A nonlinear fuzzy assisted image reconstruction algorithm for electrical capacitance tomography
A nonlinear method based on a Fuzzy Inference System (FIS) to improve the images obtained from Electrical Capacitance Tomography (ECT) is proposed. Estimation of the molten metal characteristic in the Lost Foam Casting (LFC) process is a novel application in the area of the tomography process. The c...
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| Veröffentlicht in: | ISA transactions 2010, Vol.49 (1), p.10-18 |
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| creator | Deabes, W.A. Abdelrahman, M.A. |
| description | A nonlinear method based on a Fuzzy Inference System (FIS) to improve the images obtained from Electrical Capacitance Tomography (ECT) is proposed. Estimation of the molten metal characteristic in the Lost Foam Casting (LFC) process is a novel application in the area of the tomography process. The convergence rate of iterative image reconstruction techniques is dependent on the accuracy of the first image. The possibility of the existence of metal in the first image is computed by the proposed fuzzy system. This first image is passed to an iterative image reconstruction technique to get more precise images and to speed up the convergence rate. The proposed technique is able to detect the position of the metal on the periphery of the imaging area by using just eight capacitive sensors. The final results demonstrate the advantage of using the FIS compared to the performance of the iterative back projection image reconstruction technique. |
| doi_str_mv | 10.1016/j.isatra.2009.10.005 |
| format | Article |
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Estimation of the molten metal characteristic in the Lost Foam Casting (LFC) process is a novel application in the area of the tomography process. The convergence rate of iterative image reconstruction techniques is dependent on the accuracy of the first image. The possibility of the existence of metal in the first image is computed by the proposed fuzzy system. This first image is passed to an iterative image reconstruction technique to get more precise images and to speed up the convergence rate. The proposed technique is able to detect the position of the metal on the periphery of the imaging area by using just eight capacitive sensors. The final results demonstrate the advantage of using the FIS compared to the performance of the iterative back projection image reconstruction technique.</description><identifier>ISSN: 0019-0578</identifier><identifier>EISSN: 1879-2022</identifier><identifier>DOI: 10.1016/j.isatra.2009.10.005</identifier><identifier>PMID: 19900672</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Algorithms ; Applied sciences ; Artificial intelligence ; Capacitance measurements ; Computer science; control theory; systems ; ECT ; Electric Capacitance ; Exact sciences and technology ; Finite Element Analysis ; Fluid dynamics ; Fundamental areas of phenomenology (including applications) ; Fuzzy Logic ; Fuzzy systems ; Image Processing, Computer-Assisted - methods ; Instrumentation for fluid dynamics ; Nonlinear Dynamics ; Pattern recognition. Digital image processing. 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Estimation of the molten metal characteristic in the Lost Foam Casting (LFC) process is a novel application in the area of the tomography process. The convergence rate of iterative image reconstruction techniques is dependent on the accuracy of the first image. The possibility of the existence of metal in the first image is computed by the proposed fuzzy system. This first image is passed to an iterative image reconstruction technique to get more precise images and to speed up the convergence rate. The proposed technique is able to detect the position of the metal on the periphery of the imaging area by using just eight capacitive sensors. The final results demonstrate the advantage of using the FIS compared to the performance of the iterative back projection image reconstruction technique.