{"id":"https://openalex.org/W2810380205","doi":"https://doi.org/10.1109/fskd.2017.8393139","title":"Spectrum segmentation and peak-seeking method based on spectral energy","display_name":"Spectrum segmentation and peak-seeking method based on spectral energy","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2810380205","doi":"https://doi.org/10.1109/fskd.2017.8393139","mag":"2810380205"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5101877207","display_name":"Lu Yang","orcid":"https://orcid.org/0000-0001-6745-2261"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lu Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110115820","display_name":"Qing Song","orcid":"https://orcid.org/0009-0006-7776-8845"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Song","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438125","display_name":"Zhihui Wang","orcid":"https://orcid.org/0000-0002-7547-8864"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihui Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100723209","display_name":"Shihui Zhang","orcid":"https://orcid.org/0000-0002-6418-5513"},"institutions":[{"id":"https://openalex.org/I169071405","display_name":"Hebei North University","ror":"https://ror.org/03hqwnx39","country_code":"CN","type":"education","lineage":["https://openalex.org/I169071405"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shihui Zhang","raw_affiliation_strings":["HeBei North University"],"affiliations":[{"raw_affiliation_string":"HeBei North University","institution_ids":["https://openalex.org/I169071405"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101877207"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20943333,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2347","last_page":"2353"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7295874953269958},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.6590308547019958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6159890294075012},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6088539361953735},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4612746238708496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4576171934604645},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4521671533584595},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4480099678039551},{"id":"https://openalex.org/keywords/wavelength","display_name":"Wavelength","score":0.4100492298603058},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.26322710514068604},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24787241220474243},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.14125534892082214},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07755520939826965}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7295874953269958},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.6590308547019958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6159890294075012},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6088539361953735},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4612746238708496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4576171934604645},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4521671533584595},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4480099678039551},{"id":"https://openalex.org/C6260449","wikidata":"https://www.wikidata.org/wiki/Q41364","display_name":"Wavelength","level":2,"score":0.4100492298603058},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26322710514068604},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24787241220474243},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.14125534892082214},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07755520939826965},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1975843157","https://openalex.org/W1985196743","https://openalex.org/W2013640928","https://openalex.org/W2032370961","https://openalex.org/W2041823554","https://openalex.org/W2042756512","https://openalex.org/W2061216943","https://openalex.org/W2071368424","https://openalex.org/W2079529761","https://openalex.org/W2093825454","https://openalex.org/W2095395919","https://openalex.org/W2115717467","https://openalex.org/W2120620624","https://openalex.org/W2129120544","https://openalex.org/W2131945024","https://openalex.org/W2136334331","https://openalex.org/W2342441290","https://openalex.org/W3101284843","https://openalex.org/W6658878989","https://openalex.org/W6679444976"],"related_works":["https://openalex.org/W1952261593","https://openalex.org/W2014494654","https://openalex.org/W1975321310","https://openalex.org/W2990323019","https://openalex.org/W3130349901","https://openalex.org/W1579833936","https://openalex.org/W2095350775","https://openalex.org/W2115713555","https://openalex.org/W1578916557","https://openalex.org/W2072289174"],"abstract_inverted_index":{"Spectroscopic":[0],"analysis":[1],"is":[2,17,59,103,214],"a":[3,60,120,130,141,158,199,204],"very":[4],"important":[5],"method":[6,77,81,147,178,213,221],"in":[7,63,123,192],"identifying":[8],"substances":[9],"and":[10,39,78,101,134,145,162,176,190,203,222],"determining":[11],"their":[12],"chemical":[13],"composition.":[14],"Since":[15],"it":[16,102,117],"often":[18,90],"necessary":[19],"to":[20,26,48,54,106],"seek":[21,41,55],"the":[22,28,42,45,50,56,64,99,108,114,126,163,173,186,196,209,217,223],"peaks":[23,43,57,94],"of":[24,31,188,198,211],"spectrum":[25,38,143,174],"get":[27],"corresponding":[29],"wavelength":[30,193],"these":[32,83],"substances,":[33],"we":[34,139,168],"should":[35],"first":[36],"segment":[37],"then":[40],"from":[44],"spectral":[46,150,181],"lines":[47],"make":[49],"final":[51],"identification.":[52],"So,":[53],"well":[58],"critical":[61],"step":[62],"spectroscopic":[65],"analysis.":[66],"At":[67],"present,":[68],"widely":[69],"adopted":[70],"methods":[71,85],"include":[72],"Gaussian":[73],"Mixture":[74],"Model(GMM)":[75],"fitting":[76,225],"traditional":[79],"manual":[80,115,218],"but":[82],"two":[84],"have":[86,169],"inherent":[87],"limitation.":[88],"GMM":[89,127,224],"misses":[91],"some":[92],"bottom":[93],"which":[95],"can":[96,118,156,183],"finally":[97],"reduces":[98],"recall":[100],"rather":[104],"difficult":[105],"control":[107],"appropriate":[109],"fit":[110],"orders.":[111],"As":[112],"for":[113],"method,":[116],"achieve":[119,157],"higher":[121],"accuracy":[122,161],"contrast":[124],"with":[125,195],"by":[128],"spending":[129],"lot":[131],"more":[132],"time":[133,164],"manpower.":[135],"In":[136],"this":[137],"paper,":[138],"propose":[140],"new":[142],"segmentation":[144,175,189],"peak-seeking":[146,154,177,191],"based":[148,179],"on":[149,180],"energy.":[151],"This":[152],"intelligent":[153],"algorithm":[155],"trade-off":[159],"between":[160],"cost.":[165],"The":[166],"experiments":[167],"performed":[170],"show":[171],"that":[172],"energy":[182],"effectively":[184],"solve":[185],"problems":[187],"calibration":[194],"result":[197],"95.67%":[200],"average":[201,206],"precision":[202],"77.80%":[205],"recall.":[207],"Obviously,":[208],"effect":[210],"our":[212],"better":[215],"than":[216],"peak":[219],"seeking":[220],"method.":[226]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}