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Heterogeneous IoT Sensor Data Classification for Emergency Detection using Machine Learning
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논문 기본 정보

Type
Proceeding
Author
Cosmas Ifeanyi Nwakanma (Kumoh National Institute of Technology) Ade Pitra Hermawan (Kumoh National Institute of Technology) Jae-Min Lee (Kumoh National Institute of Technology) Dong Seong Kim (Kumoh National Institute of Technology)
Journal
Korea Institute Of Communication Sciences Proceedings of Symposium of the Korean Institute of communications and Information Sciences 2021년도 한국통신학회 동계종합학술발표회 논문집
Published
2021.2
Pages
263 - 264 (2page)

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Heterogeneous IoT Sensor Data Classification for Emergency Detection using Machine Learning
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Inertial Measurement Unit (IMU) and Ultra Wide Band (UWB) sensors were integrated to collect heterogeneous data in a smart factory scenario. In this paper, various machine learning algorithms were used to classify the data with a view to detect normal and anomaly situations based on threshold values of the sensor data. System was simulated using keras with GPU 1xTesla K80, 2496 CUDA cores and 12GB GDDR5 VRAM on top of Google colaboratory. Training and testing data we^_@span style=color:#999999 ^_# ... ^_@/span^_#^_@a href=javascript:; onclick=onClickReadNode('NODE10547511');fn_statistics('Z354','null','null'); style='color:#999999;font-size:14px;text-decoration:underline;' ^_#View All^_@/a^_#
#Artificial Intelligence #IoT #sensor data #smart factories #trade off

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