{"id":"https://openalex.org/W3015215580","doi":"https://doi.org/10.1177/1550147720916405","title":"Near-field sound source localization using principal component analysis\u2013multi-output support vector regression","display_name":"Near-field sound source localization using principal component analysis\u2013multi-output support vector regression","publication_year":2020,"publication_date":"2020-04-01","ids":{"openalex":"https://openalex.org/W3015215580","doi":"https://doi.org/10.1177/1550147720916405","mag":"3015215580"},"language":"en","primary_location":{"id":"doi:10.1177/1550147720916405","is_oa":true,"landing_page_url":"https://doi.org/10.1177/1550147720916405","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/1550147720916405","source":{"id":"https://openalex.org/S64417657","display_name":"International Journal of Distributed Sensor Networks","issn_l":"1550-1329","issn":["1550-1329","1550-1477"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Distributed Sensor Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.sagepub.com/doi/pdf/10.1177/1550147720916405","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057730913","display_name":"Lanmei Wang","orcid":"https://orcid.org/0000-0003-3095-6655"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lanmei Wang","raw_affiliation_strings":["School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100319045","display_name":"Yao Wang","orcid":"https://orcid.org/0000-0003-4207-5273"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Wang","raw_affiliation_strings":["School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042203658","display_name":"Guibao Wang","orcid":"https://orcid.org/0000-0002-6860-4594"},"institutions":[{"id":"https://openalex.org/I187175081","display_name":"Shaanxi University of Technology","ror":"https://ror.org/056m91h77","country_code":"CN","type":"education","lineage":["https://openalex.org/I187175081"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guibao Wang","raw_affiliation_strings":["School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong, China","institution_ids":["https://openalex.org/I187175081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036697609","display_name":"Jianke Jia","orcid":"https://orcid.org/0000-0003-3055-9000"},"institutions":[{"id":"https://openalex.org/I187175081","display_name":"Shaanxi University of Technology","ror":"https://ror.org/056m91h77","country_code":"CN","type":"education","lineage":["https://openalex.org/I187175081"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianke Jia","raw_affiliation_strings":["School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong, China","institution_ids":["https://openalex.org/I187175081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5057730913"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":{"value":2200,"currency":"USD","value_usd":2200},"apc_paid":{"value":2200,"currency":"USD","value_usd":2200},"fwci":0.1515,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.39986833,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"16","issue":"4","first_page":"155014772091640","last_page":"155014772091640"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10822","display_name":"Acoustic Wave Phenomena Research","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/principal-component-analysis","display_name":"Principal component analysis","score":0.7570379376411438},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6434356570243835},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5914497375488281},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5789597034454346},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5632128119468689},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.48090267181396484},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4703545570373535},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.45517709851264954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.446052610874176},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4455564022064209},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4185999035835266},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4131677448749542},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3253132700920105},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20765027403831482},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07752224802970886}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7570379376411438},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6434356570243835},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5914497375488281},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5789597034454346},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5632128119468689},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.48090267181396484},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4703545570373535},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.45517709851264954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.446052610874176},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4455564022064209},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4185999035835266},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4131677448749542},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3253132700920105},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20765027403831482},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07752224802970886},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1177/1550147720916405","is_oa":true,"landing_page_url":"https://doi.org/10.1177/1550147720916405","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/1550147720916405","source":{"id":"https://openalex.org/S64417657","display_name":"International