Browsing by Keyword "Computational Theory and Mathematics"
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Item Adaptive Multifactorial Evolutionary Optimization for Multitask Reinforcement Learning(2022-04-01) Martinez, Aritz D.; Del Ser, Javier; Osaba, Eneko; Herrera, Francisco; IA; QuantumEvolutionary computation has largely exhibited its potential to complement conventional learning algorithms in a variety of machine learning tasks, especially those related to unsupervised (clustering) and supervised learning. It has not been until lately when the computational efficiency of evolutionary solvers has been put in prospective for training reinforcement learning models. However, most studies framed so far within this context have considered environments and tasks conceived in isolation, without any exchange of knowledge among related tasks. In this manuscript we present A-MFEA-RL, an adaptive version of the well-known MFEA algorithm whose search and inheritance operators are tailored for multitask reinforcement learning environments. Specifically, our approach includes crossover and inheritance mechanisms for refining the exchange of genetic material, which rely on the multilayered structure of modern deep-learning-based reinforcement learning models. In order to assess the performance of the proposed approach, we design an extensive experimental setup comprising multiple reinforcement learning environments of varying levels of complexity, over which the performance of A-MFEA-RL is compared to that furnished by alternative nonevolutionary multitask reinforcement learning approaches. As concluded from the discussion of the obtained results, A-MFEA-RL not only achieves competitive success rates over the simultaneously addressed tasks, but also fosters the exchange of knowledge among tasks that could be intuitively expected to keep a degree of synergistic relationship.Item Adaptive simulation of unsteady flow past the submerged part of a floating wind turbine platform(International Center for Numerical Methods in Engineering, 2015) Jansson, Johan; Nava, Vincenzo; Sanchez, Miren; Aguirre, Goren; De Abreu, Rodrigo Vilela; Hoffman, Johan; Villate, Jose Luis; Muscari, Roberto; Broglia, Riccardo; Salvatore, Francesco; RENOVABLES OFFSHORE; Tecnalia Research & Innovation; GENERALOffshore floating platforms for wind turbines represent challenging concepts for designers trying to combine an optimal compromise between cost effectiveness and performance. Modelling of the hydrodynamic behaviour of the structure is still the subject of wide debate in the technical communities. The assessment of the hydrodynamics of the support structure is not an easy task as the floaters consist of an assembly of columns, braces and pontoons, commonly also with heave plates: Each of these components corresponds to a different hydrodynamic model and it further interacts with the other elements. This results in very complex non-linear modeling, which makes it necessary to resort to computational fluid dynamics (CFD) methods for the evaluation of the combined hydrodynamics. In the framework of the collaboration between the Basque Centre for Applied Mathematics (BCAM) and Tecnalia R&I, the interaction of the sea flow with a semisubmersible floating offshore wind platform have been calculated by using the open source solver Unicorn in the FEniCS-HPC framework when subject to a steady inflow. The prototype of the platform consists in a semi-submersible 4-columns column stabilized platform - NAUTILUS Floating Solutions concept-; columns are connected by a rigid ring pontoon provided with heave damping plates at the bottom. The novelty of the approach in FEniCS-HPC hinges upon an implicit formulation for the turbulence, a cheap free slip model of the boundary layer and goal-oriented mesh adaptivity [8, 6, 9, 20, 1]. We find that the results are consistent with experimental results for cylinders at high Reynolds number.Item A Blockchain-Based Audit Trail Mechanism: Design and Implementation: Design and implementation(2021-11-26) Regueiro, Cristina; Seco, Iñaki; Gutiérrez-Agüero, Iván; Urquizu, Borja; Mansell, Jason; Tecnalia Research & Innovation; CIBERSEC&DLTAudit logs are a critical component in today’s enterprise business systems as they provide several benefits such as records transparency and integrity and security of sensitive information by creating a layer of evidential support. However, current implementations are vulnerable to attacks on data integrity or availability. This paper presents a Blockchain-based audit trail mechanism that leverages the security features of Blockchain to enable secure and reliable audit trails and to address the aforementioned vulnerabilities. The architecture design and specific implementation are described in detail, resulting in a real prototype of a reliable, secure, and user-friendly audit trail mechanism.Item Building an interoperability API for Sky computing(2011) Petcu, Dana; Crǎciun, Ciprian; Neagul, Marian; Lazcanotegui, Iñigo; Rak, Massimiliano; SGThe federation of Cloud resources can be treated at different levels of abstractions. In this paper we focus on application programming