A5101937810- Remote-Sensing Image Classification
- Advanced Image Fusion Techniques
- Remote Sensing in Agriculture
- Civil and Geotechnical Engineering Research
- Cloud Computing and Resource Management
- Grey System Theory Applications
- IoT and Edge/Fog Computing
- Nuclear reactor physics and engineering
- Advanced Graph Neural Networks
- Landslides and related hazards
- Quantum chaos and dynamical systems
- Data Quality and Management
- Image and Signal Denoising Methods
- Software-Defined Networks and 5G
- Soil, Finite Element Methods
- Video Surveillance and Tracking Methods
- Advanced SAR Imaging Techniques
- Nuclear Engineering Thermal-Hydraulics
- Automated Road and Building Extraction
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Control and Stability of Dynamical Systems
- Topic Modeling
- Machine Learning and Data Classification
- Nuclear Physics and Applications
- Data Mining Algorithms and Applications
Northeast Forestry University
2022-2025
China Tobacco
2023
Inner Mongolia Agricultural University
2019
Remote sensing images obtained by a variety of sensors have been widely used in different Earth observation tasks. However, owing to budget and sensor technology constraints, single cannot simultaneously provide observational with both high spatial temporal resolution. This brings difficulties remote research which requires resolution data. To solve the above spatiotemporal fusion (STF) method was proposed has received widespread attention. The main challenge for STF is reconstruct...
With the rapid development of data center network, traditional traffic scheduling method can easily cause problems such as link congestion and load imbalance. Therefore, this paper proposes a novel dynamic flow algorithm GA-ACO (Genetic Algorithm Ant COlony algorithms). al gorithm obtains global perspective network under SDN (Software defined network) architecture. It then calculates optimal path for elephant on link, reroutes it. Extensive experiments have been executed to evaluate...
Remote-sensing (RS) images with high spatial and temporal resolutions play a significant role in monitoring periodic landscape changes for earth observation science. To enrich RS images, spatiotemporal fusion (STF) is considered promising approach. The key challenge the current STF-based methods requirement large-scale data. In this work, we propose deep-learning-based method called multilayer perceptron (StfMLP) to tackle challenge. First, our focuses on given data manner of transductive...
Mining entity and relation from unstructured text is important for knowledge graph construction expansion. Recent approaches have achieved promising performance while still suffering inherent limitations, such as the computation efficiency redundancy of prediction. In this paper, we propose a novel hybrid attention dilated convolution network (HADNet), an end-to-end solution extraction mining. HADNet designs encoder architecture integrated with mechanism, convolutions, gated unit to further...
When the traditional C4.5 algorithm deals with big data a large number of multidimensional continuous attribute values, it may cause issue low classification accuracy related discretization method. This paper proposes novel method to discretize based on th e k-means algorithm. The generates clusters by combining continuous, unfeatured corresponding class labels, and then takes approximate boundary points cluster as candidate splitting-points attribute. Based this, information gain ratio is...
Forest height is a key forest parameter which of great significance for monitoring resources, calculating biomass, and observing the global carbon cycle. Because PolInSAR system could provide various object information including height, shape direction sensitivity, spatial distribution, it becomes powerful means measuring height. The proposed framework utilizes deep learning builds upon traditional DEM differencing coherence amplitude inversion algorithms. By using L band data, new CNN model...
From the Heisenberg equation of motion we elucidate so-called invariant eigenoperator method, its application to various Hamiltonian systems are presented.