Advertisement

The Transition Probabilities from Captive Animal’s Behavior by Non-invasive Sensing Method Using Stochastic Multilevel State Model

  • Phudinan Singkahmfu
  • Pruet Boonma
  • Wijak Srisujjalertwaja
  • Anurak Panyanuwat
  • Natapot Warrit
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 848)

Abstract

This article proposes a stochastic multilevel-state framework to model the animal’s behavior. The motivation of this article is the variety behavior influenced by several factors, which can lead to the state explosion. The proposed framework processes data from an automated sensing system and constructs the model. The data gathered from captive Antelope goral (Naemorhedusgriseus) in Chiang Mai Night Safari, Thailand. The data is gathered from the activity and the environmental factors in the cage by none invasive method. The model separated observed data into two main classes: the upper-level data and the lower-level data. The upper-level data represents the environment data such as temperature, humidity, and light density. Moreover, the landscape of the captivity area also takes into consideration. On the other hand, the lower-level represents the location of the animal of interested in the captivity area. The working strategy of this work is to cluster the each type of data and link them together by a stochastic approach. Both layers of data will be handled independently in clustering algorithm and determine probabilistic of state transaction. From the data observed from the sensor, the Probabilistic Automaton (PA) function is constructed. It is a function of producing the next stage based on the previous behavior states. The initial state of the framework is in the lower-level data (the current location of the animal). Then, the PA using the current lower stage and the current upper stage generates the next stage, location of the animal. Both the lower stage and the upper stage are traverse along the constructed automaton. The result can suggest computing methods, which can utilize to zoo research, which performs behavior monitoring, and in the other studies area, or subject of study, such as, air pollution dispersion that tracks the movement of pollutant according to the environment. The benefits of the proposed methods also can be used to create the application to attract the tourist to the area, which animally is likely to display themselves.

Keywords

Behavior monitoring Zoo monitoring Pattern gathering Data from sensor Sensor data cleaning Antelope goral Stochastic multilevel 

Notes

Acknowledgments

This research has been supported partially by Chiang Mai University graduation school’s research scholarship and Chiang Mai University International College ASEAN+3 co-research grant.

References

  1. Chandrashekaran, M.K.: Biological rhythms research: a personal account in the perspective, pp. 546–555 (1998)Google Scholar
  2. Aschoff, J.: Cireadian rhythms in man. Science 148, 1427–1432 (1965)CrossRefGoogle Scholar
  3. Berger, A.: Activity patterns, chronobiology and the assessment of stress and welfare in zoo and wild animals. Int. Zoo Yb. 45, 80–90 (2002). 2011CrossRefGoogle Scholar
  4. Hunter, A.: Sensor base animal tracking. Geometric Engineering UCGE Report number 20258 (2007)Google Scholar
  5. Pinter-Wollman, N., Mabry, K.E.: Remote-sensing of behavior. In: Encyclopedia of Animal Behavior, pp. 33–40 (2010)Google Scholar
  6. Coyne, M.S., Godley, B.J.: Satellite Tracking and Analysis Tool (STAT): an integrated system or archiving, analyzing and mapping animal tracking data. Marine Ecology Progress Series Published, 11 October 2005Google Scholar
  7. Manteuffel, G., Schön, P.C.: Measuring pig welfare by automatic monitoring of stress calls. Bornimer Agrartechnische Berichte (2004)Google Scholar
  8. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood for incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  9. MacQueen, J.B.: Some methods for classification and analysis of multivariate observation. In: 5th Berkeley Symposium on Mathematical Statistics and Probability (1967)Google Scholar
  10. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of ACM SIGKDD Conference (1996)Google Scholar
  11. Serfozo, R.: Basics of Applied Stochastic Processes, p. 2. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-89332-5. ISBN 978-3-540-89332-5CrossRefzbMATHGoogle Scholar
  12. Wilson, R.P., Grémillet, D., Syder, J., Kierspel, M.A.M., et al.: Remote-sensing systems and seabirds: their use, abuse and potential for measuring marine environmental variables. Mar. Ecol. Prog. Ser. 228, 241–261 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Phudinan Singkahmfu
    • 1
    • 2
  • Pruet Boonma
    • 3
  • Wijak Srisujjalertwaja
    • 1
  • Anurak Panyanuwat
    • 4
  • Natapot Warrit
    • 5
  1. 1.Computer Science Department, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
  2. 2.Software Engineering Department, College of Arts Media and TechnologyChiang Mai UniversityChiang MaiThailand
  3. 3.Department of Computer Engineer, Engineering FacultyChiang Mai UniverityChiang MaiThailand
  4. 4.College of Arts Media and TechnologyChiang Mai UniversityChiang MaiThailand
  5. 5.Department of Biology, Faculty of SciencesChulalongkorn UniversityBangkokThailand

Personalised recommendations