TECNALIA Publications Repository :: Browsing by Keyword "Modeling and Simulation"

Browsing by Keyword "Modeling and Simulation"

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Admittance control for collaborative dual-arm manipulation
(Institute of Electrical and Electronics Engineers Inc., 2019-12) Tarbouriech, Sonny; Navarro, Benjamin; Fraisse, Philippe; Crosnier, Andre; Cherubini, Andrea; Salle, Damien; ROBOTICA_FLEX
Human-robot collaboration is an appealing solution to increase the flexibility of production lines. In this context, we propose a kinematic control strategy for dual-arm robotic platforms physically collaborating with human operators. Based on admittance control, our approach aims at improving the performance of object transportation tasks by acting on two levels: estimating and compensating gravity effects on one side, and considering human intention in the cooperative task space on the other. An experimental study using virtual reality reveals the effectiveness of our method in terms of reduced human energy expenditure.
Amplitude control of the neutral-point voltage oscillations in the three-level converter
(2008) Zaragoza, J.; Pou, J.; Arias, A.; Ceballos, S.; Robles, E.; Ibáñez, P.; Gabiola, I.; POWER ELECTRONICS AND SYSTEM EQUIPMENT; RENOVABLES OFFSHORE; SG
This paper presents a control method to regulate the amplitude of the voltage oscillations that appear in the neutral-point of the three-level diode-clamped converter for some operating conditions. The control is applied to the hybrid modulation technique, which is based on combining two modulation techniques. One of them is based on two modulation signals per phase, and can remove completely the low-frequency voltage oscillations on the neutral point. However, such a modulation strategy has an important drawback; it increases significantly switching losses on the converter. The hybrid modulation used here combines such a modulation with sinusoidal PWM. The main characteristic of this hybrid modulation is the capacity to reduce switching losses at the cost of having some low-frequency voltage oscillations on the neutral point. The amplitude of these oscillations can be regulated thanks to the proposed controller, which defines the exact degree of mixture between the two modulation strategies. Some simulation and experimental results are presented in this paper.
ARTIST methodology and framework: A novel approach for the migration of legacy software on the cloud
(IEEE Computer Society, 2013) Menychtas, Andreas; Santzaridou, Christina; Kousiouris, George; Varvarigou, Theodora; Orue-Echevarria, Leire; Alonso, Juncal; Gorronogoitia, Jesus; Bruneliere, Hugo; Strauss, Oliver; Senkova, Tatiana; Pellens, Bram; Stuer, Peter; Tecnalia Research & Innovation; HPA
Nowadays Cloud Computing is considered as the ideal environment for engineering, hosting and provisioning applications. A continuously increasing set of cloud-based solutions is available to application owners and developers to tailor their applications exploiting the advanced features of this paradigm for elasticity, high availability and performance. Although these offerings provide many benefits to new applications, they also incorporate constrains to the modernization and migration of legacy applications by obliging the use of specific technologies and explicit architectural design approaches. The modernization and adaptation of legacy applications to cloud environments is a great challenge for all involved stakeholders, not only from the technical perspective, but also in business level with the need to adapt the business processes and models of the modernized application that will be offered from now on, as a service. In this paper we present a novel model-driven approach for the migration of legacy applications in modern cloud environments which covers all aspects and phases of the migration process, as well as an integrated framework that supports all migration process.
Building an interoperability API for Sky computing
(2011) Petcu, Dana; Crǎciun, Ciprian; Neagul, Marian; Lazcanotegui, Iñigo; Rak, Massimiliano; SG
The 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.