interfaces for building Cloud-based applications using services from multiple Cloud providers. A full stack of APIs is proposed to decouple the development of a Cloud-based application from its deployment and execution. A particular attention is paid to the design of the interoperability API aiming to provide programming language interoperability and protocol syntax or semantic enforcements.Item Cloud application monitoring: The mOSAIC approach(2011) Rak, Massimiliano; Venticinque, Salvatore; Máhr, Tamás; Echevarria, Gorka; Esnal, Gorka; Tecnalia Research & InnovationCloud computing delegates the management of any kind of resources, such as the computing environment or storage systems for example, to the network. The wide-spread permeation of the Cloud paradigm implies the need of new programming models that are able to utilize such new features. Once the problem of enabling developers to manage Cloud resources in a clear and flexible way is solved, a new problem emerges: the monitoring of the quality of the acquired resources and of the services offered to final users. As the first step, the mOSAIC API and framework aim at offering a solution for the development of interoperable, portable and Cloud-provider independent Cloud applications. As the second step, this paper introduces the mOSAIC monitoring components that facilitate the building of custom monitoring systems for Cloud applications using the mOSAIC API.Item Communication architectures and experiences for web-connected physical smart objects(2010) Vazquez, Juan Ignacio; Ruiz-De-Garibay, Jonathan; Eguiluz, Xabier; Doamo, Iker; Rentería, Silvia; Ayerbe, Ana; Digital Base; GENERALWe are witnessing a tremendous hype on the Internet of Things paradigm, with not only research projects, but also commercial products claiming to implement its fundamental mechanisms. Smart-connected-objects designers often have to face decisions on the global architecture of the service, since no single solution is valid for all the cases. In this paper, we explore the different criteria for designing architectures for Internet of Things solutions, along with illustrative examples of prototypes that implement these approaches.Item Computation and experimental validation of the oblique cutting process in AL2024-T4 and AISI 4340(2005) Cerro, Iván; Alcaraz, José L.; López De Lacalle, Luis N.; Gonzalo, Oscar; Centros PRE-FUSION TECNALIA - (FORMER)Oblique cutting is considered in this paper by implementing a numerical simulation model with ABAQUS/Explicit applied to two metallic materials, Al 2024-T4 and AISI 4340. An explicit algorithm for thermomechanically coupled problems is used. Two basic parts are considered: the workpiece and the cutting tool (a hard metal), this one being assumed also deformable. A thermoviscoplastic behavior is included for the workpiece by means of the Johnson-Cook law. An interface between the chip and machined workpiece is defined. Along this zone a cumulative damage failure criterion is applied and chip formation is brought about by the elimination of failing elements. Force components and temperature are analyzed in terms of different model parameters: the cutting velocity, the edge angle, the rake angle and the interface thickness. Several hollow cylinders have been turned for the results validation. Forces and temperature are measured by means of a piezoelectric platform and a thermographic camera, respectively. Force components in the cutting and normal direction fit well with numerical results. The effect of the cutting parameters in temperature variation is also in agreement with the numerical simulations.Item Computer vision with Microsoft Kinect for control of functional electrical stimulation: ANN classification of the grasping intentions(Institute of Electrical and Electronics Engineers Inc., 2014-01-15) Štrbac, Matija D.; Popović, Dejan B.; Reljin, Branimir; Stankovic, Srdan; SGWe present a method for recognizing intended grasp type based on data from the Microsoft Kinect. A computer vision algorithm estimates the vertical and the transversal distance of the hand from the center of the object and the hand orientation from the Kinect depth images. Based on this set of features in the reaching phase of grasp artificial neural network recognizes the intended grasp type. This is demonstrated with an example of a coffee cup on a working desk. Trained neural network classified the grasp with accuracy above 85%. By adding this feature to the existing computer vision system for control of the functional electrical stimulation assisted grasping we facilitate the compliance between the applied electrical stimulation and the user intentions.Item A Control Testing Framework for automated driving functionalities using modular architecture with ROS/CARLA environment(Institute of Electrical and Electronics Engineers Inc., 2021) Arizala, Asier; Lattarulo, Ray; Zubizarreta, Asier; Perez, Joshue; Ferariu, Lavinia; Matcovschi, Mihaela-Hanako; Ungureanu, Florina; CCAMInterest in Automated Vehicles (AV) has increased in the last years due to the need of providing more efficient and safe transportation systems. However, the development of AV functionalities