All Outputs (9)

Real-time social media sentiment analysis for rapid impact assessment of floods (2023)
Journal Article
Bryan-Smith, L., Godsall, J., George, F., Egode, K., Dethlefs, N., & Parsons, D. (2023). Real-time social media sentiment analysis for rapid impact assessment of floods. Computers & geosciences, 178, Article 105405. https://doi.org/10.1016/j.cageo.2023.105405

Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic env... Read More about Real-time social media sentiment analysis for rapid impact assessment of floods.

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning (2023)
Journal Article
Chatterjee, J., Alvela Nieto, M. T., Gelbhardt, H., Dethlefs, N., Ohlendorf, J., Greulich, A., & Thoben, K. (2023). Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning. Environmental Data Science, 2, 1-15. https://doi.org/10.1017/eds.2023.9

Wind energy’s ability to liberate the world from conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational f... Read More about Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning.

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines (2022)
Journal Article
Chatterjee, J., & Dethlefs, N. (2022). Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines. IEEE Access, 10, 84710-84737. https://doi.org/10.1109/ACCESS.2022.3197167

Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promisin... Read More about Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines.

XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines (2021)
Journal Article
Chatterjee, J., & Dethlefs, N. XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines. https://doi.org/10.48550/arXiv.2012.10489. Manuscript submitted for publication

Condition-based monitoring (CBM) has been widely utilised in the wind industry for monitoring operational inconsistencies and failures in turbines, with techniques ranging from signal processing and vibration analysis to artificial intelligence (AI)... Read More about XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines.

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes (2021)
Journal Article
Schoene, A. M., Turner, A. P., De Mel, G., & Dethlefs, N. (in press). Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes. IEEE Transactions on Affective Computing, https://doi.org/10.1109/TAFFC.2021.3057105

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furth... Read More about Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes.

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.

Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines (2020)
Journal Article
Chatterjee, J., & Dethlefs, N. (2020). Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind energy, 23(8), 1693-1710. https://doi.org/10.1002/we.2510

The last decade has witnessed an increased interest in applying machine learning techniques to predict faults and anomalies in the operation of wind turbines. These efforts have lately been dominated by deep learning techniques which, as in other fie... Read More about Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines.

Domain transfer for deep natural language generation from abstract meaning representations (2017)
Journal Article
Dethlefs, N. (2017). Domain transfer for deep natural language generation from abstract meaning representations. IEEE computational intelligence magazine, 12(3), 18-28. https://doi.org/10.1109/mci.2017.2708558

Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail... Read More about Domain transfer for deep natural language generation from abstract meaning representations.

Information density and overlap in spoken dialogue (2015)
Journal Article
Dethlefs, N., Hastie, H., Cuayáhuitl, H., Yu, Y., Rieser, V., & Lemon, O. (2016). Information density and overlap in spoken dialogue. Computer speech & language, 37, 82-97. https://doi.org/10.1016/j.csl.2015.11.001

Incremental dialogue systems are often perceived as more responsive and natural because they are able to address phenomena of turn-taking and overlapping speech, such as backchannels or barge-ins. Previous work in this area has often identified disti... Read More about Information density and overlap in spoken dialogue.