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Tejas G. Puranik
Tytuł
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An application of dbscan clustering for flight anomaly detection during the approach phase
K Sheridan, TG Puranik, E Mangortey, OJ Pinon-Fischer, M Kirby, ...
AIAA Scitech 2020 Forum, 1851, 2020
1212020
Anomaly Detection in General-Aviation Operations Using Energy Metrics and Flight-Data Records
TG Puranik, DN Mavris
Journal of Aerospace Information Systems 15 (1), 22-35, 2018
872018
Towards online prediction of safety-critical landing metrics in aviation using supervised machine learning
TG Puranik, N Rodriguez, DN Mavris
Transportation Research Part C: Emerging Technologies 120, 102819, 2020
722020
Natural language processing based method for clustering and analysis of aviation safety narratives
RL Rose, TG Puranik, DN Mavris
Aerospace 7 (10), 143, 2020
582020
Critical parameter identification for safety events in commercial aviation using machine learning
HK Lee, S Madar, S Sairam, TG Puranik, AP Payan, M Kirby, OJ Pinon, ...
Aerospace 7 (6), 73, 2020
552020
Application of structural topic modeling to aviation safety data
RL Rose, TG Puranik, DN Mavris, AH Rao
Reliability Engineering & System Safety 224, 108522, 2022
542022
Energy-Based Metrics for Safety Analysis of General Aviation Operations
T Puranik, H Jimenez, D Mavris
Journal of Aircraft 54 (6), 2285-2297, 2017
502017
Trajectory clustering within the terminal airspace utilizing a weighted distance function
SJ Corrado, TG Puranik, OJ Pinon, DN Mavris
Proceedings 59 (1), 7, 2020
422020
Application of machine learning techniques to parameter selection for flight risk identification
E Mangortey, D Monteiro, J Ackley, Z Gao, TG Puranik, M Kirby, ...
AIAA scitech 2020 forum, 1850, 2020
412020
Identification of Instantaneous Anomalies in General Aviation Operations Using Energy Metrics
TG Puranik, DN Mavris
Journal of Aerospace Information Systems 17 (1), 1-15, 2019
392019
A clustering-based quantitative analysis of the interdependent relationship between spatial and energy anomalies in ADS-B trajectory data
SJ Corrado, TG Puranik, OP Fischer, DN Mavris
Transportation Research Part C: Emerging Technologies 131, 103331, 2021
332021
A supervised learning approach for safety event precursor identification in commercial aviation
JL Ackley, TG Puranik, D Mavris
AIAA Aviation 2020 Forum, 2880, 2020
322020
Classification and analysis of go-arounds in commercial aviation using ADS-B data
SG Kumar, SJ Corrado, TG Puranik, DN Mavris
Aerospace 8 (10), 291, 2021
312021
Empirical assessment of deep gaussian process surrogate models for engineering problems
D Rajaram, TG Puranik, S Ashwin Renganathan, WJ Sung, OP Fischer, ...
Journal of Aircraft 58 (1), 182-196, 2021
292021
Deep Gaussian process enabled surrogate models for aerodynamic flows
D Rajaram, TG Puranik, A Renganathan, WJ Sung, OJ Pinon-Fischer, ...
AIAA scitech 2020 forum, 1640, 2020
292020
Deep spatio-temporal neural networks for risk prediction and decision support in aviation operations
HK Lee, TG Puranik, DN Mavris
Journal of Computing and Information Science in Engineering 21 (4), 041013, 2021
272021
Reduced order modeling methods for aviation noise estimation
A Behere, D Rajaram, TG Puranik, M Kirby, DN Mavris
Sustainability 13 (3), 1120, 2021
272021
Randomized algorithms for non-intrusive parametric reduced order modeling
D Rajaram, C Perron, TG Puranik, DN Mavris
AIAA Journal 58 (12), 5389-5407, 2020
242020
Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations
TG Puranik, H Jimenez, DN Mavris
AIAA Information Systems-AIAA Infotech@ Aerospace, 2017
232017
Identifying Instantaneous Anomalies in General Aviation Operations
TG Puranik, DN Mavris
17th AIAA Aviation Technology, Integration, and Operations Conference, 2017
222017
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