{"id":"https://openalex.org/W7127044023","doi":"https://doi.org/10.1088/2058-6272/ae4009","title":"LIBS spectrum and image information fusion for waste aluminum alloy classification","display_name":"LIBS spectrum and image information fusion for waste aluminum alloy classification","publication_year":2026,"publication_date":"2026-02-01","ids":{"openalex":"https://openalex.org/W7127044023","doi":"https://doi.org/10.1088/2058-6272/ae4009"},"language":"en","primary_location":{"id":"doi:10.1088/2058-6272/ae4009","doi":"https://doi.org/10.1088/2058-6272/ae4009","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2058-6272/ae4009","pdf_url":null,"source":{"id":"https://openalex.org/S199470047","display_name":"Plasma Science and Technology","issn_l":"1009-0630","issn":["1009-0630","2058-6272"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Plasma Science and Technology","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W7127044023"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124708125","display_name":"Kaige ZHANG","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaige \u51ef\u6b4c ZHANG \u5f20","raw_affiliation_strings":["Shenyang Institute of Automation, Chinese Academy of Science, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Institute of Automation, Chinese Academy of Science, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124712886","display_name":"Tong Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong \u5f64 CHEN \u9648","raw_affiliation_strings":["Shenyang Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124732801","display_name":"Meiting Guo","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meiting \u7f8e\u4ead GUO \u90ed","raw_affiliation_strings":["Shenyang Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124735346","display_name":"Liming Zheng","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liming \u9ece\u660e ZHENG \u90d1","raw_affiliation_strings":["Shenyan Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyan Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124703891","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0001-9690-7026","observed_orcids":["https://orcid.org/0000-0001-9690-7026"]},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng \u9e4f ZHANG \u5f20","raw_affiliation_strings":["Shenyang Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Institute of Automation, Chinese Academy of Sciences, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124709023","display_name":"Lanxiang Sun","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lanxiang \u5170\u9999 SUN \u5b59","raw_affiliation_strings":["Shenyang Institute of Automation, Chinese Academy of Science, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Institute of Automation, Chinese Academy of Science, No.114\u3000Nanta Street, Shenhe District, Shenyang, Liaoning, 110016, CHINA","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124709023"],"corresponding_institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"],"apc_list":null,"apc_paid":null,"fwci":1.2587,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70112146,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"28","issue":"4","first_page":"044003","last_page":"044003"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11854","display_name":"Laser-induced spectroscopy and plasma","score":0.7235999703407288,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11854","display_name":"Laser-induced spectroscopy and plasma","score":0.7235999703407288,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.13109999895095825,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.027799999341368675,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/laser-induced-breakdown-spectroscopy","display_name":"laser-induced breakdown spectroscopy","score":0.968999981880188},{"id":"https://openalex.org/keywords/resnet-18","display_name":"ResNet-18","score":0.6869999766349792},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9890000224113464},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9879999756813049},{"id":"https://openalex.org/keywords/matrix-effects","display_name":"matrix effects","score":0.5339999794960022},{"id":"https://openalex.org/keywords/lightweight-feature-extraction","display_name":"lightweight feature extraction","score":0.5109999775886536}],"concepts":[{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.6348000168800354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5645999908447266},{"id":"https://openalex.org/C2780026712","wikidata":"https://www.wikidata.org/wiki/Q37756","display_name":"Alloy","level":2,"score":0.545799970626831},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5024999976158142},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42640000581741333},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4154999852180481},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C513153333","wikidata":"https://www.wikidata.org/wiki/Q663","display_name":"Aluminium","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.373199999332428},{"id":"https://openalex.org/C32891209","wikidata":"https://www.wikidata.org/wiki/Q483666","display_name":"Spectroscopy","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C2985906921","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Spectral