The random movement of a non-stabilized camera platform during the shooting process can lead to jitter in the captured video sequences. We propose a digital image stabilization algorithm based on time-varyingfilter-b...
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The random movement of a non-stabilized camera platform during the shooting process can lead to jitter in the captured video sequences. We propose a digital image stabilization algorithm based on time-varying filter-based empirical mode decomposition (TVF-EMD) and probability density function (PDF) evaluation criterion. First, an accelerated robust feature algorithm obtains the global motion sequence. Then, TVF-EMD decomposes the global motion sequence to get the intrinsic mode functions (IMFs) and separates the intentional and jitter motion-dominated IMFs using PDFs. Finally, the deliberate motion sequence is reconstructed by summing the intentional motion-dominated IMFs. The experimental results demonstrate that, compared with existing methods, the video sequences reconstructed using the algorithm proposed in this paper exhibit a higher peak signal-to-noise ratio and structural similarity index, as well as a lower gradient magnitude similarity deviation, which proves the effectiveness of the technique.
A real-time and accurate storm surge prediction model is of great scientific value and practical significance in reducing human casualties and economic losses in coastal areas. For this purpose, a novel storm surge mu...
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A real-time and accurate storm surge prediction model is of great scientific value and practical significance in reducing human casualties and economic losses in coastal areas. For this purpose, a novel storm surge multi-step forecasting framework integrating time-varyingfiltered empirical modal decomposition (TVF-EMD), fast Fourier transform (FFT), phase space reconstruction, convolutional neural network (CNN), and long short-term memory neural network (LSTM) is proposed in this study. Among the supplementary strategies, the TVF-EMD is used to extract the fluctuation features of the storm surge data and decompose the storm surge time series into a number of IMFs;the FFT is employed to calculate the frequency values of each IMF, and the subsequences with similar frequency values are combined and reconstructed. Meanwhile, CNN is adopted to predict the preprocessed high-frequency components, while the low-frequency is predicted by LSTM. Subsequently, the ultimate prediction results of the raw storm surge are calculated by superimposing the predicted values of all components. Three datasets collected from southeastern coastal region of China and five relevant comparison models are carried out to evaluate the proposed approach, where the corresponding results demonstrate that: (1) data preprocessing strategy applying TVF-EMD and FFT can significantly improve forecasting performance;(2) the TVF-EMD decomposition method is more effective under the influence of low sampling rate and noise;(3) by observing the characteristics of the subsequence, the prediction by modules can achieve better results. In addition, in order to apply the model to engineering, the proposed model is transferred to the small data domain as a pre-trained model using a transfer learning approach. According to the prediction results of Wenzhou station in the 7821-storm surge event, it can be found the proposed model still has good robustness and generalization ability even though the new sample data
Precise water level forecasting plays a decisive role in improving the efficiency of flood prevention and disaster reduction, optimizing water resource management, enhancing the safety of waterway transportation, redu...
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Precise water level forecasting plays a decisive role in improving the efficiency of flood prevention and disaster reduction, optimizing water resource management, enhancing the safety of waterway transportation, reducing flood risks, and promoting ecological and environmental protection, which is crucial for the sustainable development of society. This study proposes a hybrid water level forecasting model based on time-varying filter-based empirical mode decomposition (TVFEMD), Inverse-Free Extreme Learning Machine (IFELM), and error correction. Firstly, historical water level data are decomposed into different modes using TVFEMD;secondly, the Improved Jellyfish Search (IJS) algorithm is employed to optimize the IFELM, and subsequently, the optimized IFELM independently forecasts each sub-sequence and obtains the predictive results of each sub-sequence;thirdly, an Online Sequential Extreme Learning Machine (OSELM) model is used to correct data errors, and the initial predictive results and error prediction results are added together to obtain the final prediction for the sub-sequence;and finally, the final prediction for the sub-sequences are added to obtain the prediction results of the entire water level sequence. Taking the daily water level data from 2006 to 2018 in Taihu, China as the research object, this paper compares the proposed model with the ELM, BP, LSTM, IFELM, TVFEMD-IFELM, and TVFEMD-IFELM-OSELM models. The results show that the TVFEMD-IJS-IFELM-OSELM model established in this study has high prediction accuracy and strong stability and is suitable for water level forecasting.
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