{"id":"https://openalex.org/W2905243803","doi":"https://doi.org/10.1109/icce-berlin.2018.8576188","title":"Robust Indoor/Outdoor Detection Method based on Sparse GPS Positioning Information","display_name":"Robust Indoor/Outdoor Detection Method based on Sparse GPS Positioning Information","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2905243803","doi":"https://doi.org/10.1109/icce-berlin.2018.8576188","mag":"2905243803"},"language":"en","primary_location":{"id":"doi:10.1109/icce-berlin.2018.8576188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin.2018.8576188","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5047621201","display_name":"Sae Iwata","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sae Iwata","raw_affiliation_strings":["Dept. of Computer Science and Communications Engineering, Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Communications Engineering, Waseda University","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101045421","display_name":"Kazuaki Ishikawa","orcid":null},"institutions":[{"id":"https://openalex.org/I110995367","display_name":"Sprint (United States)","ror":"https://ror.org/04rxdpa15","country_code":"US","type":"company","lineage":["https://openalex.org/I110995367"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kazuaki Ishikawa","raw_affiliation_strings":["Zenrin DataCom Co., LTD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zenrin DataCom Co., LTD","institution_ids":["https://openalex.org/I110995367"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107174531","display_name":"Toshinori Takayama","orcid":null},"institutions":[{"id":"https://openalex.org/I110995367","display_name":"Sprint (United States)","ror":"https://ror.org/04rxdpa15","country_code":"US","type":"company","lineage":["https://openalex.org/I110995367"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Toshinori Takayama","raw_affiliation_strings":["Zenrin DataCom Co., LTD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zenrin DataCom Co., LTD","institution_ids":["https://openalex.org/I110995367"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061982025","display_name":"Masao Yanagisawa","orcid":"https://orcid.org/0000-0002-5168-3214"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masao Yanagisawa","raw_affiliation_strings":["Dept. of Computer Science and Communications Engineering, Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Communications Engineering, Waseda University","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087516286","display_name":"Nozomu Togawa","orcid":"https://orcid.org/0000-0003-3400-3587"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nozomu Togawa","raw_affiliation_strings":["Dept. of Computer Science and Communications Engineering, Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Communications Engineering, Waseda University","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9769999980926514,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9767000079154968,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.8400972485542297},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7142507433891296},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6597760319709778},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5722092390060425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5640344619750977},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4695722758769989},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4310178756713867},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42786848545074463},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.41393908858299255},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3559005856513977},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3468506336212158}],"concepts":[{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.8400972485542297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7142507433891296},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6597760319709778},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5722092390060425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5640344619750977},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4695722758769989},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4310178756713867},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42786848545074463},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.41393908858299255},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3559005856513977},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3468506336212158},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-berlin.2018.8576188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin.2018.8576188","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W273955616","https://openalex.org/W1981920455","https://openalex.org/W1992087792","https://openalex.org/W2054602086","https://openalex.org/W2114063559","https://openalex.org/W2340491974","https://openalex.org/W2621761902","https://openalex.org/W2744655549","https://openalex.org/W2761903093","https://openalex.org/W2801621005","https://openalex.org/W6610017368"],"related_works":["https://openalex.org/W3162200841","https://openalex.org/W3193043704","https://openalex.org/W2586280620","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W2805505483","https://openalex.org/W2334071950","https://openalex.org/W2384744344","https://openalex.org/W2889302474","https://openalex.org/W2358868262"],"abstract_inverted_index":{"Cell":[0],"phones":[1],"with":[2,148],"GPS":[3,8,25,49],"function":[4],"as":[5,7],"well":[6],"loggers":[9],"are":[10],"widely":[11],"used":[12],"and":[13,83],"we":[14,39,72,108],"can":[15],"easily":[16],"obtain":[17],"users'":[18],"geographic":[19],"information.":[20],"Now":[21],"classifying":[22],"the":[23,33,67,74,103,110,120,133],"measured":[24,61,87,114,143],"positions":[26,29,62,88,115,118,144],"into":[27,116,145],"indoor/outdoor":[28,43,117,146],"is":[30],"one":[31],"of":[32,58,60,86,102,113,139],"major":[34],"challenges.":[35],"In":[36],"this":[37],"paper,":[38],"propose":[40],"a":[41,56,94],"robust":[42],"detection":[44],"method":[45,131],"based":[46],"on":[47],"sparse":[48],"positioning":[50,79],"information":[51],"utilizing":[52],"machine":[53],"learning.":[54],"Given":[55],"set":[57],"clusters":[59],"whose":[63],"center":[64],"position":[65],"shows":[66],"user's":[68],"estimated":[69],"stayed":[70],"position,":[71],"calculate":[73],"feature":[75,85,100],"values":[76,101],"composed":[77],"of:":[78],"accuracy,":[80],"spatial":[81],"features":[82],"temporal":[84],"included":[89],"in":[90],"every":[91],"cluster.":[92],"Then":[93],"random":[95,122],"forest":[96,123],"classifier":[97],"learns":[98],"these":[99],"known":[104],"data":[105],"set.":[106],"Finally,":[107],"classify":[109],"unknown":[111],"sequence":[112],"using":[119],"learned":[121],"classifier.":[124],"The":[125],"experiments":[126],"demonstrate":[127],"that":[128],"our":[129],"proposed":[130],"realizes":[132],"F":[134],"<sub":[135],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[136],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[137],"measure":[138],"0.9836,":[140],"which":[141],"classifies":[142],"ones":[147],"almost":[149],"no":[150],"errors.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}