Outputs (5)

Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks (2021)
Journal Article
Liu, Q., Zhang, J., Liu, X., Zhang, Y., Xu, X., Khosravi, M., & Bilal, M. (2022). Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks. Physical Communication, 51, Article 101584. https://doi.org/10.1016/j.phycom.2021.101584

The intensification of the greenhouse effect is driving the implementation of energy saving and emission reduction policies, which lead to a wide variety of energy saving solutions benefiting from the advancement of emerging technologies such as Wire... Read More about Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks.

A Residual Concatenate FCN Approach to Beam Blockage Correction in Weather Radars (2021)
Presentation / Conference Contribution
Wu, H., Liu, Q., Liu, X., Zhang, Y., Xu, X., & Bilal, M. (2021, October). A Residual Concatenate FCN Approach to Beam Blockage Correction in Weather Radars. Paper presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), Online

A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation (2021)
Journal Article
Ding, C., Wang, G., Zhang, X., Liu, Q., & Liu, X. (2021). A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation. Environmental and Ecological Statistics, 128(3), 503-522. https://doi.org/10.1007/s10651-021-00501-8

Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government... Read More about A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation.

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment (2021)
Journal Article
Liu, Q., Wu, X., Jiang, Y., Liu, X., Zhang, Y., Xu, X., & Qi, L. (2021). A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment. International Journal of Intelligent Systems, 36(5), 2387-2411. https://doi.org/10.1002/int.22383

Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective... Read More about A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment.

A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid (2021)
Journal Article
Anaadumba, R., Liu, Q., Marah, B. D., Nakoty, F. M., Liu, X., & Zhang, Y. (2021). A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid. Cybersecurity, 4, Article 1 (2021). https://doi.org/10.1186/s42400-020-00065-3

Energy forecasting using Renewable energy sources (RESs) is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment. Not only does energy forecasting using renewable energy sources help mitigate th... Read More about A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid.