{"id":"https://openalex.org/W2999516274","doi":"https://doi.org/10.1109/tii.2020.2965202","title":"Efficient Multitask Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography","display_name":"Efficient Multitask Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography","publication_year":2020,"publication_date":"2020-01-09","ids":{"openalex":"https://openalex.org/W2999516274","doi":"https://doi.org/10.1109/tii.2020.2965202","mag":"2999516274"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2020.2965202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.2965202","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.research.ed.ac.uk/en/publications/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053773849","display_name":"Shengheng Liu","orcid":"https://orcid.org/0000-0001-6579-9798"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengheng Liu","raw_affiliation_strings":["School of Information Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-6579-9798","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056225611","display_name":"Yongming Huang","orcid":"https://orcid.org/0000-0003-3616-4616"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongming Huang","raw_affiliation_strings":["School of Information Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-3616-4616","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054328410","display_name":"Hancong Wu","orcid":"https://orcid.org/0000-0001-8411-1127"},"institutions":[{"id":"https://openalex.org/I84884186","display_name":"Newcastle University","ror":"https://ror.org/01kj2bm70","country_code":"GB","type":"education","lineage":["https://openalex.org/I84884186"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hancong Wu","raw_affiliation_strings":["School of Engineering, Newcastle University, Newcastle upon Tyne, U.K"],"raw_orcid":"https://orcid.org/0000-0001-8411-1127","affiliations":[{"raw_affiliation_string":"School of Engineering, Newcastle University, Newcastle upon Tyne, U.K","institution_ids":["https://openalex.org/I84884186"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068911557","display_name":"Chao Tan","orcid":"https://orcid.org/0000-0001-5146-4807"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Tan","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0001-5146-4807","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058004272","display_name":"Jiabin Jia","orcid":"https://orcid.org/0000-0001-5073-5126"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jiabin Jia","raw_affiliation_strings":["School of Engineering, The University of Edinburgh, Edinburgh, U.K"],"raw_orcid":"https://orcid.org/0000-0001-5073-5126","affiliations":[{"raw_affiliation_string":"School of Engineering, The University of Edinburgh, Edinburgh, U.K","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.2867,"has_fulltext":true,"cited_by_count":115,"citation_normalized_percentile":{"value":0.98617893,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"17","issue":"1","first_page":"463","last_page":"472"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10572","display_name":"Geophysical and Geoelectrical Methods","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12537","display_name":"Flow Measurement and Analysis","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electrical-impedance-tomography","display_name":"Electrical impedance tomography","score":0.7689089775085449},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6614850163459778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5796543955802917},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5783179402351379},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5430530905723572},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.525459885597229},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.49613484740257263},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4914959669113159},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4631580412387848},{"id":"https://openalex.org/keywords/electrical-impedance","display_name":"Electrical impedance","score":0.45640385150909424},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.45464855432510376},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4497253894805908},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4271446764469147},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.4124802350997925},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3240373432636261},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1557934582233429},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09885203838348389},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.08545222878456116},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.07804104685783386}],"concepts":[{"id":"https://openalex.org/C155175808","wikidata":"https://www.wikidata.org/wiki/Q1326472","display_name":"Electrical impedance tomography","level":3,"score":0.7689089775085449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6614850163459778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5796543955802917},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5783179402351379},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5430530905723572},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.525459885597229},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.49613484740257263},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4914959669113159},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4631580412387848},{"id":"https://openalex.org/C17829176","wikidata":"https://www.wikidata.org/wiki/Q179043","display_name":"Electrical