A5101881223- Tropical and Extratropical Cyclones Research
- Ocean Waves and Remote Sensing
- Tensor decomposition and applications
- Computational Physics and Python Applications
- Metaheuristic Optimization Algorithms Research
- Remote Sensing and Land Use
- Distributed Control Multi-Agent Systems
- Climate variability and models
- Flood Risk Assessment and Management
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Vehicle Routing Optimization Methods
Donghua University
2018-2020
Tropical cyclone (TC) intensity estimation is an important task in meteorological research. Meanwhile, TC performance can be improved by developing advanced machine learning techniques using the newly emerged high-quality multispectral images (MSIs) acquired FY-4 satellite of China. To this end, article proposes a novel model, tensor-based convolutional neural network (TCNN). Not only being deep entirely formulated tensor algebra, but TCNN also establishes mathematical connections between...
Tropical cyclone (TC) intensity estimation is vital to disastrous weather forecasting. In this paper, the task approached as a classification problem, regarding levels class labels. Multispectral Imagery (MSI) captured by recently launched satellite, No. 4 meteorological satellite (FY-4) of China, used raw data for classification. To solve paper proposes machine learning framework with three major parts: useable band determination, band-wise and fusion. The compatible arbitrary classifiers...
Tropical cyclone intensity estimation is important to catastrophic weather forecast. In this paper, it treated as a classification task, with the categories class labels. Normally, traditional supervised methods require large amount of prior knowledge for training. However, in reality, only small labeled samples can be available. Therefore, paper proposes novel semisupervised deep learning framework based on convolutional neural networks (CNNs) FY-4 multispectral images (MSI). The new model...
It is challenging to estimate wind speed of tropical cyclones directly using remote sensing image patterns. This paper approaches the task in two major steps: cyclone category estimation and regression. A novel framework based on Tensor Convolutional Neural Network (Tensor CNN) proposed solve problem. Not only does combine analysis for dimensionality reduction deep neural networks pattern recognition, CNN also provides a unitary concise mathematical representation form significant models....
In this paper we present a heterogeneous multi-colony ant optimization with novel interaction strategy named pheromone fusion to balance the search ability and convergence speed of conventional colony optimization. The performs directly effectively by interchange matrices. It could exploit benefits distribution take full use advantages sub-colonies. There are also two states defined in study control interaction. global state based on KL divergence determines which sub-colonies should...
Tropical cyclone (TC) intensity estimation is a challenging task. Using the multispectral images (MSIs) captured by China's FY-4 Satellite, this paper addresses issue proposing novel deep learning framework, combining Coupled Convolutional Neural Network (Coupled CNN) for categorization and Class-wise Regressors wind speed estimation. The CNN constructed two parallel CNNs with specially-designed Transformation layers to process MSIs different spectral dimensions. could reduce dimensions of...
Maximum wind speed (MWS) is an important characteristic of tropical cyclone (TC). Estimation MWS with remote sensing images TCs via machine learning a relatively new and challenging task. Here we propose novel effective method, Regularized Tensor Network (RTN), to estimate using multispectral (MSIs). RTN transductive regression model, built on deep (TN) combined two regularizations: manifold categorization error. Experimental results showed that outperformed several classic methods as well...