controller, Calderon’s method, and fast calculation of the Dirichlet-to-Neumann map. Capacitances measured in an electrode array of the ECT sensor are compared with the feedback, and the difference is input to the controller. Fuzzy rules are used to automatically adjust the three parameters of the controller, i.e., [Formula Omitted], [Formula Omitted], and [Formula Omitted]. The controller passes the difference to Calderon’s method for reconstructing permittivity distribution. Reconstructed distribution is used to calculate a boundary map for feedback, by fast calculation of the Dirichlet-to-Neumann map, and serves as an updated reference for measured capacitances. A smooth segmentation method is also introduced to deal with the binary distribution and release the fluctuation in the tuning of the PID controller. Numerical simulations were done to verify the performance of the proposed iterative Calderon’s method for binary distributions. Experiments on real phantoms were also carried out using an ECT system to evaluate the proposed method. Several distributions were set up with solid particles and air. The results show that the proposed method can produce images with clear edges and shapes of binary distributions.  | 
    
| Author | Xu, Lijun Gao, Xin Yang, Wuqiang Tian, Yu Cao, Zhang Hu, Die  | 
    
| Author_xml | – sequence: 1 givenname: Yu orcidid: 0000-0001-6732-5924 surname: Tian fullname: Tian, Yu organization: School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing, China – sequence: 2 givenname: Zhang orcidid: 0000-0003-3649-9512 surname: Cao fullname: Cao, Zhang email: zh_cao@buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China – sequence: 3 givenname: Die orcidid: 0000-0001-6870-1522 surname: Hu fullname: Hu, Die organization: School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing, China – sequence: 4 givenname: Xin orcidid: 0000-0001-6221-9794 surname: Gao fullname: Gao, Xin organization: School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing, China – sequence: 5 givenname: Lijun orcidid: 0000-0003-0488-9604 surname: Xu fullname: Xu, Lijun organization: School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing, China – sequence: 6 givenname: Wuqiang orcidid: 0000-0002-7201-1011 surname: Yang fullname: Yang, Wuqiang organization: School of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K  | 
    
| BookMark | eNp9kLtPwzAQhy1UJFpgR2KxxMCUcnaeHqHlEYkKhjJHjn0Bo2AHx0Vq_3oSFTEwMJ10-n33-GZkYp1FQs4YzBkDcbUuV3MOnM1jSDlPxAGZsjTNI5FlfEKmAKyIRJJmR2TW9-8AkGdJPiXumt5tdrstfS6X0cLZ4F3boqZlQC-D-UK6kK1G7-xlT1cY3pymjfP0xljpt3Rp-uBNvQnGWWosvW1RDQ0l24HrpDJBWoV07T7cq5fd2_aEHDay7fH0px6Tl7vb9eIheny6LxfXj5HigoUoZZAI1jAhUWlZq0YXBdOxlk2dYp3IAmoGRa7qWAsuMIY8l0pjrgElSC7iY3Kxn9t597nBPlTvbuPtsLLiSZGlgucchlS2Tynv-t5jU40Xj88EL01bMahGudUgtxrlVj9yBxD-gJ03H4OR_5DzPWIQ8TcuYl4wIeJvxReIVA | 
    
| CODEN | IEIMAO | 
    
| CitedBy_id | crossref_primary_10_1109_TIM_2022_3229726 crossref_primary_10_1021_acs_iecr_3c01772 crossref_primary_10_1109_TIM_2023_3240989 crossref_primary_10_1109_TPAMI_2024_3354928 crossref_primary_10_1109_JSEN_2023_3297993 crossref_primary_10_1088_1361_6501_acddd8 crossref_primary_10_1088_1361_6501_ad0867 crossref_primary_10_1088_1361_6501_ad3182 crossref_primary_10_1109_TIM_2021_3132829 crossref_primary_10_1016_j_ress_2022_108509 crossref_primary_10_1063_5_0144464 crossref_primary_10_1109_JSEN_2021_3099241  | 
    
