Articles de revista
http://hdl.handle.net/2117/2253
2016-04-30T05:29:20ZUncertainty analysis of the DLT-Lines calibration algorithm for cameras with radial distortion
http://hdl.handle.net/2117/86383
Uncertainty analysis of the DLT-Lines calibration algorithm for cameras with radial distortion
Galego, Ricardo; Ortega, Agustin; Ferreira, Ricardo; Bernardino, Alexandre; Andrade-Cetto, Juan; Gaspar, José
3D metric data of environmental structures is nowadays present in many information sources (maps, GIS) and can be easily acquired with modern depth sensing technology (RGBD, laser). This wealth of information can be readily used for single view calibration of 2D cameras with radial distortion, provided that image structures can be matched with the 3D data. In this paper we present an analysis of the level of accuracy that can be obtained when such calibration is performed with the 2D-3D DLT-Lines algorithm. The analysis propagates uncertainty in the detection of features at the image level to camera pose, and from there to 3D reconstruction. The analytic error propagation expressions are derived using first order uncertainty models, and are validated with Monte Carlo simulations in a virtual indoor environment. The method is general and can be applied to other calibration methods, as long as explicit or implicit expressions can be derived for the transformation from image coordinates to 3D reconstruction. We present results with real data for two applications: i) the 3D reconstruction of an outdoors building for which 3D information is given by a map, observed by a mobile phone camera: and ii) the uncertainty in the localization at the floor plane of points observed by a fixed camera calibrated by a robot equipped with an RGBD camera navigating in a typical indoor environment. (C) 2015 Elsevier Inc. All rights reserved.
2016-04-28T13:59:59ZGalego, RicardoOrtega, AgustinFerreira, RicardoBernardino, AlexandreAndrade-Cetto, JuanGaspar, José3D metric data of environmental structures is nowadays present in many information sources (maps, GIS) and can be easily acquired with modern depth sensing technology (RGBD, laser). This wealth of information can be readily used for single view calibration of 2D cameras with radial distortion, provided that image structures can be matched with the 3D data. In this paper we present an analysis of the level of accuracy that can be obtained when such calibration is performed with the 2D-3D DLT-Lines algorithm. The analysis propagates uncertainty in the detection of features at the image level to camera pose, and from there to 3D reconstruction. The analytic error propagation expressions are derived using first order uncertainty models, and are validated with Monte Carlo simulations in a virtual indoor environment. The method is general and can be applied to other calibration methods, as long as explicit or implicit expressions can be derived for the transformation from image coordinates to 3D reconstruction. We present results with real data for two applications: i) the 3D reconstruction of an outdoors building for which 3D information is given by a map, observed by a mobile phone camera: and ii) the uncertainty in the localization at the floor plane of points observed by a fixed camera calibrated by a robot equipped with an RGBD camera navigating in a typical indoor environment. (C) 2015 Elsevier Inc. All rights reserved.Nonlinear distributed parameter observer design for fuel cell systems
http://hdl.handle.net/2117/85550
Nonlinear distributed parameter observer design for fuel cell systems
Luna Pacho, Julio Alberto; Husar, Attila Peter; Serra, Maria
This paper presents the development of a nonlinear state observer to estimate the different gas species concentration profiles in a Proton Exchange Membrane Fuel Cell energy system. The selection of the estimated states follows functionality and fuel cell performance criteria. The implementation is based on the finite element discretisation of a fuel cell distributed parameter model. Forward and backwards discretisation of the partial derivative equations is performed to take advantage of the boundary conditions of the problem and also to apply lumped systems theory in the synthesis procedure of the observer. A second-order sliding-mode super-twisting corrective input action is implemented to reduce the estimation error to zero in a finite amount of time. The sliding-mode control approach grants a suitable corrective action without incrementing the model-dependency of the observer. Simulation results are presented to show the performance of the proposed observer of the fuel cell internal states and to extract conclusions for future research work.
