Outputs (444)

Long‐Term Rain Attenuation Measurement for Short‐Range mmWave Fixed Link Using DSD and ITU‐R Prediction Models (2022)
Journal Article
Zahid, O., & Salous, S. (2022). Long‐Term Rain Attenuation Measurement for Short‐Range mmWave Fixed Link Using DSD and ITU‐R Prediction Models. Radio Science, 57(4), https://doi.org/10.1029/2021rs007307

Several millimeter Wave (mmWave) bands, which suffer from rain attenuation, were identified in the World Radiocommunication Conference 2019 (WRC-19) for fifth generation (5G) radio networks. In this paper, long-term attenuation is measured over typic... Read More about Long‐Term Rain Attenuation Measurement for Short‐Range mmWave Fixed Link Using DSD and ITU‐R Prediction Models.

Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review (2022)
Journal Article
Hua, W., Chen, Y., Qadrdan, M., Jiang, J., Sun, H., & Wu, J. (2022). Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review. Renewable and Sustainable Energy Reviews, 161, Article 112308. https://doi.org/10.1016/j.rser.2022.112308

Governments’ net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advan... Read More about Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review.

Data-driven prosumer-centric energy scheduling using convolutional neural networks (2022)
Journal Article
Hua, W., Jiang, J., Sun, H., Tonello, A., Qadrdan, M., & Wu, J. (2022). Data-driven prosumer-centric energy scheduling using convolutional neural networks. Applied Energy, 308, Article 118361. https://doi.org/10.1016/j.apenergy.2021.118361

The emerging role of energy prosumers (both producers and consumers) enables a more flexible and localised structure of energy markets. However, it leads to challenges for the energy scheduling of individual prosumers in terms of identifying idiosync... Read More about Data-driven prosumer-centric energy scheduling using convolutional neural networks.

On the Maximum Agreement Subtree Conjecture for Balanced Trees (2022)
Journal Article
Bordewich, M., Linz, S., Owen, M., St. John, K., Semple, C., & Wicke, K. (2022). On the Maximum Agreement Subtree Conjecture for Balanced Trees. SIAM Journal on Discrete Mathematics, 36(1), 336-354. https://doi.org/10.1137/20m1379678

We give a counterexample to the conjecture of Martin and Thatte that two balanced rooted binary leaf-labelled trees on n leaves have a maximum agreement subtree (MAST) of size at least n 1 2 . In particular, we show that for any c > 0, there exist tw... Read More about On the Maximum Agreement Subtree Conjecture for Balanced Trees.

Consumer-centric decarbonization framework using Stackelberg game and Blockchain (2022)
Journal Article
Hua, W., Jiang, J., Sun, H., Teng, F., & Strbac, G. (2022). Consumer-centric decarbonization framework using Stackelberg game and Blockchain. Applied Energy, 309, Article 118384. https://doi.org/10.1016/j.apenergy.2021.118384

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative... Read More about Consumer-centric decarbonization framework using Stackelberg game and Blockchain.

Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems (2021)
Presentation / Conference Contribution
Hua, W., Jing, R., Zhou, Y., Zhang, X., Jiang, J., & Sun, H. (2023, November). Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems. Presented at 13th International Conference on Applied Energy (ICAE 2021), Bangkok

Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems d... Read More about Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems.

Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul (2021)
Presentation / Conference Contribution
Maryopi, D., Yingjia, H., & Ikhlef, A. (2021, December). Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul. Presented at 2021 IEEE GLOBECOM Workshops, Madrid, Spain

The limited fronthaul capacity is known to be one of the main problems in cloud radio access networks (CRANs), especially in the wireless fronthaul links. In this paper, we consider the uplink of a CRAN system, where massive multiple-input multiple-o... Read More about Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul.

Short-term load forecasting using artificial neural networks and social media data (2021)
Presentation / Conference Contribution
Boyd, A., Sun, H., Black, M., & Jesson, S. (2021, September). Short-term load forecasting using artificial neural networks and social media data. Presented at CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution, Online Conference

Evolving practices around energy generation, storage and trading within the UK have made it more necessary than ever to provide accurate means of forecasting electricity demand. This paper considers deep neural networks with convolutional and recurre... Read More about Short-term load forecasting using artificial neural networks and social media data.

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application (2021)
Journal Article
Shi, C., Dai, X., Wang, Y., Zhou, J., & Salous, S. (2022). Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application. IEEE Systems Journal, 16(4), 6669-6680. https://doi.org/10.1109/jsyst.2021.3134657

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the op... Read More about Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application.

Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network (2021)
Journal Article
Shi, C., Wang, Y., Salous, S., Zhou, J., & Yan, J. (2022). Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network. IEEE Transactions on Aerospace and Electronic Systems, 58(4), 2762-2778. https://doi.org/10.1109/taes.2021.3138869

In this paper, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization a... Read More about Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network.

Appliance Classification using BiLSTM Neural Networks and Feature Extraction (2021)
Presentation / Conference Contribution
Correa-Delval, M., Sun, H., Matthews, P., & Jiang, J. (2021, October). Appliance Classification using BiLSTM Neural Networks and Feature Extraction. Presented at IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Espoo, Finland

One significant challenge in Non-Intrusive Load Monitoring (NILM) is to identify and classify active appliances used in a building. This research focuses on the classifying process, exploring different approaches for the feature extraction of the app... Read More about Appliance Classification using BiLSTM Neural Networks and Feature Extraction.

