75 research outputs found
Degradation Detection and Diagnosis of Inductors in LCL Filter Integrated With Active Front End Rectifier
No full text10.1109/TPEL.2017.2685421IEEE TRANSACTIONS ON POWER ELECTRONICS3321622-163
Detection and Isolation of Interturn Faults in Inductors of LCL Filter for Marine Electric Propulsion System
No full text10.1109/TTE.2017.2788189IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION41232-24
Condition Monitoring and Winding Fault Detection of Inductors in LCL Filter Integrated with Active Front End Rectifier
No full text10.1109/ECCE.2015.7310214IEEE Energy Conversion Congress and Exposition (ECCE)3922-392
Winding Fault Detection in Coupled Inductors using a Single Flux Sensor
No full text10.1109/IECON.2016.779393442nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)1554-155
Inter-Turn Fault Detection of Dry-Type Transformers Using Core-Leakage Fluxes
No full textIEEE TRANSACTIONS ON POWER DELIVERY3441230-124
Concentration in Knowledge Output: A case of Economics Journals
No full textThis paper assesses the degree of author concentration in seven economics journals, which were published in India during 1990-2002. To measure the degree of author concentration, Lotka's Law was used. Moreover, we also make an exploratory analysis of the geographic, economics subfield and institutional concentration in 704 economics journals. An important finding of this paper is that specialized journals in the sample report the highest degree of author concentration. This result is quite similar to the findings by Cox and Chung (1991). Furthermore, there are several instances showing that the journals lean towards certain norms; this may affect the flow of innovative ideas into economics. We conclude that a knowledge activity, involving the high degree of concentration and a biased publication process, may affect the flow of new ideas into the discipline.Concentration, Lotka's Law
Inter-turn Fault Detection of Induction Machines Using the Voltage Across the Star-points of the Machine and the Source
No full text10.1109/PEDS44367.2019.89987652019 IEEE 13th International Conference on Power Electronics and Drive Systems (PEDS)2019-Jul
Diagnosis and Prognosis of LCL Filter in Marine Electric Propulsion Systems
No full text10.1109/PEDS44367.2019.89988522019 IEEE 13th International Conference on Power Electronics and Drive Systems (PEDS)2019-Jul
Benchmarking Federated Learning Frameworks For Aviation Network Data
No full textAutomatic Dependent Surveillance-Broadcast (ADS-B) is an alternative technology adopted by the FAA instead of ground radar to enhance accurate navigation by relying on GPS satellites for precise aircraft position information. Factors such as jamming, multipath fading, and solar activities influence GPS data integrity issues, causing dropouts or missing data, thus affecting flight safety and navigational accuracy. To mitigate such potential GPS dropout-related incidents, there is a need for robust data-driven models. This thesis focuses on multiple studies: (1) investigate five distinct machine learning (ML) models to impute missing data on ADS-B/GPS information; (2) design a federated learning (FL) framework for aviation network data; and (3) conduct a benchmarking study to validate multiple quality attributes for the proposed aviation Fed-CPS framework. Preliminary results indicate (a) k-NN yields better accuracy over other ML models (Bayesian Ridge, Random Forest, AdaBoost, Extra Tree, and k-NN) even at the highest missing rate of 30%; (b) deployment of LSTM and k-Means in a federated setting indicate that LSTM results in both MAPE and computation run-time savings. Specifically, LSTM shows (i) performance-per-dollar of 1.5 times (client) and 0.5 times (server) than k-Means and (ii) energy-efficiency-per-watt of 1.5 times (client) and 0.5 times (server) than k-Means
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