Ponències/Comunicacions de congressos
http://hdl.handle.net/2117/3920
Mon, 24 Apr 2017 23:12:46 GMT2017-04-24T23:12:46ZRandom cubic planar graphs revisited
http://hdl.handle.net/2117/103660
Random cubic planar graphs revisited
Rué Perna, Juan José; Noy Serrano, Marcos; Requile, Clement
The goal of our work is to analyze random cubic planar graphs according to the
uniform distribution. More precisely, let G be the class of labelled cubic planar
graphs and let gn be the number of graphs with n vertices. Then each graph in G
with n vertices has the same probability 1/gn.
Mon, 24 Apr 2017 10:12:03 GMThttp://hdl.handle.net/2117/1036602017-04-24T10:12:03ZRué Perna, Juan JoséNoy Serrano, MarcosRequile, ClementThe goal of our work is to analyze random cubic planar graphs according to the
uniform distribution. More precisely, let G be the class of labelled cubic planar
graphs and let gn be the number of graphs with n vertices. Then each graph in G
with n vertices has the same probability 1/gn.Genera Esfera: Interacting with a trackball mapped onto a sphere to explore generative visual worlds
http://hdl.handle.net/2117/103657
Genera Esfera: Interacting with a trackball mapped onto a sphere to explore generative visual worlds
Barrière Figueroa, Eulalia; Carreras, Anna
Genera Esfera is an interactive installation that allows the audience to interact and easily become a VJ (visual
DJ) in a world of generative visuals. It is an animated and generative graphic environment with a music playlist,
a visual spherical world related with and suggested by the music, which reacts and evolves. The installation has
been presented at MIRA Live Visual Arts Festival 2015, in Barcelona. Genera Esfera was envisioned, developed
and programmed on the basis of two initial ideas: first, to generate our spherical planets we need to work with
spherical geometry and program 3D graphics; second, the interaction should be easy to understand, proposing a
direct mapping between the visuals and the interface. Our main goal is that participants can focus on exploring the
graphic worlds rather than concentrate on understanding the interface. For that purpose we use a trackball to map
its position onto sphere rotations. In this paper, we present the interactive installation Genera Esfera, the design
guidelines, the mathematics behind the generative visuals and its results.
Mon, 24 Apr 2017 09:27:48 GMThttp://hdl.handle.net/2117/1036572017-04-24T09:27:48ZBarrière Figueroa, EulaliaCarreras, AnnaGenera Esfera is an interactive installation that allows the audience to interact and easily become a VJ (visual
DJ) in a world of generative visuals. It is an animated and generative graphic environment with a music playlist,
a visual spherical world related with and suggested by the music, which reacts and evolves. The installation has
been presented at MIRA Live Visual Arts Festival 2015, in Barcelona. Genera Esfera was envisioned, developed
and programmed on the basis of two initial ideas: first, to generate our spherical planets we need to work with
spherical geometry and program 3D graphics; second, the interaction should be easy to understand, proposing a
direct mapping between the visuals and the interface. Our main goal is that participants can focus on exploring the
graphic worlds rather than concentrate on understanding the interface. For that purpose we use a trackball to map
its position onto sphere rotations. In this paper, we present the interactive installation Genera Esfera, the design
guidelines, the mathematics behind the generative visuals and its results.A geometric approach to dense Cayley digraphs of finite Abelian groups
http://hdl.handle.net/2117/103505
A geometric approach to dense Cayley digraphs of finite Abelian groups
Aguiló Gost, Francisco de Asis L.; Fiol Mora, Miquel Àngel; Pérez Mansilla, Sonia
We give a method for constructing infinite families of dense (or eventually likely dense) Cayley digraphs of finite Abelian groups. The diameter of the digraphs is obtained by means of the related {\em minimum distance diagrams}. A {\em dilating} technique for these diagrams, which can be used for any degree of the digraph, is applied to generate the digraphs of the family. Moreover, two infinite families of digraphs with distinguished metric properties will be given using these methods. The first family contains digraphs with asymptotically large ratio between the order and the diameter as the degree increases (moreover it is the first known asymptotically dense family). The second family, for fixed degree $d=3$, contains digraphs with the current best known density.
