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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.

SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures (2020)
Presentation / Conference Contribution
Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., & Papadopoulos, Y. SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Presented at IMBSA: International Symposium on Model-Based Safety and Assessment, Lisbon

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.

Failure Mode Reasoning in Model Based Safety Analysis (2020)
Presentation / Conference Contribution
Jahanian, H., Parker, D., Zeller, M., McIver, A., & Papadopoulos, Y. Failure Mode Reasoning in Model Based Safety Analysis. Presented at International Symposium on Model-Based Safety and Assessment, Lisbon, Portugal

© 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.

An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems (2020)
Presentation / Conference Contribution
Bressan, L., de Oliveira, A. L., Campos, F., Papadopoulos, Y., & Parker, D. An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems. Presented at Model-Based Safety and Assessment 7th International Symposium, IMBSA 2020, Lisbon, Portugal

© 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.

The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines (2020)
Presentation / Conference Contribution
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.

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease (2020)
Presentation / Conference Contribution
Alabed, A., Kambhampati, C., & Gordon, N. Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Presented at Computing 2020, London

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by rem... Read More about Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease.

Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification (2020)
Journal Article
Ahmad, M., Mazzara, M., Raza, R. A., Distefano, S., Asif, M., Sarfraz, M. S., Khan, A. M., & Sohaib, A. (2020). Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification. Applied Sciences, 10(14), Article 4739. https://doi.org/10.3390/app10144739

Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried ou... Read More about Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification.