Francisco Javier Cara Cañas

Universidad Politécnica de Madrid

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Jesús Juan

Universidad Politécnica de Madrid

Enrique Alarcón

Clinica Universidad de Navarra

Enrique Alarcón Álvarez

Universidad Autónoma de Madrid

Jaime Carpio

Universidad Politécnica de Madrid

Daniel Bouzon Nagem Assad

Universidad Politécnica de Madrid

Míguel Ortega-Mier

Universidad Politécnica de Madrid

Yun Zhou

Hunan University

Magd Abdel Wahab

Ghent University Hospital

Thaís Spiegel

Universidade do Estado do Rio de Janeiro

Enrique Alarcón Álvarez

Florida State University

Cooperative Institutions

Universidad Politécnica de Madrid

Universidade do Estado do Rio de Janeiro

Universidade do Porto

The University of Tokyo

KU Leuven

Universidade Federal do Rio de Janeiro

Universidad de Granada

Hong Kong Polytechnic University

Tongji University

Southeast University

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Periodically correlated models for short-term electricity load forecasting

Applied Mathematics and Computation (2019)

Eduardo Caro Jesús Juan Francisco Javier Cara Cañas

10.1016/j.amc.2019.124642

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Citations (33)

Numerical Solution of Stochastic Differential Equations Used in Random Vibration

International Review of Mechanical Engineering (IREME) (2017)

Abdelmadjid Chabani Francisco Javier Cara Cañas Abdelmadjid Berga

The vibratory environment found in the majority of cases consists of random vibrations. Therefore, each recording of the same phenomena results in a signal different from the previous ones. Such vibrations can be only analyzed in a stochastic manner. In this paper, we present applications of the stochastic differential equations in random vibrations of Mass-Spring-Damper System. The motivation behind stochastic calculus is to define a calculus for situations in which ordinary methods of calculus do not apply due to randomness concerned with the system or the input. In this work, we will construct and implement methods for solving the stochastic differential equation modeling the Mass-Spring-Damper System with disturbances at the source (stochastic input) and stochastic differential equations with random coefficients.

Random vibration

Stochastic Calculus

10.15866/ireme.v11i1.9800

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Operational Modal Analysis of Wind Turbine Structures: A Maximum Likelihood Approach

Lecture notes in civil engineering (2024)

Francisco Javier Cara Cañas Filipe Magalhães

Operational Modal Analysis

10.1007/978-3-031-61425-5_55

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Computational Methods for Identification of Vibrating Structures

Civil-comp proceedings (2009)

Francisco Javier Cara Cañas Jaime Carpio Jesús Juan Enrique Alarcón

This paper describes a theoretical and experimental research work in relation to the system identification of an steel arch bridge.This bridge forms part of the Spanish highway network and is located about 30 km at the south of Granada (Spain).Ambient vibration tests were conducted during two days and the response of the structure was measured at selected points.The scope of this paper is present some modal identification results obtained using state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms) applied to the ambient vibration data measured in two of this points.Point parameter estimation and interval parameter estimation are presented.A complete method for interval parameter estimation using state space models and subspace system identification is developed.This method includes the construction of a plot similar to stabilization diagram which permits differentiate system modes from spurious modes for a given system order.Additionally, the obtained experimental results are compared with those from a three dimensional finite element analysis.

Identification

10.4203/ccp.91.176

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State space model and its estimation for Operational Modal Analysis with non-white noise inputs

Procedia Engineering (2017)

Francisco Javier Cara Cañas Jesús Juan Enrique Alarcón

Operational Modal Analysis consists of estimating the modal parameters of civil/structural systems using only the response of the systems, while the unknown inputs are considered as realizations of white noise processes. Sometimes this last hypothesis is not correct, that is, the unknown input does not have a flat spectrum. The consequence is that the peaks of the input's spectrum are observed in the system response, and, since the input is unmeasured, it is not possible to separate system poles (the modal parameters) from the input poles. The method proposed in this work is based on recording the response of the structure under different (unknown) excitations, including both white noise and non-white noise excitations. Then, a joint analysis of the records is performed: the common poles will correspond to system poles and the specific poles will correspond to input poles.

Operational Modal Analysis

10.1016/j.proeng.2017.09.233

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Joint statistical analysis of multiple measurement setups for the estimation of vibrational modes

Francisco Javier Cara Cañas Jesús Juan Enrique Alarcón Álvarez

Computing the modal parameters of large structures in Operational Modal Analysis often requires to process data from multiple non simultaneously recorded setups of sensors. These setups share some sensors in common, the so-called reference sensors that are fixed for all the measurements, while the other sensors are moved from one setup to the next. One possibility is to process the setups separately what result in different modal parameter estimates for each setup. Then the reference sensors are used to merge or glue the different parts of the mode shapes to obtain global modes, while the natural frequencies and damping ratios are usually averaged. In this paper we present a state space model that can be used to process all setups at once so the global mode shapes are obtained automatically and subsequently only a value for the natural frequency and damping ratio of each mode is computed. We also present how this model can be estimated using maximum likelihood and the Expectation Maximization algorithm. We apply this technique to real data measured at a footbridge.

Merge (version control)

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Maximization

Natural frequency

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Forecasting hourly electricity demand in Spain using a revised version of the Cancelo‐Espasa Model

Eduardo Caro Huertas Jesús Juan Francisco Javier Cara Cañas

The Cancelo‐Espasa Model is a great procedure for hourly electrcity demand forecasting (IJF,2008) that has been used for 30 years by the Spanish TSO (REE).

Electricity demand

Source

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Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak

Bulletin of Mathematical Biology (2022)

Daniel Bouzon Nagem Assad Francisco Javier Cara Cañas Míguel Ortega-Mier

Univariate

Predictive modelling

10.1007/s11538-022-01112-5

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Citations (8)