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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö > Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö ¼ÒÇÁÆ®¿þ¾î ¹× µ¥ÀÌÅÍ °øÇÐ

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Current Result Document : 5 / 10 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) Áú°¨ ºÐ¼®°ú CNNÀ» ÀÌ¿ëÇÑ ÀâÀ½¿¡ °­ÀÎÇÑ µÅÁö È£Èí±â Áúº´ ½Äº°
¿µ¹®Á¦¸ñ(English Title) Noise-Robust Porcine Respiratory Diseases Classification Using Texture Analysis and CNN
ÀúÀÚ(Author) ÃÖ¿ëÁÖ   ÀÌÁ¾¿í   ¹Ú´ëÈñ   Á¤¿ëÈ­   Yongju Choi   Jonguk Lee   Daihee Park   Yongwha Chung  
¿ø¹®¼ö·Ïó(Citation) VOL 07 NO. 03 PP. 0091 ~ 0098 (2018. 03)
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(Korean Abstract)
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(English Abstract)
Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.
Å°¿öµå(Keyword) µÅÁö È£Èí±â Áúº´   ÀâÀ½ °­Àμº   ¼Ò¸® ºÐ¼®   DNS   CNN   Porcine Respiratory Diseases   Noise Robustness   Sound Analysis   Dominant Neighborhood Structure   Convolutional Neural Network  
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