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JRM Vol.28 No.1 pp. 40-49
doi: 10.20965/jrm.2016.p0040
(2016)

Paper:

A Novel Approach to Quantitative Evaluation of Tangle Formations for Seaweeds in Stirrer Cultivation

Jun Ogawa*, Hiroyuki Iizuka*, Masahito Yamamoto*, and Masashi Furukawa**

*Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan

**Department of System and Informatics, Hokkaido Information University
59-2 Nishi Nopporo, Ebetsu, Hokkaido 069-8585, Japan

Received:
April 1, 2015
Accepted:
November 18, 2015
Published:
February 20, 2016
Keywords:
seaweed cultivation, physical simulation, tangle formations, quantitative evaluation, support vector machine
Abstract

A Novel Approach to Quantitative Evaluation of Tangle Formations for Seaweeds in Stirrer Cultivation

Seaweed tangle formation

We discuss novel approaches to the control of seaweed tangle formations in stirrer cultivation. Cultivating seaweed is one important way to avoid such formation. Because defining such formation is difficult based on human recognition alone, there is currently no quantitative evaluation criterion for formation. We develop physical simulation for analyzing formations in a water flow field and model three factors – physical, geometric and time – for characterizing formations. Our criterion is that formations are created by using these factors as input to a nonlinear support vector machine. To show the effectiveness of our simulation and criteria, we confirm the control effects of the water flow in simulation and the real world. Results show that our simulation model is useful for avoiding such formation in the real world.

Cite this article as:
J. Ogawa, H. Iizuka, M. Yamamoto, and M. Furukawa, “A Novel Approach to Quantitative Evaluation of Tangle Formations for Seaweeds in Stirrer Cultivation,” J. Robot. Mechatron., Vol.28, No.1, pp. 40-49, 2016.
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References
  1. [1] Y. Chisti, “Biodiesel from microalgae,” newblock Biotechnology advances, Vol.25, No.3, pp. 294-306, 2007.
  2. [2] G. A. Jackson, “Marine biomass production through seaweed aquaculture,” Biochemical and photosynthetic aspects of energy production, pp. 31-58, 1980.
  3. [3] K. T. Bird and P. H. Benson, “Seaweed cultivation for renewable resources,” 1987.
  4. [4] A. Demirbas and M. F. Demirbas, “Importance of algae oil as a source of biodiesel,” Energy Conversion and Management, Vol.52, No.1, pp. 163-170, 2011.
  5. [5] M. Watanabe, “Future perspective of technology for algal biomass energy,” Jpn. Soc. Mechanical Engineers, Vol.113, pp. 342-345, 2010.
  6. [6] Takenaka Corporation, “Ocean carbon dioxide fixation and utilization technology research business of fiscal 2012,” 2012 (in Japanese).
  7. [7] M. Müuller, J. Stam, D. James, and N. Thüurey, “Real time physics: class notes,” ACM SIGGRAPH 2008 classes, p. 88, 2008.
  8. [8] E. Ogasa, “An introduction to high dimensional knots,” 2013.
  9. [9] S. Koshizuka, “Moving-particle semi-implicit method for fragmentation of incompressible fluid,” Nuclear science and engineering, Vol.123, No.3, pp. 421-434, 1996.
  10. [10] S. Koshizuka, H. Tamako, and Y. Oka, “A particle method for incompressible viscous flow with fluid fragmentation,” Comput. Fluid Dynamics J., 1995.
  11. [11] J. J. Monagha, “Smoothed particle hydrodynamics,” Annual review of astronomy and astrophysics, Vol.30, pp. 543-574, 1992.
  12. [12] S. Chen and G. D. Doolen, “Lattice Boltzmann method for fluid flows,” Annual Review of Fluid Mechanics, Vol.30, pp. 329-364, 1998.
  13. [13] G. McNamara and G. Zanetti, “Use of the Boltzmann equation to simulate lattice-gas automata,” Phys. Rev. Lett., Vol.61, pp. 2332-2335, 1988.
  14. [14] J. Ogawa, H. Iizuka, M. Yamamoto, and M. Furukawa, “Estimation of seaweed twist based on diffusion kernels in physical simulation,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.18, No.5, pp. 823-829, 2014.
  15. [15] J. Ogawa, I. Suzuki, M. Yamamoto, and M. Furukawa, “Control of water flow to avoid twining of artificial seaweed,” Artificial Life and Robotics, Vol.17, No.3-4, pp. 383-387, 2013.
  16. [16] K. Wu, W. Koegler, J. Chen, and A. Shoshani, “Using bitmap index for interactive exploration of large datasets,” 15th Int. Conf. on Scientific and Statistical Database Management 2003, pp. 65-74, 2003.
  17. [17] K. Suzuki, I. Horiba, and N. Sugie, “Linear-time connected-component labeling based on sequential local operations,” Computer Vision and Image Understanding, Vol.89, No.1, pp. 1-23, 2003.
  18. [18] L. Shapiro and G. Stockman, “Computer vision,” Prentice Hall, 2002.
  19. [19] L. He, Y. Chao, K. Suzuki, and K. Wu, “Fast connected-component labeling,” Pattern Recognition, Vol.42, No.9, pp. 1977-1987, 2009.
  20. [20] Y. Han and R. A. Wagner, “An efficient and fast parallel-connected component algorithm,” J. of the ACM (JACM), Vol.37, No.3, pp. 626-642, 1990.
  21. [21] M. B. Dillencourt, H. Samet, and M. Tamminen, “A general approach to connected-component labeling for arbitrary image representations,” J. of the ACM (JACM), Vol.39, No.2, pp. 253-280, 1992.

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Last updated on Aug. 20, 2018