{"id":"https://openalex.org/W4393047710","doi":"https://doi.org/10.3390/s24061992","title":"Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction","display_name":"Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction","publication_year":2024,"publication_date":"2024-03-21","ids":{"openalex":"https://openalex.org/W4393047710","doi":"https://doi.org/10.3390/s24061992","pmid":"https://pubmed.ncbi.nlm.nih.gov/38544255"},"language":"en","primary_location":{"id":"doi:10.3390/s24061992","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24061992","pdf_url":"https://www.mdpi.com/1424-8220/24/6/1992/pdf?version=1711004466","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/24/6/1992/pdf?version=1711004466","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076289546","display_name":"Syed Danish Ali","orcid":"https://orcid.org/0000-0001-5204-5073"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]},{"id":"https://openalex.org/I4210126421","display_name":"Forest Products Laboratory","ror":"https://ror.org/031sp1p50","country_code":"US","type":"facility","lineage":["https://openalex.org/I1313416372","https://openalex.org/I1336096307","https://openalex.org/I4210126421"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Syed Danish Ali","raw_affiliation_strings":["Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA","USDA Forest Service, Forest Products Laboratory, Madison, WI 53726, USA"],"raw_orcid":"https://orcid.org/0000-0001-5204-5073","affiliations":[{"raw_affiliation_string":"Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"USDA Forest Service, Forest Products Laboratory, Madison, WI 53726, USA","institution_ids":["https://openalex.org/I4210126421"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026864211","display_name":"Sameen Raut","orcid":"https://orcid.org/0000-0002-7088-5946"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sameen Raut","raw_affiliation_strings":["Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA"],"raw_orcid":"https://orcid.org/0000-0002-7088-5946","affiliations":[{"raw_affiliation_string":"Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052556118","display_name":"Joseph Dahlen","orcid":"https://orcid.org/0009-0002-7231-4845"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Dahlen","raw_affiliation_strings":["Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069250833","display_name":"Laurence R. Schimleck","orcid":"https://orcid.org/0000-0003-1958-6059"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Laurence Schimleck","raw_affiliation_strings":["Department of Wood Science and Engineering, Oregon State University, Corvallis, OR 97331, USA"],"raw_orcid":"https://orcid.org/0000-0003-1958-6059","affiliations":[{"raw_affiliation_string":"Department of Wood Science and Engineering, Oregon State University, Corvallis, OR 97331, USA","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090086262","display_name":"Richard Bergman","orcid":"https://orcid.org/0000-0003-1798-1717"},"institutions":[{"id":"https://openalex.org/I4210126421","display_name":"Forest Products Laboratory","ror":"https://ror.org/031sp1p50","country_code":"US","type":"facility","lineage":["https://openalex.org/I1313416372","https://openalex.org/I1336096307","https://openalex.org/I4210126421"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard Bergman","raw_affiliation_strings":["USDA Forest Service, Forest Products Laboratory, Madison, WI 53726, USA"],"raw_orcid":"https://orcid.org/0000-0003-1798-1717","affiliations":[{"raw_affiliation_string":"USDA Forest Service, Forest Products Laboratory, Madison, WI 53726, USA","institution_ids":["https://openalex.org/I4210126421"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054881624","display_name":"Zhou Zhang","orcid":"https://orcid.org/0000-0001-7816-672X"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhou Zhang","raw_affiliation_strings":["Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA"],"raw_orcid":"https://orcid.org/0000-0001-7816-672X","affiliations":[{"raw_affiliation_string":"Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045814524","display_name":"Vahid Nasir","orcid":"https://orcid.org/0000-0002-6906-3224"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vahid Nasir","raw_affiliation_strings":["Department of Wood Science and Engineering, Oregon State University, Corvallis, OR 97331, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Wood Science and Engineering, Oregon State University, Corvallis, OR 97331, USA","institution_ids":["https://openalex.org/I131249849"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5045814524"],"corresponding_institution_ids":["https://openalex.org/I131249849"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":5.0095,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.96073205,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"24","issue":"6","first_page":"1992","last_page":"1992"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10484","display_name":"Wood Treatment and Properties","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6280291676521301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.586297333240509},{"id":"https://openalex.org/keywords/near-infrared-spectroscopy","display_name":"Near-infrared