Ernesto C. Marujo

Dr. Ernesto C. Marujo

orcid

Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2020 in Neural Networks
Verified
Journal Article
Closed access
BRBruno P. Roquette
Bruno P. Roquette
HNHitoshi Nagano
Hitoshi Nagano
EMErnesto C. Marujo
Ernesto C. Marujo
AMAlexandre C. Maiorano
Alexandre C. Maiorano

1 September 2019
Verified
Conference Paper
Closed access
FSFelipe Coelho Silva
Felipe Coelho Silva
PCPaulo Andre Lima de Castro
Paulo Andre Lima de Castro
HRHelio Ricardo
Helio Ricardo
EMErnesto C. Marujo
Ernesto C. Marujo
Colorization is a challenging task that has recently been tackled by deep learning. Line art colorization is particularly difficult because there is no grayscale value to indicate the color intensities as there is in black-and-white photograph images. When designing a character, concept artists often need to try different color schemes, however, colorization is a timeconsuming task. In this article, we propose a semi-automatic framework for colorizing manga concept arts by letting concept artists try different color schemes and obtain colorized results in fashion time. Our approach uses Conditional Generative Adversarial Networks (cGAN) and outperforms current hint-based line-art colorization techniques by providing natural-looking arts with only minor coloring mistakes.

6 December 2023 in Sustainability
Verified
Journal Article
Open access
PDF file
EMErnesto C. Marujo
Ernesto C. Marujo
JAJosé R. U. C. Almeida
José R. U. C. Almeida
LSLuiz F. L. Souza
Luiz F. L. Souza
ACAlan R. S. P. Costa
Alan R. S. P. Costa
PMPaulo César de Miranda
Paulo César de Miranda
PMPOTIRA MANAUARA SOUZA MELO
POTIRA MANAUARA SOUZA MELO
In this article, we discuss the uncertainties involved in the models and in the measurements necessary to estimate the emissions of greenhouse gases (GHG) in a chemical industry. When these uncertainties cannot be neglected and some measurements exhibit correlations with others, estimating the final emission is not a trivial task. Even if we intend to determine a simple point estimate for the mean emissions, we will need to use the average values of the measurements as well as information about their uncertainties and correlations in complex computations. To solve this problem, we propose a Monte Carlo method to estimate the mean and confidence interval of CO2 emissions in the context of uncertainties and correlations. We validated our approach through a case study involving a traditional chemical company in Brazil. Our results indicated that previously, there was an overestimation of the emission because the company did not consider uncertainties and correlations. The overestimation was modest since the parameters involved present relatively little uncertainty, but the bias effect was clear. This research has demonstrated the importance of accounting for uncertainties and correlations in emission estimates, providing a practical framework for analyses in industrial settings.

28 March 2011 in Revista de Administração
Verified
Journal Article
Open access
PDF file
PNPericles da Silva Pereira Neto
Pericles da Silva Pereira Neto
EMErnesto C. Marujo
Ernesto C. Marujo
Airline alliances: permanent advantages?The aim of this paper is to examine the effects of passenger airline alliances. The following tend to be included in the list of benefits of such alliances: the possibility of allocating the best aircraft for each flight segment with cost savings, network expansion, a greater offering of services, and a larger number of city pairs serviced with the same total pax. km flown. All else being equal, these benefits seem clear in analytical terms. However, the expected benefits did not materialize as anticipated. The reason for this is that even though all firms in the alliance flew longer stretches, carrying a larger number of passengers, these carriers were affected by the drop in fare prices, driven by industry-related issues, such as the sector’s heavy competition, caused by deregulation, and the emergence of low cost airlines. All of this resulted in alliance member carriers achieving only modest earnings