3D wave simulation based on a deep learning model for spatiotemporal prediction

Li, Ying; Zhang, Xiaohui; Cheng, Lingxiao; Xie, Mingxing; Cao, Kai

doi:10.1016/j.oceaneng.2022.112420

Cited by 12 publications

(2 citation statements)

References 21 publications

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“…Where

represents the three-dimensional characteristics of each time window, and the

represents the hidden state.

represents the Sigmoid function, and

correspond to the input gate, forget gate, cell gate ( Li et al, 2022 ). The weights and biases indexed by

are learned through backpropagation.…”

Section: Epileptic-states Classificationmentioning

confidence: 99%

Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN

Liu,

Li,

Lou

et al. 2024

Front. Neuroinform.

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Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient’s daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time–space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time–space nonlinear feature fusion is effective.

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“…Where

represents the three-dimensional characteristics of each time window, and the

represents the hidden state.

represents the Sigmoid function, and

correspond to the input gate, forget gate, cell gate ( Li et al, 2022 ). The weights and biases indexed by

are learned through backpropagation.…”

Section: Epileptic-states Classificationmentioning

confidence: 99%

Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN

Liu,

Li,

Lou

et al. 2024

Front. Neuroinform.

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“…Leu extracted wavelength from SPOT-3 panchromatic images using the Fast Fourier Transform (FFT) method to obtain the inverted water depth in Taichung Harbor [36]. This method does not rely on other environmental data, such as water quality and bottom reflectance, and does not require additional observed water depths in bathymetric operations [36][37][38][39]. It has practical application value as it does not depend on the simultaneously observed wind speed and surface flow velocity in radar image analysis.…”

Section: Introductionmentioning

confidence: 99%

Nearshore Depth Estimation Using Fine-Resolution Remote Sensing of Ocean Surface Waves

Liu,

Zhu,

Cheng

et al. 2023

Sensors

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In the field of water depth inversion using imagery, the commonly used methods are based on water reflectance and wave extraction. Among these methods, the Optical Bathymetry Method (OBM) is significantly influenced by bottom sediment and climate, while the wave method requires a specific study area. This study introduces a method combining the FFT and spatial profile measurement to invert the wavelength of the wave bathymetry method (WBM), which enhances accuracy and reduces workload. The method was applied to remote sensing images of Sanya Bay in China, obtained from the Worldview satellite. The average error of the inverted depth results after applying the wavelength inversion technique was 15.9%, demonstrating consistency with the depth measurements obtained through the OBM in clear water of the bay. The WBM has notable advantages over the OBM, as it is unaffected by water quality. In addition, the influence of wave period on the accuracy of water depth retrieval was theoretically evaluated, revealing that a larger wave period leads to a better depth measurement. The depth measurement from two images with different wave periods aligned with the theoretical analysis. These results showcase the applicability and potential of the WBM for accurately estimating water depth in various coastal environments.

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