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A divide-and-conquer approach to neural natural language generation from structured data (2021)
Journal Article
Dethlefs, N., Schoene, A., & Cuayáhuitl, H. (2021). A divide-and-conquer approach to neural natural language generation from structured data. Neurocomputing, 433, 300-309. https://doi.org/10.1016/j.neucom.2020.12.083

Current approaches that generate text from linked data for complex real-world domains can face problems including rich and sparse vocabularies as well as learning from examples of long varied sequences. In this article, we propose a novel divide-and-... Read More about A divide-and-conquer approach to neural natural language generation from structured data.

Nonlinear adaptive backstepping control of permanent magnet synchronous motor (2021)
Journal Article
Ali, N., Alam, W., Pervaiz, M., & Iqbal, J. (2021). Nonlinear adaptive backstepping control of permanent magnet synchronous motor. Revue Roumaine des Sciences Techniques : Série Électrotechnique et Énergétique, 66(1), 9-14

This paper addresses the speed tracking problem of a permanent magnet synchronous motor (PMSM) under the influence of parametric uncertainties and external load torque disturbances. The nonlinear dynamics associated with both PMSM and load is conside... Read More about Nonlinear adaptive backstepping control of permanent magnet synchronous motor.

Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines (2020)
Conference Proceeding
Chatterjee, J. (2020). Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines. In J. M. Alonso, & U. Cortés (Eds.), Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI2020-proceedings) (53-54)

As global efforts in transitioning to sustainable energy sources rise, wind energy has become a leading renewable energy resource. However, turbines are complex engineering systems and rely on effective operations & maintenance (O&M) to prevent catas... Read More about Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines.

Refining the Teaching of Programming (2020)
Conference Proceeding
Gordon, N., Cargill, M., Grey, S., & Brayshaw, M. (2020). Refining the Teaching of Programming. In INSPIRE XXV : e-Learning as a solution during unprecedented times in the 21st Century (97-107)

This paper considers issues around the teaching of programming, a critical yet challenging part of the computing education at all levels. This paper begins by outlining some of the key concerns around computing education-from secondary school, throug... Read More about Refining the Teaching of Programming.

Identifying Gaps in Cybersecurity Teaching and Learning (2020)
Conference Proceeding
Brayshaw, M., Gordon, N., & Karatazgianni, A. (2020). Identifying Gaps in Cybersecurity Teaching and Learning. In INSPIRE XXV : e-Learning as a solution during unprecedented times in the 21st Century (165-178)

This paper explores perceptions and expectations of privacy when using computer-mediated communication and social media. In this paper we present the results of an empirical survey into this topic and explore the pedagogic implications for the teachi... Read More about Identifying Gaps in Cybersecurity Teaching and Learning.

A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease (2020)
Conference Proceeding
Rana, S. S., Ma, X., Pang, W., & Wolverson, E. (2020). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. In 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) (9-18). https://doi.org/10.1109/BDCAT50828.2020.00013

Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utili... Read More about A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease.

A Model-based RCM Analysis Method (2020)
Conference Proceeding
Mian, Z., Jia, S., Shi, X., Tang, C., Chen, J., & Gao, Y. (2020). A Model-based RCM Analysis Method. In IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C) (301-307). https://doi.org/10.1109/QRS-C51114.2020.00059

The reliability-centered maintenance (RCM) is one of the most advanced maintenance plan generating technologies for equipments. At present, the key technologies such as FMEA and FMECA supporting the RCM analysis remains in the manual stage in some en... Read More about A Model-based RCM Analysis Method.

Computational Intelligence for Safety Assurance of Cooperative Systems of Systems (2020)
Journal Article
Kabir, S., & Papadopoulos, Y. (2020). Computational Intelligence for Safety Assurance of Cooperative Systems of Systems. Computer, 53(12), 24-34. https://doi.org/10.1109/MC.2020.3014604

Cooperative systems of systems (CSoSs) form a new technological frontier for their enormous economic and societal potentials in various domains. This article presents a novel framework for dynamic safety assurance of CSoSs that integrates design time... Read More about Computational Intelligence for Safety Assurance of Cooperative Systems of Systems.

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., …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.

Comparative review of pipelines monitoring and leakage detection techniques (2020)
Conference Proceeding
Aljuaid, K. G., Albuoderman, M. A., Alahmadi, E. A., & Iqbal, J. (2020). Comparative review of pipelines monitoring and leakage detection techniques. In 2nd International Conference on Computer and Information Sciences (ICCIS). https://doi.org/10.1109/ICCIS49240.2020.9257602

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)
Conference Proceeding
Chatterjee, J., & Dethlefs, N. (2020). Deep reinforcement learning for maintenance planning of offshore vessel transfer. In C. Guedes Soares (Ed.), Developments in Renewable Energies Offshore Proceedings of the 4th International Conference on Renewable Energies Offshore (RENEW 2020, 12 - 15 October 2020, Lisbon, Portugal) (435-443)

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.

A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines (2020)
Conference Proceeding
Chatterjee, J., & Dethlefs, N. (2020). A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9206839

© 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)
Journal Article
Chatterjee, J., & Dethlefs, N. (2020). Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI. Journal of Physics: Conference Series, 1618(2), Article 022022. https://doi.org/10.1088/1742-6596/1618/2/022022

© 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.

SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures (2020)
Journal Article
Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., & Papadopoulos, Y. (2020). SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Lecture notes in computer science, 12297, 197-211. https://doi.org/10.1007/978-3-030-58920-2_13

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with... Read More about SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures.

An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems (2020)
Journal Article
Bressan, L., de Oliveira, A. L., Campos, F., Papadopoulos, Y., & Parker, D. (2020). An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems. Lecture notes in computer science, 12297 LNCS, 179-193. https://doi.org/10.1007/978-3-030-58920-2_12

© 2020, Springer Nature Switzerland AG. Component-based approaches and software product lines have been adopted by industry to manage the diversity of configurations on safety-critical software. Safety certification demands compliance with standards.... Read More about An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems.

Failure Mode Reasoning in Model Based Safety Analysis (2020)
Journal Article
Jahanian, H., Parker, D., Zeller, M., McIver, A., & Papadopoulos, Y. (2020). Failure Mode Reasoning in Model Based Safety Analysis. Lecture notes in computer science, 12297 LNCS, 130-145. https://doi.org/10.1007/978-3-030-58920-2_9

© 2020, Springer Nature Switzerland AG. Failure Mode Reasoning (FMR) is a novel approach for analyzing failure in a Safety Instrumented System (SIS). The method uses an automatic analysis of an SIS program to calculate potential failures in parts of... Read More about Failure Mode Reasoning in Model Based Safety Analysis.

The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines (2020)
Presentation / Conference
Chatterjee, J., & Dethlefs, N. (2020, August). The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines. Paper presented at Fragile Earth: Data Science for a Sustainable Planet. KDD 2020, Virtual Conference

The global pursuit towards sustainable development is leading to increased adaptation of renewable energy sources. Wind turbines are promising sources of clean energy, but regularly suffer from failures and down-times, primarily due to the complex en... Read More about The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines.