Worldwide, the demand for power load forecasting is increasing. A multi-step power-load model established based on Informer, which takes historical data as input to realize prediction of in future. The constructed abandons common recurrent neural network deal with time-series problems, and uses seq2seq structure sparse self-attention mechanism main body, supplemented by specific output modules ...

Machine learning is widely used in various practical applications with deep models demonstrating advantages handling huge data. Treating music as a special language and using to accomplish melody recognition, generation, analysis has proven feasible. In certain music-related research, recurrent neural networks have been replaced transformers. This achieved significant results. traditional appro...

Nowadays, modern technologies in power systems have been attracting more attention, and households can supply a portion of or all their electricity based on on-site generation at location. This be challenging for utilities terms monitoring recording the data because households’ facilities generate consume energy without passing it through meter, increasing complexity distribution network. The s...

Radar echo extrapolation is a commonly used approach for convective nowcasting. The evolution of systems over very short term can be foreseen according to the extrapolated reflectivity images. Recently, deep neural networks have been widely applied radar and achieved better forecasting performance than traditional approaches. However, it difficult existing methods combine predictive flexibility...

Epidemic models are powerful tools in understanding infectious disease. However, as they increase size and complexity, can quickly become computationally intractable. Recent progress modelling methodology has shown that surrogate be used to emulate complex epidemic with a high-dimensional parameter space. We show deep sequence-to-sequence (seq2seq) serve accurate surrogates for sequence based m...

Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...