Car parking assistance based on time-or-flight camera
(Institute of Electrical and Electronics Engineers Inc., 2019-06) Pelaez, Luis Paarup; Recalde, Myriam E.Vaca; Munoz, Enrique D.Marti; Larrauri, Jesus Murgoitio; Rastelli, Joshue M.Perez; Druml, Norbert; Hillbrand, Bernhard; Tecnalia Research & Innovation; CCAM
External sensing for automative applications are key tools for the development of Advanced Driver Assistance Systems (ADAS), since they can sense and analyse the environment around the vehicle by providing pictures of the scene behind the vehicle. Parking assistance systems are already available in the market. However, most of these applications are based on ultrasonic sensors, wide-angle image cameras, RADAR, etc, which present some drawbacks such as dependency to light conditions or high maintenance cost, among others. This paper proposes an approach for assisting drivers to park through the processing of data derived by a 3D Time-of-Flight (ToF) camera and the reconstruction of the objects identified around the vehicle. The proposed technique is focused on fusion of two parallel processing technologies, a visual one through the intensity image and a spatial one through the point cloud. Both of them are centered on the detection of the vehicle's plate to estimate its position and determine free spots in the parking. This novel methodology improves the detection of surrounding elements, since it helps to solve two main problems with this kind of devices: 1) the degraded performance under bright ambient light problem (occurring mainly in outdoor parkings), that causes shadows and brightness in the images, hindering its process to detect objects; and 2) the limited detection of low reflection objects such as dark cars. Moreover, this fusion allows to link each pixel of the image with a 3D position, and vice versa, giving the point cloud a visual reference. The system is evaluated through a Renault Twizy platform in real conditions.
A collaborative platform integrating multi-physical and neighborhood-aware building performance analysis driven by the optimized HOLISTEEC building design methodology
(CRC Press/Balkema, 2016) Pruvost, H.; Scherer, R. J.; Linhard, K.; Dangl, G.; Robert, S.; Mazza, D.; Mediavilla Intxausti, A.; Van Maercke, D.; Michaelis, E.; Kira, G.; Häkkinen, T.; Delponte, E.; Ferrando, C.; Christodoulou, Symeon E.; Scherer, Raimar; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN
The paper presents the technical developments made in the course of the HOLISTEEC project. HOLISTEEC is an EU FP7 project whose objective is to first, formalize a new optimized building design methodology, and second, to develop the software platform and modules that implement that design methodology. The paper focuses on the developments made on that software infrastructure that shall at the end of the project provide a BIM-based collaborative platform supporting the optimization of the building design in terms of performances and also enhancing the building design process itself. As an important driver for that optimized methodology the project sets a focus on the consideration of interactions between the building and its neighborhood as well as multi-physical aspects including energy, acoustics, lighting and environmental impact.
Community detection in graphs based on surprise maximization using firefly heuristics
(Institute of Electrical and Electronics Engineers Inc., 2016-11-14) Del Ser, Javier; Lobo, Jesus L.; Villar-Rodriguez, Esther; Bilbao, Miren Nekane; Perfecto, Cristina; IA; Quantum
The detection of node clusters (communities) in graphs has been at the core of many modeling paradigms emerging in different fields and disciplines such as Social Sciences, Biology, Chemistry, Telecommunications and Linguistics. When evaluating the quality of a clustering arrangement unsupervised metrics can be utilized (e.g. modularity), which all rely on structural and topological characteristics of the cluster space rather than on an observed ground of truth that should be achieved. One of such metrics is the recently published Surprise, which evaluates how statistically unlikely a given clustering arrangement is with respect to a random network featuring the same distribution of nodes per cluster. To maximize this metric, a number of algorithms have been proposed in the literature, but their comparative performance varies significantly between networks of different shape and size. In this article a novel heuristic community detection approach is proposed as a means to achieve a universally well-performing tool for graph clustering based on Surprise maximization. The heuristic scheme relies on the search procedure of the so-called Firefly Algorithm, a nature-inspired meta-heuristic solver based on the collective behavior, mutual attractiveness and random yet controlled movement of these insects. The proposed technique emulates these observed behavioral patterns of fireflies in the genotype of the graph clustering problem rather than on an encoded representation of its search space (phenotype). Simulation results evince that the performance of our community detection scheme generalizes better than other schemes when applied over synthetically generated graphs with varying properties.
A comparative study of two hybrid grouping evolutionary techniques for the capacitated P-median problem
(2012-09) Landa-Torres, I.; Del Ser, J.; Salcedo-Sanz, S.; Gil-Lopez, S.; Portilla-Figueras, J. A.; Alonso-Garrido, O.; Tecnalia Research & Innovation; IA
This paper addresses the application of two different grouping-based algorithms to the so-called capacitated P-median problem (CPMP). The CPMP is an NP-complete problem, well-known in the operations research field, arising from a wide spectrum of applications in diverse fields such as telecommunications, manufacturing and industrial engineering. The CPMP problem has been previously tackled by using distinct algorithmic approaches, among which we focus on evolutionary computation techniques. The work presented herein elaborates on these evolutionary computation algorithms when applied to the CPMP, by evaluating the performance of a novel grouping genetic algorithm (GGA) and a novel grouping harmony search approach (GHS). Both GGA and GHS are hybridized with a specially tailored local search procedure for enhancing the overall performance of the algorithm in the particular CPMP scenario under consideration. This manuscript delves into the main characteristics of the proposed GGA and GHS schemes by thoroughly describing the grouping encoding procedure, the evolutionary operators (GGA) and the improvisation process (GHS), the aforementioned local search procedure and a repairing technique that accounts for the feasibility of the solutions iteratively provided by both algorithms. The performance of the proposed algorithms is compared with that of several existing evolutionary-based algorithms for CPMP instances of varying size, based on which it is concluded that GGA and GHS dominate any other approaches published so far in the literature, specially when the size of the CPMP increases. The experimental section of the paper tries to evaluate the goodness of the grouping encoding, and also the differences in behavior between the GGA and GHS due to the meta-heuristic algorithm used.
A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models
(IEEE, 2019-06) Matute, Jose A.; Lattarulo, Ray; Zubizarreta, Asier; Perez, Joshue; CCAM
In this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.
A comparison of the Essence 1.0 and SPEM 2.0 specifications for software engineering methods
(2013) Elvesæter, Brian; Benguria, Gorka; Ilieva, Sylvia; HPA
In this paper we present a comparison of the draft Essence 1.0 and SPEM 2.0 specifications for software engineering methods. The comparison is based on results from the REMICS research project where we are defining an agile methodology for model-driven modernization of legacy applications to service clouds.
Composite Mould Design with Multiphysics FEM Computations Guidance
(2023-02) Garmendia, Iñaki; Vallejo, Haritz; Osés, Usue; MAQUINAS
Composite moulds constitute an attractive alternative to classical metallic moulds when used for components fabricated by processes such as Resin Transfer Moulding (RTM). However, there are many factors that have to be accounted for if a correct design of the moulds is sought after. In this paper, the Finite Element Method (FEM) is used to help in the design of the mould. To do so, a thermo-electrical simulation has been performed through MSC-Marc in the preheating phase in order to ensure that the mould is able to be heated, through the Joule’s effect, according to the thermal cycle specified under operating conditions. Mean temperatures of 120 °C and 100 °C are predicted for the lower and upper semi-mould parts, respectively. Additionally, a thermo-electrical-mechanical calculation has been completed with MSC-Marc to calculate the tensile state along the system during the preheating stage. For the filling phase, the filling process itself has been simulated through RTM-Worx. Both the uniform- and non-uniform temperature distribution approaches have been used to assess the resulting effect. It has been found that this piece of software cannot model the temperature dependency of the resin and a numerical trick must have been applied in the second case to overcome it. Results have been found to be very dependent on the approach, the filling time being 73% greater when modelling a non-uniform temperature distribution. The correct behaviour of the mould during the filling stage, as a consequence of the filling pressure, has been also proved with a specific mechanical analysis conducted with MSC-Marc. Finally, the thermo-elastic response of the mould during the curing stage has been numerically assessed. This analysis has been made through MSC-Marc, paying special attention to the curing of the resin and the exothermic reaction that takes place. For the sake of accuracy, a user subroutine to include specific curing laws has been used. Material properties employed are also described in detail following a modified version of the Scott model, with curing properties extracted from experiments. All these detailed calculations have been the cornerstone to designing the composite mould and have also unveiled some capabilities that were missed in the commercial codes employed. Future versions of these commercial codes will have to deal with these weak points but, as a whole, the Finite Element Method is shown to be an appropriate tool for helping in the design of composite moulds.
