By Doctor Graham Wild

Conference Papers

Jansz PV; Wild G; Hinckley S, 2007, 'Stepped mirrored structures for generating true time delays in stationary optical delay line proof-of-principle experiments for application to optical coherence tomography - art. no. 68011H', in Krolikowski WZ; Soukoulis CM; Lam PK; Davis TJ; Fan S; Kivshar YS (eds.), PHOTONICS: DESIGN, TECHNOLOGY, AND PACKAGING III, SPIE-INT SOC OPTICAL ENGINEERING, AUSTRALIA, Canberra, pp. H8011 - H8011, presented at Conference on Photonics: Design, Technology, and Packaging III, AUSTRALIA, Canberra, 05 December 2007 - 07 December 2007

Wild G; Hinckley S, 2007, 'Fiber Bragg grating sensors for acoustic emission and transmission detection applied to robotic NDE in structural health monitoring', in Proceedings of the 2007 IEEE Sensors Applications Symposium Sas, http://dx.doi.org/10.1109/SAS.2007.374388

Wild G; Hinckley S, 2007, 'Electro-acoustic and acousto-optic communications for robotic agents in smart structures', in Proceedings of SPIE the International Society for Optical Engineering, http://dx.doi.org/10.1117/12.695750

Wild G; Hinckley S, 2007, 'Fiber bragg grating sensors for acoustic emission and transmission detection applied to robotic NDE in structural health monitoring', in 2007 IEEE SENSORS APPLICATIONS SYMPOSIUM, IEEE, CA, San Diego, pp. 219 - +, presented at 2nd IEEE Sensors Applications Symposium, CA, San Diego, 06 February 2007 - 08 February 2007

Preprints

Wild G, 2026, War in Social Media: Soft Systems Modelling of NATO Fracture Through Grey Zone Operations, http://dx.doi.org/10.20944/preprints202601.1500.v1

Somerville A; Lynar T; Joiner K; Wild G, 2026,

Virtual Reality Flight Simulation: A Quasi-Transfer of Training Study, http://dx.doi.org/10.20944/preprints202601.0964.v1

Nanyonga A; Joiner K; Turhan U; Wild G, 2025, Deep Learning Approaches for Classifying Aviation Safety Incidents: Evidence from Australian Data, http://dx.doi.org/10.20944/preprints202508.2196.v1

Nanyonga A; Wild G, 2025, Utilizing AI for Aviation Post-Accident Analysis Classification, http://dx.doi.org/10.48550/arxiv.2506.00169

Nanyonga A; Wild G, 2025, Classification of Operational Records in Aviation Using Deep Learning Approaches, http://dx.doi.org/10.48550/arxiv.2501.01222

Nanyonga A; Wasswa H; Turhan U; Molloy O; Wild G, 2025, Sequential Classification of Aviation Safety Occurrences with Natural Language Processing, http://dx.doi.org/10.36227/techrxiv.173895150.01666498/v1

Nanyonga A; Wasswa H; Wild G, 2025, Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports, http://dx.doi.org/10.48550/arxiv.2501.07923

Nanyonga A; Wasswa H; Turhan U; Joiner K; Wild G, 2025, Exploring Aviation Incident Narratives Using Topic Modeling and Clustering Techniques, http://dx.doi.org/10.48550/arxiv.2501.07924

Nanyonga A; Wasswa H; Wild G, 2025, Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset, http://dx.doi.org/10.48550/arxiv.2501.07925

Nanyonga A; Wasswa H; Molloy O; Turhan U; Wild G, 2025, Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences, http://dx.doi.org/10.48550/arxiv.2501.06564

Nanyonga A; Wasswa H; Turhan U; Molloy O; Wild G, 2025, Sequential Classification of Aviation Safety Occurrences with Natural Language Processing, http://dx.doi.org/10.48550/arxiv.2501.06490

Nanyonga A; Wild G, 2025, Analyzing Aviation Safety Narratives with LDA, NMF and PLSA: A Case Study Using Socrata Datasets, http://dx.doi.org/10.48550/arxiv.2501.01690

Nanyonga A; Joiner K; Turhan U; Wild G, 2025, Applications of natural language processing in aviation safety: A review and qualitative analysis, http://dx.doi.org/10.48550/arxiv.2501.06210

