[1]CHANG Mingyu,TIAN Le,GUO Maozu.Research on the HHT-LSTM-based operation trend prediction method of temporary facilities for the Winter Olympic Games[J].CAAI Transactions on Intelligent Systems,2024,19(1):228-237.[doi:10.11992/tis.202303003]
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Research on the HHT-LSTM-based operation trend prediction method of temporary facilities for the Winter Olympic Games

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 1 Page number: 228-237 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2024-01-05
Title:
Research on the HHT-LSTM-based operation trend prediction method of temporary facilities for the Winter Olympic Games
Author(s):
CHANG Mingyu1; 2;  TIAN Le1; 2;  GUO Maozu1; 2
1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Research on Intelligent Processing Method of Building Big Data Beijing Key Laboratory, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Keywords:
time series; Hilbert-Huang transform; long short-term memory network; signal processing; temporary facilities; temporary facilities; prediction methods; data analysis; natural language processing
CLC:
TP911.7
DOI:
10.11992/tis.202303003
Abstract:
For the safety and availability of temporary facilities in the Yanqing area of the Winter Olympic Games, by sufficiently combining signal processing algorithm and deep neural network, this paper proposes a brand-new model that consists of two parts: Hilbert-Huang transform (HHT) used for signal decomposition and extraction of signal feature for time-series data, and long short-term memory (LSTM) for prediction of the operation trend of temporary facility. Based on the real vibration and tilt angle data measured while it is affected by a series of exogenous factors such as grandstand vibration induced by severe cold weather and heavy passenger flow, the model realizes effective prediction for facilities, so as to avoid safety problems and solve the problem of low prediction accuracy due to the interference of some irrelevant feature factors in the data. By comparing with such operational trend prediction methods as recurrent neural network (RNN), gated recurrent neural network (GRU), bi-directional RNN and bi-directional GRU, the feasibility and effectiveness of the method was demonstrated. The experimental results also show that the proposed model performs very well in such tasks.
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