[1]常明煜,田乐,郭茂祖.基于HHT-LSTM的冬奥会临时设施运行趋势预测方法研究[J].智能系统学报,2024,19(1):228-237.[doi:10.11992/tis.202303003]
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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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]
基于HHT-LSTM的冬奥会临时设施运行趋势预测方法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第1期
页码:
228-237
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2024-01-05
- Title:
- Research on the HHT-LSTM-based operation trend prediction method of temporary facilities for the Winter Olympic Games
- Keywords:
- time series; Hilbert-Huang transform; long short-term memory network; signal processing; temporary facilities; temporary facilities; prediction methods; data analysis; natural language processing
- 分类号:
- TP911.7
- 文献标志码:
- 2023-07-31
- 摘要:
- 针对冬奥会延庆赛区临时设施的安全性和可使用性，本文充分结合信号处理算法与深度神经网络，提出了一种由希尔伯特黄变换（Hilbert-Huang transform，HHT）对时序数据进行信号分解和信号特征提取，长短期记忆网络（long short-term memory，LSTM）进行临时设施运行趋势预测2部分构成模型。该模型基于受严寒天气和大客流诱发的看台振动等一系列外因影响所测得的真实振动和倾角数据，实现对设施进行有效的预测，以避免发生安全问题，解决了由于受数据中一些无关特征因素的干扰导致预测准确度低的问题。论文提出的方法与循环神经网络（recurrent neural network, RNN）、门控循环网络（gated recurrent neural network，GRU）、双向RNN和双向GRU等运行趋势预测方法进行比较，验证了本文方法的可行性和有效性，实验结果也说明所提出的模型在此类任务中表现非常出色。
- 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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- 相似文献/References:
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[1]严修红,许伦辉,董世畅.基于数据预处理灰色神经网络组合和集成预测[J].智能系统学报,2007,2(4):58.
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备注/Memo
收稿日期:2023-03-06。
基金项目:科技部科技冬奥重点专项（2021YFF0306303）；国家自然科学基金项目（62271036).
作者简介:常明煜，硕士研究生，主要研究方向为人工智能、计算机网络和大数据处理。E-mail：changmingyu258@163.com;田乐，副教授，博士，主要研究方向为计算机网络、无线通信和大数据处理。主持国家重点研发子项目、北京市教委项目 10 余项，授权发明专利 4 项，获中国发明协会一等奖。发表学术论文 20 余篇，出版专著 1 部。E-mail：tianle@bucea.edu.cn;郭茂祖，教授，博士生导师，中国计算机学会生物信息学专委会副主任、中国人工智能学会机器学习专委会常委、中国建筑学会计算性设计学术委员会常委、中国自动化学会智能健康与生物信息专委会委员、国家自然基金委重大研究计划指导专家组成员，主要研究方向为机器学习与人工智能、智能建造与智慧城市、生物信息学与计算生物学。主持国家重点研发项目、国家自然科学基金重点和面上项目等国家、省部级项目 20 余项，获教育部自然科学奖、吴文俊人工智能自然科学奖、省自然科学奖（均排名第 1），北京市优秀教师、宝钢优秀教师奖、校教学名师获得者。发表学术论文300 余篇。E-mail：guomaozu@bucea.edu.cn
通讯作者:郭茂祖. E-mail：guomaozu@bucea.edu.cn
更新日期/Last Update:
1900-01-01