Abstract
Fatigued bills have a harmful influence on the daily operation of automated teller machines (ATMs). To make the classification of fatigued bills more efficient, the development of an automatic fatigued bill classification method with a continu ous fatigue level is desirable. We propose a new method to estimate the bending rigidity of bills using the acoustic signal feature of banking machines. The estimated bending rigidities are used as the continuous fatigue level for the classification of fatigued bills. By using a supervised self-organizing map (SOM), we effectively estimate the bending rigidity using only the acoustic energy pattern. The experimental results with real bill samples show the effectiveness of the proposed method.
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This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008
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Teranishi, M., Omatu, S. & Kosaka, T. Continuous fatigue level estimation for the classification of fatigued bills based on an acoustic signal feature by a supervised SOM. Artif Life Robotics 13, 547–550 (2009). https://doi.org/10.1007/s10015-008-0622-5
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DOI: https://doi.org/10.1007/s10015-008-0622-5