IOT device code translators using LSTM networks

2017 IEEE National Aerospace and Electronics Conference (NAECON)(2017)

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摘要
Currently, the Internet of Things (IOT) platform used by large buildings to manage the indoor climate uses different controllers and sensors from multiple manufacturers. Communication between these devices requires a human in the loop to translate each devices data to to be compatible with a common integration engine and storage historian. The subject matter expert needs to decipher the non-standard naming convention used for each device and manually translate thousands of codes each time a new device has to be integrated. To aid the human translator, we propose a technique to implement a smart translator using Deep Neural Networks (DNN) by automatically assigning any registers with recognized data patterns to standardized labels.
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关键词
LSTM,seq2seq,vector embedding,deep learning,smart translators,tensorflow
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