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Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Xiang, Yusheng 1; Tang, Tian 1; Su, Tianqing; Brach, Christine; Liu, Libo; Mao, Samuel S.; Geimer, Marcus 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)


The CRDNN is a combined neural network that can increase the holistic efficiency of torque based mobile working machines by about 9% by means of accurately detecting the truck loading cycles. On the one hand, it is a robust but offline learning algorithm so that it is more accurate and much quicker than the previous methods. However, on the other hand, its accuracy cannot always be guaranteed because of the diversity of the mobile machines industry and the nature of the offline method. To address the problem, we utilize the transfer learning algorithm and the Internet of Things (IoT) technology. Concretely, the CRDNN is first trained by computer and then saved in the on-board ECU. In case that the pre-trained CRDNN is not suitable for the new machine, the operator can label some new data by our App connected to the on-board ECU of that machine through Bluetooth. With the newly labeled data, we can directly further train the pre-trained CRDNN on the ECU without overloading since transfer learning requires less computation effort than training the networks from scratch. In our paper, we prove this idea and show that CRDNN is always competent, with the help of transfer learning and IoT technology by field experiment, even the new machine may have a different distribution. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000137763
Veröffentlicht am 24.09.2021
DOI: 10.1109/ACCESS.2021.3110288
Zitationen: 5
Web of Science
Zitationen: 4
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2169-3536
KITopen-ID: 1000137763
Erschienen in IEEE Access
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 9
Seiten 124253 - 124267
Nachgewiesen in Dimensions
Web of Science
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