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Flatfish Measurement Performance Improvement Based on Multi-sensor Data Fusion

  • Intelligent Control and Applications
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International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

In this study, a multi-sensor data fusion system using a load cell and vision sensor was considered in the development of a flatfish classifier for systematic fish management in aquaculture. In the single-sensor measurement method, each sensor has disadvantages. A load cell shows high performance in the measurement of adult fish, but the measurement of fry is affected significantly due to water weight (water weight disturbance). A vision sensor shows high performance in the measurement of fry, but the movement of fish (movement disturbance) affects the accurate measurement of adult fish. Therefore, in this study, these disturbances were compensated for using a datafusion algorithm, of which the performance was evaluated by a comparison between single sensor measurements and multi-sensor data fusion results.

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Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, Geumjeong-gu, Busandaehak-ro 63 Beon-gil 2 (Jangjeon-dong), Busan, 46241, Korea

    Kang Hyun Hwang & Jae Weon Choi

  2. Graduate School of Technology Entrepreneurship, Pusan National University, Geumjeong-gu, Busandaehak-ro 63Beon-gil 2 (Jangjeon-dong), Busan, 46241, Korea

    Chang Ho Yu

Authors
  1. Kang Hyun Hwang
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Correspondence to Jae Weon Choi.

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Recommended by Associate Editor DaeEun Kim under the direction of Editor Euntai Kim.

Kang Hyun Hwang received his B.S. degree in mechanical engineering from Pusan National University in 2017. He is currently pursuing an M.S. degree at the same University. His research focuses on smart aqua plants.

Chang Ho Yu received his B.S. degree in mechanical engineering from Pusan National University, Busan, Korea, in 2002 and received his Ph.D. degree in intelligent mechanical engineering from Pusan National University, Busan, Korea in 2014. He is currently a Research Professor at the Graduate School of Technology Entrepreneurship, Pusan National University, Busan, Korea. He is a Member of ICROS, KSME, and KATA. His current research interests include target tracking filter design, guidance and control theories, sensor network technologies, sensor localization algorithms, technology entrepreneurship, and ICT-based start-ups.

Jae Weon Choi received his B.S., M.S., and Ph.D. degrees all in control and instrumentation engineering from Seoul National University, Seoul, Korea, in 1987, 1989, and 1995, respectively. Since 1996, he has been with School of Mechanical Engineering, Pusan National University. He was a visiting professor at M.I.T., Cambridge, MA, from 2003 to 2004, and at The George Washington University, Washington D.C., from 2011 to 2012. He served as Dean of Office for Planning and Finance from 2013 to 2014, Pusan National University. Since 2018, he has been serving as the Dean of both the Research Institute of Mechanical Technology, and the Office for Education Accreditation, Pusan National University, Korea. He served from 2003 to 2011 as an Associate and Editor for the International Journal of Control, Automation and Systems, and also had served as an Associate Editor for over ten years in Conference Editorial Board of IEEE Control Systems Society since 2000. His current research interests include spectral theory for linear time-varying systems, tracking filter design, and control and sensor network technologies with applications to aquaculture plants.

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Hwang, K.H., Yu, C.H. & Choi, J.W. Flatfish Measurement Performance Improvement Based on Multi-sensor Data Fusion. Int. J. Control Autom. Syst. 19, 1988–1997 (2021). https://doi.org/10.1007/s12555-019-0653-9

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  • DOI: https://doi.org/10.1007/s12555-019-0653-9

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