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JACIII Vol.18 No.5 pp. 823-829
doi: 10.20965/jaciii.2014.p0823
(2014)

Paper:

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Estimation of Seaweed Twist Based on Diffusion Kernels in Physical Simulation

Jun Ogawa*, 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:
December 15, 2013
Accepted:
June 1, 2014
Published:
September 20, 2014
Creative Commons License
Keywords:
seaweed cultivation, twist phenomenon, physical simulation, estimation method, diffusion kernel
Abstract
In the optimization of seaweed cultivation now being extensively researched, a problem arises in avoiding twisting seaweed. Twisting is a complex phenomenon and difficult to formulate. Producing the optimal water flow, requires calculating the risk of twisting occurring. In this paper, we propose a method to calculate and estimate the twist state based on the results of physical simulation. We devise a seaweed model using multiple rigid bodies that mutually and physically interfere. One result of physical interference, is that the model has two internal state variables – contact time and the number of contact points between individual pieces of seaweed. We introduce an evaluation function to quantify twisting using these state variables in each time step, and propose a way to calculate twist risk based on the von Neumann and Laplacian diffusion kernels, in a dynamic network.
Cite this article as:
J. Ogawa, M. Yamamoto, and M. Furukawa, “Estimation of Seaweed Twist Based on Diffusion Kernels in Physical Simulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 823-829, 2014.
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References
  1. [1] T. Abbasi and S. A. Abbasi, “Biomass energy and the environmental impacts associated with its production and utilization,” Renewable and Sustainable Energy Reviews, Vol.14, No.3, pp. 919-937, 2010.
  2. [2] A. Vergara-Fernández, G. Vargas, N. Alarcón, and A. Velasco, “Evaluation of marine algae as a source of biogas in a two-stage anaerobic reactor system,” Biomass and Bioenergy, Vol.32, No.4, pp. 338-344, 2008.
  3. [3] 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.
  4. [4] G. A. Jackson, “Marine biomass production through seaweed aquaculture,” Biochemical and photosynthetic aspects of energy production, pp. 31-58, 1980.
  5. [5] K. T. Bird and P. H. Benson, “Seaweed cultivation for renewable resources” Elsevier Science Ltd., 1987.
  6. [6] W. Schramm, M. D. Guiry, G. Blunden et al., “Seaweeds for waste water treatment and recycling of nutrients,” Seaweed resources in Europe: uses and potential, pp. 149-168, 1991.
  7. [7] J. Ogawa, I. Suzuki, M. Yamamoto, and M. Furukawa, “Control of water flow to avoid twining of artificial seaweed,” J. of Artificial Life and Robotics, Vol.17, No.3-4, pp. 383-387, 2013.
  8. [8] A. J. Smola and R. Kondor, “Kernels and regularization on graphs,” Learning theory and kernel machines, pp. 144-158, 2003.
  9. [9] H. Lee, Z. Tu, M. Deng, F. Sun, and T. Chen, “Diffusion kernelbased logistic regression models for protein function prediction,” Omics: a journal of integrative biology, Vol.10, No.1, pp. 40-55, 2006.
  10. [10] K. Tsuda and W. S. Noble, “Learning kernels from biological networks by maximizing entropy,” Bioinformatics, Vol.20, No.1, pp. 326-333, 2004.
  11. [11] Y. Yamanishi, J.-P. Vert, and M. Kanehisa, “Protein network inference from multiple genomic data: a supervised approach,” Bioinformatics, Vol.20, No.1, pp. 363-370, 2004.
  12. [12] T. Ito, M. Shimbo, T. Kudo, and Y. Matsumoto, “Application of kernels to link analysis,” Proc. of the eleventh ACM SIGKDD Int. Conf. on Knowledge discovery in data mining, pp. 586-592, 2005.
  13. [13] D. Nitsch, J. P. Gonçalves, F. Ojeda, B. de Moor, and Y. Moreau, “Candidate gene prioritization by network analysis of differential expression using machine learning approaches,” BMC bioinformatics, Vol.11, No.1, p. 460, 2010.
  14. [14] J. Kunegis, A. Lommatzsch, and C. Bauckhage, “Alternative similarity functions for graph kernels,” 19th Int. Conf. on Pattern Recognition 2008 (ICPR 2008), pp. 1-4, 2008.
  15. [15] J. Kunegis and A. Lommatzsch, “Learning spectral graph transformations for link prediction,” Proc. of the 26th Annual Int. Conf. on Machine Learning, pp. 561-568, 2009.
  16. [16] J. Kunegis, E. W. de Luca, and S. Albayrak, “The link prediction problem in bipartite networks,” Computational intelligence for knowledge-based systems design, pp. 380-389, 2010.
  17. [17] NVIDIA PhysX: http://www.nvidia.co.jp/object/physx_new_jp.html [Accessed August 19, 2014]
  18. [18] M. Müller, J. Stam, D. James, and N. Thürey, “Real time physics: class notes,” ACM SIGGRAPH 2008 classes, p. 88, 2008.
  19. [19] S. Chen and G. D. Doolen, “Lattice Boltzmann method for fluid flows,” Annual review of fluid mechanics, Vol.30, No.1, pp. 329-364, 1998.
  20. [20] R I. Kondor and J. Lafferty, “Diffusion kernels on graphs and other discrete input spaces,” ICML, Vol.2, pp. 315-322, 2002.
  21. [21] J. Kandola, J. Shawe-Taylor, and N. Cristianini, “Learning Semantic Similarity,” In NIPS 15, pp. 657-664, 2002.

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