{"id":"https://openalex.org/W3006521650","doi":"https://doi.org/10.1109/uemcon47517.2019.8993083","title":"Control System Response Improvement via Denoising Using Deep Neural Networks","display_name":"Control System Response Improvement via Denoising Using Deep Neural Networks","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3006521650","doi":"https://doi.org/10.1109/uemcon47517.2019.8993083","mag":"3006521650"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon47517.2019.8993083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","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/A5068165802","display_name":"Kiavash Fathi","orcid":"https://orcid.org/0000-0002-1844-8001"},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Kiavash Fathi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran","institution_ids":["https://openalex.org/I170013655"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079150810","display_name":"Mehdi Mahdavi","orcid":"https://orcid.org/0000-0003-4701-9377"},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mehdi Mahdavi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran","institution_ids":["https://openalex.org/I170013655"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170013655"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21679367,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"0377","last_page":"0382"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9954000115394592,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9954000115394592,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.992900013923645,"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/T11236","display_name":"Control Systems and Identification","score":0.9918000102043152,"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":0.6289931535720825},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.573905348777771},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.533386766910553},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48558515310287476},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.39553016424179077},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.34719789028167725},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.33855152130126953},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1586824357509613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6289931535720825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.573905348777771},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.533386766910553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48558515310287476},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.39553016424179077},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.34719789028167725},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.33855152130126953},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1586824357509613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon47517.2019.8993083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W83533993","https://openalex.org/W590134283","https://openalex.org/W1534096605","https://openalex.org/W2025768430","https://openalex.org/W2028828529","https://openalex.org/W2050259033","https://openalex.org/W2145094598","https://openalex.org/W2182936301","https://openalex.org/W2405774341","https://openalex.org/W2502475384","https://openalex.org/W2888499542","https://openalex.org/W2908735595","https://openalex.org/W2912799857","https://openalex.org/W2915622878","https://openalex.org/W2997574889","https://openalex.org/W3216905214","https://openalex.org/W4210968171","https://openalex.org/W4249314718","https://openalex.org/W4285719527","https://openalex.org/W6681096077","https://openalex.org/W6686220060","https://openalex.org/W6804521139"],"related_works":["https://openalex.org/W1982160337","https://openalex.org/W1971380437","https://openalex.org/W1983548084","https://openalex.org/W2141691317","https://openalex.org/W2110846987","https://openalex.org/W2160609742","https://openalex.org/W2979526210","https://openalex.org/W1997414638","https://openalex.org/W2031091109","https://openalex.org/W2049254832"],"abstract_inverted_index":{"Noise":[0],"is":[1,20,63,182,192,230,247],"an":[2],"inseparable":[3],"part":[4],"of":[5,16,31,39,52,65,72,75,113,132,148,159,176,212,224,233],"control":[6,18],"systems.":[7,40,227,251],"Every":[8],"sensor":[9,32],"reading":[10],"used":[11],"for":[12,125],"determining":[13],"the":[14,26,37,48,53,58,67,73,76,88,114,126,133,152,171,174,177,179,189,207,213,218,222,225,238,244,250],"state":[15],"a":[17,107,120,139,144,185,195,198],"system":[19],"corrupted":[21,68,153],"with":[22,157,210],"noise.,":[23],"therefor":[24,62],"increasing":[25],"signal":[27,56,70,209,242,246],"to":[28,81,95,105,169,184,206,237,249],"noise":[29],"ratio":[30],"readings":[33],"can":[34],"significantly":[35],"improve":[36],"performance":[38,223],"The":[41,111,129,201],"proposed":[42,134,180,190],"filter":[43,135,181,191],"in":[44,57,87,164,167,221,231],"this":[45],"paper":[46],"captures":[47],"underlying":[49],"probability":[50],"distribution":[51,74],"noise-free":[54,215,240],"input":[55,69,154],"training":[59],"stage":[60],"and":[61,101,122,143,173,197],"capable":[64],"refining":[66],"regardless":[71],"added":[77],"noise.":[78,187],"In":[79],"order":[80,168],"acquire":[82],"better":[83],"data-driven":[84],"based":[85],"results":[86],"suggested":[89],"approach,":[90],"it":[91],"has":[92],"been":[93],"decided":[94],"use":[96],"different":[97],"neural":[98,116,141],"network":[99,117,142],"structures":[100],"stack":[102],"these":[103,160],"layers":[104],"form":[106],"hybrid":[108],"multilayer":[109],"filter.":[110],"properties":[112],"stacked":[115],"sublayers":[118,161],"guarantee":[119],"robust":[121],"general":[123],"solution":[124],"intended":[127],"purpose.":[128],"key":[130],"elements":[131],"are":[136,162],"two":[137],"auto-encoders.,":[138],"dense":[140],"convolutional":[145],"layer.":[146],"Effects":[147],"every":[149],"sublayer":[150],"on":[151,194],"signal.,":[155,216],"along":[156],"fine-tuning":[158],"discussed":[163],"detail.":[165],"Afterwards":[166],"assess":[170],"generality":[172],"robustness":[175],"method.,":[178],"exposed":[183],"non-Gaussian":[186],"Finally.,":[188],"tested":[193],"linear":[196],"nonlinear":[199],"system.":[200],"comparison":[202],"between":[203],"systems\u201d":[204,234],"output":[205,235,241],"reconstructed":[208,245],"that":[211],"original":[214],"suggests":[217],"substantial":[219],"improvement":[220,229],"given":[226],"This":[228],"terms":[232],"resemblance":[236],"reference":[239],"when":[243],"applied":[248]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}