Tue, 18 Apr 2017 10:02:58 GMThttp://hdl.handle.net/2117/1035052017-04-18T10:02:58ZAguiló Gost, Francisco de Asis L.Fiol Mora, Miquel ÀngelPérez Mansilla, SoniaWe give a method for constructing infinite families of dense (or eventually likely dense) Cayley digraphs of finite Abelian groups. The diameter of the digraphs is obtained by means of the related {\em minimum distance diagrams}. A {\em dilating} technique for these diagrams, which can be used for any degree of the digraph, is applied to generate the digraphs of the family. Moreover, two infinite families of digraphs with distinguished metric properties will be given using these methods. The first family contains digraphs with asymptotically large ratio between the order and the diameter as the degree increases (moreover it is the first known asymptotically dense family). The second family, for fixed degree $d=3$, contains digraphs with the current best known density.The Kernel Matrix Diffie-Hellman assumption
http://hdl.handle.net/2117/103241
The Kernel Matrix Diffie-Hellman assumption
Morillo Bosch, M. Paz; Rafols Salvador, Carla; Villar Santos, Jorge Luis
We put forward a new family of computational assumptions, the Kernel Matrix Diffie-Hellman Assumption. Given some matrix A sampled from some distribution D, the kernel assumption says that it is hard to find “in the exponent” a nonzero vector in the kernel of A¿ . This family is a natural computational analogue of the Matrix Decisional Diffie-Hellman Assumption (MDDH), proposed by Escala et al. As such it allows to extend the advantages of their algebraic framework to computational assumptions. The k-Decisional Linear Assumption is an example of a family of decisional assumptions of strictly increasing hardness when k grows. We show that for any such family of MDDH assumptions, the corresponding Kernel assumptions are also strictly increasingly weaker. This requires ruling out the existence of some black-box reductions between flexible problems (i.e., computational problems with a non unique solution).
The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-662-53887-6_27
Tue, 04 Apr 2017 05:23:07 GMThttp://hdl.handle.net/2117/1032412017-04-04T05:23:07ZMorillo Bosch, M. PazRafols Salvador, CarlaVillar Santos, Jorge LuisWe put forward a new family of computational assumptions, the Kernel Matrix Diffie-Hellman Assumption. Given some matrix A sampled from some distribution D, the kernel assumption says that it is hard to find “in the exponent” a nonzero vector in the kernel of A¿ . This family is a natural computational analogue of the Matrix Decisional Diffie-Hellman Assumption (MDDH), proposed by Escala et al. As such it allows to extend the advantages of their algebraic framework to computational assumptions. The k-Decisional Linear Assumption is an example of a family of decisional assumptions of strictly increasing hardness when k grows. We show that for any such family of MDDH assumptions, the corresponding Kernel assumptions are also strictly increasingly weaker. This requires ruling out the existence of some black-box reductions between flexible problems (i.e., computational problems with a non unique solution).Sensor fault detection in a damage detection approach based on piezodiagnostics
http://hdl.handle.net/2117/103182
Sensor fault detection in a damage detection approach based on piezodiagnostics
Ruiz Ordóñez, Magda; Camacho-Navarro, Jhonatan; Villamizar Mejía, Rodolfo; Mujica Delgado, Luis Eduardo
Online monitoring systems demand an adequate operation of sensor system used to acquire structural state measurements. If a damaged sensor record is incorporated in the diagnosis algorithm, it could be generate uncertainties and generate unsuitable alarms. Thus,
appropriate operation of sensor system is a critical requirement in order to obtain a high reliability for structural damage diagnosis algorithms. In this work a data-driven procedure is studied in order to mitigate the faulty sensor effect in a monitoring system. The studied
method takes advantage of piezo-diagnostics approach, where piezoelectric devices are attached to the surface of the monitored structure to produce guided waves. Thus, piezoelectric measurements are analyzed by applying principal component analysis and cross-correlation, in order to detect abnormal behaviors. In this sense, the squared prediction error Q and Hotelling squared statistical indices are used to observe a typical behaviour caused by sensor problems or structural damages. The methodology is validated on a lab carbon steel pipe section by using scenarios that include electric power failures,
disconnecting power cords as well as mass adding. As concluding remark, in this work was possible to separate structural damage and fault sensor states at different clusters.
