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ETRI journal v.27 no.3, 2005년, pp.294 - 303  

Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi-level Cellular Neural Networks

Bilgili, Erdem    (Department of Electrical Engineering, Gebze High Technology Institute   ); Goknar, I. Cem    (Department of Electronics and Communications Engineering, DOGUS University   ); Albora, Ali Muhittin    (Department of Geophysics Engineering, Istanbul University   ); Ucan, Osman Nuri    (Department of Electrical and Electronics Engineering, Istanbul University  );
  • 초록

    In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. ML-CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas-Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.


  • 주제어

    Cellular neural networks (CNN) .   genetic algorithm .   data processing .   information retrieval .   potential anomaly separation .   archeo-geophysics.  

  • 참고문헌 (24)

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