{"id":"https://openalex.org/W2920230375","doi":"https://doi.org/10.1109/tencon.2018.8650362","title":"Adaptive Balance of Quality-resources of Neural Networks with Principle Component Analysis","display_name":"Adaptive Balance of Quality-resources of Neural Networks with Principle Component Analysis","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2920230375","doi":"https://doi.org/10.1109/tencon.2018.8650362","mag":"2920230375"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2018.8650362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","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/A5051204193","display_name":"Cuiping Shao","orcid":"https://orcid.org/0000-0002-2219-2328"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Cuiping Shao","raw_affiliation_strings":["Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100614997","display_name":"Huiyun Li","orcid":"https://orcid.org/0000-0003-0157-1393"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiyun Li","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology, Chinese, Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese, Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101996455","display_name":"Zhiheng Yang","orcid":"https://orcid.org/0009-0005-3721-2650"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiheng Yang","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology, Chinese, Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese, Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083257261","display_name":"Jiayan Fang","orcid":"https://orcid.org/0009-0008-9079-4655"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayan Fang","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology, Chinese, Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese, Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5051204193"],"corresponding_institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19079885,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2253","last_page":"2258"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.8145084381103516},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7086910009384155},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.585636556148529},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5628657937049866},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5283556580543518},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4959436357021332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4746996760368347},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.42279669642448425},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.42088478803634644},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4106445610523224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3895719349384308},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.18133172392845154},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11506333947181702}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8145084381103516},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7086910009384155},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.585636556148529},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5628657937049866},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5283556580543518},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4959436357021332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4746996760368347},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42279669642448425},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.42088478803634644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4106445610523224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3895719349384308},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.18133172392845154},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11506333947181702},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2018.8650362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","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":25,"referenced_works":["https://openalex.org/W1555148682","https://openalex.org/W1690739335","https://openalex.org/W1821462560","https://openalex.org/W2057434193","https://openalex.org/W2086309060","https://openalex.org/W2094190665","https://openalex.org/W2117539524","https://openalex.org/W2160508786","https://openalex.org/W2253855427","https://openalex.org/W2285660444","https://openalex.org/W2294370754","https://openalex.org/W2319920447","https://openalex.org/W2337344472","https://openalex.org/W2516141709","https://openalex.org/W2563860341","https://openalex.org/W2565960208","https://openalex.org/W2612864759","https://openalex.org/W2964118293","https://openalex.org/W4251875201","https://openalex.org/W6633204899","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6700264148","https://openalex.org/W6703414193","https://openalex.org/W6730589793"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W2531880140","https://openalex.org/W3205213561","https://openalex.org/W2126145365","https://openalex.org/W2036609560","https://openalex.org/W346861917","https://openalex.org/W3024018414","https://openalex.org/W385273440","https://openalex.org/W4380081032"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs)":[3],"push":[4],"the":[5,28,48,51,57,63,72,90,96,103,117,122,130,136,144,148,152,171,182,201,207],"state-of-the-art":[6],"in":[7,40],"many":[8],"machine":[9],"learning":[10],"applications,":[11],"they":[12],"often":[13],"require":[14],"millions":[15],"of":[16,30,53,76,92,98,105,116,125,129,138,150,174,191,206,211],"expensive":[17],"floating-point":[18],"operations":[19],"for":[20],"each":[21],"input":[22,93],"classification.":[23],"This":[24],"computation":[25],"overhead":[26],"limits":[27],"applicability":[29],"DNNs":[31],"to":[32,70,143],"low-power,":[33],"embedded":[34],"platforms":[35],"and":[36,59,74,95,107,114,127],"incurs":[37],"high":[38],"cost":[39],"data":[41],"centers.":[42],"In":[43,155],"this":[44,156],"paper,":[45,157],"by":[46,56,62,101,179],"exploiting":[47],"gap":[49],"between":[50],"level":[52,190],"accuracy":[54],"required":[55,189],"applications/users":[58],"that":[60,170],"provided":[61],"computing":[64],"system,":[65],"we":[66],"propose":[67],"a":[68,134,158],"model":[69,119],"balance":[71,118,124],"resource":[73],"quality":[75,126,153],"neural":[77,131,209],"network":[78,86,99,139,161,175,210],"with":[79],"principal":[80],"components":[81],"analysis":[82],"(PCA),":[83],"which":[84,195],"simplifies":[85],"structure":[87],"(such":[88],"as":[89,164],"number":[91,97,173],"nodes":[94,176],"layers)":[100],"reducing":[102],"dimension":[104],"dataset":[106],"achieve":[108],"diverse":[109],"optimizations.":[110],"The":[111,167],"continuous":[112],"iteration":[113],"optimization":[115],"can":[120,187],"acquire":[121],"best":[123],"resources":[128,140],"network.":[132],"As":[133],"result,":[135],"consumption":[137],"is":[141,162,177,196],"reduced":[142,178],"greatest":[145],"extent":[146],"under":[147],"condition":[149],"satisfying":[151],"requirements.":[154],"pedestrian":[159,212],"recognition":[160,183,192,203],"taken":[163],"an":[165],"example.":[166],"results":[168],"illustrate":[169],"total":[172],"69%":[180],"while":[181],"rate":[184,193,204],"achieved":[185],"(95.99%)":[186],"reach":[188],"(95%),":[194],"only":[197],"2%":[198],"lower":[199],"than":[200],"highest":[202],"(97.01%)":[205],"original":[208],"recognition.":[213]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}