A comprehensive ontologies-based framework to support the retrofitting design of energy-efficient districts
(CRC Press/Balkema, 2016) Costa, G.; Sicilia; Lilis, G. N.; Rovas, D. V.; Izkara, J.; Christodoulou, Symeon E.; Scherer, Raimar; Tecnalia Research & Innovation; LABORATORIO DE TRANSFORMACIÓN URBANA
As part of the Europe 2020strategy, one of the challenges for the European construction sector is to reduce the energy footprint and CO2 emissions from new and renovated buildings. This interest is also fostered at a district scale with new technological solutions being developed to achieve more efficient designs. In response to this challenge, a web-based platform for district energy-efficient retrofitting design projects has been proposed in the context of OptEEmAL research project. In order to provide data integration and interoperability between BIM/GIS models and energy simulation tools through this platform, a District Data Model (DDM) has been devised. In this model, fields for urban sustainable regeneration (energy, social, environment, comfort, urban morphology and economic) are related to existing ontological models based on the CityGML and IFC schemas. This paper discusses how the semantic representation from IFC and CityGML files with different levels of detail can be integrated to obtain consistent description of the district in the proposed district data model.
Conservative approximations in nonlinear optimization. theory and examples
(1991) Longo, A.; Nó, M.; Aizpitarte, M.; Unzueta, J.; Centros PRE-FUSION TECNALIA - (FORMER)
A new nonlinear optimization approach with strong convergence properties is presented. This approach is based on approximate subproblems, a nondifferentiable penalty function and a set active strategy, and is well suited for solution of design problems in engineering, where the number of variables may be large and function and gradient evaluations are very expensive (e.g. in structural optimization). The main theoretical results are presented, which lead to a general algorithm from which well-known methods (e.g. Pschenichny's baseline, method of hybrid approximations) can be seen as particular cases. Also, a new kind of convex approximation called SOC (second order correction) is introduced in this context, and some examples are solved by a practical algorithm implemented in a C module called ACPM.
Cooperative game concepts in solving global optimization
(Institute of Electrical and Electronics Engineers Inc., 2019-06) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Ser, Javier Del; Osaba, Eneko; Quantum
Nowadays, cooperative game theory has been applied to many domains of human activities. In this study, the cooperative game concept needed for calculating Shapley value is used in solving global optimization. Precisely, the marginal contribution that an agent carries by joining a coalition is calculated as an increase in population diversity of coalition. This concept is incorporated into differential evolution and its self-adaptive variants jDE in order to show that distributing the monolithic population of solutions into more coalitions and their parallel evolution can improve the results of the original algorithms.
Corrigendum to “Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters” [Energy 239, Part D, 2022, 122318] (Energy (2022) 239(PD), (S0360544221025664), (10.1016/j.energy.2021.122318))
(2022-08-15) Lumbreras, Mikel; Garay-Martinez, Roberto; Arregi, Beñat; Martin-Escudero, Koldobika; Diarce, Gonzalo; Raud, Margus; Hagu, Indrek; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA
The authors regret to inform that, even after careful revisions in all stages of the manuscript, a relevant typographic error has been found in the published version of the paper. The error is found in the definition of the so-called Q-algorithm in Eq. (1), where the selection among two formulae is performed based on the actual heat load (Q) compared to a reference heat load (QREF). The correct formulation for equation can be found in Eq. (1) below. [Formula presented] The authors would like to apologise for any inconvenience caused.
Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters
(2022-01-15) Lumbreras, Mikel; Garay-Martinez, Roberto; Arregi, Beñat; Martin-Escudero, Koldobika; Diarce, Gonzalo; Raud, Margus; Hagu, Indrek; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA
An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study.
Data-driven Predictive Modeling of Traffic and Air Flow for the Improved Efficiency of Tunnel Ventilation Systems
(Institute of Electrical and Electronics Engineers Inc., 2020-09-20) Laña, Ibai; Olabarrieta, Ignacio Iñaki; Ser, Javier Del; Rodriguez, Luis; IA
Tunnel ventilation systems are strictly controlled by safety regulations. Such regulations define not only their operating conditions during fire situations, but also the way in which they should be activated when the accumulation of pollutant gases reaches certain thresholds that are considered unsafe. In addition to these exceptional circumstances, evacuation of tunnel gases is produced naturally on a regular basis, due to causes like air currents originated in pressure differences among the tunnel portals, or the well known piston effect, as a result of vehicles pushing the air when they pass. This work elaborates on the prediction of air-flow inside the tunnels boosted by traffic flow prediction, in order to assist the system activation, be it automated or manual. After experiments made over real tunnel data with a benchmark of machine learning predictive algorithms, results suggest that traffic flow inside the studied tunnels can be effectively predicted and used to enhance air flow predictions, specially in those cases where an air flow predictor alone is not enough to obtain an actionable forecast. The relevance of these results comes from their direct applicability wherein improving the ventilation activation cycles, by adjusting their automation or by informing operators of future air flow levels.
