{"id":"https://openalex.org/W7124285441","doi":"https://doi.org/10.3390/pr14020313","title":"Real-Time Detection of Electrohydrodynamic Atomization Modes via a YOLOv8-Based Deep Learning Model","display_name":"Real-Time Detection of Electrohydrodynamic Atomization Modes via a YOLOv8-Based Deep Learning Model","publication_year":2026,"publication_date":"2026-01-15","ids":{"openalex":"https://openalex.org/W7124285441","doi":"https://doi.org/10.3390/pr14020313"},"language":"en","primary_location":{"id":"doi:10.3390/pr14020313","doi":"https://doi.org/10.3390/pr14020313","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr14020313","pdf_url":"https://www.mdpi.com/2227-9717/14/2/313/pdf","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2227-9717/14/2/313/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065604076","display_name":"Xiong Ran","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiong Ran","raw_affiliation_strings":["School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123076486","display_name":"Heming Xu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405280939","display_name":"Bozhou University","ror":"https://ror.org/03mhrs691","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heming Xu","raw_affiliation_strings":["Department of Electronic and Information Engineering, Bozhou University, Bozhou 236800, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Information Engineering, Bozhou University, Bozhou 236800, China","institution_ids":["https://openalex.org/I4405280939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123107955","display_name":"Xiangfei Wei","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405280939","display_name":"Bozhou University","ror":"https://ror.org/03mhrs691","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangfei Wei","raw_affiliation_strings":["Department of Electronic and Information Engineering, Bozhou University, Bozhou 236800, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Information Engineering, Bozhou University, Bozhou 236800, China","institution_ids":["https://openalex.org/I4405280939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100376128","display_name":"Jinxin Wang","orcid":"https://orcid.org/0000-0003-3972-4949","observed_orcids":["https://orcid.org/0000-0003-3972-4949","https://orcid.org/0000-0001-6855-2539"]},"institutions":[{"id":"https://openalex.org/I4405280939","display_name":"Bozhou University","ror":"https://ror.org/03mhrs691","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280939"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinxin Wang","raw_affiliation_strings":["Department of Electronic and Information Engineering, Bozhou University, Bozhou 236800, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Information Engineering, Bozhou University, Bozhou 236800, China","institution_ids":["https://openalex.org/I4405280939"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064753053","display_name":"Wei\u2010Cheng Yan","orcid":"https://orcid.org/0000-0002-6820-8815","observed_orcids":["https://orcid.org/0000-0002-6820-8815"]},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei-Cheng Yan","raw_affiliation_strings":["School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, China","institution_ids":["https://openalex.org/I115592961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5064753053","https://openalex.org/A5100376128"],"corresponding_institution_ids":["https://openalex.org/I115592961","https://openalex.org/I4405280939"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2896},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2896},"fwci":1.7657,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.7875,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"14","issue":"2","first_page":"313","last_page":"313"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12776","display_name":"Electrohydrodynamics and Fluid Dynamics","score":0.7310000061988831,"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/T12776","display_name":"Electrohydrodynamics and Fluid Dynamics","score":0.7310000061988831,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.25929999351501465,"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/T10616","display_name":"Smart Agriculture and AI","score":0.0035000001080334187,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electrospray","display_name":"electrospraying","score":0.9990000128746033},{"id":"https://openalex.org/keywords/yolov8","display_name":"YOLOv8","score":0.8989999890327454},{"id":"https://openalex.org/keywords/real-time-object-detection","display_name":"real-time object detection","score":0.906000018119812},{"id":"https://openalex.org/keywords/lightweight-deep-learning-models","display_name":"lightweight deep learning","score":0.6830000281333923},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.40700000524520874},{"id":"https://openalex.org/keywords/feature-fusion","display_name":"feature fusion","score":0.3160000145435333},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.4269999861717224}],"concepts":[{"id":"https://openalex.org/C95238685","wikidata":"https://www.wikidata.org/wiki/Q1326902","display_name":"Electrohydrodynamics","level":3,"score":0.6809999942779541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6769000291824341},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6759999990463257},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6279000043869019},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5611000061035156},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3158000111579895},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2987000048160553},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29840001463890076},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.274399995803833},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2703000009059906},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/pr14020313","doi":"https://doi.org/10.3390/pr14020313","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr14020313","pdf_url":"https://www.mdpi.com/2227-9717/14/2/313/pdf","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/pr14020313","doi":"https://doi.org/10.3390/pr14020313","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr14020313","pdf_url":"https://www.mdpi.com/2227-9717/14/2/313/pdf","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2104998916","display_name":null,"funder_award_id":"2408085QE164","funder_id":"https://openalex.org/F4320334897","funder_display_name":"Natural Science Foundation of Anhui Province"},{"id":"https://openalex.org/G342501132","display_name":null,"funder_award_id":"2024YFA1510003","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320330919","display_name":"Bozhou University","ror":"https://ror.org/03mhrs691"},{"id":"https://openalex.org/F4320334897","display_name":"Natural Science Foundation of Anhui Province","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7124285441.pdf","grobid_xml":"https://content.openalex.org/works/W7124285441.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"YOLOv8-based":[1],"deep":[2],"learning":[3],"model":[4,82],"was":[5,24],"developed":[6],"to":[7,26,61],"address":[8],"real-time":[9,137],"detection":[10,138],"and":[11,42,63,68,70,89,117,128],"dynamic":[12],"regulation":[13],"needs":[14],"of":[15,29,91,96,139],"the":[16,45,49,53,78,81,114],"electrohydrodynamic":[17],"atomization":[18,32],"process.":[19],"An":[20],"EHDA":[21,140],"experimental":[22],"system":[23],"built":[25],"obtain":[27],"images":[28],"six":[30],"typical":[31],"modes,":[33],"forming":[34],"a":[35,58,84],"dataset":[36,46],"with":[37,109],"6000":[38],"images.":[39],"After":[40],"annotation":[41],"mosaic":[43],"augmentation,":[44],"served":[47],"as":[48,119,121],"training":[50],"data":[51],"for":[52,136],"model.":[54],"The":[55],"YOLOv8":[56],"adopts":[57],"\u201cbackbone-neck-head\u201d":[59],"architecture":[60],"extract":[62],"fuse":[64],"features,":[65],"decouple":[66],"classification":[67],"detection,":[69],"optimize":[71],"performance.":[72],"Experimental":[73],"results":[74],"demonstrate":[75],"that":[76],"on":[77],"test":[79],"set,":[80],"attains":[83],"precision":[85,116],"value,":[86],"recall":[87],"rate,":[88],"mAP50":[90],"0.995,":[92],"alongside":[93],"an":[94],"mAP50-95":[95],"0.8.":[97],"Additionally,":[98],"its":[99],"prediction":[100],"accuracy":[101,127],"exceeds":[102],"0.99":[103],"across":[104],"all":[105],"operational":[106],"modes.":[107,141],"Compared":[108],"10":[110],"models,":[111],"it":[112],"has":[113],"best":[115],"mAP50,":[118],"well":[120],"low":[122],"computational":[123],"complexity,":[124],"combining":[125],"high":[126],"lightweight":[129],"advantages,":[130],"which":[131],"can":[132],"be":[133],"effectively":[134],"used":[135]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2026-01-16T00:00:00"}