Mon, 03 Apr 2017 08:15:28 GMThttp://hdl.handle.net/2117/1031822017-04-03T08:15:28ZRuiz Ordóñez, MagdaCamacho-Navarro, JhonatanVillamizar Mejía, RodolfoMujica Delgado, Luis EduardoOnline monitoring systems demand an adequate operation of sensor system used to acquire structural state measurements. If a damaged sensor record is incorporated in the diagnosis algorithm, it could be generate uncertainties and generate unsuitable alarms. Thus,
appropriate operation of sensor system is a critical requirement in order to obtain a high reliability for structural damage diagnosis algorithms. In this work a data-driven procedure is studied in order to mitigate the faulty sensor effect in a monitoring system. The studied
method takes advantage of piezo-diagnostics approach, where piezoelectric devices are attached to the surface of the monitored structure to produce guided waves. Thus, piezoelectric measurements are analyzed by applying principal component analysis and cross-correlation, in order to detect abnormal behaviors. In this sense, the squared prediction error Q and Hotelling squared statistical indices are used to observe a typical behaviour caused by sensor problems or structural damages. The methodology is validated on a lab carbon steel pipe section by using scenarios that include electric power failures,
disconnecting power cords as well as mass adding. As concluding remark, in this work was possible to separate structural damage and fault sensor states at different clusters.Sensor selection based on principal component analysis for fault detection in wind turbines
http://hdl.handle.net/2117/103181
Sensor selection based on principal component analysis for fault detection in wind turbines
Pozo Montero, Francesc; Vidal Seguí, Yolanda
Growing interest for improving the reliability of safety-critical structures, such as wind turbines, has led to the advancement of structural health monitoring (SHM). Existing techniques for fault detection can be broadly classified into two major categories: model-based methods and signal processing-based methods. This work focuses in the signal-processing-based fault detection by using principal component analysis (PCA) as a way to condense and extract information from the collected signals. In particular, the goal of this work is to select a reduced number of sensors to be used. From a practical point of view, a reduced number of sensors installed in the structure leads to a reduced cost of installation and maintenance. Besides, from a computational point of view, less sensors implies lower computing time, thus the detection time is shortened.
The overall strategy is to firstly create a PCA model measuring a healthy wind turbine. Secondly, with the model, and for each fault scenario and each possible subset of sensors, it measures the Euclidean distance between the arithmetic mean of the projections into the PCA model that come from the healthy wind turbine and the mean of the projections that come from the faulty one. Finally, it finds the subset of sensors that separate the most the data coming from the healthy wind turbine and the data coming from the faulty one.
Numerical simulations using a sophisticated wind turbine model (a modern 5MW turbine implemented in the FAST software) show the performance of the proposed method under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics.
Mon, 03 Apr 2017 08:05:19 GMThttp://hdl.handle.net/2117/1031812017-04-03T08:05:19ZPozo Montero, FrancescVidal Seguí, YolandaGrowing interest for improving the reliability of safety-critical structures, such as wind turbines, has led to the advancement of structural health monitoring (SHM). Existing techniques for fault detection can be broadly classified into two major categories: model-based methods and signal processing-based methods. This work focuses in the signal-processing-based fault detection by using principal component analysis (PCA) as a way to condense and extract information from the collected signals. In particular, the goal of this work is to select a reduced number of sensors to be used. From a practical point of view, a reduced number of sensors installed in the structure leads to a reduced cost of installation and maintenance. Besides, from a computational point of view, less sensors implies lower computing time, thus the detection time is shortened.
The overall strategy is to firstly create a PCA model measuring a healthy wind turbine. Secondly, with the model, and for each fault scenario and each possible subset of sensors, it measures the Euclidean distance between the arithmetic mean of the projections into the PCA model that come from the healthy wind turbine and the mean of the projections that come from the faulty one. Finally, it finds the subset of sensors that separate the most the data coming from the healthy wind turbine and the data coming from the faulty one.
Numerical simulations using a sophisticated wind turbine model (a modern 5MW turbine implemented in the FAST software) show the performance of the proposed method under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics.Fault tolerant control design of floating offshore wind turbines
http://hdl.handle.net/2117/103180
Fault tolerant control design of floating offshore wind turbines
Rodellar Benedé, José; Tutivén Gálvez, Christian; Acho Zuppa, Leonardo; Vidal Seguí, Yolanda
This work is concerned with active vibration mitigation in wind turbines (WT) but not through the use of specifically tailored devices. Instead, a general control scheme is designed for torque and pitch controllers based on a super-twisting algorithm, which uses additional feedback of the fore-aft and side-to-side acceleration signals at the top of the WT tower to mitigate the vibrational behavior. In general, proposed methods to improve damping through pitch and torque control suffer from increased blade pitch actuator usage. However, in this work the blade pitch angle is smoothed leading to a decrease of the pitch actuator effort, among other benefits evidenced through numerical experiments. The most frequent faults induce vibrations in the corresponding WT subsystems. In fact, vibration monitoring has been recently used for fault diagnosis Thus, by means of vibration mitigation, different faulty conditions can be alleviated leading to a passive fault tolerant control. In this work, coupled non-linear aero-hydro- servo-elastic simulations of a floating offshore wind turbine are carried out for one of the most common pitch actuator faults.
