A5049437018- Arctic and Antarctic ice dynamics
- Cryospheric studies and observations
- Climate change and permafrost
- Soil Moisture and Remote Sensing
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Advanced Algorithms and Applications
- Energy Load and Power Forecasting
- Advanced SAR Imaging Techniques
- Retinal Imaging and Analysis
- Machine Learning and Data Classification
- Forecasting Techniques and Applications
- Stock Market Forecasting Methods
- Spectroscopy and Chemometric Analyses
- Face and Expression Recognition
Central South University
2023-2024
Anhui Agricultural University
2017-2018
This paper aims to develop an effective way predict the inventory demand of agricultural materials. Focusing on pesticide, author introduced backpropagation neural network (BPNN) and optimized BPNN prediction model by multiple interpolation method. In this way, a novel strategy was created, with national macro policy, pest disease resistance, market role other factors as part BPNN. For lack input samples, method adopted restore missing data. Then, replacement values were combined in...
Arctic sea ice prediction holds significant importance for facilitating route planning, optimizing fisheries management, and advancing the field of dynamics research. While various deep learning models have been developed prediction, they predominantly operate at seasonal or sub-seasonal scale, often focusing on localized areas, few cater to full-region daily scale prediction. This study introduces use spatiotemporal sequence data models, namely, convolutional LSTM (ConvLSTM) predictive...
Arctic sea ice prediction is of great practical significance in facilitating route planning, optimizing fisheries management, and advancing the field dynamics research. While various deep learning models have been developed for prediction, they predominantly operate at seasonal or sub-seasonal scale, often focusing on localized areas, few cater to full-region daily-scale prediction. This study introduces use spatiotemporal sequence data models, namely, convolutional LSTM (ConvLSTM)...
Phase unwrapping (PU) is one of the core procedures interferometric synthetic aperture radar (InSAR). The traditional phase methods estimate absolute gradient based on assumption that spatially continuous. However, this often not true due to large gradient. In paper, a local enhanced U-net (LEU-Net) was developed determine discontinuity SAR differential interferograms in areas with substantial elevation change. predicted by network used as priori information for maximum flow\minimum cut...
Accurate prediction of inventory can improve value, speed up the capital turnover and profitability enterprises. It has become a hot research topic in recent years. Taking business process agricultural enterprise as background. Using multiple imputation methods to optimize BP (Back Propagation) neural network forecasting model, combined with demand personnel functional units, we propose novel method based on network. The takes four factors conventional, policy, drug resistance market predict...