Yuhan Yu
Biographic Data
| ID | 7945306 |
|---|---|
| NAME | Yuhan Yu |
| GIVEN NAMES | Yuhan |
| FAMILY NAME | Yu |
| SIGNATURE | YU Y |
| AFFILIATIONS | Zhejiang University |
| ORCID | 0000-0002-0705-6774 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Boosting PM2.5 short-term prediction by coupling graph attention and koopman networks
Accurately predicting PM2.5 concentrations remains a major challenge due to the complex and nonlinear nature of its formation and transport processes. Traditional models often struggle to capture the spatiotemporal dynamics of PM2.5, particularly under varying meteorological conditions. In recent years, Koopman operator theory has attracted increasing attention for its ability to transform nonlinear systems into linear representations, thereby en…
Slbrin
The spatial learned index constructs a spatial index by learning the spatial distribution, which performs a lower cost of storage and query than the spatial indices. The current update strategies of spatial learned indices can only solve limited updates at the cost of query performance. We propose a novel spatial learned index structure based on a Block Range Index (SLBRIN for short). Its core idea is to cooperate history range and current range …
Gatr
Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then …
No prominent works on this page.
Slbrin
The spatial learned index constructs a spatial index by learning the spatial distribution, which performs a lower cost of storage and query than the spatial indices. The current update strategies of spatial learned indices can only solve limited updates at the cost of query performance. We propose a novel spatial learned index structure based on a Block Range Index (SLBRIN for short). Its core idea is to cooperate history range and current range …
Gatr
Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then …
Boosting PM2.5 short-term prediction by coupling graph attention and koopman networks
Accurately predicting PM2.5 concentrations remains a major challenge due to the complex and nonlinear nature of its formation and transport processes. Traditional models often struggle to capture the spatiotemporal dynamics of PM2.5, particularly under varying meteorological conditions. In recent years, Koopman operator theory has attracted increasing attention for its ability to transform nonlinear systems into linear representations, thereby en…
Automated Road and Building Extraction (2 works) · Computer Science (2 works) · Data mining (2 works) · Graph (2 works) · Mathematics (2 works) · Traffic Prediction and Management Techniques (2 works) · Air Quality and Health Impacts (1 work) · Air Quality Monitoring and Forecasting (1 work) · Artificial Intelligence (1 work) · Correlation (1 work)