Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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A Similarity-based Inference System for Identifying Insects in the Ubiquitous Environments

유비쿼터스 환경에서의 유사도 기반 곤충 종 추론검색시스템

  • 전응섭 (인덕대학 컴퓨터소프트웨어과) ;
  • 장용식 (한신대학교 e-비즈니스학과) ;
  • 권영대 (경기도산림환경연구소 나무연구팀) ;
  • 김용남 (엠비즈테크 기술연구소)
  • Received : 2011.01.25
  • Accepted : 2011.03.18
  • Published : 2011.03.31
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Abstract

Since insects play important roles in existence of plants and other animals in the natural environment, they are considered as necessary biological resources from the perspectives of those biodiversity conservation and national utilization strategy. For the conservation and utilization of insect species, an observational learning environment is needed for non-experts such as citizens and students to take interest in insects in the natural ecosystem. The insect identification is a main factor for the observational learning. A current time-consuming search method by insect classification is inefficient because it needs much time for the non-experts who lack insect knowledge to identify insect species. To solve this problem, we proposed an smart phone-based insect identification inference system that helps the non-experts identify insect species from observational characteristics in the natural environment. This system is based on the similarity between the observational information by an observer and the biological insect characteristics. For this system, we classified the observational characteristics of insects into 27 elements according to order, family, and species, and proposed similarity indexes to search similar insects. In addition, we developed an insect identification inference prototype system to show this study's viability and performed comparison experimentation between our system and a general insect classification search method. As the results, we showed that our system is more effective in identifying insect species and it can be more efficient in search time.

곤충 종은 환경생태학적 종 다양성 보존과 국가적 생물자원 활용전략 관점에서 중요한 역할을 하기 때문에 생태계의 주요 구성요소로 인식되고 있다. 곤충 종 보존과 육성을 위해서는 곤충전문가는 물론 곤충비전문가인 일반인과 학생들도 곤충에 관심을 가질 수 있는 곤충관찰학습 환경이 요구된다. 그러므로, 곤충식별은 관찰학습에 있어서 주요학습의 동기유발 요인이 된다. 현재 서비스하고 있는 온라인 곤충 종 분류검색시스템은 시간 소모적이며, 곤충 종에 대한 지식이 부족한 일반인들이 곤충식별의 도구로 사용하기에는 많은 노력을 요구하기 때문에 비효율적이다. 본 연구에서는 이러한 문제를 해결하기 위하여, 일반인들이 자연 생태계에서 관찰한 내용을 바탕으로 곤충식별을 도와주는 스마트폰 기반의 유러닝시스템인 곤충 종 식별추론 시스템을 제안하였다. 본 시스템은 사용자의 곤충관찰정보와 생물학적 곤충특성과의 유사도에 기반하여 추론검색을 수행한다. 이를 위해, 생물학적 곤충특성을 목, 과, 종 단위의 27개 항목으로 분류하고, 관찰 단계별 유사도 지표를 제안하였다. 또한, 본 연구의 유용성을 보이기 위하여 추론검색 프로토타입시스템을 개발하고, 기존의 분류검색시스템과의 곤충식별 비교테스트를 하였다. 실험결과, 본 연구의 추론검색 방법이 곤충식별의 효과성에 있어 더 우수함을 보였고, 검색시간에 있어서도 보다 효율적인 시스템이 될 수 있음을 보였다.

