LSTM Autoencoder를 활용한 스마트 온실 데이터 이상 탐지 구현 = Implementation of Smart Farm Data Anomaly Detection Using LSTM Autoencoder

다국어 초록 (Multilingual Abstract)

In this paper, LSTM (Long-Short Term Memory), a sequence data processing model, is used as an Autoencoder method that reconstructs input smart farm sensor data composed of encoders and decoders to implement an artificial intelligence model that detects anomaly in smart farm. The LSTM Autoencoder was trained by using the normal data collected from the 60 sensors installed for precise control of the greenhouse environment of the smart farm as input sequence values. The trained model obtained a very low train and validation error in the learning process, and the LSTM Autoencoder model that has finally completed the learning process contains information about normal data distribution in the representation vector, so sequence input is restored based on that information. In this restoration precess, when a sequence input that is out of the normal data distribution is received, it is applied to anomaly detection by taking advantage of the fact that the reconstruction error increases because accurate restoration does not proceed. The error that becomes the threshold value that distinguishes normal and abnormal data can be set using the loss distribution obtained in the learning process, and the error for sequence input can be visualized as an image and used for anomaly detection.