Mon, 03 Apr 2017 08:00:22 GMThttp://hdl.handle.net/2117/1031802017-04-03T08:00:22ZRodellar Benedé, JoséTutivén Gálvez, ChristianAcho Zuppa, LeonardoVidal Seguí, YolandaThis work is concerned with active vibration mitigation in wind turbines (WT) but not through the use of specifically tailored devices. Instead, a general control scheme is designed for torque and pitch controllers based on a super-twisting algorithm, which uses additional feedback of the fore-aft and side-to-side acceleration signals at the top of the WT tower to mitigate the vibrational behavior. In general, proposed methods to improve damping through pitch and torque control suffer from increased blade pitch actuator usage. However, in this work the blade pitch angle is smoothed leading to a decrease of the pitch actuator effort, among other benefits evidenced through numerical experiments. The most frequent faults induce vibrations in the corresponding WT subsystems. In fact, vibration monitoring has been recently used for fault diagnosis Thus, by means of vibration mitigation, different faulty conditions can be alleviated leading to a passive fault tolerant control. In this work, coupled non-linear aero-hydro- servo-elastic simulations of a floating offshore wind turbine are carried out for one of the most common pitch actuator faults.Multidimensional big data processing for damage detection in real pipelines using a smart pig tool
http://hdl.handle.net/2117/102797
Multidimensional big data processing for damage detection in real pipelines using a smart pig tool
Ruiz Ordóñez, Magda; Mujica Delgado, Luis Eduardo; Alférez Baquero, Edwin Santiago; Quintero, Mario; Villamizar Mejía, Rodolfo
The history of the hydrocarbons business in Colombia dates back to the early twentieth century where mining and energy sector has been one of the principal pillars for the its development. Thus, the pipelines currently in service have over 30 years and most of them are buried and phenomena like metal losses, corrosion, mechanical stress, strike by excavation machinery and other type of damages are presented. Since it can generate social and environmental problems, monitoring tools and programs should be developed in order to prevent catastrophic situations. However, the maintaining of these structures is very expensive and it is normally developed by foreign companies. In order to overcome this situation, recently the native research institute “Research Institute of Corrosion - CIC (Corporación para la Investigación de la Corrosión)” developed an in-line inspection tool to be operated in Colombian pipelines (especially gas) to get valuable information of their current state along of thousand kilometres. The recorded data is of big size and its processing demand a high computational cost and adequate tool analysis to determine a certain pipeline damage condition. On other hand, the author from UPC and UIS have been bringing its expertise in processing and analysing this type of big data by using mainly Principal Component Analysis (PCA) as an effective tool to detect and locate different damages. In previous papers, multidimensional data matrix was used to locate possible damages along the pipeline, however most of activated points were considered false alarms since they corresponded to weld points. Thus, in this paper it is proposed no considering piecewise weld points (tube sections) and an extension of PCA named Multiway PCA (MPCA) is applied for each each one of the tube sections that form the pipeline. Therefore, if a tube section is found outside from overall indices found by using the MPCA model, an alarm activated in that section and a precise location can be obtained by analyzing only data from that specific tube section.