Journal of Distributed Sensor Networks","issn_l":"1550-1329","issn":["1550-1329","1550-1477"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Distributed Sensor Networks","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:sae:intdis:v:16:y:2020:i:4:p:1550147720916405","is_oa":false,"landing_page_url":"https://journals.sagepub.com/doi/10.1177/1550147720916405","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:50c50e2259f44f188312a6f00819e437","is_oa":true,"landing_page_url":"https://doaj.org/article/50c50e2259f44f188312a6f00819e437","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Distributed Sensor Networks, Vol 16 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1177/1550147720916405","is_oa":true,"landing_page_url":"https://doi.org/10.1177/1550147720916405","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/1550147720916405","source":{"id":"https://openalex.org/S64417657","display_name":"International Journal of Distributed Sensor Networks","issn_l":"1550-1329","issn":["1550-1329","1550-1477"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Distributed Sensor Networks","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8626294138","display_name":null,"funder_award_id":"61772398, 61972239","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3015215580.pdf","grobid_xml":"https://content.openalex.org/works/W3015215580.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1983600173","https://openalex.org/W1987450470","https://openalex.org/W1997483434","https://openalex.org/W2097334502","https://openalex.org/W2097981690","https://openalex.org/W2112977844","https://openalex.org/W2119040979","https://openalex.org/W2125804487","https://openalex.org/W2148603752","https://openalex.org/W2159935273","https://openalex.org/W2163679811","https://openalex.org/W2483880719","https://openalex.org/W2776323726","https://openalex.org/W2800229517","https://openalex.org/W2803130179","https://openalex.org/W2885219692","https://openalex.org/W2886299913","https://openalex.org/W2897361856","https://openalex.org/W2901785876","https://openalex.org/W2911787226","https://openalex.org/W2913100182","https://openalex.org/W2921843297","https://openalex.org/W2944432420","https://openalex.org/W3104947433"],"related_works":["https://openalex.org/W2090269531","https://openalex.org/W2105715935","https://openalex.org/W2355203151","https://openalex.org/W94476185","https://openalex.org/W4318261617","https://openalex.org/W3097684468","https://openalex.org/W2939200583","https://openalex.org/W2372606255","https://openalex.org/W2357491648","https://openalex.org/W2360054605"],"abstract_inverted_index":{"In":[0],"this":[1,201],"article,":[2],"principal":[3,43],"component":[4,44,66,144],"analysis":[5,45,67,145],"method,":[6],"which":[7],"is":[8,16,48,68,85,91,98,146,167,194,221],"applied":[9],"to":[10,70,100,140,148,180],"image":[11],"compression":[12],"and":[13,30,39,52,78,87,104,165,187,207,210,218,231],"feature":[14,133,172],"extraction,":[15],"introduced":[17],"into":[18],"the":[19,56,60,63,72,82,88,102,105,110,121,137,150,153,158,162,170,174,182,188,219,226,232],"dimension":[20,46],"reduction":[21,47],"of":[22,26,33,36,62,75,109,152,157,161,173,191,225],"input":[23,118,134,171],"characteristic":[24],"variable":[25],"support":[27,95,176],"vector":[28,96,177],"regression,":[29],"a":[31,132],"method":[32,202],"joint":[34],"estimation":[35,185,190,205],"near-field":[37,183],"angle":[38],"range":[40],"based":[41],"on":[42],"proposed.":[49],"Signal-to-noise":[50],"ratio":[51,84],"calculation":[53,89],"amount":[54,90],"are":[55,114],"decisive":[57],"factors":[58],"affecting":[59],"performance":[61,220],"algorithm.":[64,237],"Principal":[65,143],"used":[69,99,116,147,168],"fuse":[71],"main":[73],"characteristics":[74],"training":[76,129,138,208],"data":[77],"discard":[79],"redundant":[80],"information,":[81],"signal-to-noise":[83,216],"improved,":[86],"reduced":[92],"accordingly.":[93],"Similarly,":[94],"regression":[97,178],"model":[101],"signal,":[103,164],"upper":[106,126,154],"triangular":[107,127,155],"elements":[108],"signal":[111,193,235],"covariance":[112,122,159],"matrix":[113,123,160],"usually":[115],"as":[117,131,169],"features.":[119],"Since":[120],"has":[124,203,211],"more":[125],"elements,":[128],"it":[130,166],"will":[135],"affect":[136],"speed":[139],"some":[141],"extent.":[142],"reduce":[149],"dimensionality":[151],"element":[156],"known":[163],"multi-output":[175],"machine":[179],"construct":[181],"parameter":[184,189],"model,":[186],"unknown":[192],"herein":[195],"obtained.":[196],"Simulation":[197],"results":[198],"show":[199],"that":[200,224],"high":[204],"accuracy":[206],"speed,":[209],"strong":[212],"adaptability":[213],"at":[214],"low":[215],"ratio,":[217],"better":[222],"than":[223],"back-propagation":[227],"neural":[228],"network":[229],"algorithm":[230],"two-step":[233],"multiple":[234],"classification":[236]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}