is a complex task, as multiple technologies have to be tested and integrated to fulfill the required automation level. Moreover, the number of different scenarios that have to be dealt with Cooperative and Connected Automated Mobility (CCAM) solutions makes traditional track testing a non-optimal approach. Due to this, in recent years interest in the development of simulation-based testing frameworks has arisen, with open-source and commercial solutions trying to fulfill the requirements of AV development. This work introduces an automated vehicle testing framework that combines the widely used open-source simulation environment CARLA with a self-developed modular control framework AUDRIC. The communication between both is made using the ROS environment. The proposed approach provides the advantages of both environments in terms of flexibility and modularity, allowing the development of automated functionalities for the different modules of AV architecture. The validity of the approach is demonstrated by presenting two use cases: a lane following application and an obstacle avoidance scenario.Item A deep learning approach to the inversion of borehole resistivity measurements(2020-04-13) Shahriari, M.; Pardo, D.; Picon, A.; Galdran, A.; Del Ser, J.; Torres-Verdín, C.; COMPUTER_VISION; IABorehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical complexity, inversion techniques are often used to estimate layer-by-layer electrical properties from measurements. When used for well geosteering purposes, it becomes essential to invert the measurements into layer-by-layer values of electrical resistivity in real time. We explore the possibility of using deep neural networks (DNNs) to perform rapid inversion of borehole resistivity measurements. Accordingly, we construct a DNN that approximates the following inverse problem: given a set of borehole resistivity measurements, the DNN is designed to deliver a physically reliable and data-consistent piecewise one-dimensional layered model of the surrounding subsurface. Once the DNN is constructed, we can invert borehole measurements in real time. We illustrate the performance of the DNN for inverting logging-while-drilling (LWD) measurements acquired in high-angle wells via synthetic examples. Numerical results are promising, although further work is needed to achieve the accuracy and reliability required by petrophysicists and drillers.Item Effect of the generator sizing on a wave energy converter considering different control strategies(2013-07-01) Alberti, Luigi; Tedeschi, Elisabetta; Bianchi, Nicola; Santos, Maider; Fasolo, Alessandro; POWER SYSTEMSPurpose - The purpose of this paper is to investigate the impact of control strategy selection on the power performance of wave energy converters for different ratings of the Power Take-Off (PTO) system. Design/methodology/ approach - The case of a point absorber equipped with an all-electric PTO is considered. The effect of control techniques and electrical generator design is analyzed from a theoretical standpoint and then veriï ed through integrated hydrodynamic-electric simulations. Findings - It has been proved that control parameters that maximize the power extraction from the waves can be derived based on the power and torque constraints imposed by the electrical machine. Originality/value - An optimized and integrated approach to the control strategy selection and generator design for point absorbers has been presented, which maximizes the electric power generation from sea waves under real conditions and represents a good trade-off for the PTO from both the technical and the economic standpoint.Item Focusing on the hybrid quantum computing - Tabu search algorithm: New results on the Asymmetric Salesman Problem(Association for Computing Machinery, Inc, 2021-07-07) Osaba, Eneko; Villar-Rodriguez, Esther; Oregi, Izaskun; Moreno-Fernandez-De-Leceta, Aitor; QuantumQuantum Computing is an emerging paradigm which is gathering a lot of popularity in the current scientific and technological community. Widely conceived as the next frontier of computation, Quantum Computing is still at the dawn of its development. Thus, current solving systems suffer from significant limitations in terms of performance and capabilities. Some interesting approaches have been devised by researchers and practitioners in order to overcome these barriers, being quantum-classical hybrid algorithms one of the most often used solving schemes. The main goal of this paper is to extend the results and findings of the recently proposed hybrid Quantum Computing - Tabu Search Algorithm for partitioning problems. To do that, we focus our research on the adaptation of this method to the Asymmetric Traveling Salesman Problem. In overall, we have employed six well-known instances belonging to TSPLIB to assess the performance of Quantum Computing - Tabu Search Algorithm in comparison to QBSolv. Furthermore, as an additional contribution, this work also supposes the first solving of the Asymmetric Traveling Salesman Problem using a Quantum Computing based method. Aiming to boost whole community's research in QC, we have released the project's