Wed, 22 Mar 2017 12:43:59 GMThttp://hdl.handle.net/2117/1027972017-03-22T12:43:59ZRuiz Ordóñez, MagdaMujica Delgado, Luis EduardoAlférez Baquero, Edwin SantiagoQuintero, MarioVillamizar Mejía, RodolfoThe history of the hydrocarbons business in Colombia dates back to the early twentieth century where mining and energy sector has been one of the principal pillars for the its development. Thus, the pipelines currently in service have over 30 years and most of them are buried and phenomena like metal losses, corrosion, mechanical stress, strike by excavation machinery and other type of damages are presented. Since it can generate social and environmental problems, monitoring tools and programs should be developed in order to prevent catastrophic situations. However, the maintaining of these structures is very expensive and it is normally developed by foreign companies. In order to overcome this situation, recently the native research institute “Research Institute of Corrosion - CIC (Corporación para la Investigación de la Corrosión)” developed an in-line inspection tool to be operated in Colombian pipelines (especially gas) to get valuable information of their current state along of thousand kilometres. The recorded data is of big size and its processing demand a high computational cost and adequate tool analysis to determine a certain pipeline damage condition. On other hand, the author from UPC and UIS have been bringing its expertise in processing and analysing this type of big data by using mainly Principal Component Analysis (PCA) as an effective tool to detect and locate different damages. In previous papers, multidimensional data matrix was used to locate possible damages along the pipeline, however most of activated points were considered false alarms since they corresponded to weld points. Thus, in this paper it is proposed no considering piecewise weld points (tube sections) and an extension of PCA named Multiway PCA (MPCA) is applied for each each one of the tube sections that form the pipeline. Therefore, if a tube section is found outside from overall indices found by using the MPCA model, an alarm activated in that section and a precise location can be obtained by analyzing only data from that specific tube section.Embedded piezodiagnostics for online structural damage detection based on PCA algorithm
http://hdl.handle.net/2117/102795
Embedded piezodiagnostics for online structural damage detection based on PCA algorithm
Camacho-Navarro, Jhonatan; Ruiz Ordóñez, Magda; Villamizar Mejía, Rodolfo; Mujica Delgado, Luis Eduardo; Ariza, Fabian
This work discusses a methodology used to implement a data-driven strategy for Structural Health Monitoring. First, the instrumentation of the equipment is detailed by describing the main components to be installed in the test structure in order to produce guide d waves. Specifically, an active piezo active system is used for this purpose , which consists of piezoelectric devices attached to the test structure surface and an ac quisition system. Then, the programming procedure to embed the damage detection algorithm is defined. In particular, the mathematical foundations and software requirements for impleme nting the preprocessing stage, baseline model building, and statistical index computation are specified. As a result, the Odroid-U3 computational core has the capability t o perform online damage assessment. Finally, some validation tests are presented through videos and short real time demonstration. Experimental data are recorded from two test specimens: i.) a lab carbon steel pipe loop built to emulate leak scenarios, and ii.) an aluminum plate, where mass adding is used to emulate reversible damages. The results reported i n this work show the high feasibility of the proposal methodology for obtaining an online embedded monitoring system with several advantages such as low cost, easy configuration, expandability and few computational resources
Wed, 22 Mar 2017 12:27:24 GMThttp://hdl.handle.net/2117/1027952017-03-22T12:27:24ZCamacho-Navarro, JhonatanRuiz Ordóñez, MagdaVillamizar Mejía, RodolfoMujica Delgado, Luis EduardoAriza, FabianThis work discusses a methodology used to implement a data-driven strategy for Structural Health Monitoring. First, the instrumentation of the equipment is detailed by describing the main components to be installed in the test structure in order to produce guide d waves. Specifically, an active piezo active system is used for this purpose , which consists of piezoelectric devices attached to the test structure surface and an ac quisition system. Then, the programming procedure to embed the damage detection algorithm is defined. In particular, the mathematical foundations and software requirements for impleme nting the preprocessing stage, baseline model building, and statistical index computation are specified. As a result, the Odroid-U3 computational core has the capability t o perform online damage assessment. Finally, some validation tests are presented through videos and short real time demonstration. Experimental data are recorded from two test specimens: i.) a lab carbon steel pipe loop built to emulate leak scenarios, and ii.) an aluminum plate, where mass adding is used to emulate reversible damages. The results reported i n this work show the high feasibility of the proposal methodology for obtaining an online embedded monitoring system with several advantages such as low cost, easy configuration, expandability and few computational resourcesSobre la inversa de grupo de grafos distancia regulares
http://hdl.handle.net/2117/102776
Sobre la inversa de grupo de grafos distancia regulares
Carmona Mejías, Ángeles
En este trabajo estudiamos cuando la inversa de grupo del Laplacia no com- binatorio de un grafo distancia–regular es una M –matriz. Cuando esto ocurre decimos que el grafo tiene la M –propiedad. Aquí probamos que sólo grafos distancia regulares con diámetro menor que cuatro pueden tener la M –propiedad y damos una caracterización en términos del vector de intersección
Wed, 22 Mar 2017 09:38:08 GMThttp://hdl.handle.net/2117/1027762017-03-22T09:38:08ZCarmona Mejías, ÁngelesEn este trabajo estudiamos cuando la inversa de grupo del Laplacia no com- binatorio de un grafo distancia–regular es una M –matriz. Cuando esto ocurre decimos que el grafo tiene la M –propiedad. Aquí probamos que sólo grafos distancia regulares con diámetro menor que cuatro pueden tener la M –propiedad y damos una caracterización en términos del vector de intersección