Tilting micromirror platform based on liquid dielectrophoresis (2021)
Journal Article
Frozanpoor, I., Cooke, M., Alvarez-Ruiz, D., Ambukan, V., Gallant, A., & Balocco, C. (2021). Tilting micromirror platform based on liquid dielectrophoresis. Sensors and Actuators A: Physical, 332(2), Article 113177. https://doi.org/10.1016/j.sna.2021.113177

This study presents an electrically actuated tilting micro platform based on liquid dielectrophoresis with three axes movement using three droplets situated 120° apart from each other. The interdigitated electrodes produce a non-uniform electric fiel... Read More about Tilting micromirror platform based on liquid dielectrophoresis.

Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres (2021)
Presentation / Conference Contribution
Mackie, M., Sun, H., & Jiang, J. (2021, October). Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres. Presented at IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) 2021, Espoo, Finland

This paper proposes a method of migrating workload among geo-distributed data centres that are equipped with on-site renewable energy sources (RES), such as solar and wind energy, to decarbonise data centres. It aims to optimise the performance of su... Read More about Reinforcement Learning Based Load Balancing for Geographically Distributed Data centres.

Evolving to Non-round Weingarten Spheres: Integer Linear Hopf Flows (2021)
Journal Article
Guilfoyle, B., & Klingenberg, W. (2021). Evolving to Non-round Weingarten Spheres: Integer Linear Hopf Flows. Partial Differential Equations and Applications, 2(6), Article 72. https://doi.org/10.1007/s42985-021-00128-1

In the 1950’s Hopf gave examples of non-round convex 2-spheres in Euclidean 3-space with rotational symmetry that satisfy a linear relationship between their principal curvatures. In this paper, we investigate conditions under which evolving a smooth... Read More about Evolving to Non-round Weingarten Spheres: Integer Linear Hopf Flows.

Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties (2021)
Journal Article
You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties. Applied Energy, 305, Article 117899. https://doi.org/10.1016/j.apenergy.2021.117899

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT’s predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency and cost saving.energy efficiency,... Read More about Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties.

Autoencoders Without Reconstruction for Textural Anomaly Detection (2021)
Presentation / Conference Contribution
Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021, July). Autoencoders Without Reconstruction for Textural Anomaly Detection. Presented at 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China

Automatic anomaly detection in natural textures is a key component within quality control for a range of high-speed, high-yield manufacturing industries that rely on camera-based visual inspection techniques. Targeting anomaly detection through the u... Read More about Autoencoders Without Reconstruction for Textural Anomaly Detection.

Blockchain-Based Peer-to-Peer Energy Trading Method (2021)
Journal Article
Thompson, M. J., Sun, H., & Jiang, J. (2022). Blockchain-Based Peer-to-Peer Energy Trading Method. CSEE journal of power and energy systems, 8(5), 1318-1326. https://doi.org/10.17775/cseejpes.2021.00010

Blockchain-enabled peer-to-peer energy trading provides a method for neighbours and communities to trade energy generated from local and distributed renewable energy sources. Effective matching can facilitate greater energy efficiency during transmis... Read More about Blockchain-Based Peer-to-Peer Energy Trading Method.

Verification of an Intelligent Ray Launching Algorithm in Indoor Environments in the Ka‐Band (2021)
Journal Article
Yang, W., Huang, J., Zhang, J., Gao, Y., Salous, S., & Zhang, J. (2021). Verification of an Intelligent Ray Launching Algorithm in Indoor Environments in the Ka‐Band. Radio Science, 56(9), https://doi.org/10.1029/2020rs007252

This paper presents the verification of indoor propagation channel simulations based on an intelligent ray launching algorithm (IRLA) in the Ka-band of the millimeter-wave (mmWave) spectrum in various indoor environments, including a classroom, an of... Read More about Verification of an Intelligent Ray Launching Algorithm in Indoor Environments in the Ka‐Band.

A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming (2021)
Presentation / Conference Contribution
You, M., Zheng, G., & Sun, H. (2021, June). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. Presented at ICC 2021 - IEEE International Conference on Communications, Montreal, Quebec

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in the multiuser multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major chal... Read More about A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming.

Joint Design of Fronthaul and Access Links in Massive MIMO Multi-UAV-enabled CRANs (2021)
Journal Article
Huang, Y., & Ikhlef, A. (2021). Joint Design of Fronthaul and Access Links in Massive MIMO Multi-UAV-enabled CRANs. IEEE Wireless Communications Letters, 10(11), 2355-2359. https://doi.org/10.1109/lwc.2021.3100320

This paper proposes a novel architecture of multi-unmanned aerial vehicle (UAV)-enabled cloud radio access network (CRAN). In particular, we propose to deploy the UAVs as flying remote radio heads (RRHs) to serve ground user equipments (UEs). The bas... Read More about Joint Design of Fronthaul and Access Links in Massive MIMO Multi-UAV-enabled CRANs.