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All Outputs (184)

Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization (2024)
Journal Article
Zeng, L., Liu, Q., Shen, S., & Liu, X. (2024). Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization. Tsinghua Science and Technology, 29(3), 806 - 817. https://doi.org/10.26599/TST.2023.9010058

Edge computing nodes undertake more and more tasks as business density grows. How to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge. An edge task scheduling approach based on an impr... Read More about Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization.

PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing (2024)
Journal Article
Zhang, Z., Liu, Q., Liu, X., Zhang, Y., Du, Z., & Cao, X. (2024). PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13(1), Article 76. https://doi.org/10.1186/s13677-024-00637-5

In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional met... Read More about PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing.

A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system (2024)
Journal Article
Liu, Q., Pan, L., Cao, X., Gan, J., Huang, X., & Liu, X. (2024). A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system. Concurrency and Computation: Practice and Experience, 36(13), Article e8060. https://doi.org/10.1002/cpe.8060

As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real-time (RT) load forecasting. In this article, an edge-cloud co... Read More about A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system.

An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment (2024)
Journal Article
Liu, Q., Jin, Y., Cao, X., Liu, X., Zhou, X., Zhang, Y., Xu, X., & Qi, L. (2024). An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment. IEEE Transactions on Computational Social Systems, 11(4), 5308 - 5318. https://doi.org/10.1109/TCSS.2023.3342873

Fake news is a prevalent issue in modern society, leading to misinformation and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awa... Read More about An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing (2024)
Journal Article
Liu, Q., Sun, J., Zhang, Y., & Liu, X. (2024). DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13, Article 32. https://doi.org/10.1186/s13677-024-00607-x

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric re... Read More about DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing.

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.

DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar (2023)
Journal Article
Liu, Q., Sun, J., & Liu, X. (in press). DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar. Journal on Artificial Intelligence,

In the realm of meteorological research, extensive global radar networks serve to detect and provide early warnings for a diverse array of weather phenomena. However, the inherently discontinuous nature of radar observations often results in the pres... Read More about DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar.

A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems (2023)
Journal Article
Liu, Q., Darteh, O. F., Bilal, M., Huang, X., Attique, M., Liu, X., & Acakpovi, A. (2023). A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems. Sustainable Computing, 40, Article 100892. https://doi.org/10.1016/j.suscom.2023.100892

The drive for smarter, greener, and more livable cities has led to research towards more effective solar energy forecasting techniques and their integration into traditional power systems. However, the availability of real-time data, data storage, an... Read More about A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems.

PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images (2023)
Journal Article
Liu, Q., Zhang, Z., Liu, X., Zhang, Y., & Du, Z. (in press). PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images. Intelligent Automation and Soft Computing,

Automatic extraction of water body information from high-resolution remote sensing images is one of the core tasks of remote sensing image interpretation. Since the complex multi-scale characteristics of high-resolution remote sensing images, it is d... Read More about PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images.

A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter (2023)
Journal Article
Liu, Q., Zeng, L., Bilal, M., Song, H., Liu, X., Zhang, Y., & Cao, X. (2023). A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter. IEEE Transactions on Green Communications and Networking, 7(4), 1667 - 1677. https://doi.org/10.1109/tgcn.2023.3283509

Supply chain management is a vital part of ensuring service quality and production efficiency in industrial applications. With the development of cloud computing and data intelligence in modern industries, datacenters have become an important basic s... Read More about A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter.

CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images (2023)
Journal Article
Liu, Q., Li, Y., Bilal, M., Liu, X., Zhang, Y., Wang, H., & Xu, X. (2023). CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 2481-2491. https://doi.org/10.1109/jstars.2023.3244336

In recent years, AI and Deep Learning (DL) methods have been widely used for object classification, recognition, and segmentation of high-resolution multispectral remote sensing images. These DL-based solutions perform better compare to traditional s... Read More about CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images.

Explainable AI-Based DDOS Attack Identification Method for IoT Networks (2023)
Journal Article
Kalutharage, C. S., Liu, X., Chrysoulas, C., Pitropakis, N., & Papadopoulos, P. (2023). Explainable AI-Based DDOS Attack Identification Method for IoT Networks. Computers, 12(2), Article 32. https://doi.org/10.3390/computers12020032

The modern digitized world is mainly dependent on online services. The availability of online systems continues to be seriously challenged by distributed denial of service (DDoS) attacks. The challenge in mitigating attacks is not limited to identify... Read More about Explainable AI-Based DDOS Attack Identification Method for IoT Networks.

A survey of intelligent load monitoring in IoT-enabled distributed smart grids (2022)
Journal Article
Gan, J., Zeng, L., Liu, Q., & Liu, X. (2023). A survey of intelligent load monitoring in IoT-enabled distributed smart grids. International Journal of Ad Hoc and Ubiquitous Computing, 42(1), 12. https://doi.org/10.1504/ijahuc.2023.127781

Power load monitoring has been a research hotspot since a few years ago. With development of artificial intelligence, construction of smart grid has become the most important part of power load monitoring. At the same time, task scheduling mechanism... Read More about A survey of intelligent load monitoring in IoT-enabled distributed smart grids.

A Survey on the Integration of Blockchain and IoT: Challenges and Opportunities (2022)
Book Chapter
Abubakar, M., Jaroucheh, Z., Al-Dubai, A., & Liu, X. (2022). A Survey on the Integration of Blockchain and IoT: Challenges and Opportunities. In R. Jiang, A. Bouridane, C. Li, D. Crookes, S. Boussakta, F. Hao, & E. A. Edirisinghe (Eds.), Big Data Privacy and Security in Smart Cities (197-221). Springer. https://doi.org/10.1007/978-3-031-04424-3_11

Since Satoshi Nakamoto first introduced the blockchain as an open-source project for secure financial transactions, it has attracted the scientific community’s interest, paving the way for addressing problems in domains other than cryptocurrencies, o... Read More about A Survey on the Integration of Blockchain and IoT: Challenges and Opportunities.

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.