An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars

Wu, Hao; Liu, Qi; Liu, Xiaodong; Zhang, Yonghong; Yang, Zhiyun

doi:10.1007/s11280-021-00988-y

High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network (2022)
Presentation / Conference Contribution
Zhang, Z., Li, Y., Liu, Q., & Liu, X. (2022, September). High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

A basic stage of hydrological research is to automatically extract water body information from high-resolution remote sensing images. Various methods based on deep learning convolutional neural networks have been proposed in recent studies to achieve... Read More about High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network.

Emerging Simulation Frameworks for Analyzing Smart Grid Cyberattack: A Literature Review (2022)
Presentation / Conference Contribution
Darteh, O. F., Liu, Q., Liu, X., Bah, I., Nakoty, F. M., & Acakpovi, A. (2022, September). Emerging Simulation Frameworks for Analyzing Smart Grid Cyberattack: A Literature Review. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

The transition of the conventional power grid into the Smart Grid (SG), a widely distributed energy delivery network characterized by a two-way flow of electricity and information, is key for energy sector stakeholders. Despite the SG’s clear improve... Read More about Emerging Simulation Frameworks for Analyzing Smart Grid Cyberattack: A Literature Review.

Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction (2022)
Presentation / Conference Contribution
Sun, J., Wu, H., Liu, Q., Liu, X., & Ma, J. (2022, September). Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

The weather radar will receive a lot of non-meteorological echo information during the body scan process, such as: object echoes, co-wave interference echoes, airplanes, flocks of birds, etc. These non-meteorological echoes will cause pollution to no... Read More about Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction.

An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models (2022)
Presentation / Conference Contribution
Wang, Y., Yang, Z., Liu, Q., & Liu, X. (2022, September). An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

Short-term heavy rainfall can have a significant impact on people's production, life and travel. Numerical Weather Prediction (NWP) is complex. It can predict weather conditions for the next week or even two weeks, but cannot predict the weather in t... Read More about An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models.

Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract) (2022)
Presentation / Conference Contribution
Sampath Kalutharage, C., Liu, X., & Chrysoulas, C. (2022, September). Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract). Presented at 27th European Symposium on Research in Computer Security (ESORICS) 2022, Copenhagen, Denmark

Over the past few decades, Machine Learning (ML)-based intrusion detection systems (IDS) have become increasingly popular and continue to show remarkable performance in detecting attacks. However, the lack of transparency in their decision-making pro... Read More about Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract).

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 Lightweight and User-centric Two-factor Authentication Mechanism for IoT Based on Blockchain and Smart Contract (2022)
Presentation / Conference Contribution
Abubakar, M., Jaroucheh, Z., Al Dubai, A., & Liu, X. (2022, May). A Lightweight and User-centric Two-factor Authentication Mechanism for IoT Based on Blockchain and Smart Contract. Presented at 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia

Two-factor authentication (2FA) is commonly used in Internet of Things (IoT) authentication to provide multi-layer protection. Tokens, often known as One-Time Passwords (OTP), are used to offer additional information. While this technique provides fl... Read More about A Lightweight and User-centric Two-factor Authentication Mechanism for IoT Based on Blockchain and Smart Contract.

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.

Intelligent Question Answering System Based on Knowledge Graph (2022)
Presentation / Conference Contribution
Feng, X., Liu, Q., & Liu, X. (2021, December). Intelligent Question Answering System Based on Knowledge Graph. Presented at IEEE SmartCity-2021, Hainan, China

In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. The intelligent question answering system based on the knowledge graph is dedicated t... Read More about Intelligent Question Answering System Based on Knowledge Graph.

A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment (2022)
Presentation / Conference Contribution
Lu, H., Kamoto, K. M., Liu, Q., Zhang, Y., Liu, X., Xu, X., & Qi, L. (2021, December). A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment. Presented at 11th EAI International Conference, CloudComp 2021, Online

It is of great significance to study the positive characteristics of concrete bearing cracks, fire and other adverse environment for the safety of human life and property and the protection of environmental resources. However, there are still some ch... Read More about A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment.

Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach (2022)
Presentation / Conference Contribution
Zhang, J., Sun, J., Gan, J., Liu, Q., & Liu, X. (2021, October). Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach. Presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), AB, Canada [Online]

The past decade have seen a growth in Internet technology, the overlap of cyberspace and social space provides great convenience for people's life. The in-depth study of non-intrusive load management (NILM) promotes the development of multi-integrati... Read More about Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach.

An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data (2022)
Presentation / Conference Contribution
Wu, Z., Wu, X., Liu, Q., & Liu, X. (2021, October). An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data. Presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), AB, Canada [Online]

There are more than 10 million new stroke cases worldwide every year, and stroke has become one of the main causes of death and disability. In recent years, with the rapid development of computer science and technology, through the combination of Int... Read More about An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data.

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.

Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems (2022)
Journal Article
Liu, Q., Liu, X., Grosu, R., & Yang, C. (2022). Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems. Computer Modeling in Engineering and Sciences, 131(1), 23-26. https://doi.org/10.32604/cmes.2022.020646

This article has no abstract.

Tracking Stream Quality Issues in Combined Physical and Radar Sensors for IoT-based Data-driven Actuation (2021)
Presentation / Conference Contribution
Bamgboye, O., Liu, X., Cruickshank, P., Liu, Q., & Zhang, Y. (2021, December). Tracking Stream Quality Issues in Combined Physical and Radar Sensors for IoT-based Data-driven Actuation. Presented at 2021 CIE International Conference on Radar (Radar), Haikou, Hainan, China

In this paper, a stream quality tracking for measurements from combined radar and physical sensors is developed. The authors proposed the use of RDF stream processing system and semantic rules to provide semantic reasoning for tracking erroneous data... Read More about Tracking Stream Quality Issues in Combined Physical and Radar Sensors for IoT-based Data-driven Actuation.

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.

All Outputs (185)