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Radar Quantitative Precipitation Estimation Based on the gated recurrent unit neural network and Echo-Top Data

Advances in Atmospheric Sciences 2023年第6期40卷 1043-1057页

作者： Haibo ZOU Shanshan WU Miaoxia TIAN Key Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education&School of Geography and Environmental Science Jiangxi Normal UniversityNanchang 330022China Meteorological Sciences Institute of Jiangxi Province Nanchang 330046China Key Laboratory of Natural Disaster Monitoring Early Warning and Assessment of Jiangxi ProvinceNanchang 330022China Jiangxi Climate Center Nanchang 330046China

The gated recurrent unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are *** further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

关键词： quantitative precipitation estimation gated recurrent unit neural network Z-R relationship echo-top height

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A computational method for the load spectra of large-scale structures with a data-driven learning algorithm

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Science China(Technological Sciences) 2023年第1期66卷 141-154页

作者： CHEN XianJia YUAN Zheng LI Qiang SUN ShouGuang WEI YuJie The State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBejing 100190China School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBejing 100044China School of Enginering Sciences University of Chinese Academy of SciencesBeijing 100049China

For complex engineering systems, such as trains, planes, and offshore oil platforms, load spectra are cornerstone of their safety designs and fault diagnoses. We demonstrate in this study that well-orchestrated machine learning modeling, in combination with limited experimental data, can effectively reproduce the high-fidelity, history-dependent load spectra in critical sites of complex engineering systems, such as high-speed trains. To meet the need for in-service monitoring, we propose a segmentation and randomization strategy for long-duration historical data processing to improve the accuracy of our data-driven model for longterm load-time history prediction. Results showed the existence of an optimal length of subsequence, which is associated with the characteristic dissipation time of the dynamic system. Moreover, the data-driven model exhibits an excellent generalization capability to accurately predict the load spectra for different levels of passenger-dedicated lines. In brief, we pave the way, from data preprocessing, hyperparameter selection, to learning strategy, on how to capture the nonlinear responses of such a dynamic system, which may then provide a unifying framework that could enable the synergy of computation and in-field experiments to save orders of magnitude of expenses for the load spectrum monitoring of complex engineering structures in service and prevent catastrophic fatigue and fracture in those solids.

关键词： load spectrum computational mechanics deep learning data-driven modeling gated recurrent unit neural network

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Prediction of landslide displacement with dynamic features using intelligent approaches

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International Journal of Mining Science and Technology 2022年第3期32卷 539-549页

作者： Yonggang Zhang Jun Tang Yungming Cheng Lei Huang Fei Guo Xiangjie Yin Na Li Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical EngineeringTongji UniversityShanghai 200092China College of Civil Engineering Huaqiao UniversityXiamen 316000China School of Civil Engineering Qingdao University of TechnologyQingdao 266000China College of Civil Engineering and Architecture Wenzhou UniversityWenzhou 325000China Shenzhen Antai Data Monitoring Technology Co. Ltd.Shenzhen 518000China Key Laboratory of Disaster Prevention and Mitigation of Hubei Province China Three Gorges UniversityYichang 443002China School of Resources&Safety Engineering Central South UniversityChangsha 410083China

Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly *** the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic *** models may have bad performances,when the dynamic features of landslide triggers are *** paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic *** procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement *** case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise *** contrast,the accuracy can be significantly improved using the dynamic predictive *** paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.

关键词： Landslide displacement prediction Artificial intelligent methods gated recurrent unit neural network CEEMDAN Landslide monitoring

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A HybridManufacturing ProcessMonitoringMethod Using Stacked gated recurrent unit and Random Forest

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Intelligent Automation & Soft Computing 2024年第2期39卷 233-254页

作者： Chao-Lung Yang Atinkut Atinafu Yilma Bereket Haile Woldegiorgis Hendrik Tampubolon Hendri Sutrisno Department of Industrial Management National Taiwan University of Science and TechnologyTaipeiTaiwanChina Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology(BiT)Bahir DarUniversityBahir Dar6000Ethiopia Department of Computer Science and Information Engineering National Taiwan University of Science and TechnologyTaipeiTaiwanChina Department of Information System Faculty of Engineering and Computer ScienceKridaWacana Christian UniversityJakartaIndonesia Department of Information Management National Dong Hwa UniversityHualienTaiwanChina

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing *** real-time production process monitoring is critical in today’s smart *** more robust the monitoring model,the more reliable a process is to be under *** the past,many researchers have developed real-time monitoring methods to detect process shifts ***,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and *** this paper,a robust monitoring model combining gated recurrent unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts *** effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution ***,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal *** results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal *** experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring *** result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

关键词： Smart manufacturing process monitoring quality control gated recurrent unit neural network random forest

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Hybrid Deep neural network based on SDAE and GRUNN

Hybrid Deep Neural Network based on SDAE and GRUNN

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第三十九届中国控制会议

作者： Yingyong Zou Jun Yu Jiangen Tang Yongde Zhang School of Mechanical and Power Engineering Harbin University of Science and Technology College of Mechanical and Vehicular Engineering Changchun University Key Laboratory of Advanced Manufacturing and Intelligent Technology Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

Stacked autoencoder（SAE） is hard to achieve satisfactory performance,when input data are complex and ***,the identification performance of recurrent neural network（RNN） may decrease rapidly under noisy *** order to deal with these problems,a novel hybrid deep neural network（DNN） based on stacked denoising autoencoder（SDAE） and gated recurrent unit neural network（GRUNN） is ***,the structure of the presented hybrid DNN is *** hybrid DNN contains a SDAE,a GRUNN,and a softmax ***,the training algorithm based on action discovery（AD） is proposed to train the presented hybrid *** experimental studies indicate the presented hybrid DNN processes strong anti-noise ability and adaptability to time-varying signals.

关键词： deep neural network stacked denoising autoencoder gated recurrent unit neural network denoising ability action discovery

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