Nanyonga A; Wasswa H; Wild G, 2025, Comparative Study of Deep Learning Architectures for Textual Damage Level Classification, http://dx.doi.org/10.48550/arxiv.2501.01694

Nanyonga A; Wasswa H; Turhan U; Joiner K; Wild G, 2025, Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language Processing, http://dx.doi.org/10.48550/arxiv.2501.01227

Pollock L; Wild G, 2024, Data-driven shape sensing of a hypersonic inlet ramp, http://dx.doi.org/10.31224/3817

Wild G, 2024, Airbus A32x vs Boeing 737 Safety Occurrences, http://dx.doi.org/10.48550/arxiv.2405.00044

Nanyonga A; Wasswa H; Wild G, 2024, Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models, http://dx.doi.org/10.48550/arxiv.2403.04788

Wild G; Richardson S, 2022, Automated formative assessments marking, feedback, and analytics with multiple choice face-to-face quizzes, http://dx.doi.org/10.31219/osf.io/vsr4q

Wild G, 2022, Improving Capstone Research Projects: Using Computational Thinking to Provide Choice and Structured Active Learning, https://arxiv.org/abs/2203.15947v1

Beling C; Wild G, 2021, The association between emotional intelligence and decision making for pilots, http://dx.doi.org/10.31224/osf.io/gqfsc

Wild G; Baxter G; Srisaeng P; Richardson S, 2021, Machine Learning for Air Transport Planning and Management, http://dx.doi.org/10.48550/arxiv.2112.01301

Pollock L; Wild G, 2021, An initial review of hypersonic vehicle accidents, https://arxiv.org/abs/2110.06438v1

Pollock L; Wild G, 2021, Passive Phased Array Acoustic Emission Localisation via Recursive Signal-Averaged Lamb Waves with an Applied Warped Frequency Transformation, https://arxiv.org/abs/2110.06457v1

Wild G, 2021, On the Origins and Relevance of the Equal Transit Time Fallacy to Explain Lift, https://arxiv.org/abs/2110.00690v1

Nanyonga A; Wasswa H; Joiner K; Turhan U; Wild G, A Multi-Head Attention-Based Transformer Model for Predicting Causes in Aviation Incident, http://dx.doi.org/10.20944/preprints202502.1196.v1

Eqbal M; Marino M; Fernando N; Wild G, Design Factors of High- speed Turbo-electric Distributed Propulsion System, http://dx.doi.org/10.21203/rs.3.rs-1986257/v1

Nanyonga A; Joiner K; Turhan U; Wild G, Does the Choice of Topic Modeling Technique Impact the Interpretation of Aviation Incident Reports? A Methodological Assessment, http://dx.doi.org/10.20944/preprints202503.2171.v1

Nanyonga A; Wasswa H; Joiner K; Turhan U; Wild G, Explainable Supervised Learning Models for Aviation Predictions in Australia, http://dx.doi.org/10.20944/preprints202502.0998.v1

Wild G; Baxter G; Srisaeng P; Richardson S, Machine Learning for Air Transport Planning and Management, http://dx.doi.org/10.31224/osf.io/35rqj

Nanyonga A; Joiner K; Turhan U; Wild G, Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia, http://dx.doi.org/10.20944/preprints202503.2251.v1

Wild G, Potential Designs for Miniature Distributed Optical Fiber Smart Sensors Systems for Use in Aerospace Flight Vehicles, http://dx.doi.org/10.20944/preprints202503.0993.v1

Nanyonga A; Wasswa H; Joiner K; Turhan U; Wild G, Predicting Probable Causes of Aviation Incidents Using NLP and Variational Autoencoders, http://dx.doi.org/10.36227/techrxiv.174431146.68041296/v1

Nanyonga A; Joiner K; Turhan U; Wild G, Semantic Topic Modeling of Aviation Safety Reports: A Comparative Analysis Using BERTopic and PLSA, http://dx.doi.org/10.20944/preprints202505.1509.v1

Somerville A; Lynar T; Wild G, The Nature and Costs of Civil Aviation Flight Training Safety Occurrences, http://dx.doi.org/10.2139/ssrn.4191514

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