2016-04-12T11:00:03ZLuna Pacho, Julio AlbertoHusar, Attila PeterSerra, MariaThis paper presents the development of a nonlinear state observer to estimate the different gas species concentration profiles in a Proton Exchange Membrane Fuel Cell energy system. The selection of the estimated states follows functionality and fuel cell performance criteria. The implementation is based on the finite element discretisation of a fuel cell distributed parameter model. Forward and backwards discretisation of the partial derivative equations is performed to take advantage of the boundary conditions of the problem and also to apply lumped systems theory in the synthesis procedure of the observer. A second-order sliding-mode super-twisting corrective input action is implemented to reduce the estimation error to zero in a finite amount of time. The sliding-mode control approach grants a suitable corrective action without incrementing the model-dependency of the observer. Simulation results are presented to show the performance of the proposed observer of the fuel cell internal states and to extract conclusions for future research work.MSClique: Multiple structure discovery through the maximum weighted clique problem
http://hdl.handle.net/2117/84927
MSClique: Multiple structure discovery through the maximum weighted clique problem
Sanromà Güell, Gerard; Peñate Sánchez, Adrián; Alquézar Mancho, René; Serratosa Casanelles, Francesc; Moreno-Noguer, Francesc; Andrade-Cetto, Juan; González Ballester, Miguel Ángel
We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods.
2016-03-31T10:01:35ZSanromà Güell, GerardPeñate Sánchez, AdriánAlquézar Mancho, RenéSerratosa Casanelles, FrancescMoreno-Noguer, FrancescAndrade-Cetto, JuanGonzález Ballester, Miguel ÁngelWe present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods.Variable symmetry breaking in numerical constraint problems
http://hdl.handle.net/2117/84830
Variable symmetry breaking in numerical constraint problems
Goldsztejn, Alexandre; Jermann, Christophe; Ruiz de Angulo García, Vicente; Torras, Carme
Symmetry breaking has been a hot topic of research in the past years, leading to many theoretical developments as well as strong scaling strategies for dealing with hard applications. Most of the research has however focused on discrete, combinatorial, problems, and only few considered also continuous, numerical, problems. While part of the theory applies in both contexts, numerical problems have specificities that make most of the technical developments inadequate.
In this paper, we present the rlex constraints, partial symmetry-breaking inequalities corresponding to a relaxation of the famous lex constraints extensively studied in the discrete case. They allow (partially) breaking any variable symmetry and can be generated in polynomial time. Contrarily to lex constraints that are impractical in general (due to their overwhelming number) and inappropriate in the continuous context (due to their form), rlex constraints can be efficiently handled natively by numerical constraint solvers. Moreover, we demonstrate their pruning power on continuous domains is almost as strong as that of lex constraints, and they subsume several previous work on breaking specific symmetry classes for continuous problems. Their experimental behavior is assessed on a collection of standard numerical problems and the factors influencing their impact are studied. The results confirm rlex constraints are a dependable counterpart to lex constraints for numerical problems.
2016-03-29T17:56:04ZGoldsztejn, AlexandreJermann, ChristopheRuiz de Angulo García, VicenteTorras, CarmeSymmetry breaking has been a hot topic of research in the past years, leading to many theoretical developments as well as strong scaling strategies for dealing with hard applications. Most of the research has however focused on discrete, combinatorial, problems, and only few considered also continuous, numerical, problems. While part of the theory applies in both contexts, numerical problems have specificities that make most of the technical developments inadequate.