Keywords

References

  1. Aquatic Insects of Korea, http://www.waterinsect.org.
  2. F. Arrignon, M. Deconchat, J.-P. Sarthou, G. Balent, and C. Monteil, "Modelling the Overwintering Strategy of a Beneficial Insect in a Heterogeneous Landscape using a Multi-agent System," EcologicalModelling, Vol. 205, pp.423-436, 2007.
  3. M. G. Baek et al., "Korean Insects List," Nature and Ecology, 2010.
  4. W. D. Batchelor, R. W. McClendon, D. B. Adams, and J.W. Jones, "Evaluation of SMARTSOY: An Expert Simulation System for Insect Pest Management," AgriculturalSystems, Vol. 31, pp.67-81, 1989.
  5. H. W. Beck, P. Jones, and J. W. Jones, "SOYBUG: An Expert System for Soybean Insect Pest Management," Agricultural Systems, Vol. 30, pp.269-286, 1989. https://doi.org/10.1016/0308-521X(89)90091-7
  6. Bric, http://bric.postech.ac.kr.
  7. Digital Insect Collection of Seoul National University, http://insect.snu. ac.kr.
  8. Ecological Information System, http://ecoinfo.seoul.go.kr.
  9. Encyber, http://www.encyber.com.
  10. F-L. Francis, "Qualitative Reasoning and Integrated Management of the Quality of Stored Grain_a Promising New Approach," Journal of Stored Products Research, Vol. 38, pp.191-218, 2002. https://doi.org/10.1016/S0022-474X(01)00022-4
  11. T. L. Friedman, "Code Green", 21 Century Books, 2008.
  12. Green Gyeonggi-do with Beautiful Forests, http://forest.gg.go.kr.
  13. K. S. Han, "Introduction to Entomology and Insect Utilization Cases Applied to Industry," Shingu University, 2008.
  14. Insect Collection, http://insect.naac.go.kr.
  15. A.T. Jozsef, "Kenneth M. Ford and Patrick J. Hayes, eds., Reasoning Agent in a Dynamic World: The Frame Problem," Artificial Intelligence, Vol. 73, pp.323-369, 1995. https://doi.org/10.1016/0004-3702(95)90043-8
  16. E. S. Jun, Y. S. Chang, Y. D. Kwon, C. S. Ko, and Y. N. Kim. "Smart phone-based Inference Framework for Identifying Insect Species in the Environment of U-Learning Observational Study", in Proceedings of the 2010 Autumn Conference, Korea Intelligent Information Systems Society pp.324-334, 2010.
  17. S. Kaloudis, D. Anastopoulos, C. P. Yialouris, N.A.Lorentzos, and A.B.Sideridis, "Insect Identification Expert System for Forest Protection," Expert Systems with Applications, Vol. 28, pp.445-452, 2005. https://doi.org/10.1016/j.eswa.2004.12.005
  18. Y. D. Kim, "The Reality of Insect Diversity: The Biological Classification," NaverCast, http://navercast.naver.com/ science/biology/
  19. J. I. Kim and W. G. Lee, "Korean Insects in the Neighborhood," Heonamsa, 1999.
  20. C. S. Ko, E. S. Jun, S. K. Park, S. B. Kim, and S. T. Chung, "WEB-Mobile Service Systems for the Ecology of Insects and the Environment Information based on U-Learning," The Journal of Korea Information Processing Society, Vol. 4, No. 3, pp. 207-224, 2009.
  21. Korean Bioinformation Center, http://www.kobic. re.kr.
  22. Korean Insects Society, http://insectkorea.kr.
  23. G. O. Kwon, "The Concept of Insecta that Teachers of Elementary Schools Understand," Master's Thesis, Daegu National University of Education, 2007.
  24. D. P. McKenzie and R. S. Forsyth, "Classification by Similarity: An Overview of Statistical Methods of Case-based Reasoning," Computersin Human Behavior, Vol. 11, No. 2, pp.273-288, 1995. https://doi.org/10.1016/0747-5632(94)00036-H
  25. Michigan State University, http://animaldiversity.ummz.umich.edu/site/accounts/information/insect.html.
  26. The Mystery of ButterFlies, http://nabisinbi.com.
  27. National Academy of Agricultural Science, http://goodin.sect.naac.go.kr.
  28. National Biological Species Knowledge Information Systems, http://www.nature. go.kr.
  29. National Institute of Biological Resources, http://www.nibr.go.kr.
  30. G. T. Park, Y. S. Bae, and Y. D. Kwon, "Biological Resources in Gyeonggi-do(Insects)," Gyeonggi-do Forest Environment Research Institute, 2004.
  31. G. M. Pasqual, and J. Mansfield, "Development of a Prototype Expert System for Identification and Control of Insect Pests," Computers and Electronicsin Agriculture, Vol. 2, pp.263-276, 1988. https://doi.org/10.1016/0168-1699(88)90002-6
  32. U-EREMS, http:// new.induk.ac.kr.
  33. C. Wen, D. E. Guyer, and W. Li, "Local Feature-based Identification and Classification for Orchard Insects," BiosystemsEngineering, Vol. 104, pp.299-307, 2009.
  34. The Wonderful World of Insects. http://www.earthlife.net/insects.

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