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第三十九届中国控制会议

作者： Qing Peng Zhi-Wei Liu School of Articial Intelligence and Automation Huazhong University of Science and Technology

Short-term residential load forecasting（STRLF） is the crucial step of customer side demand response（CSDR） that is mainly applied to peak cut through the adjustment of electricity price. Compared with load forecasting of high voltage level,STRLF is a more challenging task due to the high volatility and randomness of load. Most studies focus on STRLF using traditional machine learning and recursive neural network technology, which are difficult to maintain long-term load memory.temporal convolutional networks（TCN）, a deep learning method, is put forward to predict residential load which can not only keep the load memory longer, but also process the load information in parallel. Based on AMPds2 smart meter data set,experiments show that the proposed method has a great advantage over the state-of-the-art methods.

关键词： Short-term residential load forecasting temporal convolutional networks residential load

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Realtime prediction of hard rock TBM advance rate using temporal convolutional network(TCN)with tunnel construction big data

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Frontiers of Structural and Civil Engineering 2022年第4期16卷 401-413页

作者： Zaobao LIU Yongchen WANG Long LI Xingli FANG Junze WANG Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Institute of Deep Engineering and Intelligent TechnologyNortheastern UniversityShenyang 110819China State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu 610065China Software College Northeastern UniversityShenyang 110819China

Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction.This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big data.The prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in China.The TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the model.The TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two.The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment.On the contrary,the influence of the cutterhead rotation and total thrust is moderate.The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel tunnel bore machine advance rate prediction temporal convolutional networks soft computing construction big data

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temporal convolutional Network for Speech Bandwidth Extension

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China Communications 2023年第11期20卷 142-150页

作者： Chundong Xu Cheng Zhu Xianpeng Ling Dongwen Ying School of Information Engineering Jiangxi University of Science and TechnologyJiangxi 341000China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.

关键词： speech bandwidth extension temporal convolutional networks time-frequency loss

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A Spatio-temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

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Computer Systems Science & Engineering 2023年第11期47卷 2563-2582页

作者： Tao Liu Kejia Zhang Jingsong Yin Yan Zhang Zihao Mu Chunsheng Li Yanan Hu School of Computer&Information Technology Northeast Petroleum UniversityDaqing163318China School of Qinhuangdao Northeast Petroleum UniversityQinhuangdao066004China

Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

关键词： Spatiotemporal heterogeneity data data accuracy complex topology structure graph convolutional networks temporal convolutional networks

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