Outputs (249)

The Effect of Conducted Emissions of Grid-Tied Three-Phase Adjustable Drives (2023)
Journal Article
Genc, S., Muneeswaran, V., Thomas, D., Greedy, S., Gundogdu, B., Sarma, N., & Ozgonenel, O. (2023). The Effect of Conducted Emissions of Grid-Tied Three-Phase Adjustable Drives. Engineering Science and Technology, an International Journal, 46, Article 101505. https://doi.org/10.1016/j.jestch.2023.101505

Electromagnetic Interference (EMI) is generated and mitigated in power converters. EMI problems are related to high-speed power converters. This article focuses on conducted electromagnetic interference in adjustable-speed drive (ASD) systems. The el... Read More about The Effect of Conducted Emissions of Grid-Tied Three-Phase Adjustable Drives.

Modelling low-velocity impact damage and compression after impact of 3D woven structures considering compaction (2023)
Journal Article
Millen, S., Dahale, M., Fisher, T., Samy, A., Thompson, K., Ramaswamy, K., Ralph, C., Archer, E., McIlhagger, A., Ullah, Z., & Falzon, B. (2023). Modelling low-velocity impact damage and compression after impact of 3D woven structures considering compaction. Composite Structures, 318, Article 117104. https://doi.org/10.1016/j.compstruct.2023.117104

A novel finite element modelling approach is presented which incorporates representative binder yarn compaction, for simulating the low-velocity impact (LVI) and compression after impact (CAI) response of 3D woven layer-to-layer carbon/epoxy composit... Read More about Modelling low-velocity impact damage and compression after impact of 3D woven structures considering compaction.

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening (2023)
Presentation / Conference Contribution
Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023, June). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection perfor... Read More about Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery (2023)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023, June). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample siz... Read More about Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery.

An Interdisciplinary Approach to Optimal Communication and Flight Operation of High-Altitude Long-Endurance Platforms (2023)
Journal Article
Javed, S., Alouini, M.-S., & Ding, Z. (2023). An Interdisciplinary Approach to Optimal Communication and Flight Operation of High-Altitude Long-Endurance Platforms. IEEE Transactions on Aerospace and Electronic Systems, 59(6), 8327-8341. https://doi.org/10.1109/taes.2023.3304615

Aerial communication platforms, stratospheric high-altitude platform stations (HAPSs), have the potential to provide/catalyze advanced mobile wireless communication services with their ubiquitous connectivity and ultrawide coverage radius. Recently,... Read More about An Interdisciplinary Approach to Optimal Communication and Flight Operation of High-Altitude Long-Endurance Platforms.

A one-class support vector machine for detecting valve stiction (2023)
Journal Article
O’Neill, H., Khalid, Y., Spink, G., & Thorpe, P. (2023). A one-class support vector machine for detecting valve stiction. Digital Chemical Engineering, 8, Article 100116. https://doi.org/10.1016/j.dche.2023.100116

In industrial processes, control valve stiction is known to be one of the primary causes for poor control loop performance. Stiction introduces oscillatory behaviour in the process, leading to increased energy consumption, variations in product quali... Read More about A one-class support vector machine for detecting valve stiction.

Modelling of hydrogen blending into the UK natural gas network driven by a solid oxide fuel cell for electricity and district heating system (2023)
Journal Article
Samanta, S., Roy, D., Roy, S., Smallbone, A., & Roskilly, A. P. (2024). Modelling of hydrogen blending into the UK natural gas network driven by a solid oxide fuel cell for electricity and district heating system. Fuel, 355, Article 129411. https://doi.org/10.1016/j.fuel.2023.129411

A thorough investigation of the thermodynamics and economic performance of a cogeneration system based on solid oxide fuel cells that provides heat and power to homes has been carried out in this study. Additionally, different percentages of green hy... Read More about Modelling of hydrogen blending into the UK natural gas network driven by a solid oxide fuel cell for electricity and district heating system.

Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset (2023)
Journal Article
Parsarad, S., Saeedizadeh, N., Soufi, G. J., Shafieyoon, S., Hekmatnia, F., Zarei, A. P., Soleimany, S., Yousefi, A., Nazari, H., Torabi, P., S. Milani, A., Madani Tonekaboni, S. A., Rabbani, H., Hekmatnia, A., & Kafieh, R. (2023). Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset. Journal of Imaging, 9(8), Article 159. https://doi.org/10.3390/jimaging9080159

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy... Read More about Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset.

Convolutional neural network framework for wind turbine electromechanical fault detection (2023)
Journal Article
Stone, E., Giani, S., Zappalá, D., & Crabtree, C. (2023). Convolutional neural network framework for wind turbine electromechanical fault detection. Wind Energy, 26(10), 1082 - 1097. https://doi.org/10.1002/we.2857

Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight Convolutional Neural Networ... Read More about Convolutional neural network framework for wind turbine electromechanical fault detection.

Stochastic Optimization of an Active Network Management Scheme for a DER-Rich Distribution Network Comprising Various Aggregators (2023)
Presentation / Conference Contribution
Adeniji, O., & Kazemtabrizi, B. (2023, June). Stochastic Optimization of an Active Network Management Scheme for a DER-Rich Distribution Network Comprising Various Aggregators. Presented at EEEIC2023: 23rd International Conference on Environment and Electrical Engineering, Madrid, Spain

With large-scale acceptance of solar and wind energy generation into electric grids, large energy storage is expected to provide sufficient flexibility for the safe, stable and economic operation of power systems under uncertainty. Active Network Man... Read More about Stochastic Optimization of an Active Network Management Scheme for a DER-Rich Distribution Network Comprising Various Aggregators.