Zobrazeno 1 - 10 of 35 pro vyhledávání: '"Rafael Orellana"'
1
Akademický článek
Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements
Autor: Angel L. Cedeno, Maria Coronel, Rafael Orellana, Patricio Varas, Rodrigo Carvajal, Boris I. Godoy, Juan C. Aguero
Publikováno v: IEEE Access, Vol 12, Pp 60883-60895 (2024)
In this paper, we have proposed a new paradigm for modeling of SAG mills. Typically, important parameters found in the modeling of such processes are described as state-space system model rather than unknown parameters. Here, we propose to estimate t
Externí odkaz: https://doaj.org/article/ad55845f3f7541f08c1ca825f73af511
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2
Akademický článek
Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models
Autor: Angel L. Cedeno, Rafael Orellana, Rodrigo Carvajal, Boris I. Godoy, Juan C. Aguero
Publikováno v: IEEE Access, Vol 11, Pp 24615-24630 (2023)
In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-in-Variables systems is developed. We consider that the noise-free input signal is Gaussian-mixture distributed. We propose an Expectation-Maximization-based a
Externí odkaz: https://doaj.org/article/81937253501049938ed7396050b0a04f
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3
Akademický článek
On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models
Autor: Rafael Orellana, Rodrigo Carvajal, Pedro Escárate, Juan C. Agüero
Publikováno v: Sensors, Vol 21, Iss 11, p 3837 (2021)
In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and
Externí odkaz: https://doaj.org/article/2442809b0bf14f5888c22662cc7e6773
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4
Akademický článek
A data augmentation approach for a class of statistical inference problems.
Autor: Rodrigo Carvajal, Rafael Orellana, Dimitrios Katselis, Pedro Escárate, Juan Carlos Agüero
Publikováno v: PLoS ONE, Vol 13, Iss 12, p e0208499 (2018)
We present an algorithm for a class of statistical inference problems. The main idea is to reformulate the inference problem as an optimization procedure, based on the generation of surrogate (auxiliary) functions. This approach is motivated by the M
Externí odkaz: https://doaj.org/article/acb58627c4834d6e98ba5eefd0f95dd0
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6
Investigar las configuraciones subjetivas que los adolescentes construyen para enfrentar su realidad cuando uno o ambos padres migran irregularmente
Autor: Karla Josseline Ortiz Ponce, Lucia Cavalletti, Rafael Orellana Sibrián
Publikováno v: ECA: Estudios Centroamericanos. 76:207-239
El presente trabajo representa un encuadre fructífero que señala aspectos claves para reflexionar sobre la configuración de las estrategias con que los adolescentes enfrentan los cambios en sus condiciones de vida cuando uno o ambos padres migran
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::94252da7278671e2b463406a3e88cdb2
https://doi.org/10.51378/eca.v76i765.6450
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7
Combating IPV With Community Leaders in Honduras: An Evaluation of an IPV Training Program Among Teachers and Health Professionals
Autor: Eduardo Rafael Orellana, Chunrye Kim, Hung-En Sung, Joel A. Capellan
Publikováno v: Violence Against Women. 28:991-1007
Intimate partner violence (IPV) among women in Latin America, including Honduras, is serious. To help IPV victims, a community-based educational program has been implemented. This study aims to examine the impact of IPV training among teachers and he
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a85f5b781faf1209927e63aaa08d80f
https://doi.org/10.1177/10778012211008995
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8
On the Uncertainty Modelling for Linear Continuous-Time Systems Utilising Sampled Data and Gaussian Mixture Models
Autor: Rodrigo Carvajal, Pedro Escárate, Rafael Orellana, Juan C. Agüero, María Coronel, Ramon A. Delgado
Publikováno v: IFAC-PapersOnLine. 54:589-594
In this paper a Maximum Likelihood estimation algorithm for model error modelling in a continuous-time system is developed utilising sampled data and a Stochastic Embedding approach. Orthonormal basis functions are used to model both the continuous-t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ea2ebb052655e98e73d2a16cd9745198
https://doi.org/10.1016/j.ifacol.2021.08.424
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