DECIDO Portal for evidence-Based Policy-Making
(Institute of Electrical and Electronics Engineers Inc., 2023) Alexakis, Konstantinos; Martinez, Jabier; Kokkinakos, Panagiotis; Filograna, Antonio; Glikman, Yury; Uriarte, Xabier; Askounis, Dimitris; SWT
Public policy-making has a strong impact on our daily lives and on most of the aspects of our society including its relation with the environment. Sustainability as a global goal cannot be achieved without effective policies supporting it. The current trend in policy-making has two main pillars, policies are a) co-created with citizens and other relevant stakeholders and b) data-based to take informed decisions. Information technologies are needed to support these pillars and facilitate evidence-based policy-making. We present the DECIDO Portal that acts as a catalyst towards putting these general principles into practice gathering the technical components to be used within the whole policy life cycle. Notably, the portal lays its foundations on a well-defined co-creation methodology that can be seamlessly followed from a unique portal and provides best-practices for participatory processes and data-driven analyses. A set of real-world experimental policies in different countries serves as case studies to showcase and discuss the usage of the portal.
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
(Institute of Electrical and Electronics Engineers Inc., 2020-09-20) Del Ser, Javier; Laña, Ibai; Manibardo, Eric L.; Oregi, Izaskun; Osaba, Eneko; Lobo, Jesus L.; Bilbao, Miren Nekane; Vlahogianni, Eleni I.; IA; Quantum
In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activity reported in this long-standing research field has been lately dominated by different Deep Learning approaches, yielding overly complex forecasting models that in general achieve accuracy gains of questionable practical utility. In this work we elaborate on the performance of Deep Echo State Networks for this particular task. The efficient learning algorithm and simpler parametric configuration of these alternative modeling approaches make them emerge as a competitive traffic forecasting method for real ITS applications deployed in devices and systems with stringently limited computational resources. An extensive comparison benchmark is designed with real traffic data captured over the city of Madrid (Spain), amounting to more than 130 Automatic Traffic Readers (ATRs) and several shallow learning, ensembles and Deep Learning models. Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
Design-time safety assessment of robotic systems using fault injection simulation in a model-driven approach
(Institute of Electrical and Electronics Engineers Inc., 2019-09) Juez Uriagereka, Garazi; Amparan, Estibaliz; Martinez Martinez, Cristina; Martinez, Jabier; Ibanez, Aurelien; Morelli, Matteo; Radermacher, Ansgar; Espinoza, Huascar; Burgueno, Loli; Burgueno, Loli; Pretschner, Alexander; Voss, Sebastian; Chaudron, Michel; Kienzle, Jorg; Volter, Markus; Gerard, Sebastien; Zahedi, Mansooreh; Bousse, Erwan; Rensink, Arend; Polack, Fiona; Engels, Gregor; Kappel, Gerti; Tecnalia Research & Innovation; CIBERSEC&DLT; SWT
The rapid advancement of autonomy in robotic systems together with the increasing interaction with humans in shared workspaces (e.g. collaborative robots), raises pressing concerns about system safety. In recent years, the need of model-driven approaches for safety analysis during the design stage has gained a lot of attention. In this context, simulation-based fault injection combined with a virtual robot is a promising practice to complement traditional safety analysis. Fault injection is used to identify the potential safety hazard scenarios and to evaluate the controller's robustness to certain faults. Besides, it enables a quantitative assessment w.r.t. other techniques that only give qualitative hints, such as FMEA. Thus, it facilitates the refinement of safety requirements and the conception of concrete mitigation actions. This paper presents a tool-supported approach that leverages models and simulation-assisted fault injection to assess safety and reliability of robotic systems in the early phases of design. The feasibility of this method is demonstrated by applying it to the design of a real-time cartesian impedance control system in torque mode as a use case scenario.

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