Prediction of Egional Temperature Change Trend Based on LSTM Algorithm

Wu, Tao; Liu, Changchun; He, Cheng

doi:10.1109/itnec48623.2020.9084842

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…The long short-term memory (LSTM) network addresses this problem by introducing memory units into the network architecture. The LSTM network has great advantages in analyzing large-scale data [13,14], and it has high prediction accuracy for time-series data such as atmospheric and ocean temperature, traffic flow, and vibration signals [15,16]. For example, Milad et al applied the LSTM model to the temperature prediction of pavement roads [17].…”

Section: Introductionmentioning

confidence: 99%

Time Series Recovery Using Adjacent Channel Data Based on LSTM: A Case Study of Subway Vibrations

Xin

Zheng³

et al. 2022

Applied Sciences

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Multi-sensor technology has been widely applied in the condition monitoring of rail transit. In practice, the data of some channels in the high channel counts are often abnormal or lost due to the abnormality and damage of the sensors, thus resulting in a large amount of data waste. A method for the data recovery of lost channels by using adjacent channel data is proposed to solve this problem. Based on the LSTM network algorithm, a data recovery model is established based on the “sequence-to-sequence” regression analysis of adjacent channel data. Taking the measured vibration data of a subway as an example, the network is trained with multi-channel measured data to recover the lost channel data of time-series characteristics. The results show that this multi-channel data recovery model is feasible, and the accuracy is up to 98%. This method can also further reduce the number of channels that need to be collected.

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