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책임교수 박강령
논문명 Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images
논문종류 SCI
제1저자 권승용
교신저자 박강령
공동저자 Tuyen Danh Pham (팜단투엔), 정대식, 윤성수
Impact Factor 2.033
개제학술지명 Sensors
Keyword fitness classification; contact image sensor; fuzzy system; USD; KRW; Indian rupee (INR)
게재일 2016 년 06 월
Fitness classification is a technique to assess the quality of banknotes in order to determine
whether they are usable. Banknote classification techniques are useful in preventing problems that arise from the circulation of substandard banknotes (such as recognition failures, or bill jams in automated teller machines (ATMs) or bank counting machines). By and large, fitness classification continues to be carried out by humans, and this can cause the problem of varying fitness classifications for the same bill by different evaluators, and requires a lot of time. To address these problems, this study proposes a fuzzy system-based method that can reduce the processing time needed for fitness classification, and can determine the fitness of banknotes through an objective, systematic method rather than subjective judgment. Our algorithm was an implementation to actual banknote counting machine. Based on the results of tests on 3856 banknotes in United States currency (USD), 3956 in Korean currency (KRW), and 2300 banknotes in Indian currency (INR) using visible light reflection (VR) and near-infrared light transmission (NIRT) imaging, the proposed method was found to yield higher accuracy than prevalent banknote fitness classification methods. Moreover, it was confirmed that the proposed algorithm can operate in real time, not only in a normal PC environment, but also in an embedded system environment of a banknote counting machine.
(*ITRC  기여율 = 0.5)