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Capacitance measurements</subject><subject>Computer science; control theory; systems</subject><subject>ECT</subject><subject>Electric Capacitance</subject><subject>Exact sciences and technology</subject><subject>Finite Element Analysis</subject><subject>Fluid dynamics</subject><subject>Fundamental areas of phenomenology (including applications)</subject><subject>Fuzzy Logic</subject><subject>Fuzzy systems</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Instrumentation for fluid dynamics</subject><subject>Nonlinear Dynamics</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Physics</subject><subject>Tomography - methods</subject><issn>0019-0578</issn><issn>1879-2022</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1rGzEQhkVpaJyk_6AUXUpP64yk3ZV0KYSQNoVAL81ZjLWSI7O7ciVtwfn1lbFpbz0NDM98vA8hHxisGbD-drcOGUvCNQfQtbUG6N6QFVNSNxw4f0tWAEw30El1Sa5y3gEA77R6Ry6Z1gC95Cti7ugc5zHMDhP1y-vrgWLOIRc30DDh1tHkbJxzSYstIc4Ux21MobxM1MdE3ehsScHiSC3u0YaCs3W0xCluE-5fDjfkwuOY3ftzvSbPXx9-3j82Tz--fb-_e2qs0Kw0bgOWbdrOi1Yr3nPNWwDB-g1vpZQMlPUChBdD26Hqei-xVZphV4Mgdl6La_L5tHef4q_F5WKmkK0bR5xdXLKRQqheMCkr2Z5Im2LOyXmzTzVpOhgG5mjW7MzJrDmaPXar2Tr28Xxg2Uxu-Dd0VlmBT2cAc_XhUzUR8l-O1yRcSlW5LyfOVR2_g0sm2-CqtSFU1cUMMfz_kz-j6Jlm</recordid><startdate>2010</startdate><enddate>2010</enddate><creator>Deabes, W.A.</creator><creator>Abdelrahman, M.A.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>2010</creationdate><title>A nonlinear fuzzy assisted image reconstruction algorithm for electrical capacitance tomography</title><author>Deabes, W.A. ; Abdelrahman, M.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c391t-eb0c1b45f349826292400316b24777108cf303f3d45a856f7a4891a5199aa5f93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Capacitance measurements</topic><topic>Computer science; control theory; systems</topic><topic>ECT</topic><topic>Electric Capacitance</topic><topic>Exact sciences and technology</topic><topic>Finite Element Analysis</topic><topic>Fluid dynamics</topic><topic>Fundamental areas of phenomenology (including applications)</topic><topic>Fuzzy Logic</topic><topic>Fuzzy systems</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Instrumentation for fluid dynamics</topic><topic>Nonlinear Dynamics</topic><topic>Pattern recognition. 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Computational geometry</topic><topic>Physics</topic><topic>Tomography - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Deabes, W.A.</creatorcontrib><creatorcontrib>Abdelrahman, M.A.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>ISA transactions</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Deabes, W.A.</au><au>Abdelrahman, M.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A nonlinear fuzzy assisted image reconstruction algorithm for electrical capacitance tomography</atitle><jtitle>ISA transactions</jtitle><addtitle>ISA Trans</addtitle><date>2010</date><risdate>2010</risdate><volume>49</volume><issue>1</issue><spage>10</spage><epage>18</epage><pages>10-18</pages><issn>0019-0578</issn><eissn>1879-2022</eissn><abstract>A nonlinear method based on a Fuzzy Inference System (FIS) to improve the images obtained from Electrical Capacitance Tomography (ECT) is proposed. Estimation of the molten metal characteristic in the Lost Foam Casting (LFC) process is a novel application in the area of the tomography process. The convergence rate of iterative image reconstruction techniques is dependent on the accuracy of the first image. The possibility of the existence of metal in the first image is computed by the proposed fuzzy system. This first image is passed to an iterative image reconstruction technique to get more precise images and to speed up the convergence rate. The proposed technique is able to detect the position of the metal on the periphery of the imaging area by using just eight capacitive sensors. The final results demonstrate the advantage of using the FIS compared to the performance of the iterative back projection image reconstruction technique.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><pmid>19900672</pmid><doi>10.1016/j.isatra.2009.10.005</doi><tpages>9</tpages></addata></record> |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Algorithms Applied sciences Artificial intelligence Capacitance measurements Computer science control theory systems ECT Electric Capacitance Exact sciences and technology Finite Element Analysis Fluid dynamics Fundamental areas of phenomenology (including applications) Fuzzy Logic Fuzzy systems Image Processing, Computer-Assisted - methods Instrumentation for fluid dynamics Nonlinear Dynamics Pattern recognition. Digital image processing. Computational geometry Physics Tomography - methods |
| title | A nonlinear fuzzy assisted image reconstruction algorithm for electrical capacitance tomography |
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