Outputs (835)

Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050 (2020)
Journal Article
Golizadeh Akhlaghi, Y., Aslansefat, K., Zhao, X., Sadati, S., Badiei, A., Xiao, X., Shittu, S., Fan, Y., & Ma, X. (2021). Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050. Applied energy, 281, Article 116062. https://doi.org/10.1016/j.apenergy.2020.116062

The empirical success of the Artificial Intelligence (AI), has enhanced importance of the transparency in black box Machine Learning (ML) models. This study pioneers in developing an explainable and interpretable Deep Neural Network (DNN) model for a... Read More about Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050.

Effect of IDT position parameters on SAW yarn tension sensor sensitivity (2020)
Journal Article
Lei, B., Lu, W., Mian, Z., & Bao, W. (2020). Effect of IDT position parameters on SAW yarn tension sensor sensitivity. Measurement and Control, 53(9-10), 2055-2062. https://doi.org/10.1177/0020294020965620

In this paper, the effect of the interdigital transducer (IDT) position parameters on the surface acoustic wave (SAW) yarn tension sensor sensitivity is investigated. The stress–strain characteristic of substrate was studied by the combination of fin... Read More about Effect of IDT position parameters on SAW yarn tension sensor sensitivity.

Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation (2020)
Journal Article
Ramirez Rivera, A., Khan, A., Bekkouch, I. E. I., & Sheikh, T. S. (2022). Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation. IEEE Transactions on Neural Networks and Learning Systems, 33(1), 281-291. https://doi.org/10.1109/TNNLS.2020.3027667

Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality o... Read More about Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation.

Comparative review of pipelines monitoring and leakage detection techniques (2020)
Presentation / Conference Contribution
Aljuaid, K. G., Albuoderman, M. A., Alahmadi, E. A., & Iqbal, J. (2020, October). Comparative review of pipelines monitoring and leakage detection techniques. Presented at 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia

The oil and gas industry owns expensive and widely-spread assets. Any fault in this complex transportation network may result in accidents and/or huge losses thereby triggering various environmental and economic issues. Thus, real-time monitoring and... Read More about Comparative review of pipelines monitoring and leakage detection techniques.

Deep reinforcement learning for maintenance planning of offshore vessel transfer (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020, October). Deep reinforcement learning for maintenance planning of offshore vessel transfer. Presented at 4th International Conference on Renewable Energies Offshore (RENEW 2020), Lisbon, Portugal

Offshore wind farm operators need to make short-term decisions on planning vessel transfers to turbines for preventive or corrective maintenance. These decisions can play a pivotal role in ensuring maintenance actions are carried out in a timely and... Read More about Deep reinforcement learning for maintenance planning of offshore vessel transfer.

Model-connected safety cases (2020)
Thesis
Retouniotis, A. Model-connected safety cases. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4223157

Regulatory authorities require justification that safety-critical systems exhibit acceptable levels of safety. Safety cases are traditionally documents which allow the exchange of information between stakeholders and communicate the rationale of how... Read More about Model-connected safety cases.

A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020, July). A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK

© 2020 IEEE. Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines an... Read More about A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines.

Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI. Presented at The Science of Making Torque from Wind (TORQUE 2020), Online, Netherlands

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, inclu... Read More about Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI.

GANS-based data augmentation for citrus disease severity detection using deep learning (2020)
Journal Article
Zeng, Q., Ma, X., Cheng, B., Zhou, E., & Pang, W. (2020). GANS-based data augmentation for citrus disease severity detection using deep learning. IEEE Access, 8, 172882-172891. https://doi.org/10.1109/ACCESS.2020.3025196

Recently, many Deep Learning models have been employed to classify different kinds of plant diseases, but very little work has been done for disease severity detection. However, it is more important to master the severities of plant diseases accurate... Read More about GANS-based data augmentation for citrus disease severity detection using deep learning.

Predictive control using active aerodynamic surfaces to improve ride quality of a vehicle (2020)
Journal Article
Ahmad, E., Iqbal, J., Khan, M. A., Liang, W., & Youn, I. (2020). Predictive control using active aerodynamic surfaces to improve ride quality of a vehicle. Electronics, 9(9), Article 1463. https://doi.org/10.3390/electronics9091463

This work presents a predictive control strategy for a four degrees of freedom (DOF) half-car model in the presence of active aerodynamic surfaces. The proposed control strategy consists of two parts: the feedback control deals with the tracking erro... Read More about Predictive control using active aerodynamic surfaces to improve ride quality of a vehicle.