Enrique López Droguett
University of California, Los Angeles
189 Papers
370 Citations
Enrique López Droguett is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 21, co-authored 151 publications. Previous affiliations of Enrique López Droguett include University of Maryland, College Park & University of Chile.
Chat about Author
Papers
Embedding resilience in the design of the electricity supply for industrial clients.
Márcio das Chagas Moura,Helder Henrique Lima Diniz,Enrique López Droguett,Beatriz Sales da Cunha,Isis Didier Lins,Vicente Ribeiro Simoni +5 more
TL;DR: Investments in pre-event actions, if implemented, can enhance the resilience of power grids serving industrial clients because the impacts of disruptions either are experienced only for a short time period or are completely avoided.
Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model
Márcio das Chagas Moura,Rafael Valença Azevedo,Enrique López Droguett,Leandro Rego Chaves,Isis Didier Lins,Romulo Fernando Vilela,Romero Luiz Mendonça Sales Filho +6 more
TL;DR: A Bayesian population variability method is proposed for the estimation of the distributions of the rates of accident and recovery and a Markov-based model will be used to estimate the uncertainty over the expected number of accidents and the work time loss.
19
Human reliability analysis of conventional maritime pilotage operations supported by a prospective model
Danilo Taverna Martins Pereira de Abreu,Marcos Coelho Maturana,Enrique López Droguett,Marcelo Ramos Martins +3 more
TL;DR: In this paper , a methodology for human reliability analysis (HRA) supported by the prospective Technique for Early Consideration of Human Reliability (TECHR) combined with Bayesian Networks (BNs) is proposed.
19
Capsule Neural Networks for structural damage localization and quantification using transmissibility data
Joaquin Eduardo Figueroa Barraza,Enrique López Droguett,Viviana Meruane Naranjo,Marcelo Ramos Martins +3 more
TL;DR: A novel CapsNets-based method for dual classification–regression task in SHM and analysis of both routing algorithms (dynamic routing and Expectation–Maximization routing) in the context of SHM show better results than Convolutional Neural Networks (CNN), especially when it comes to false positive values.
19