{"id":"https://openalex.org/W2800869392","doi":null,"title":"Automated Classification of Steel Samples : An investigation using Convolutional Neural Networks","display_name":"Automated Classification of Steel Samples : An investigation using Convolutional Neural Networks","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2800869392","mag":"2800869392"},"language":"en","primary_location":{"id":"pmh:oai:DiVA.org:kth-209669","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209669","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209669","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033337582","display_name":"Bj\u00f6rn Ahlin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bj\u00f6rn Ahlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5050405563","display_name":"Marcus G\u00e4rdin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marcus G\u00e4rdin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7675999999046326,"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":0.7675999999046326,"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/T11126","display_name":"Metallurgical Processes and Thermodynamics","score":0.07129999995231628,"subfield":{"id":"https://openalex.org/subfields/2506","display_name":"Metals and Alloys"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials 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":0.06289999932050705,"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/inclusions-in-steel","display_name":"steel inclusions","score":0.7200000286102295},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9430000185966492},{"id":"https://openalex.org/keywords/automated-image-recognition","display_name":"automated image recognition","score":0.7649999856948853},{"id":"https://openalex.org/keywords/binarization","display_name":"binarization","score":0.7480000257492065},{"id":"https://openalex.org/keywords/steelmaking","display_name":"steel industry","score":0.4860000014305115},{"id":"https://openalex.org/keywords/non-destructive-testing","display_name":"non-destructive testing","score":0.4959999918937683}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7394762635231018},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.6223992705345154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5685073137283325},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5684272646903992},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5238792300224304},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4975002110004425},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:DiVA.org:kth-209669","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209669","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},{"id":"mag:2800869392","doi":null,"is_oa":false,"landing_page_url":"http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1113529","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:DiVA.org:kth-209669","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209669","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W122552260","https://openalex.org/W353957520","https://openalex.org/W1546411676","https://openalex.org/W1849277567","https://openalex.org/W1959220709","https://openalex.org/W1969868988","https://openalex.org/W1972110362","https://openalex.org/W1976228831","https://openalex.org/W1981759979","https://openalex.org/W1996497733","https://openalex.org/W2064962023","https://openalex.org/W2083924420","https://openalex.org/W2091120953","https://openalex.org/W2163605009","https://openalex.org/W2514440090","https://openalex.org/W2583450985","https://openalex.org/W2591544917"],"related_works":["https://openalex.org/W3213450470","https://openalex.org/W4388177888","https://openalex.org/W4286252812","https://openalex.org/W4376956860","https://openalex.org/W2924762335","https://openalex.org/W2981444274","https://openalex.org/W3125089330","https://openalex.org/W2338137735","https://openalex.org/W1981759979","https://openalex.org/W4378190034"],"abstract_inverted_index":{"Automated":[0],"image":[1,68],"recognition":[2],"software":[3,24,81],"has":[4],"earlier":[5],"been":[6,55],"used":[7,117],"for":[8,59,106],"various":[9],"analyses":[10],"in":[11,172],"the":[12,19,44,50,80,121,126,131,144,147,170],"steel":[13,34,40,46,162,174],"making":[14],"industry.":[15],"In":[16],"this":[17],"study,":[18],"possibility":[20],"to":[21,25,118,160],"apply":[22],"such":[23],"classify":[26,161],"Scanning":[27],"Electron":[28],"Microscope":[29],"(SEM)":[30],"images":[31,108,115,135],"of":[32,43,63,79,140,169,186],"two":[33,39],"samples":[35,41,163,175],"was":[36,75,82,100,151],"investigated.":[37],"The":[38,77,98,133,153],"were":[42,116],"same":[45],"grade":[47],"but":[48],"with":[49,57,84,111],"difference":[51],"that":[52,120,156,177],"they":[53],"had":[54],"treated":[56],"calcium":[58],"a":[60,70,137],"different":[61],"length":[62],"time.":[64],"To":[65],"enable":[66,183],"automated":[67,184],"recognition,":[69],"Convolutional":[71],"Neural":[72],"Network":[73],"(CNN)":[74],"built.":[76],"construction":[78],"performed":[83],"open":[85],"source":[86],"code":[87],"provided":[88],"by":[89],"Keras":[90],"Documentation,":[91],"thus":[92],"ensuring":[93],"an":[94],"easily":[95],"reproducible":[96],"program.":[97],"network":[99],"trained,":[101],"validated":[102],"and":[103,109,129],"tested,":[104],"first":[105],"non-binarized":[107,134],"then":[110],"binarized":[112,145],"images.":[113],"Binarized":[114],"ensure":[119],"network's":[122],"prediction":[123],"only":[124],"considers":[125],"inclusion":[127],"information":[128],"not":[130],"substrate.":[132],"gave":[136],"classification":[138,148,185],"accuracy":[139,149],"99.99":[141],"%.":[142],"For":[143],"images,":[146],"obtained":[150],"67.9%.":[152],"results":[154],"show":[155],"it":[157],"is":[158,176],"possible":[159],"using":[164],"CNNs.":[165],"One":[166],"interesting":[167],"aspect":[168],"success":[171],"classifying":[173],"further":[178],"studies":[179],"on":[180],"CNNs":[181],"could":[182],"inclusions.":[187]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2018-05-17T00:00:00"}