repository as open source code for further application and improvements.Item Gait phase detection optimization based on variational bayesian inference of feedback sensor signal(Institute of Electrical and Electronics Engineers Inc., 2014-01-15) Malešević, Nebojša; Malešević, Jovana; Keller, Thierry; Reljin, Branimir; Stankovic, Srdan; SG; Tecnalia Research & InnovationStroke patients often suffer from gait disorders which can remain chronic. Mechanical or electrical aids designed to deal with this problem often rely on accurate estimation of current gait phase as this information is used for active ankle joint control. In this paper we present the method for optimization of the gait phase detection algorithm. The method is based on Variational Bayesian inference which is employed on signals from feedback sensors positioned on both paretic and healthy foot of patient. Main aim of Variational Bayesian inference application was to remove noise and provide smooth sensor signal which is suitable for robust gait phase detection algorithm. We modeled foot trajectory with linear model. Results presented in this paper show significant reduction of high frequency noise in gyroscope signal. The reduction was dominant during transitions between gait phases making our method applicable in any algorithm based on signal features in time domain.Item H.264/SVC rate-resiliency tradeoff in faulty communications through 802.16e railway networks(2009) Unanue, Iraide; Del Ser, Javier; Sanchez, Pedro; Casasempere, Jon; IA; Centros PRE-FUSION TECNALIA - (FORMER)Focusing on 802.16e-based railway networks, our work deals with the degradation of the perceived quality of multimedia communications in presence of handover events. The movement of mobile nodes (MN) across coverage areas of distinct Base Stations (BS) involves global disconnection periods, which leads to a degradation of the perceived video quality. During this period, video data packets are dropped, and therefore video frame information is lost. The study presented here concentrates on the specific dynamism characteristics of conventional video surveillance applications and the transmission of the recorded video information over 802.16e networks. We conclude, through intensive simulations, that the GOP structure of the H.264/SVC encoded video information should not exclusively emphasize on maximizing the compression rate of the stream. Instead, the utilized GOP structure should be selected according to both the motion pattern of the processed video and the handover statistics of the railway communication channel which, in turn, are extremely predictable in railway scenarios. This conclusion is corroborated by proposing the insertion of intra-period frames into the GOP structure, in order to maximize the average Peak Signal to Noise Ratio (PSNR) of the received stream at a negligible video size penalty cost.Item A heuristic approach to the multicriteria design of IaaS cloud infrastructures for Big Data applications(2018-10) Arostegi, María; Torre-Bastida, Ana; Bilbao, Miren Nekane; Del Ser, Javier; IA; Tecnalia Research & Innovation; HPAThe rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed great opportunities for companies to shift their business model towards a fully digital strategy. A major obstacle in this matter is the requirement of highly specialized ICT infrastructures that are expensive and difficult to manage. It is at this point that the IaaS (infrastructure as a service) model offers an efficient and cost-affordable solution to supply companies with their required computing resources. In the Big Data context, it is often a hard task to design an optimal IaaS solution that meets user requirements. In this context, we propose a methodology to optimize the definition of IaaS cloud models for hosting Big Data platforms, following a threefold criterion: cost, reliability, and computing capacity. Specifically, the proposed methodology hinges on evolutionary heuristics in order to find IaaS configurations in the cloud that optimally balance such objectives. We also define measures to quantify the aforementioned metrics over a Big Data platform hosted within an IaaS cloud model. The proposed method is validated by using real information from three IaaS providers and three Big Data platforms. The obtained results provide an insightful input for system managers when initially designing cloud infrastructures for Big Data applications.Item Hybrid Classical-Quantum Computing: Are we Forgetting the Classical Part in the Binomial?