{"id":"https://openalex.org/W4406356522","doi":"https://doi.org/10.1109/wcsp62071.2024.10827093","title":"Deep Mean-Kernel Learning for Bayesian Optimization in IoT-Enabled Data Center","display_name":"Deep Mean-Kernel Learning for Bayesian Optimization in IoT-Enabled Data Center","publication_year":2024,"publication_date":"2024-10-24","ids":{"openalex":"https://openalex.org/W4406356522","doi":"https://doi.org/10.1109/wcsp62071.2024.10827093"},"language":"en","primary_location":{"id":"doi:10.1109/wcsp62071.2024.10827093","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp62071.2024.10827093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 16th International Conference on Wireless Communications and Signal Processing (WCSP)","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":null,"display_name":"Jinhui Dou","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinhui Dou","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100640942","display_name":"Bo Cheng","orcid":"https://orcid.org/0000-0003-2160-2839"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Cheng","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090367723","display_name":"Jinan Li","orcid":"https://orcid.org/0000-0002-3021-972X"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinan Li","raw_affiliation_strings":["Guangdong Branch, China Unicom Group Co., Ltd.,Guangzhou,China,510627"],"affiliations":[{"raw_affiliation_string":"Guangdong Branch, China Unicom Group Co., Ltd.,Guangzhou,China,510627","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101431727","display_name":"Yu Sun","orcid":"https://orcid.org/0009-0003-6220-0163"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Sun","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112595560","display_name":"Gaoxiang Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaoxiang Jiang","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043286765","display_name":"Haibo Zhou","orcid":"https://orcid.org/0000-0002-6549-8917"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibo Zhou","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.3345,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.69712765,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1169","last_page":"1174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.821399986743927,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10320","display_name":"Neural Networks and Applications","score":0.821399986743927,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12676","display_name":"Machine Learning and ELM","score":0.7416999936103821,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.6909999847412109,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7040812373161316},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6006983518600464},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5847712159156799},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.5434922575950623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5424923896789551},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5408724546432495},{"id":"https://openalex.org/keywords/center","display_name":"Center (category theory)","score":0.47912856936454773},{"id":"https://openalex.org/keywords/data-center","display_name":"Data center","score":0.47818830609321594},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4402579665184021},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11876225471496582},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08828526735305786},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.07614710927009583}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7040812373161316},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6006983518600464},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5847712159156799},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.5434922575950623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5424923896789551},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5408724546432495},{"id":"https://openalex.org/C2779463800","wikidata":"https://www.wikidata.org/wiki/Q5062222","display_name":"Center (category theory)","level":2,"score":0.47912856936454773},{"id":"https://openalex.org/C153740404","wikidata":"https://www.wikidata.org/wiki/Q671224","display_name":"Data center","level":2,"score":0.47818830609321594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4402579665184021},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11876225471496582},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08828526735305786},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.07614710927009583},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C8010536","wikidata":"https://www.wikidata.org/wiki/Q160398","display_name":"Crystallography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcsp62071.2024.10827093","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp62071.2024.10827093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 16th International Conference on Wireless Communications and Signal Processing (WCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2000359198","https://openalex.org/W2522710757","https://openalex.org/W3194654576","https://openalex.org/W3214421366","https://openalex.org/W4393075204","https://openalex.org/W4396941418","https://openalex.org/W6730042731","https://openalex.org/W6734090873","https://openalex.org/W6745256532","https://openalex.org/W6786190416","https://openalex.org/W6791136995","https://openalex.org/W6801226822","https://openalex.org/W6847204548","https://openalex.org/W6858269668","https://openalex.org/W6868974054"],"related_works":["https://openalex.org/W4245926026","https://openalex.org/W4311097251","https://openalex.org/W2586548817","https://openalex.org/W3106461837","https://openalex.org/W2625093826","https://openalex.org/W4200598720","https://openalex.org/W2921026492","https://openalex.org/W4247463117","https://openalex.org/W4361251261","https://openalex.org/W4401858220"],"abstract_inverted_index":{"In":[0,99],"efforts":[1],"to":[2,41,79,107],"mitigate":[3],"energy":[4],"consumption":[5],"in":[6,52,84,143],"large":[7],"data":[8,97,147,159],"centers,":[9],"numerous":[10],"optimization":[11,24,35,70,141],"strategies":[12],"have":[13],"been":[14],"explored.":[15],"However,":[16],"these":[17],"methods":[18,51,142],"often":[19],"encounter":[20],"challenges":[21],"including":[22],"prolonged":[23],"times":[25],"and":[26,45,66,88,160],"settling":[27],"on":[28,64],"local":[29],"optima.":[30,167],"Among":[31],"diverse":[32],"algorithms,":[33],"Bayesian":[34,50],"(BO)":[36],"demonstrates":[37],"exceptional":[38],"performance.":[39],"Due":[40],"the":[42,58,61,80,93,109,120,124,144,165],"slow":[43],"convergence":[44,163],"limited":[46],"scalability":[47],"of":[48,60,96,136,146],"existing":[49,140],"handling":[53],"high-dimensional":[54,158],"data,":[55],"we":[56,132],"explore":[57],"influence":[59],"surrogate":[62],"model":[63],"BO,":[65],"introduce":[67],"an":[68,114],"innovative":[69],"approach":[71,155],"deep":[72],"mean-kernel":[73],"learning,":[74],"which":[75],"can":[76],"converge":[77],"rapidly":[78],"global":[81,166],"optimal":[82],"solution":[83],"high":[85],"dimensional":[86],"space":[87],"is":[89,105],"specifically":[90],"designed":[91],"for":[92],"refrigeration":[94],"systems":[95],"centers.":[98],"our":[100,137,154],"approach,":[101],"a":[102],"neural":[103,116],"network":[104,117],"employed":[106],"approximate":[108],"conventional":[110],"mean":[111],"function,":[112,122],"while":[113],"infinite-width":[115],"serves":[118],"as":[119],"kernel":[121],"modifying":[123],"Gaussian":[125],"process.":[126],"Utilizing":[127],"two":[128],"prevalent":[129],"acquisition":[130],"functions,":[131],"conduct":[133],"comparative":[134],"experiments":[135],"algorithm":[138],"against":[139],"context":[145],"center":[148],"scenarios.":[149],"Our":[150],"findings":[151],"demonstrate":[152],"that":[153],"adeptly":[156],"manages":[157],"exhibits":[161],"rapid":[162],"towards":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}