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논문 기본 정보

자료유형
학술저널
저자정보
이창준 (한경국립대학교) 이정근 (한경국립대학교)
저널정보
제어로봇시스템학회 제어로봇시스템학회 논문지 제어로봇시스템학회 논문지 제29권 제4호
발행연도
2023.4
수록면
360 - 365 (6page)
DOI
10.5302/J.ICROS.2023.23.0005

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초록· 키워드

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Bulk trailers are vehicles that are used for transporting powdered construction materials such as cement and fly ash. To ensure high transportation efficiency and low transportation costs, materials stored in the tank of bulk trailers must be quickly exhausted. One approach is to replace the existing manual exhaust operation with an automatic control system. For the development of an automatic control system for bulk trailers, measurement of the instantaneous exhaust of powdered materials is required. However, flowmeters for powders are expensive and also tricky to install on bulk trailers requiring a straight pipeline of a certain length or more. Therefore, in this paper, a deep learning-based method was proposed for estimating the instantaneous exhaust from the pressure signals of a bulk trailer without using a flowmeter. In the proposed method, a recurrent neural network (i.e., a long short-term memory network) is used to estimate the instantaneous exhaust of powder by using the pressure signals. For the training and validation of the proposed neural network, the signals from pressure sensors and flowmeter were collected during actual cement exhaust operations. The performance of the proposed method was experimentally validated: the normalized root mean square errors of instantaneous and accumulated exhausts were 9.29% and 3.32%, respectively. Thus, the results showed that the instantaneous exhaust information can be effectively estimated using low-cost pressure sensor signals instead of a flowmeter.

목차

Abstract
I. 서론
II. 방법
III. 결과
IV. 고찰 및 결론
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UCI(KEPA) : I410-ECN-0101-2023-003-001319592