乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T13:52:12Z","timestamp":1726408332617},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030130008"},{"type":"electronic","value":"9783030130015"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-13001-5_11","type":"book-chapter","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T10:03:29Z","timestamp":1568023409000},"page":"153-170","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Benchmark Performance for the Sussex-Huawei Locomotion and Transportation Recognition Challenge 2018"],"prefix":"10.1007","author":[{"given":"Lin","family":"Wang","sequence":"first","affiliation":[]},{"given":"Hristijan","family":"Gjoreski","sequence":"additional","affiliation":[]},{"given":"Mathias","family":"Ciliberto","sequence":"additional","affiliation":[]},{"given":"Sami","family":"Mekki","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Valentin","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Roggen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,10]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.trc.2013.09.016","volume":"37","author":"W Brazil","year":"2013","unstructured":"Brazil W, Caulfield B (2013) Does green make a difference: the potential role of smartphone technology in transport behaviour. Transp Res Part C Emerg Technol 37:93\u2013101","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"1","key":"11_CR2","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/MITS.2014.2328673","volume":"7","author":"G Castignani","year":"2015","unstructured":"Castignani G, Derrmann T, Frank R, Engel T (2015) Driver behavior profiling using smartphones: a low-cost platform for driver monitoring. IEEE Intell Transp Syst Mag 7(1):91\u2013102","journal-title":"IEEE Intell Transp Syst Mag"},{"issue":"3","key":"11_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1\u201327","journal-title":"ACM Trans Intell Syst Technol"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Ciliberto M, Ordonez FJ, Gjoreski H, Mekki S, Valentin S, Roggen D (2017) High reliability Android application for multidevice multimodal mobile data acquisition and annotation. In: Proceedings of ACM conference on embedded networked sensor systems, pp 1\u20132","DOI":"10.1145\/3131672.3136977"},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.future.2013.07.014","volume":"37","author":"C Dobre","year":"2014","unstructured":"Dobre C, Xhafa F (2014) Intelligent services for Big Data science. Futur Gener Comput Syst 37:267\u2013281","journal-title":"Futur Gener Comput Syst"},{"issue":"10","key":"11_CR6","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1049\/iet-its.2014.0248","volume":"9","author":"J Engelbrecht","year":"2015","unstructured":"Engelbrecht J, Booysen MJ, van Rooyen GJ, Bruwer FJ (2015) Survey of smartphone-based sensing in vehicles for intelligent transportation system applications. IET Intell Transp Syst 9(10):924\u2013935","journal-title":"IET Intell Transp Syst"},{"issue":"8","key":"11_CR7","doi-asserted-by":"publisher","first-page":"6111","DOI":"10.1109\/JSEN.2017.2737825","volume":"16","author":"SH Fang","year":"2017","unstructured":"Fang SH, Fei YX, Xu Z, Tsao Y (2017) Learning transportation modes from smartphone sensors based on deep neural network. IEEE Sens J 16(8):6111\u20136118","journal-title":"IEEE Sens J"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.trc.2013.09.014","volume":"37","author":"T Feng","year":"2013","unstructured":"Feng T, Timmermans H (2013) Transportation mode recognition using GPS and accelerometer data. Transp Res Part C Emerg Technol 37:118\u2013130","journal-title":"Transp Res Part C Emerg Technol"},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"42592","DOI":"10.1109\/ACCESS.2018.2858933","volume":"6","author":"H Gjoreski","year":"2018","unstructured":"Gjoreski H, Ciliberto M, Wang L, Ordonez FJ, Mekki S, Valentin S, Roggen D (2018) The university of Sussex-Huawei locomotion-transportation dataset for multimodal analytics with mobile devices. IEEE Access 6:42592\u201342604","journal-title":"IEEE Access"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Hemminki S, Nurmi P, Tarkoma S (2013) Accelerometer-based transportation mode detection on smartphones. In: Proceedings of ACM conference on embedded networked sensor systems, pp 