{"id":"https://openalex.org/W4409561270","doi":"https://doi.org/10.1109/icvisp64524.2024.10959700","title":"Gaussian-Weighted Trend-Seasonal Decomposition Interactive Attention LSTM for Wind Turbine Drivetrain State Forecasting","display_name":"Gaussian-Weighted Trend-Seasonal Decomposition Interactive Attention LSTM for Wind Turbine Drivetrain State Forecasting","publication_year":2024,"publication_date":"2024-12-27","ids":{"openalex":"https://openalex.org/W4409561270","doi":"https://doi.org/10.1109/icvisp64524.2024.10959700"},"language":"en","primary_location":{"id":"doi:10.1109/icvisp64524.2024.10959700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp64524.2024.10959700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP)","raw_type":"proceedings-article"},"type":"article","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/A5050025702","display_name":"Hui-Ni Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hui-Ni Sun","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006546237","display_name":"Hao Zheng","orcid":"https://orcid.org/0009-0004-9280-2592"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Zheng","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100955332","display_name":"Shuang Bai","orcid":"https://orcid.org/0009-0003-8376-5964"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bai Shuang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050025702"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27868066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9944999814033508,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9944999814033508,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9605000019073486,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12759","display_name":"Vehicle Noise and Vibration Control","score":0.9279999732971191,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/drivetrain","display_name":"Drivetrain","score":0.8853675127029419},{"id":"https://openalex.org/keywords/turbine","display_name":"Turbine","score":0.6893072128295898},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5968471765518188},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5204496383666992},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5017983913421631},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.41387248039245605},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.4111928343772888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3716104030609131},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.33101457357406616},{"id":"https://openalex.org/keywords/torque","display_name":"Torque","score":0.2715236246585846},{"id":"https://openalex.org/keywords/aerospace-engineering","display_name":"Aerospace engineering","score":0.1952439546585083},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16714921593666077},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09649932384490967},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06445685029029846}],"concepts":[{"id":"https://openalex.org/C105637607","wikidata":"https://www.wikidata.org/wiki/Q1786258","display_name":"Drivetrain","level":3,"score":0.8853675127029419},{"id":"https://openalex.org/C2778449969","wikidata":"https://www.wikidata.org/wiki/Q130760","display_name":"Turbine","level":2,"score":0.6893072128295898},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5968471765518188},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5204496383666992},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5017983913421631},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.41387248039245605},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.4111928343772888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3716104030609131},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.33101457357406616},{"id":"https://openalex.org/C144171764","wikidata":"https://www.wikidata.org/wiki/Q48103","display_name":"Torque","level":2,"score":0.2715236246585846},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.1952439546585083},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16714921593666077},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09649932384490967},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06445685029029846},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icvisp64524.2024.10959700","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp64524.2024.10959700","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.800000011920929,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W4293776453","https://openalex.org/W4294631345","https://openalex.org/W4313814297","https://openalex.org/W4365453948","https://openalex.org/W4366266931","https://openalex.org/W4367597417","https://openalex.org/W4381995982","https://openalex.org/W4386259599","https://openalex.org/W4387780009","https://openalex.org/W4393234968","https://openalex.org/W4399531112","https://openalex.org/W4401685196","https://openalex.org/W4402725327","https://openalex.org/W4404447855"],"related_works":["https://openalex.org/W3152769028","https://openalex.org/W2566458058","https://openalex.org/W2090101458","https://openalex.org/W2216607741","https://openalex.org/W2594287858","https://openalex.org/W3125878227","https://openalex.org/W2540967753","https://openalex.org/W2191685641","https://openalex.org/W2763886546","https://openalex.org/W1694426859"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,163],"of":[2,6,15,22,36,66,75,89,143,162],"the":[3,13,19,33,53,63,70,79,87,94,110,119,125,132,141,154],"operational":[4,43,64],"states":[5,65,134],"wind":[7,23,37,67,76,95,167],"turbines":[8],"is":[9,83,114],"crucial":[10],"for":[11,61,165],"improving":[12],"stability":[14],"their":[16],"operation":[17],"and":[18,72,121,135],"utilization":[20],"efficiency":[21],"power.":[24],"Currently,":[25],"most":[26],"data-driven":[27],"studies":[28],"focus":[29],"primarily":[30],"on":[31],"forecasting":[32],"output":[34],"power":[35],"turbines,":[38],"without":[39],"addressing":[40],"other":[41,158],"important":[42],"data":[44],"such":[45],"as":[46],"rotor":[47],"loads.":[48],"Therefore,":[49],"this":[50],"paper":[51],"proposes":[52],"Gaussian-Weighted":[54,80],"Trend-Seasonal":[55,81],"Decomposition":[56],"Interactive":[57,111],"Attention":[58,112],"LSTM":[59,113],"model":[60,156],"predicting":[62],"turbines.":[68],"Given":[69],"diverse":[71],"complex":[73],"nature":[74],"farm":[77,96],"data,":[78,97],"Decom-position":[82],"designed":[84,115],"to":[85,102,116,128,146],"decompose":[86],"features":[88],"different":[90],"time":[91,107,137],"patterns":[92],"within":[93],"with":[98],"Gaussian":[99],"weighting":[100],"applied":[101],"emphasize":[103],"information":[104,145],"from":[105],"adjacent":[106],"points.":[108],"Additionally,":[109],"separately":[117],"predict":[118],"trend":[120],"seasonal":[122],"components,":[123],"using":[124],"attention":[126],"mechanism":[127],"enable":[129],"interaction":[130],"between":[131],"current":[133],"historical":[136,144],"points,":[138],"thus":[139],"enhancing":[140],"contribution":[142],"future":[147],"feature":[148],"predictions.":[149],"Experimental":[150],"results":[151],"demonstrate":[152],"that":[153],"proposed":[155],"outperforms":[157],"models":[159],"in":[160],"terms":[161],"accuracy":[164],"multiple":[166],"turbine":[168],"states.":[169]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