| Cites_doi | 10.1016/j.partic.2018.05.009 10.1108/SR-01-2016-0027 10.1016/j.ces.2018.01.032 10.1109/JSEN.2020.2965731 10.1109/TIM.2019.2921441 10.1088/0957-0233/19/9/094014 10.1088/0957-0233/22/10/104001 10.1109/TIM.2014.2329738 10.1109/TIM.2018.2851839 10.1016/j.apm.2020.01.063 10.1109/JSEN.2017.2750741 10.1002/aic.15879 10.1109/TIM.2015.2450351 10.1002/aic.11406 10.1016/j.applthermaleng.2020.115311 10.1016/j.ces.2019.01.020 10.1109/TIM.2011.2113010 10.1109/JSEN.2019.2919923 10.1016/j.ijmultiphaseflow.2019.103085 10.1109/TIM.2012.2236731 10.1109/TIM.2012.2232475 10.1007/s40815-017-0401-3 10.1109/TIM.2018.2811228 10.1088/0957-0233/26/12/125402 10.1109/JSEN.2018.2880999 10.1088/0957-0233/20/10/104027 10.1109/JSEN.2018.2866524 10.1109/TMI.2019.2900031 10.1088/0957-0233/21/1/015502 10.1109/JSEN.2015.2446982  | 
    
| ContentType | Journal Article | 
    
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 | 
    
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 | 
    
| DBID | 97E RIA RIE AAYXX CITATION 7SP 7U5 8FD L7M  | 
    
| DOI | 10.1109/TIM.2021.3052249 | 
    
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Xplore digital library CrossRef Electronics & Communications Abstracts Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace  | 
    
| DatabaseTitle | CrossRef Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts  | 
    
| DatabaseTitleList | Solid State and Superconductivity Abstracts  | 
    
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher  | 
    
| DeliveryMethod | fulltext_linktorsrc | 
    
| Discipline | Engineering Physics  | 
    
| EISSN | 1557-9662 | 
    
| EndPage | 11 | 
    
| ExternalDocumentID | 10_1109_TIM_2021_3052249 9328199  | 
    
| Genre | orig-research | 
    
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61871017; 61961130393; 61620106004; 61827802 funderid: 10.13039/501100001809 – fundername: Newton Advanced Fellowship grantid: NAF191193  | 
    
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 5VS 6IK 85S 8WZ 97E A6W AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TN5 TWZ VH1 VJK AAYXX CITATION 7SP 7U5 8FD L7M  | 
    
| ID | FETCH-LOGICAL-c291t-510491f19aecdabcfd881d3dafb5eb4a80b1087cb3d929e3077acde7d0ea0a293 | 
    
| IEDL.DBID | RIE | 
    
| ISSN | 0018-9456 | 
    
| IngestDate | Mon Jun 30 10:19:17 EDT 2025 Thu Apr 24 22:52:54 EDT 2025 Wed Oct 01 03:46:26 EDT 2025 Wed Aug 27 02:28:15 EDT 2025  | 
    
| IsPeerReviewed | true | 
    
| IsScholarly | true | 
    
| Language | English | 
    
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html | 
    
| LinkModel | DirectLink | 
    
| MergedId | FETCHMERGED-LOGICAL-c291t-510491f19aecdabcfd881d3dafb5eb4a80b1087cb3d929e3077acde7d0ea0a293 | 
    
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14  | 
    
| ORCID | 0000-0003-3649-9512 0000-0001-6221-9794 0000-0001-6870-1522 0000-0002-7201-1011 0000-0003-0488-9604 0000-0001-6732-5924  | 
    
| PQID | 2486592720 | 
    
| PQPubID | 85462 | 
    
| PageCount | 11 | 
    
| ParticipantIDs | crossref_citationtrail_10_1109_TIM_2021_3052249 crossref_primary_10_1109_TIM_2021_3052249 proquest_journals_2486592720 ieee_primary_9328199  | 
    
