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[IEEE 2018 17th International Conference on Ground Penetrating Radar (GPR) - Rapperswil, Switzerland (2018.6.18-2018.6.21)] 2018 17th International Conference on Ground Penetrating Radar (GPR) - Noise suppression of GPR data using Variational Mode Decomposition
摘要： Ground penetrating radar (GPR) has been used in the many aspects, such as civil engineering and the earth sciences. And the analysis and noise suppression of GPR data have always been the research focus. In this study, a new self-adaptive time-frequency decomposition tool called the variational mode decomposition (VMD) is introduced. We use the VMD method to derive a set of stationary sub-components, and based on the decomposition, we separate the valid signals and the components which are corresponded to the noise. One trace of GPR data are given to test the effect of the VMD decomposition, and the empirical mode decomposition (EMD) is also employed as a comparison. And a primary noise-suppression method based on the VMD scheme is also proposed. The application of the field GPR data further demonstrates the better performance of the proposed method in both noise suppression and the retention of geophysical events.
关键词： ground penetrating radar (GPR),mode decomposition,variational mode decomposition (VMD),noise reduction or suppression
更新于2025-09-23 15:23:52
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The Photovoltaic Output Prediction Based on Variational Mode Decomposition and Maximum Relevance Minimum Redundancy
摘要： Photovoltaic output is affected by solar irradiance, ambient temperature, instantaneous cloud cluster, etc., and the output sequence shows obvious intermittent and random features, which creates great difficulty for photovoltaic output prediction. Aiming at the problem of low predictability of photovoltaic power generation, a combined photovoltaic output prediction method based on variational mode decomposition (VMD), maximum relevance minimum redundancy (mRMR) and deep belief network (DBN) is proposed. The method uses VMD to decompose the photovoltaic output sequence into modal components of different characteristics, and determines the main characteristic factors of each modal component by mRMR, and the DBN model is used to fit the modal components and the corresponding characteristic factors, then the predicted results of each modal component is superimposed to obtain the predicted value of the photovoltaic output. By using the data of a certain photovoltaic power station in Yunnan for comparative experiments, it is found that the model proposed in this paper improves the prediction accuracy of photovoltaic output.
关键词： mRMR,photovoltaic output prediction,feature selection,DBN,VMD
更新于2025-09-11 14:15:04