spectroscopy","score":0.5372384190559387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5074674487113953},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49434661865234375},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.489298552274704},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.47994378209114075},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4748254716396332},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.45931509137153625},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.41875240206718445},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41339990496635437},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.34844809770584106},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.08115065097808838}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6280291676521301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.586297333240509},{"id":"https://openalex.org/C43571822","wikidata":"https://www.wikidata.org/wiki/Q599037","display_name":"Near-infrared spectroscopy","level":2,"score":0.5372384190559387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5074674487113953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49434661865234375},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.489298552274704},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.47994378209114075},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4748254716396332},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.45931509137153625},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41875240206718445},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41339990496635437},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34844809770584106},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.08115065097808838},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000078332","descriptor_name":"Data Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000078332","descriptor_name":"Data Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000078332","descriptor_name":"Data Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000078332","descriptor_name":"Data Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008027","descriptor_name":"Light","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008027","descriptor_name":"Light","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008027","descriptor_name":"Light","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008027","descriptor_name":"Light","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014934","descriptor_name":"Wood","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014934","descriptor_name":"Wood","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014934","descriptor_name":"Wood","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014934","descriptor_name":"Wood","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D055119","descriptor_name":"Elastic Modulus","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D055119","descriptor_name":"Elastic Modulus","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D055119","descriptor_name":"Elastic Modulus","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D055119","descriptor_name":"Elastic Modulus","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.3390/s24061992","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24061992","pdf_url":"https://www.mdpi.com/1424-8220/24/6/1992/pdf?version=1711004466","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"Sensors","raw_type":"journal-article"},{"id":"pmid:38544255","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38544255","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10975370","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10975370","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10975370/pdf/sensors-24-01992.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:4ac6490025394fb99c35264b58bd9cba","is_oa":false,"landing_page_url":"https://doaj.org/article/4ac6490025394fb99c35264b58bd9cba","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 24, Iss 6, p 1992 (2024)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/24/6/1992/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s24061992","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"},{"id":"pmh:oai:osti.gov:2472267","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/2472267","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.3390/s24061992","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24061992","pdf_url":"https://www.mdpi.com/1424-8220/24/6/1992/pdf?version=1711004466","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4393047710.pdf"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W74037523","https://openalex.org/W1972268602","https://openalex.org/W1981883640","https://openalex.org/W1998466489","https://openalex.org/W2002163331","https://openalex.org/W2028801527","https://openalex.org/W2034568337","https://openalex.org/W2058645560","https://openalex.org/W2077341198","https://openalex.org/W2078841605","https://openalex.org/W2109606373","https://openalex.org/W2129208771","https://openalex.org/W2146806697","https://openalex.org/W2148440374","https://openalex.org/W2152159071","https://openalex.org/W2331622956","https://openalex.org/W2356694407","https://openalex.org/W2467562285","https://openalex.org/W2526674818","https