| ProviderPackageCode | CITATION AAYXX  | 
    
| PublicationCentury | 2000 | 
    
| PublicationDate | 20210000 2021-00-00 20210101  | 
    
| PublicationDateYYYYMMDD | 2021-01-01 | 
    
| PublicationDate_xml | – year: 2021 text: 20210000  | 
    
| PublicationDecade | 2020 | 
    
| PublicationPlace | New York | 
    
| PublicationPlace_xml | – name: New York | 
    
| PublicationTitle | IEEE transactions on instrumentation and measurement | 
    
| PublicationTitleAbbrev | TIM | 
    
| PublicationYear | 2021 | 
    
| 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 | ref13 ref12 ref15 ref14 ref30 ref11 ref10 ref2 ye (ref18) 2015; 64 ref1 ref17 ref16 ref19 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5  | 
    
| References_xml | – ident: ref2 doi: 10.1016/j.partic.2018.05.009 – ident: ref10 doi: 10.1108/SR-01-2016-0027 – ident: ref3 doi: 10.1016/j.ces.2018.01.032 – ident: ref13 doi: 10.1109/JSEN.2020.2965731 – ident: ref19 doi: 10.1109/TIM.2019.2921441 – ident: ref21 doi: 10.1088/0957-0233/19/9/094014 – ident: ref24 doi: 10.1088/0957-0233/22/10/104001 – volume: 64 start-page: 89 year: 2015 ident: ref18 article-title: Image reconstruction for electrical capacitance tomography based on sparse representation publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2014.2329738 – ident: ref26 doi: 10.1109/TIM.2018.2851839 – ident: ref14 doi: 10.1016/j.apm.2020.01.063 – ident: ref29 doi: 10.1109/JSEN.2017.2750741 – ident: ref16 doi: 10.1002/aic.15879 – ident: ref6 doi: 10.1109/TIM.2015.2450351 – ident: ref7 doi: 10.1002/aic.11406 – ident: ref4 doi: 10.1016/j.applthermaleng.2020.115311 – ident: ref11 doi: 10.1016/j.ces.2019.01.020 – ident: ref9 doi: 10.1109/TIM.2011.2113010 – ident: ref17 doi: 10.1109/JSEN.2019.2919923 – ident: ref1 doi: 10.1016/j.ijmultiphaseflow.2019.103085 – ident: ref8 doi: 10.1109/TIM.2012.2236731 – ident: ref23 doi: 10.1109/TIM.2012.2232475 – ident: ref28 doi: 10.1007/s40815-017-0401-3 – ident: ref12 doi: 10.1109/TIM.2018.2811228 – ident: ref15 doi: 10.1088/0957-0233/26/12/125402 – ident: ref30 doi: 10.1109/JSEN.2018.2880999 – ident: ref22 doi: 10.1088/0957-0233/20/10/104027 – ident: ref27 doi: 10.1109/JSEN.2018.2866524 – ident: ref20 doi: 10.1109/TMI.2019.2900031 – ident: ref25 doi: 10.1088/0957-0233/21/1/015502 – ident: ref5 doi: 10.1109/JSEN.2015.2446982  | 
    
| SSID | ssj0007647 | 
    
| Score | 2.4313414 | 
    
| Snippet | Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical... | 
    
| SourceID | proquest crossref ieee  | 
    
| SourceType | Aggregation Database Enrichment Source Index Database Publisher  | 
    
| StartPage | 1 | 
    
| SubjectTerms | Binary distribution Boundary maps Calderon’s method Capacitance Capacitance measurement Controllers Dirichlet problem electrical capacitance tomography (ECT) Electrodes Feedback Fuzzy control fuzzy PID controller Image reconstruction Image segmentation Iterative algorithms Iterative methods Permittivity Permittivity measurement Proportional integral derivative Sensor arrays Tomography Two phase flow  | 
    
| Title | A Fuzzy PID-Controlled Iterative Calderon's Method for Binary Distribution in Electrical Capacitance Tomography | 
    
| URI | https://ieeexplore.ieee.org/document/9328199 https://www.proquest.com/docview/2486592720  | 
    
| Volume | 70 | 
    
| hasFullText | 1 | 
    
| inHoldings | 1 | 
    
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Xplore customDbUrl: eissn: 1557-9662 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007647 issn: 0018-9456 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE  | 
    
