Art
J-GLOBAL ID:202402224813314459   Reference number:24A0247881

Transportation Mode Detection Combining CNN and Vision Transformer with Sensors Recalibration Using Smartphone Built-In Sensors

スマートフォン内蔵センサを使用したセンサ再キャリブレーションによるCNNとビジョン変圧器を組み合わせた輸送モード検出【JST・京大機械翻訳】
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Volume: 22  Issue: 17  Page: 6453  Publication year: 2022 
JST Material Number: U7015A  ISSN: 1424-8220  CODEN: SENSC9  Document type: Article
Article type: 原著論文  Country of issue: Switzerland (CHE)  Language: ENGLISH (EN)
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Transportation Mode Detection ...
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Nondestructive testing  ,  Analytical instruments 
Reference (29):
  • Veres, M.; Moussa, M. Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends. IEEE Trans. Intell. Transp. Syst. 2020, 21, 3152-3168.
  • Yürür, Ö.; Liu, C.H.; Sheng, Z.; Leung, V.C.M.; Moreno, W.; Leung, K.K. Context-awareness for mobile sensing: A survey and future directions. IEEE Commun. Surv. Tutor. 2014, 18, 68-93.
  • Bharti, P.; De, D.; Chellappan, S.; Das, S.K. HuMAn: Complex activity recognition with multi-modal multi-positional body sensing. IEEE Trans. Mob. Comput. 2018, 18, 857-870.
  • Gu, Y.; Li, D.; Kamiya, Y.; Kamijo, S. Lifelog using Mobility Context Information in Urban City Area. In Proceedings of the ION 2019 Pacific PNT Meeting (PNT2019), Honolulu, HI, USA, 8-11 April 2019.
  • Wang, L.; Gjoreski, H.; Ciliberto, M.; Mekki, S.; Valentin, S.; Roggen, D. Enabling reproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset. IEEE Access 2019, 7, 10870-10891.
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