Khazaal Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission https://doi.org/10.1515/eng-2024-0015 received January 12, 2024; accepted March 20, 2024 1 Introduction Abstract: Digital beamforming (DBF) has emerged as a pivotal technology for large-scale ...
Therefore, the objective of this paper is to develop a machine learning model based on Long Short-Term Memory (LSTM) neural networks for reconstruction and short-and long-term prediction of nearshore significant wave height (SWH), integrating bathymetric data for the first time. Time series of ...
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关键词: Long Short-Term Memory (LSTM) Extreme Gradient Boosting (XGBoost) Wave prediction DOI: 10.1016/j.mex.2024.103031 年份: 2024 收藏 引用 批量引用 报错 分享 全部来源 求助全文 dx.doi.org 相似文献An empirical method for joint inversion of wave and wind parameters based on SAR and wave ...
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data - jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction
Apart from regulations, the results of wave height predictions using the Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) methods can help fishermen determine shipping departures, thereby reducing the risk of accidents. In this study, the Grid Search hyperparameter tuning process...
A novel method to fill in short- and long-term missing values in Significant Wave Height (SWH) is proposed.Short-term missing ratios of up to 50 percent are randomly created.Influence of Kalman smoothing (KS) on filling SWH missing values is firstly discussed.KS-LSTM model shows a robust ...
This study introduces a novel LIF-based spectral analysis method that integrates a self-designed detection system and a multi-task framework, the Wavelet CNN-sLSTM-KAN-Enhanced Transformer (WaveConv-sLSTM-KET). By combining a Wavelet Transform CNN block, a scalar LSTM block, and a Kolmogorov–...
Using Deep Learning and RNN/LSTM for Time Series Learning and Prediction - 2wavetech/Time-Series-Analysis-and-Prediction
This study proposes a hybrid model (Conformer-LSTM) based on Conformer and Long Short-Term Memory networks (LSTM) to overcome the limitations of existing techniques and enhance the accuracy and generalizability of wave height predictions. The model combines the advantages of self-attention mechanisms ...