properties","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.31610000133514404},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C50497907","wikidata":"https://www.wikidata.org/wiki/Q2654990","display_name":"Laser-induced breakdown spectroscopy","level":3,"score":0.30329999327659607},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2700999975204468},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2515000104904175},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1088/2058-6272/ae4009","doi":"https://doi.org/10.1088/2058-6272/ae4009","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2058-6272/ae4009","pdf_url":null,"source":{"id":"https://openalex.org/S199470047","display_name":"Plasma Science and Technology","issn_l":"1009-0630","issn":["1009-0630","2058-6272"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Plasma Science and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12","score":0.807699978351593}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1971663289","https://openalex.org/W1976526581","https://openalex.org/W2014753194","https://openalex.org/W2081330145","https://openalex.org/W2091379531","https://openalex.org/W2271835967","https://openalex.org/W2272877979","https://openalex.org/W2772228854","https://openalex.org/W2806913352","https://openalex.org/W2911964244","https://openalex.org/W2914647584","https://openalex.org/W2928532420","https://openalex.org/W2933889259","https://openalex.org/W2953767750","https://openalex.org/W2965198367","https://openalex.org/W2980694887","https://openalex.org/W2997591727","https://openalex.org/W3000385978","https://openalex.org/W3051995256","https://openalex.org/W3095816591","https://openalex.org/W3146277255","https://openalex.org/W3162498963","https://openalex.org/W4205479008","https://openalex.org/W4226420376","https://openalex.org/W4308352302","https://openalex.org/W4313828204","https://openalex.org/W4318381876","https://openalex.org/W4384934638"],"related_works":["https://openalex.org/W4313828204","https://openalex.org/W4416899994","https://openalex.org/W4225624162","https://openalex.org/W3126675763","https://openalex.org/W4294156759","https://openalex.org/W4407134506","https://openalex.org/W2624093923","https://openalex.org/W7160695178","https://openalex.org/W2904563717","https://openalex.org/W4386713946"],"abstract_inverted_index":{"Abstract":[0],"Traditional":[1],"waste":[2,178],"aluminum":[3],"(Al)":[4],"alloy":[5,180],"sorting":[6,182],"technologies":[7],"face":[8,38],"technical":[9],"bottlenecks":[10],"such":[11],"as":[12],"low":[13],"efficiency":[14],"and":[15,42,50,69,95,113,127,141,183],"poor":[16],"accuracy.":[17],"Image-based":[18],"classification":[19,60,157],"methods":[20,37],"struggle":[21],"to":[22,28,66,161],"accurately":[23],"identify":[24],"Al":[25,179],"grades":[26],"due":[27],"the":[29,39,47,103,109,120,133,142,162],"similar":[30],"external":[31],"characteristics;":[32],"laser-induced":[33],"breakdown":[34],"spectroscopy":[35],"(LIBS)":[36],"analytical":[40],"accuracy":[41,68,126],"stability":[43],"problem":[44],"caused":[45],"by":[46,76,90],"matrix":[48],"effects":[49],"heterogeneity.":[51],"Therefore,":[52],"this":[53],"paper":[54],"proposes":[55],"an":[56,77,174],"improved":[57,78],"ResNet18-SVM-RF":[58],"fusion":[59],"model":[61,72,122,135,172],"using":[62],"image-spectral":[63],"bimodal":[64],"data":[65,140],"improve":[67],"robustness.":[70],"The":[71],"extracts":[73],"image":[74,139],"features":[75,84,88],"lightweight":[79],"ResNet18":[80,134,164],"network,":[81],"integrates":[82],"these":[83],"with":[85],"LIBS":[86],"spectral":[87,148],"processed":[89],"support":[91],"vector":[92],"machine":[93],"(SVM),":[94],"subsequently":[96],"utilizes":[97],"a":[98,153],"Random":[99],"Forest":[100],"classifier":[101],"for":[102,177],"final":[104],"classification.":[105,115],"This":[106,170],"approach":[107],"achieves":[108],"purpose":[110],"of":[111],"efficient":[112],"accurate":[114],"Experimental":[116],"results":[117],"demonstrate":[118],"that":[119],"proposed":[121,171],"exceeds":[123],"97%":[124],"on":[125,137,146],"recall":[128],"metrics,":[129],"significantly":[130],"outperforming":[131],"both":[132],"based":[136,145],"single":[138,147],"SVM":[143],"method":[144],"data.":[149],"Furthermore,":[150],"it":[151],"attains":[152],"72%":[154],"reduction":[155],"in":[156],"prediction":[158],"time":[159],"compared":[160],"original":[163],"model,":[165],"demonstrating":[166],"its":[167],"computational":[168],"efficiency.":[169],"provides":[173],"effective":[175],"solution":[176],"intelligent":[181],"holds":[184],"broad":[185],"application":[186],"prospects.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-03T00:00:00"}
