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운전자 편향을 완화한 딥 러닝 기반 굴착기 부하 측정 시스템
박경원(Kyoung-Won Park),박용진(Yong-Jin Park),장계봉(Gye-Bong Jang) 한국자동차공학회 2023 한국자동차공학회 부문종합 학술대회 Vol.2023 No.5
Measuring the weight of excavation work of an excavator on a construction site is critical for both budget reduction and accurate progress tracking of construction projects. Typically, the existing weighing systems use sensors to measure the volume of work. However, precise measurement is difficult due to sensor errors or noise, and manual adjustments are necessary to correct minor errors. In addition, the sensors collected from different equipment and operators have different distributions, making it difficult to capture information for measuring weights. To overcome these limitations, this paper proposes a CNNLSTM-based deep learning algorithm that is effective for noisy data and can learn multivariate time series patterns. Additionally, it proposes a domain adaptation learning method that is robust to driver bias information. Through the proposed domain adaptation method using 22 sensors collected from two excavator operators, it is verified that weight measurement is possible without bias to the driver domain.