Huantong Geng
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View article: DA-RTDETR: domain-adaptive RT-DETR with feature fusion and category-level constraints
DA-RTDETR: domain-adaptive RT-DETR with feature fusion and category-level constraints Open
Transformer-based object detection has attracted significant attention recently due to its promising performance. As the first work of DETR-like algorithms in the field of real time object detection, Real-Time Detection Transformer (RT-DET…
View article: Efficient-Yolom: A Real-Time and Efficient Vision Model for Generic Multi-Task
Efficient-Yolom: A Real-Time and Efficient Vision Model for Generic Multi-Task Open
View article: MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation
MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation Open
Radar echo extrapolation is a critical technique for short-term weather forecasting. Timely warnings of severe convective weather events can be provided according to the extrapolated images. However, traditional echo extrapolation methods …
View article: The MS-RadarFormer: A Transformer-Based Multi-Scale Deep Learning Model for Radar Echo Extrapolation
The MS-RadarFormer: A Transformer-Based Multi-Scale Deep Learning Model for Radar Echo Extrapolation Open
As a spatial–temporal sequence prediction task, radar echo extrapolation aims to predict radar echoes’ future movement and intensity changes based on historical radar observations. Two urgent issues still need to be addressed in deep learn…
View article: Radar-SR3: A Weather Radar Image Super-Resolution Generation Model Based on SR3
Radar-SR3: A Weather Radar Image Super-Resolution Generation Model Based on SR3 Open
To solve the problems of the current deep learning radar extrapolation model consuming many resources and the final prediction result lacking details, a weather radar image super-resolution weather model based on SR3 (super-resolution via …
View article: Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model
Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model Open
Weather radar echo extrapolation plays a crucial role in weather forecasting. However, traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data. Deep learning algorithms based o…
View article: LSTMAtU-Net: A Precipitation Nowcasting Model Based on ECSA Module
LSTMAtU-Net: A Precipitation Nowcasting Model Based on ECSA Module Open
Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0–2 h. Existing methods use radar echo maps and the Z–R relationship to directly predict future rainfall rates through deep…
View article: A Dual-Population-Based NSGA-III for Constrained Many-Objective Optimization
A Dual-Population-Based NSGA-III for Constrained Many-Objective Optimization Open
The main challenge for constrained many-objective optimization problems (CMaOPs) is how to achieve a balance between feasible and infeasible solutions. Most of the existing constrained many-objective evolutionary algorithms (CMaOEAs) are f…
View article: Cascading Alignment for Unsupervised Domain-Adaptive DETR with Improved DeNoising Anchor Boxes
Cascading Alignment for Unsupervised Domain-Adaptive DETR with Improved DeNoising Anchor Boxes Open
Transformer-based object detection has recently attracted increasing interest and shown promising results. As one of the DETR-like models, DETR with improved denoising anchor boxes (DINO) produced superior performance on COCO val2017 and a…
View article: AF-SRNet: Quantitative Precipitation Forecasting Model Based on Attention Fusion Mechanism and Residual Spatiotemporal Feature Extraction
AF-SRNet: Quantitative Precipitation Forecasting Model Based on Attention Fusion Mechanism and Residual Spatiotemporal Feature Extraction Open
Reliable quantitative precipitation forecasting is essential to society. At present, quantitative precipitation forecasting based on weather radar represents an urgently needed, yet rather challenging. However, because the Z-R relation bet…
View article: A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection
A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection Open
Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during th…
View article: GAN-rcLSTM: A Deep Learning Model for Radar Echo Extrapolation
GAN-rcLSTM: A Deep Learning Model for Radar Echo Extrapolation Open
The target of radar echo extrapolation is to predict the motion and development of radar echo in the future based on historical radar observation data. For such spatiotemporal prediction problems, a deep learning method based on Long Short…
View article: MCCS-LSTM: Extracting Full-Image Contextual Information and Multi-Scale Spatiotemporal Feature for Radar Echo Extrapolation
MCCS-LSTM: Extracting Full-Image Contextual Information and Multi-Scale Spatiotemporal Feature for Radar Echo Extrapolation Open
Precipitation nowcasting has been gaining importance in the operational weather forecast, being essential for economic and social development. Conventional methods of precipitation nowcasting are mainly focused on the task of radar echo ex…
View article: Spatiotemporal Model Based on Deep Learning for ENSO Forecasts
Spatiotemporal Model Based on Deep Learning for ENSO Forecasts Open
El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO pr…
View article: Strong Spatiotemporal Radar Echo Nowcasting Combining 3DCNN and Bi-Directional Convolutional LSTM
Strong Spatiotemporal Radar Echo Nowcasting Combining 3DCNN and Bi-Directional Convolutional LSTM Open
In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directi…
View article: A prediction scheme for the frequency of summer tropical cyclone landfalling over China based on data mining methods
A prediction scheme for the frequency of summer tropical cyclone landfalling over China based on data mining methods Open
This study examines the landfalling tropical cyclones (TCs) over China using state-of-the-art data mining methods (i.e. Finite Mixture Model (FMM) based cluster algorithm and the Classification and Regression Tree (CART)). Using the 1951–2…