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Yen-Chang Chen, Hui-Chung Yeh, Su-Pai Kao, Chiang Wei and Pei-Yi Su
In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model ...
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Ali Al-Naji, Ghaidaa A. Khalid, Jinan F. Mahdi and Javaan Chahl
Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware ...
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Xiaoxu Niu, Junwei Ma, Yankun Wang, Junrong Zhang, Hongjie Chen and Huiming Tang
The proposed decomposition-ensemble learning model can be efficiently used to enhance the prediction accuracy of landslide displacement prediction and can also be extended to other difficult forecasting tasks in the geosciences with extremely complex non...
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Muhammad Tayyab, Ijaz Ahmad, Na Sun, Jianzhong Zhou and Xiaohua Dong
Consistent streamflow forecasts play a fundamental part in flood risk mitigation. Population increase and water cycle intensification are extending not only globally but also among Pakistan’s water resources. The frequency of floods has increased i...
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Xike Zhang, Qiuwen Zhang, Gui Zhang, Zhiping Nie, Zifan Gui and Huafei Que
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Taiyong Li, Zhenda Hu, Yanchi Jia, Jiang Wu and Yingrui Zhou
Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However...
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In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is cre...
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Licheng Zhu and Abdollah Malekjafarian
In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is cre...
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Chengjiang Zhou, Ling Xing, Yunhua Jia, Shuyi Wan and Zixuan Zhou
Aiming at the problem that fault feature extraction is susceptible to background noises and burrs, we proposed a new feature extraction method based on a new decomposition method and an effective intrinsic mode function (IMF) selection method. Firstly, p...
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Emmanuel Senyo Fianu
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequen...
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Sanaz Roshanmanesh, Farzad Hayati and Mayorkinos Papaelias
In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that t...
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Shanshan Chen, Sheng Guan, Hui Wang, Ningqi Ye and Zexun Wei
Ship type identification is an important basis for ship management and monitoring. The paper proposed a new method of ship type identification by combining characteristic parameters from the energy difference between high and low frequencies and the sens...
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Jiajia Xiao, Ying Li, Chuang Zhang and Zhaoyi Zhang
The primary problem faced by the integrated navigation system based on the inertial navigation system (INS) and global positioning system (GPS) is providing reliable navigation and positioning solutions during GPS failure. Thus, this study proposes an in...
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Zhiyang He, Weidong Cheng, Jiqiang Xia, Weigang Wen and Meng Li
With the development of industrial robots and other mechanical equipment to a higher degree of automation, mechanical systems have become increasingly complex. This represents a huge challenge for condition monitoring. The separation of vibration source ...
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Jingbao Hou, Yunxin Wu, Hai Gong, A. S. Ahmad and Lei Liu
For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation en...
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Weijia Chen and Yancai Xiao
The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is...
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Taereem Kim, Ju-Young Shin, Hanbeen Kim, Sunghun Kim and Jun-Haeng Heo
Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential inform...
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Yanfeng Wu, Guangxin Zhang, Hong Shen and Y. Jun Xu
Assessment of the response of streamflow to future climate change in headwater areas is of a particular importance for sustainable water resources management in a large river basin. In this study, we investigated multiscale variation in hydroclimatic var...
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Yanfeng Wu, Guangxin Zhang, Hong Shen and Y. Jun Xu
Assessment of the response of streamflow to future climate change in headwater areas is of a particular importance for sustainable water resources management in a large river basin. In this study, we investigated multiscale variation in hydroclimatic var...
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Guimei Jiao, Tianlin Guo and Yongjian Ding
Pág. 1 - 15
Hydrogeological disasters occur frequently. Proposing an effective prediction method for hydrology data can play a guiding role in disaster prevention; however, due to the complexity and instability of hydrological data, this is difficult. This paper pro...
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