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Benjamin Nelsen, D. Alexandra Williams, Gustavious P. Williams and Candace Berrett
Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have...
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Reza Abbasi-Asl, Aboozar Ghaffari and Emad Fatemizadeh
Spatially-varying intensity noise is a common source of distortion in medical images and is often associated with reduced accuracy in medical image registration. In this paper, we propose two multi-resolution image registration algorithms based on Empiri...
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Kenan Shen and Dongbiao Zhao
Aircraft hydraulic fault diagnosis is an important technique in aircraft systems, as the hydraulic system is one of the key components of an aircraft. In aircraft hydraulic system fault diagnosis, complex environmental noises will lead to inaccurate resu...
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Michael Wood, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins and Sonia Leva
Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and...
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Miaomiao Yu, Hongyong Yuan, Kaiyuan Li and Lizheng Deng
To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization...
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Asha Jayasree, Santhosh Kumar Sasidharan, Rishidas Sivadas and Jayan A. Ramakrishnan
Rainfall forecasting is critical for the economy, but it has proven difficult due to the uncertainties, complexities, and interdependencies that exist in climatic systems. An efficient rainfall forecasting model will be beneficial in implementing suitabl...
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Chunyao Hou, Yilun Wei, Hongyi Zhang, Xuezhou Zhu, Dawen Tan, Yi Zhou and Yu Hu
In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Process...
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Gang Tang, Jingyu Zhang, Jinman Lei, Haohao Du, Hongxia Luo, Yide Wang and Yuehua Ding
The accurate prediction of significant wave height (SWH) offers major safety improvements for coastal and ocean engineering applications. However, the significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction work a no...
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Xue Chen, Xiangbin Zhao, Yongquan Liang and Xin Luan
Ocean turbulence measurement in the wild sea has contributed significantly to improving our understanding of ocean mixing processes. Restricted by observation instruments and methods, the measured turbulence signal contains much information about the mar...
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Norbert A. Agana and Abdollah Homaifar
Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is e...
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Norbert A. Agana and Abdollah Homaifar
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Xuehua Zhao, Xu Chen, Yongxin Xu, Dongjie Xi, Yongbo Zhang, Xiuqing Zheng
Pág. 1 - 17
Accurate forecasting of annual runoff is necessary for water resources management. However, a runoff series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved prediction acc...
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Xuehua Zhao, Xu Chen, Yongxin Xu, Dongjie Xi, Yongbo Zhang and Xiuqing Zheng
Accurate forecasting of annual runoff is necessary for water resources management. However, a runoff series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved prediction acc...
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Misael Lopez-Ramirez, Luis Ledesma-Carrillo, Eduardo Cabal-Yepez, Carlos Rodriguez-Donate, Homero Miranda-Vidales and Arturo Garcia-Perez
In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal f...
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Boris Medina Salgado, Leonardo Duque Muñoz
Pág. 9 - 19
AbstractDownloadsReferencesHow to Cite
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Boris Medina Salgado, Leonardo Duque Muñoz
Abstract AuthorsDownloadsReferencesHow to Cite
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Xiang Li, Linlu Dong, Biao Li, Yifan Lei and Nuwen Xu
Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) com...
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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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Jinxiu Ma, An Li, Fangjun Qin, Wenbin Gong and Hao Che
The marine atomic interferometric gravimeter is a vital precision instrument for measuring marine geophysical information, which is widely used in mineral resources exploration, military applications, and missile launches. In practical measurements, vibr...
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