1\u201314","DOI":"10.1145\/2517351.2517367"},{"issue":"5","key":"11_CR11","doi-asserted-by":"publisher","first-page":"2406","DOI":"10.1109\/TITS.2015.2405759","volume":"16","author":"A Jahangiri","year":"2015","unstructured":"Jahangiri A, Rakha HA (2015) Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE Trans Intell Transp Syst 16(5):2406\u20132417","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"11_CR12","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1109\/MCOM.2010.5560598","volume":"48","author":"ND Lane","year":"2010","unstructured":"Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140\u2013150","journal-title":"IEEE Commun Mag"},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s16010115","volume":"16","author":"FJ Ordonez","year":"2016","unstructured":"Ordonez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16:1\u201325","journal-title":"Sensors"},{"issue":"8","key":"11_CR14","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Richoz S, Ciliberto M, Wang L, Birch P, Gjoreski H, Perez-Uribe A, Roggen D (2019) Human and machine recognition of transportation modes from body-worn camera images. In: Proceedings of joint 8th international conference informatics, electronics & vision and international conference on imaging, vision & pattern recognition, pp 1\u20136","DOI":"10.1109\/ICIEV.2019.8858537"},{"issue":"1","key":"11_CR16","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"10","key":"11_CR17","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1109\/TITS.2016.2530999","volume":"17","author":"X Su","year":"2016","unstructured":"Su X, Caceres H, Tong H, He Q (2016) Online travel mode identification using smartphones with battery saving considerations. IEEE Trans Intell Transp Syst 17(10):2921\u20132934","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"10870","DOI":"10.1109\/ACCESS.2019.2890793","volume":"7","author":"L Wang","year":"2019","unstructured":"Wang L, Gjoreski H, Ciliberto M, Mekki S, Valentin S, Roggen D (2019) Enabling reproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset. IEEE Access 7:10870\u201310891","journal-title":"IEEE Access"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Wang L, Gjoreski H, Ciliberto M, Mekki S, Valentin S, Roggen D (2018a) Benchmarking the SHL recognition challenge with classical and deep-learning pipelines. In: Proceedings of 6th international workshop on human activity sensing corpus and applications (HASCA2018), pp 1626\u20131635","DOI":"10.1145\/3267305.3267531"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Wang L, Gjoreski H, Murao K, Okita T, Roggen D (2018b) Summary of the Sussex-Huawei locomotion-transportation recognition challenge. In: Proceedings of 6th international workshop on human activity sensing corpus and applications (HASCA2018), pp 1521\u20131530","DOI":"10.1145\/3267305.3267519"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Xia H, Qiao Y, Jian J, Chang Y (2014) Using smart phone sensors to detect transportation modes. Sensors, 20843\u201320865","DOI":"10.3390\/s141120843"},{"issue":"13","key":"11_CR22","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.14778\/2733004.2733015","volume":"7","author":"Meng-Chieh Yu","year":"2014","unstructured":"Yu MC, Yu T, Wang SC, Lin CJ, Chang EY (2014) Big Data small footprint: the design of a low-power classifier for detecting transportation modes. In: Proceedings of very large data base endowment, pp 1429\u20131440","journal-title":"Proceedings of the VLDB Endowment"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Zhang Z, Poslad S (2013) A new post correction algorithm (POCOA) for improved transportation mode recognition. In: Proceedings of IEEE international conference on systems, man, and cybernetics, pp 1512\u20131518","DOI":"10.1109\/SMC.2013.261"}],"container-title":["Springer Series in Adaptive Environments","Human Activity Sensing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13001-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T16:28:40Z","timestamp":1575649720000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-13001-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030130008","9783030130015"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13001-5_11","relation":{},"ISSN":["2522-5529","2522-5537"],"issn-type":[{"type":"print","value":"2522-5529"},{"type":"electronic","value":"2522-5537"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}