(Institute of Electrical and Electronics Engineers Inc., 2023) Villar-Rodriguez, Esther; Gomez-Tejedor, Aitor; Osaba, Eneko; Muller, Hausi; Alexev, Yuri; Delgado, Andrea; Byrd, Greg; QuantumThe expectations arising from the latest achievements in the quantum computing field are causing that researchers coming from classical artificial intelligence to be fascinated by this new paradigm. In turn, quantum computing, on the road towards usability, needs classical procedures. Hybridization is, in these circumstances, an indispensable step but can also be seen as a promising new avenue to get the most from both computational worlds. Nonetheless, hybrid approaches have now and will have in the future many challenges to face, which, if ignored, will threaten the viability or attractiveness of quantum computing for real-world applications. To identify them and pose pertinent questions, a proper characterization of the hybrid quantum computing field, and especially hybrid solvers, is compulsory. With this motivation in mind, the main purpose of this work is to propose a preliminary taxonomy for classifying hybrid schemes, and bring to the fore some questions to stir up researchers minds about the real challenges regarding the application of quantum computing.Item Identifying recommendation opportunities for computer-supported collaborative environments(2016-10-01) Lobo, Jesus L.; Santos, Olga C.; Boticario, Jesus G.; Del Ser, Javier; IACollaborative indicators derived from participants' interactions can be used to support and improve their collaborative behaviour. In this research, we focus on automatically identifying recommendation opportunities in the Collaborative Logical Framework from participants' interactions. Different information sources have been considered: (a) statistical collaborative indicators; (b) social interactions; (c) opinions received by the participants via ratings; and (d) users' affective state and personality. The recommendations have been elicited considering the generality and transferability of the participants' interactions provided by the Collaborative Logical Framework. As a result, three scenarios have been identified that lead us to propose meaningful grouping suggestions and recommendations, which ultimately aimed to ground an informed personalized support to the participants in intensive collaborative frameworks.Item Iterative concatenated zigzag decoding and blind data fusion of correlated sensors(2009) Del Ser, Javier; Garcia-Frias, Javier; Crespo, Pedro M.; IAThis paper addresses the sensor network scenario where several nodes sense a common information source S. When such sensors forward their correlated observations to a common shared receiver, it is necessary to combine the received information in order to obtain an estimation of S with high reliability. In this manuscript we propose the use of low-complexity concatenated Zigzag codes for the transmission of correlated sensors through orthogonal AWGN channels. In reception, a novel albeit simple correlation estimation procedure is integrated into the iterative decoding and data fusion algorithm, which is based on the Sum-Product Algorithm applied over the factor graph describing the system. Fundamental limits are also derived for the end-to-end probability of error. Simulation results verify that the Bit Error Rate (BER) performance of the proposed receiver is very close to the aforementioned fundamental limits, while requiring less decoding complexity than other capacity-approaching codes.Item KOPLE - Knowledge-oriented product line engineering(Association for Computing Machinery, 2010) Hamza, Haitham S.; Martínez, Jabier; Mugartza, Joseba Laka; SWT; SGThe maturity of Knowledge Engineering (KE) theory and practice presents a real opportunity for advancing the state-of-the-art and state-of-the-practice in software Product-line Engineering (PLE). Several challenges that face the adoption and implementation of PLE in practice can be addressed by exploiting advanced techniques from KE. This paper introduces the concept of KOPLE and describes the related one-day workshop that will be held in conjunction with SPLASH 2010.Item Material Fracture Life Prediction Under High Temperature Creep Conditions Using Support Vector Machines And Artificial Neural Networks Techniques(Institute of Electrical and Electronics Engineers Inc., 2021) Martinez, Roberto Fernandez; Jimbert, Pello; Callejo, Lorena M.; Barbero, Jose Ignacio; CIRMETAL; Tecnalia Research & Innovation; PROMETALOne of the most applied materials to manufacture critical components in power plants are martensitic steels due to their high creep and oxidation resistance. In this work, the fracture life of martensitic steels that are designed based on the P92 standard is modeled in order to better understand the relation between its service life and its composition and its thermal treatment. This feature is usually studied by performing creep tests, although carrying out tests of this type are really cost and time consuming. To solve this problem, a multivariate analysis and a training-testing model methodology were performed using a dataset formed by 344 creep tests with the final goal of obtaining a model to predict the fracture life of the material based on several nonlinear techniques like support vector machines and artificial neural networks. Once the models were defined based on predicting with the better generalization capability to cover the whole scenario of the problem, those were compared to determine which one was the most accurate among them. Finally, it was concluded that the model's performance using the proposed methodology based on artificial neural networks got the most accurate results, achieving low errors of approximately 6.14% when predicting creep behavior under long service times.