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Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges (2023)
Journal Article
Liu, Q., Yang, Z., Ji, R., Zhang, Y., Bilal, M., Liu, X., Vimal, S., & Xu, X. (2023). Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges. IEEE Systems, Man, and Cybernetics Magazine, 9(4), 4-12. https://doi.org/10.1109/msmc.2022.3216943

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this article, recent relevant scientific investigation and practical efforts using deep learning (DL) models for weather radar data analy... Read More about Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges.

A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars (2022)
Journal Article
Yang, Z., Wu, H., Liu, Q., Liu, X., Zhang, Y., & Cao, X. (2023). A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars. ISA Transactions, 132, 155-166. https://doi.org/10.1016/j.isatra.2022.06.046

In recent years, the number of weather-related disasters significantly increases across the world. As a typical example, short-range extreme precipitation can cause severe flooding and other secondary disasters, which therefore requires accurate pred... Read More about A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars.

CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets (2022)
Journal Article
Yang, Z., Liu, Q., Wu, H., Liu, X., & Zhang, Y. (2023). CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets. Computer Modeling in Engineering and Sciences, 135(1), 45-64. https://doi.org/10.32604/cmes.2022.022045

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their conc... Read More about CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets.

Near-Data Prediction Based Speculative Optimization in a Distribution Environment (2022)
Journal Article
Liu, Q., Wu, X., Liu, X., Zhang, Y., & Hu, Y. (2022). Near-Data Prediction Based Speculative Optimization in a Distribution Environment. Mobile Networks and Applications, 27(6), 2339-2347. https://doi.org/10.1007/s11036-021-01793-7

Hadoop is an open source from Apache with a distributed file system and MapReduce distributed computing framework. The current Apache 2.0 license agreement supports on-demand payment by consumers for cloud platform services, helping users leverage th... Read More about Near-Data Prediction Based Speculative Optimization in a Distribution Environment.

SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction (2022)
Journal Article
Li, Y., Lu, H., Liu, Q., Zhang, Y., & Liu, X. (2022). SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction. Remote Sensing, 14(3), Article 768. https://doi.org/10.3390/rs14030768

In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent... Read More about SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction.

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.

An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars (2021)
Journal Article
Wu, H., Liu, Q., Liu, X., Zhang, Y., & Yang, Z. (2022). An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars. World Wide Web, 25, 1923-1949. https://doi.org/10.1007/s11280-021-00988-y

Internet of Things (IoT) has been rapidly developed in recent years, being well applied in the fields of Environmental Surveillance, Smart Grid, Intelligent Transportation, and so on. As one of the typical earth-based meteorological observation metho... Read More about An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars.

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data (2021)
Journal Article
Lu, H., Liu, Q., Liu, X., & Zhang, Y. (2021). A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data. Journal of Organizational and End User Computing, 33(6), Article 6. https://doi.org/10.4018/joeuc.20211101.oa6

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing dat... Read More about A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data.

RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction (2021)
Journal Article
Zhu, L., Liu, Q., Liu, X., & Zhang, Y. (2021). RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction. EURASIP Journal on Wireless Communications and Networking, 2021, Article 171 (2021). https://doi.org/10.1186/s13638-021-02044-9

For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospher... Read More about RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction.