{"id":"https://openalex.org/W7213615746","doi":"https://doi.org/10.13272/j.issn.1671-251x.2026040097","title":"Ash content prediction of flotation tailings based on sediment hyperspectral images","display_name":"Ash content prediction of flotation tailings based on sediment hyperspectral images","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7213615746","doi":"https://doi.org/10.13272/j.issn.1671-251x.2026040097"},"language":"zh","primary_location":{"id":"pmh:oai:doaj.org/article:2e0bfe443d644f849d1cd38136836b53","doi":"https://doi.org/10.13272/j.issn.1671-251x.2026040097","is_oa":false,"landing_page_url":"https://doaj.org/article/2e0bfe443d644f849d1cd38136836b53","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Gong-kuang zidonghua, Vol 52, Iss 7, Pp 93-103 (2026)","raw_type":"article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["doaj"],"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/A5135543107","display_name":"REN Hanchi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren Hanchi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037821358","display_name":"R. Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang Ranfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5152250017","display_name":"Liu Shengyu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu Shengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5152156003","display_name":"Zhang Shuxin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang Shuxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5152222253","display_name":"FU Xiang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56432589,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.586899995803833,"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"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.586899995803833,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.16040000319480896,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10689","display_name":"Remote-Sensing Image Classification","score":0.08540000021457672,"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/flotation-tailings","display_name":"flotation tailings","score":0.996999979019165},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"hyperspectral imaging","score":0.7889999747276306},{"id":"https://openalex.org/keywords/coal-beneficiation","display_name":"coal beneficiation","score":0.8169999718666077},{"id":"https://openalex.org/keywords/u-net","display_name":"U-Net","score":0.7860000133514404},{"id":"https://openalex.org/keywords/1d-resnet","display_name":"1D ResNet","score":0.6320000290870667},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.5799999833106995},{"id":"https://openalex.org/keywords/continuum-removal","display_name":"continuum removal","score":0.734000027179718},{"id":"https://openalex.org/keywords/multiplicative-scatter-correction","display_name":"multiplicative scatter correction","score":0.7329999804496765},{"id":"https://openalex.org/keywords/isolation-forest","display_name":"Isolation Forest","score":0.6209999918937683},{"id":"https://openalex.org/keywords/online-monitoring","display_name":"online monitoring","score":0.3240000009536743}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7373999953269958},{"id":"https://openalex.org/C5166401","wikidata":"https://www.wikidata.org/wiki/Q1784525","display_name":"Tailings","level":2,"score":0.6435999870300293},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.550599992275238},{"id":"https://openalex.org/C2816523","wikidata":"https://www.wikidata.org/wiki/Q180184","display_name":"Sediment","level":2,"score":0.4848000109195709},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.45559999346733093},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C2776648687","wikidata":"https://www.wikidata.org/wiki/Q4496980","display_name":"Tailings dam","level":3,"score":0.4083000123500824},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3725999891757965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C518851703","wikidata":"https://www.wikidata.org/wiki/Q24489","display_name":"Coal","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.34610000252723694},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C130066347","wikidata":"https://www.wikidata.org/wiki/Q680509","display_name":"Spectroradiometer","level":3,"score":0.3273000121116638},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C159390177","wikidata":"https://www.wikidata.org/wiki/Q9161265","display_name":"Soil science","level":1,"score":0.29580000042915344},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C199289684","wikidata":"https://www.wikidata.org/wiki/Q83353","display_name":"Mineralogy","level":1,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:doaj.org/article:2e0bfe443d644f849d1cd38136836b53","doi":"https://doi.org/10.13272/j.issn.1671-251x.2026040097","is_oa":false,"landing_page_url":"https://doaj.org/article/2e0bfe443d644f849d1cd38136836b53","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Gong-kuang zidonghua, Vol 52, Iss 7, Pp 93-103 (2026)","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7129701337","https://openalex.org/W4403920122","https://openalex.org/W4413135642","https://openalex.org/W4411161281","https://openalex.org/W3027995052","https://openalex.org/W4390882713","https://openalex.org/W4402421903","https://openalex.org/W4399130410","https://openalex.org/W4412202687","https://openalex.org/W7123679092"],"abstract_inverted_index":{"High-precision":[0],"online":[1],"detection":[2,221],"of":[3,37,45,183,190,207,234],"ash":[4,47,87,140,174,236],"content":[5,48,88,141,175,237],"in":[6,238],"flotation":[7],"tailings":[8],"is":[9],"essential":[10],"for":[11,128,231],"intelligent":[12],"coal":[13,72],"preparation,":[14],"but":[15],"conventional":[16],"vision-based":[17],"methods":[18],"are":[19],"susceptible":[20],"to":[21,114,163],"interference":[22],"from":[23,49,69],"water":[24],"films":[25],"and":[26,42,107,123,130,158,168,171,185,196,199,215,227],"metamerism.":[27],"To":[28],"address":[29],"this":[30],"issue,":[31],"the":[32,43,58,181,191,229],"diffuse-reflectance":[33],"optical":[34],"response":[35],"mechanism":[36],"flotation-tailings":[38,86,235],"sediment":[39,50,116],"was":[40,53,62,100,135,144,203],"analyzed,":[41],"feasibility":[44],"predicting":[46],"hyperspectral":[51],"images":[52],"demonstrated.":[54],"A":[55,85,102,137,219],"dataset":[56],"spanning":[57],"full":[59],"ash-content":[60],"range":[61],"constructed":[63,146],"by":[64,147],"artificially":[65],"blending":[66],"raw":[67],"samples":[68],"an":[70],"operating":[71],"preparation":[73],"plant":[74],"at":[75],"graded":[76],"proportions":[77],"while":[78],"preserving":[79],"their":[80],"original":[81],"mineral":[82],"sliming":[83],"characteristics.":[84],"prediction":[89,142,201],"method":[90],"integrating":[91],"spatial":[92],"semantic":[93],"segmentation":[94,105],"with":[95],"a":[96,108,131,149],"deep":[97],"temporal":[98],"network":[99],"proposed.":[101],"full-band":[103],"U-Net":[104],"model":[106,143,192],"morphological":[109],"truncation":[110],"algorithm":[111],"were":[112,126,194],"developed":[113],"extract":[115,164],"regions.":[117],"Isolation":[118],"Forest,":[119],"multiplicative":[120],"scatter":[121],"correction,":[122],"continuum":[124],"removal":[125],"used":[127],"preprocessing,":[129],"dual-channel":[132],"feature":[133],"tensor":[134],"constructed.":[136],"concentration-prior-integrated":[138],"1D-RSL":[139],"then":[145],"combining":[148],"One-Dimensional":[150],"Residual":[151],"Network":[152],"(1D-ResNet),":[153],"Squeeze-and-Excitation":[154],"(SE)":[155],"channel":[156],"recalibration,":[157],"Long":[159],"Short-Term":[160],"Memory":[161],"(LSTM)":[162],"local":[165],"interband":[166],"features":[167],"long-range":[169],"dependencies":[170],"achieve":[172],"sample-level":[173],"prediction.":[176],"Test":[177],"results":[178],"showed":[179],"that":[180],"coefficient":[182],"determination":[184],"root":[186],"mean":[187],"square":[188],"error":[189,202],"predictions":[193],"0.952":[195],"1.95%,":[197],"respectively,":[198],"its":[200],"lower":[204],"than":[205],"those":[206],"models":[208],"such":[209],"as":[210],"partial":[211],"least":[212],"squares":[213],"regression":[214],"support":[216],"vector":[217],"regression.":[218],"complete":[220],"process":[222],"took":[223],"approximately":[224],"48.2":[225],"s":[226],"met":[228],"requirements":[230],"dynamic":[232],"monitoring":[233],"industrial":[239],"production.":[240]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2026-09-20T00:00:00"}