• Journal of Platform Technology
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• v.9 no.3
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• pp.63-69
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• 2021

Limited materials to make coins for countries and designs suitable for hand-carry make the shape, size, and color of coins similar. This similarity makes that it is difficult for visitors to identify each country's coins. To solve this problem, we propose the coin classification method using CNN effective to image processing. In our coin identification method, we collect the training data by using web crawling and use OpenCV for preprocessing. After preprocessing, we extract features from an image by using three CNN layers and classify coins by using two fully connected network layers. To show that our model designed in this paper is effective for coin classification, we evaluate our model using eight different coin types. From our experimental results, the accuracy for coin classification is about 99.5%.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.7-11
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• 2012

This paper develops the coin recognition and classification system using digital image processing technique. Coin images are taken by USB camera. The developed system can be used at home since it just needs USB camera and personal computers. For this development, some digital image prodessing technique is used; size recognition technique and color classification. Using Matlab, we design the graphic user interface and verify the reliability of the developed system with some simulation result.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.424-429
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• 2015

This research proposes a new system for calculating the total amount of coins in an image. The proposed system identified and classified the coins in the image in realtime. The image was obtained using a USB camera. Most previous coin calculation systems only used size information. If the size of an object was incorrectly detected, it caused a misclassification. Especially, in case of the former 10 won, it had high error rate because it was similar in size to the 50 won and 100 won coin. The proposed system combines hue histogram information with size information to reduce errors in the classification process. When we only used size information in the classification experiment of 2,290 coins, the recognition rate was on average about 88.2%. When we combined hue information with size information the recognition rate increased to about 99.3%.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.100-104
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• 2020

Over the past several years, deep learning has been widely used for feature extraction in image and video for various applications such as object classification and facial recognition. This paper introduces an implantation of embedded Linux system for embossed digits recognition using CNN based deep learning methods. For this purpose, we implemented a coin recognition system based on deep learning with the Keras open source library on Raspberry PI. The performance evaluation has been made with the success rate of coin classification using the images captured with ultra-wide angle camera on Raspberry PI. The simulation result shows 98% of the success rate on average.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.77-86
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• 2022

Recently, various cryptocurrencies (coins) based on block chains have appeared, and interest in privacy coins, which is an anonymity-based cryptocurrency that values personal information protection, is growing. In this paper, we look at coin abuse cases using privacy coins, and analyze the technology that guarantees the anonymity of 8 mainly traded privacy coins (Monero, Dash, Zcash, BEAM, Grin, Horizen, Verge, and Pirate Chain). We would like to analyze the applied technologies for We present the problems that can occur in these privacy coins, check the technology and each element applied to the privacy coin, and analyze the technical difficulty of the anonymity guarantee technology for the mainly traded coins through this, and Appropriate countermeasures and classification of privacy coins for technical difficulty were presented through the problem. Through this, the standard for re-evaluating the value of the coin according to the application of appropriate technology for the privacy coin can be presented.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.99-102
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• 2001

We propose a PC cluster using general-purpose microprocessors and a high-speed network for simulating ANN (Artificial Neural Network) processes on Linux OS. We apply this cluster to intelligent information processing such as ANN simulation. The elapsed time for simulating ANNs can be reduced from 7,295 seconds by a PE (Processing Element) to 1,226 seconds by six PEs. The reliability of a pattern-classification using ANNs can be improved by the proposed ANN, Randomized-ANN. In order to generate a Randomized-ANN, we choose three ANNs and combine the output results from three huts by means of logical AND. Results are as follows: The mean correct answer rate is 94.4%, the mean wrong answer rate is only 0.1 %, and the mean unknown answer rate is 5.5 %. We make sure that Randomized-ANN approach reduces the mean wrong answer rate within a tenth part and improves the reliability of Japanese coin classification.