Search Results for author: Marcus Geimer

Found 4 papers, 0 papers with code

An Extension of BIM Using AI: a Multi Working-Machines Pathfinding Solution

no code implementations14 May 2021 Yusheng Xiang, Kailun Liu, Tianqing Su, Jun Li, Shirui Ouyang, Samuel S. Mao, Marcus Geimer

In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime.

KIT MOMA: A Mobile Machines Dataset

no code implementations8 Jul 2020 Yusheng Xiang, Hongzhe Wang, Tianqing Su, Ruoyu Li, Christine Brach, Samuel S. Mao, Marcus Geimer

Mobile machines typically working in a closed site, have a high potential to utilize autonomous driving technology.

Autonomous Driving

Fast CRDNN: Towards on Site Training of Mobile Construction Machines

no code implementations4 Jun 2020 Yusheng Xiang, Tian Tang, Tianqing Su, Christine Brach, Libo Liu, Samuel Mao, Marcus Geimer

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.

Time Series Analysis Transfer Learning

Optimization of Operation Strategy for Primary Torque based hydrostatic Drivetrain using Artificial Intelligence

no code implementations22 Mar 2020 Yusheng Xiang, Marcus Geimer

Therefore, even with a simple regeneration process, our algorithm can improve the holistic efficiency of mobile machines up to 9% during Y cycle processes if primary torque concept is used.

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