In this paper, we present the rlex constraints, partial symmetry-breaking inequalities corresponding to a relaxation of the famous lex constraints extensively studied in the discrete case. They allow (partially) breaking any variable symmetry and can be generated in polynomial time. Contrarily to lex constraints that are impractical in general (due to their overwhelming number) and inappropriate in the continuous context (due to their form), rlex constraints can be efficiently handled natively by numerical constraint solvers. Moreover, we demonstrate their pruning power on continuous domains is almost as strong as that of lex constraints, and they subsume several previous work on breaking specific symmetry classes for continuous problems. Their experimental behavior is assessed on a collection of standard numerical problems and the factors influencing their impact are studied. The results confirm rlex constraints are a dependable counterpart to lex constraints for numerical problems.A multi-timescale modeling methodology for PEMFC performance and durability in a virtual fuel cell car
http://hdl.handle.net/2117/84753
A multi-timescale modeling methodology for PEMFC performance and durability in a virtual fuel cell car
Mayur, Manik; Strahl, Stephan; Husar, Attila Peter; Bessler, Wolfang G.
The durability of polymer electrolyte membrane fuel cells (PEMFC) is governed by a nonlinear cou-pling between system demand, component behavior, and physicochemical degradation mechanisms, occurring on timescales from the sub-second to the thousand-hour. We present a simulation methodol-ogy for assessing performance and durability of a PEMFC under automotive driving cycles. The simu-lation framework consists of (a) a fuel cell car model converting velocity to cell power demand, (b) a 2D multiphysics cell model, (c) a flexible degradation library template that can accommodate physi-cally-based component-wise degradation mechanisms, and (d) a time-upscaling methodology for ex-trapolating degradation during a representative load cycle to multiple cycles. The computational framework describes three different time scales, (1) sub-second timescale of electrochemistry, (2) minute-timescale of driving cycles, and (3) thousand-hour-timescale of cell ageing. We demonstrate an exemplary PEMFC durability analysis due to membrane degradation under a highly transient load-ing of the New European Driving Cycle (NEDC).
2016-03-18T18:55:07ZMayur, ManikStrahl, StephanHusar, Attila PeterBessler, Wolfang G.The durability of polymer electrolyte membrane fuel cells (PEMFC) is governed by a nonlinear cou-pling between system demand, component behavior, and physicochemical degradation mechanisms, occurring on timescales from the sub-second to the thousand-hour. We present a simulation methodol-ogy for assessing performance and durability of a PEMFC under automotive driving cycles. The simu-lation framework consists of (a) a fuel cell car model converting velocity to cell power demand, (b) a 2D multiphysics cell model, (c) a flexible degradation library template that can accommodate physi-cally-based component-wise degradation mechanisms, and (d) a time-upscaling methodology for ex-trapolating degradation during a representative load cycle to multiple cycles. The computational framework describes three different time scales, (1) sub-second timescale of electrochemistry, (2) minute-timescale of driving cycles, and (3) thousand-hour-timescale of cell ageing. We demonstrate an exemplary PEMFC durability analysis due to membrane degradation under a highly transient load-ing of the New European Driving Cycle (NEDC).Set-membership identification and fault detection using a Bayesian framework
http://hdl.handle.net/2117/84709
Set-membership identification and fault detection using a Bayesian framework
Fernández Canti, Rosa M.; Blesa Izquierdo, Joaquim; Puig Cayuela, Vicenç; Tornil Sin, Sebastián
This paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from the Bayesian viewpoint in order to, first, determine the feasible parameter set in the identification stage and, second, check the consistency between the measurement data and the model in the fault-detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single-output and multiple-output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple-tank process.
2016-03-18T14:10:52ZFernández Canti, Rosa M.Blesa Izquierdo, JoaquimPuig Cayuela, VicençTornil Sin, SebastiánThis paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from the Bayesian viewpoint in order to, first, determine the feasible parameter set in the identification stage and, second, check the consistency between the measurement data and the model in the fault-detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single-output and multiple-output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple-tank process.Ouput-feedback control of combined sewer networks through receding horizon control with moving horizon estimation
http://hdl.handle.net/2117/84090
Ouput-feedback control of combined sewer networks through receding horizon control with moving horizon estimation
Joseph Duran, Bernat; Ocampo-Martínez, Carlos; Cembrano Gennari, Gabriela
An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according to different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically-based model of a real case-study network as virtual reality.
