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Çмú´ëȸ ÇÁ·Î½Ãµù

Ȩ Ȩ > ¿¬±¸¹®Çå > Çмú´ëȸ ÇÁ·Î½Ãµù > Çѱ¹Á¤º¸Åë½ÅÇÐȸ Çмú´ëȸ > 2018³â Ãß°èÇмú´ëȸ

2018³â Ãß°èÇмú´ëȸ

Current Result Document : 5 / 6 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) IoT ȯ°æ¿¡¼­ÀÇ È¿À²ÀûÀÎ LSTM ±¸¼º
¿µ¹®Á¦¸ñ(English Title) Efficient LSTM Configuration in IoT Environment
ÀúÀÚ(Author) ÀÌÁ¾¿ø   ȲöÇö   À̼º¿Á   ¼ÛÇö¿Á   Á¤È¸°æ   Jongwon Lee   Chulhyun Hwang   Sungock Lee   Hyunok Song   Hoekyung Jung  
¿ø¹®¼ö·Ïó(Citation) VOL 22 NO. 02 PP. 0345 ~ 0345 (2018. 10)
Çѱ۳»¿ë
(Korean Abstract)
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(English Abstract)
Internet of Things (IoT) data is collected in real time and is treated as highly reliable data because of its high precision. However, IoT data is not always highly reliable data. Because, data be often incomplete values for reasons such as sensor aging and failure, poor operating environment, and communication problems. So, we propose the methodology for solve this problem. Our methodology implements multiple LSTM networks to individually process the data collected from the sensors and a single LSTM network that batches the input data into an array. And, we propose an efficient method for constructing LSTM in IoT environment.
Å°¿öµå(Keyword) Data Quality   IoT   Deep Learning   Recurrent Neural Network   LSTM  
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