://openalex.org/W2552705578","https://openalex.org/W2565852104","https://openalex.org/W2610332124","https://openalex.org/W2625357353","https://openalex.org/W2782812883","https://openalex.org/W2791120867","https://openalex.org/W2916521892","https://openalex.org/W2919115771","https://openalex.org/W2949676527","https://openalex.org/W2962949934","https://openalex.org/W2967869210","https://openalex.org/W2969387865","https://openalex.org/W2970009524","https://openalex.org/W3000741860","https://openalex.org/W3011642172","https://openalex.org/W3025356332","https://openalex.org/W3100340856","https://openalex.org/W3127095391","https://openalex.org/W3163225299","https://openalex.org/W3198672443","https://openalex.org/W4200272464","https://openalex.org/W4226483884","https://openalex.org/W4242289937","https://openalex.org/W4280537286","https://openalex.org/W4285388434","https://openalex.org/W4293763360","https://openalex.org/W4361275302","https://openalex.org/W4361282535","https://openalex.org/W4385858713","https://openalex.org/W6706375891","https://openalex.org/W6719886662","https://openalex.org/W6838706651","https://openalex.org/W6850504670"],"related_works":["https://openalex.org/W2786391746","https://openalex.org/W4312659495","https://openalex.org/W2991483587","https://openalex.org/W2945050830","https://openalex.org/W3101007570","https://openalex.org/W4387910575","https://openalex.org/W3176425421","https://openalex.org/W1555087354","https://openalex.org/W3091312527","https://openalex.org/W4381430104"],"abstract_inverted_index":{"Near-infrared":[0],"(NIR)":[1],"spectroscopy":[2,306],"is":[3,233],"widely":[4],"used":[5,134],"as":[6,307],"a":[7,36,102,118,230],"nondestructive":[8],"evaluation":[9],"(NDE)":[10],"tool":[11,313],"for":[12,59,91,151,241,286,314],"predicting":[13],"wood":[14,315],"properties.":[15],"When":[16],"deploying":[17],"NIR":[18,44,64,110,287,305],"models,":[19],"one":[20,236],"faces":[21],"challenges":[22],"in":[23],"ensuring":[24],"representative":[25],"training":[26,97,113,211,250,270,329],"data,":[27,98,330],"which":[28,192,272],"large":[29],"datasets":[30,132],"can":[31,243],"mitigate":[32],"but":[33],"often":[34],"at":[35,47],"significant":[37],"cost.":[38],"Machine":[39],"learning":[40,43,281,284,340,343],"and":[41,124,130,145,172,183,235,262,268,311,338],"deep":[42,283,342],"models":[45,198,303,344],"are":[46],"an":[48,278,308],"even":[49],"greater":[50],"disadvantage":[51],"because":[52,220],"they":[53],"typically":[54],"require":[55],"higher":[56],"sample":[57],"sizes":[58],"training.":[60],"In":[61],"this":[62,99,242],"study,":[63],"spectra":[65,189,205],"were":[66,133,206],"collected":[67],"to":[68,107,120,135,170,180,208],"predict":[69],"the":[70,92,96,163,174,209,221,224,238,245,248,254,266,274,293,299,322,325,331],"modulus":[71],"of":[72,75,95,165,176,223,237,247,276,296,302,324,328,334],"elasticity":[73],"(MOE)":[74],"southern":[76],"pine":[77],"lumber":[78],"(training":[79],"set":[80,85],"=":[81,86],"573":[82],"samples,":[83],"testing":[84],"145":[87],"samples).":[88],"To":[89],"account":[90],"limited":[93],"size":[94,246,327],"study":[100],"employed":[101],"generative":[103],"adversarial":[104],"network":[105],"(GAN)":[106],"generate":[108,121],"synthetic":[109,126,188,204,336],"spectra.":[111,127],"The":[112,128,290],"dataset":[114],"was":[115],"fed":[116,252],"into":[117,253],"GAN":[119,161,228,297],"313,":[122],"573,":[123],"1000":[125],"original":[129,210,267],"enhanced":[131,269],"train":[136],"artificial":[137],"neural":[138,142],"networks":[139,143],"(ANNs),":[140],"convolutional":[141],"(CNNs),":[144],"light":[146],"gradient":[147],"boosting":[148],"machines":[149],"(LGBMs)":[150],"MOE":[152],"prediction.":[153],"Overall,":[154],"results":[155,291],"showed":[156,199,257],"that":[157,345],"data":[158,251,350],"augmentation":[159,351],"using":[160,352],"improved":[162],"coefficient":[164],"determination":[166],"(R2)":[167],"by":[168,178,227],"up":[169,179],"7.02%":[171],"reduced":[173],"error":[175],"predictions":[177],"4.29%.":[181],"ANNs":[182,261],"CNNs":[184,263],"benefited":[185],"more":[186,348],"from":[187,349],"than":[190,260],"LGBMs,":[191],"only":[193],"yielded":[194],"slight":[195],"improvement.":[196],"All":[197],"optimal":[200,332],"performance":[201,219,301],"when":[202],"313":[203],"added":[207],"data;":[212],"further":[213],"additions":[214],"did":[215],"not":[216],"improve":[217],"model":[218,285],"quality":[222],"datapoints":[225],"generated":[226,335],"beyond":[229],"certain":[231],"threshold":[232],"poor,":[234],"main":[239],"reasons":[240],"be":[244],"initial":[249,326],"GAN.":[255],"LGBMs":[256],"superior":[258],"performances":[259],"on":[264,298],"both":[265],"datasets,":[271],"highlights":[273],"significance":[275],"selecting":[277],"appropriate":[279],"machine":[280,339],"or":[282,341],"spectral-data":[288],"analysis.":[289],"highlighted":[292],"positive":[294],"impact":[295,323],"predictive":[300],"utilizing":[304],"NDE":[309],"technique":[310],"monitoring":[312],"mechanical-property":[316],"evaluation.":[317],"Further":[318],"studies":[319],"should":[320],"investigate":[321],"number":[333],"spectra,":[337],"could":[346],"benefit":[347],"GANs.":[353]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":8}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}