2016-03-09T18:20:31ZJoseph Duran, BernatOcampo-Martínez, CarlosCembrano Gennari, GabrielaAn output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according to different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically-based model of a real case-study network as virtual reality.Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach
http://hdl.handle.net/2117/83960
Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach
Rotondo, Damiano; Fernández Canti, Rosa M.; Tornil Sin, Sebastián; Blesa Izquierdo, Joaquim; Puig Cayuela, Vicenç
In this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-Sugeno (TS) interval observers that consider uncertainty in a bounded context, adapting TS observers to the so-called interval approach. Design conditions for the TS interval observer based on regional pole placement are also introduced to guarantee the fault detection and isolation (FDI) performance. The fault detection test is based on checking the consistency between the measurements and the output estimations provided by the TS observers. In presence of bounded uncertainty, this check relies on determining if all the measurements lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature.
2016-03-08T11:22:35ZRotondo, DamianoFernández Canti, Rosa M.Tornil Sin, SebastiánBlesa Izquierdo, JoaquimPuig Cayuela, VicençIn this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-Sugeno (TS) interval observers that consider uncertainty in a bounded context, adapting TS observers to the so-called interval approach. Design conditions for the TS interval observer based on regional pole placement are also introduced to guarantee the fault detection and isolation (FDI) performance. The fault detection test is based on checking the consistency between the measurements and the output estimations provided by the TS observers. In presence of bounded uncertainty, this check relies on determining if all the measurements lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature.Non-linear set-membership identification approach based on the Bayesian framework
http://hdl.handle.net/2117/83901
Non-linear set-membership identification approach based on the Bayesian framework
Fernández Canti, Rosa M.; Tornil Sin, Sebastián; Blesa Izquierdo, Joaquim; Puig Cayuela, Vicenç
This study deals with the problem of set-membership identification of non-linear-in-the-parameters models. To solve this problem, this study illustrates how the Bayesian approach can be used to determine the feasible parameter set (FPS) by assuming uniform distributed estimation error and flat model prior probability distributions. The key point of the methodology is the interval evaluation of the likelihood function and the result is a set of boxes with associated credibility indices. For each box, the credibility index is in the interval (0, 1] and gives information about the amount of consistent models inside the box. The union of the boxes with credibility value equal to one provides an inner approximation of the FPS, whereas the union of all boxes provides an outer estimation. The boxes with credibility value smaller than one are located around the boundary of the FPS and their credibility index can be used to iteratively refine the inner and outer approximations up to a desired precision. The main issues and performance of the developed algorithms are discussed and illustrated by means of examples.
2016-03-07T15:49:29ZFernández Canti, Rosa M.Tornil Sin, SebastiánBlesa Izquierdo, JoaquimPuig Cayuela, VicençThis study deals with the problem of set-membership identification of non-linear-in-the-parameters models. To solve this problem, this study illustrates how the Bayesian approach can be used to determine the feasible parameter set (FPS) by assuming uniform distributed estimation error and flat model prior probability distributions. The key point of the methodology is the interval evaluation of the likelihood function and the result is a set of boxes with associated credibility indices. For each box, the credibility index is in the interval (0, 1] and gives information about the amount of consistent models inside the box. The union of the boxes with credibility value equal to one provides an inner approximation of the FPS, whereas the union of all boxes provides an outer estimation. The boxes with credibility value smaller than one are located around the boundary of the FPS and their credibility index can be used to iteratively refine the inner and outer approximations up to a desired precision. The main issues and performance of the developed algorithms are discussed and illustrated by means of examples.Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach
http://hdl.handle.net/2117/83898
Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach
Fernández Canti, Rosa M.; Blesa Izquierdo, Joaquim; Tornil Sin, Sebastián; Puig Cayuela, Vicenç
This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.
2016-03-07T15:29:46ZFernández Canti, Rosa M.Blesa Izquierdo, JoaquimTornil Sin, SebastiánPuig Cayuela, VicençThis paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.