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Na9wwEB2SQCE5NM0X3SYNOhRCod61LNuyjukmS7awoYcN5GZkaQQhqV263kP313cke5cmLaU3HyRb8EaaN57RG4APMksKzbWIhOBZlFrrokpiHjkhkcyLY-58Rnd2m9_cpV_us_st-LS5C4OIofgMh_4x5PJtY5b-V9mIuAY5MLUN27LIu7tam1NX5mmnj8lpAxMrWKckYzWaT2cUCCZ8SB8nj6WeuaDQU-WPgzh4l8k-zNbr6opKHofLthqa1QvJxv9d-Bt43dNMdtnZxQFsYX0Ie7-JDx7Cq1D8aRZH0FyyyXK1-sm-Tq-icVe7_oSWTYPiMh2HbKx9M--mvliwWWg5zYjrss_hLi-78tq7fdss9lCz69BZx4NP8ygkf2i9ZbF5862Xxz6Gu8n1fHwT9Y0YIpMo3ka0b1PFHVcajdWVcbYgmiusdlWGVaqLuOJxIU0lLLEtwlhKbSxKG6OONRGKE9ipmxrfAuPGkdmoHIUxaYGZMmmeU4gjnKBISLkBjNbYlKZXKffNMp7KEK3EqiQ0S49m2aM5gI-bGd87hY5_jD3y4GzG9bgM4GwNf9lv4UWZpIVPOcskfvf3Waew69_d_Y85g532xxLfE0Npq_Ngmr8ArzLjGA | 
    
| linkProvider | IEEE | 
    
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VIgQcgD4QC334UAkhkd04duL4WLZd7dKm4rCVeoscP6SKkiA2e2B_PWMnu6KAUG852LKlb-z5JjP-BuBEpEmuqGIRYzSNuDEuqoTNIseERfOiNnM-o1tcZdNr_vkmvdmCj5u3MNbaUHxmh_4z5PJNo5f-V9kIuQY6MPkIHqec87R7rbW5d0XGO4VMikcYecE6KRnL0XxWYCiY0CEujz5L3nNCoavKX1dx8C-Tl1Csd9aVlXwdLttqqFd_iDY-dOuv4EVPNMlpZxk7sGXrXXj-m_zgLjwJ5Z96sQfNKZksV6uf5MvsLBp31et31pBZ0FzGC5GMlW_n3dTvF6QITacJsl3yKbzmJWdefbdvnEVua3Ieeut4-HEeBuW3rbctMm--9QLZ-3A9OZ-Pp1HfiiHSiaRthCeXS-qoVFYbVWlnciS6zChXpbbiKo8rGudCV8wg30KUhVDaWGFiq2KFlOI1bNdNbd8Aodqh4cjMMq15blOpeZZhkMMcw1hIugGM1tiUutcp9-0y7soQr8SyRDRLj2bZozmAD5sZ3zuNjv-M3fPgbMb1uAzgYA1_2R_iRZnw3CedRRK__fesY3g6nReX5eXs6uIdPPPrdH9nDmC7_bG0h8hX2uoomOkvuKjmZQ | 
    
| 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=A+Fuzzy+PID-Controlled+Iterative+Calderon%27s+Method+for+Binary+Distribution+in+Electrical+Capacitance+Tomography&rft.jtitle=IEEE+transactions+on+instrumentation+and+measurement&rft.au=Tian%2C+Yu&rft.au=Cao%2C+Zhang&rft.au=Hu%2C+Die&rft.au=Gao%2C+Xin&rft.date=2021&rft.pub=IEEE&rft.issn=0018-9456&rft.volume=70&rft.spage=1&rft.epage=11&rft_id=info:doi/10.1109%2FTIM.2021.3052249&rft.externalDocID=9328199 | 
    
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9456&client=summon | 
    
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9456&client=summon | 
    
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9456&client=summon |