impedance","level":2,"score":0.45640385150909424},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.45464855432510376},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4497253894805908},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4271446764469147},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4124802350997925},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3240373432636261},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1557934582233429},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09885203838348389},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.08545222878456116},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.07804104685783386},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tii.2020.2965202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.2965202","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Liu, S, Huang, Y, Wu, H, Tan, C & Jia, J 2020, 'Efficient multi-task structure-aware sparse Bayesian learning for frequency-difference electrical impedance tomography', IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2020.2965202","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11820/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","pdf_url":"https://www.research.ed.ac.uk/en/publications/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:openaire/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/2b28df42-16e4-44ca-a8fe-d1b12ba481d0","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Liu, S, Huang, Y, Wu, H, Tan, C & Jia, J 2020, 'Efficient multi-task structure-aware sparse Bayesian learning for frequency-difference electrical impedance tomography', IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2020.2965202","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3092627289","display_name":"Cerebral Blood Flow Imaging based on 3D Electrical Impedance Tomography","funder_award_id":"EP/P006833/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4465478400","display_name":null,"funder_award_id":"SBK2019042353","funder_id":"https://openalex.org/F4320334982","funder_display_name":"Basic Research Program of Jiangsu Province"},{"id":"https://openalex.org/G745719754","display_name":null,"funder_award_id":"EP/P001661/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G780682132","display_name":null,"funder_award_id":"EP/P006833/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"},{"id":"https://openalex.org/F4320334982","display_name":"Basic Research Program of Jiangsu Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1529548832","https://openalex.org/W1531455566","https://openalex.org/W1891695974","https://openalex.org/W1971662692","https://openalex.org/W1979120317","https://openalex.org/W2009831994","https://openalex.org/W2012454583","https://openalex.org/W2025950086","https://openalex.org/W2027806577","https://openalex.org/W2033419225","https://openalex.org/W2053510218","https://openalex.org/W2055571910","https://openalex.org/W2055899967","https://openalex.org/W2056132468","https://openalex.org/W2059707276","https://openalex.org/W2113761410","https://openalex.org/W2149498546","https://openalex.org/W2156062145","https://openalex.org/W2179643578","https://openalex.org/W2278879664","https://openalex.org/W2342572800","https://openalex.org/W2405176983","https://openalex.org/W2482912839","https://openalex.org/W2496068300","https://openalex.org/W2579091821","https://openalex.org/W2620803030","https://openalex.org/W2735205850","https://openalex.org/W2735337215","https://openalex.org/W2792740237","https://openalex.org/W2794514794","https://openalex.org/W2795198316","https://openalex.org/W2897807752","https://openalex.org/W2913384339","https://openalex.org/W2913886006","https://openalex.org/W2914571636","https://openalex.org/W2919509499","https://openalex.org/W3102921918","https://openalex.org/W6741365667"],"related_works":["https://openalex.org/W2359871536","https://openalex.org/W1992681652","https://openalex.org/W2991320615","https://openalex.org/W1979972895","https://openalex.org/W4285180073","https://openalex.org/W2051428347","https://openalex.org/W1976188970","https://openalex.org/W2990541822","https://openalex.org/W1588885902","https://openalex.org/W1965352811"],"abstract_inverted_index":{"Frequency-difference":[0],"electrical":[1,86],"impedance":[2,87],"tomography":[3,88],"(fdEIT)":[4],"was":[5],"originally":[6],"developed":[7],"to":[8,41,79,131],"mitigate":[9],"the":[10,47,133,137],"systematic":[11],"artifacts":[12],"induced":[13],"by":[14,119],"modeling":[15],"errors":[16],"when":[17],"a":[18,94,121],"baseline":[19],"dataset":[20],"is":[21,91,117],"unavailable.":[22],"Instead":[23],"of":[24,136],"fine":[25],"anatomical":[26],"imaging,":[27],"only":[28],"coarse":[29],"anomaly":[30],"detection":[31],"has":[32,51],"been":[33,52],"addressed":[34],"in":[35,111],"current":[36],"fdEIT":[37,57,76],"research":[38],"mainly":[39],"due":[40],"its":[42],"low":[43],"spatial":[44,101],"resolution.":[45],"On":[46],"other":[48],"hand,":[49],"there":[50],"not":[53],"enough":[54],"study":[55],"on":[56],"reconstruction":[58,89],"algorithm":[59,71],"as":[60],"well.":[61],"In":[62],"this":[63],"article,":[64],"we":[65],"propose":[66],"an":[67,112],"efficient":[68],"and":[69,103,109],"high-spatial-resolution":[70],"for":[72],"simultaneously":[73],"reconstructing":[74],"multiple":[75,83],"frames":[77],"corresponding":[78],"inject":[80],"currents":[81],"with":[82],"frequencies.":[84],"The":[85,115],"problem":[90],"considered":[92],"within":[93],"hierarchical":[95],"Bayesian":[96],"framework,":[97],"where":[98],"both":[99],"intratask":[100],"clustering":[102],"intertask":[104],"dependency":[105],"are":[106,129],"automatically":[107],"learned":[108],"exploited":[110],"unsupervised":[113],"manner.":[114],"computation":[116],"accelerated":[118],"adopting":[120],"modified":[122],"marginal":[123],"likelihood":[124],"maximization":[125],"approach.":[126],"Real-data":[127],"experiments":[128],"conducted":[130],"verify":[132],"recovery":[134],"performance":[135],"proposed":[138],"algorithm.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":37},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":13}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}