Browsing by Keyword "Information Systems and Management"
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Item The AFarCloud ECSEL Project(IEEE, 2019-10) Castillejo, Pedro; Curuklu, Baran; Fresco, Roberto; Johansen, Gorm; Bilbao-Arechabala, Sonia; Martinez-Rodriguez, Belen; Pomante, Luigi; Martinez-Ortega, Jose-Fernan; Santic, Marco; Konofaos, Nikos; Kitsos, Paris; Tecnalia Research & Innovation; BIGDATAFarming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate identification and proper quantification of pathogens affecting plant and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate FARming in the CLOUD) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs. This platform will be integrated with farm management software and will support monitoring and decision-making solutions based on big data and real-time data mining techniques.Item Analysing Barriers to Achieving SDG 7. Managing Green Product Development in the Wind Energy Sector(Springer Science and Business Media Deutschland GmbH, 2023) García-Alonso, Rakel; Landeta-Manzano, Beñat; Arana-Landín, German; Jiménez-Redal, Rubén; Alfnes, Erlend; Romsdal, Anita; Strandhagen, Jan Ola; von Cieminski, Gregor; Romero, David; TecnologíaWind energy is seen as a promising option for sustainable energy, but its implementation also has environmental impacts. The environmental impact of wind energy systems, particularly on-shore and off-shore wind turbines, has been extensively researched using methodologies such as Life Cycle Assessment. However, creating more sustainable wind turbines involves rethinking their creation, production, and consumption. To reduce environmental impact, companies must overcome barriers to implementing eco-design and develop innovative solutions. Understanding these barriers is crucial for designing effective solution strategies in the wind energy sector. A case study was carried out on one of the top five original equipment manufacturers in the wind energy sector. Eco-design has become one of the most effective ways of incorporating environmental considerations into design activities, but also has led to increased innovation in wind turbine design and development, as well as in associated processes such as manufacturing, transportation, installation, operation, and maintenance. However, despite experience in environmental management systems and eco-design in particular, obstacles, both external and internal to the company, still need to be overcome to succeed in product eco-design. Among other barriers, technicians and managers often lack specialised ecodesign skills, which can hinder the process, and the labour market does not adequately respond to the demand for professionals with specific knowledge in this field. Top management should also develop more effective methods or improve existing ones to meet the expectations of product design and development technicians.Item AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking(2021-09) Osaba, Eneko; Del Ser, Javier; Martinez, Aritz D.; Lobo, Jesus L.; Herrera, Francisco; Quantum; IATransfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process.Item Battery Storage Demonstration Projects An Overview Across Europe(Institute of Electrical and Electronics Engineers Inc., 2021) Astero, Poria; Maki, Kari; Evens, Corentin; Papadimitriou, Christina; Efthymiou, Venizelos; Niebe, Astrid; Holly, Stefanie; Marinelli, Mattia; Gabderakhmanova, Tatiana; Melendez, Joaquim; Herraiz, Sergio; Rodriguez-Sanchez, Raul; Morch, Andrei; De Urtasun, Laura Gimenez; Fernandez, Gregorio; Divshali, Poria Hasanpor; Tecnalia Research & InnovationThis paper summarises results and experiences from several demonstration projects across European countries in the field of battery energy storage system (BESS) integration to the power system. These research projects are selected among research institutes and universities that are part of the European Energy Research Alliance (EERA) Joint Program on Smart Grids. The paper categorizes these projects according to the demonstrated applications of BESS and then reviews specific aspects of each project. This paper provides an opportunity to find out the summary of the most recent results as well as challenges and open research questions in projects focusing on different BESS application in the power system.Item Blockchain-Based Threat Registry Platform(Institute of Electrical and Electronics Engineers Inc., 2019-10) De DIego, Santiago; Goncalves, Carlos; Lage, Oscar; Mansell, Jason; Kontoulis, Michael; Moustakidis, Serafeim; Guerra, Barbara; Liapis, Angelos; Chakrabarti, Satyajit; Saha, Himadri Nath; CIBERSEC&DLTThis document presents a reference architecture for a Blockchain-based Threat Registry platform, named BBTR, to share information about treats among different actors. The design of the BBTR guarantees integrity and availability of the data stored in it and is also compatible with privacy requirements, allowing different actors to participate as users of this shared BaaS (Blockchain as a Service). The paper also shows how this approach can be combined with AI techniques to extract valuable information from the threats directly from the Blockchain, empowering the final solution with a decision-making engine. It also includes its validation in a use case in the Health care domain.Item Boosting Data Monetisation with DATAMITE(Springer Science and Business Media Deutschland GmbH, 2024) Aroca, Jordi Arjona; Osa, María José López; Barturen, Urtza Iturraspe; Siopidis, Vasileios; Votis, Konstantinos; Nikolakopoulos, Anastasios; Chondrogiannis, Efthymios; Plociennik, Marcin; Himanen, Joel; Marinakis, Achilleas; Nestorakis, Konstantinos; Maglogiannis, Ilias; Iliadis, Lazaros; Karydis, Ioannis; Papaleonidas, Antonios; Chochliouros, Ioannis; HPACompanies around the globe store large quantities of data they cannot monetise. Regarding internal monetisation, they lack tools to facilitate the governance and quality assessment of their data, resulting not really knowing what data they own or it being non reliable due to its poor quality. External monetisation is usually hindered by the unavailability of trustable mechanisms to perform this exchange or enabling the company to participate in ecosystems like EU data spaces or Gaia-X. DATAMITE is an open-source modular and multi-domain framework that focuses on monetisation through interoperability and data exchange. Its modules offer tools for enhancing data governance, quality and security, but also enabling data sharing to a collection of ecosystems like data spaces, Gaia-X, EOSC or AIoD through a plugin-based approach. It also includes a series of additional support tools to assist on data discovery, ingestion, harmonization or evaluate data fairness, among other.Item A case analysis of enabling continuous software deployment through knowledge management(2018-06) Colomo-Palacios, Ricardo; Fernandes, Eduardo; Soto-Acosta, Pedro; Larrucea, Xabier; Tecnalia Research & InnovationContinuous software engineering aims to accelerate software development by automating the whole software development process. Knowledge management is a cornerstone for continuous integration between software development and its operational deployment, which must be implemented using sound methodologies and solid tools. In this paper, the authors present and analyse a case study on the adoption of such practices by a software company. Results show that, beyond tools, knowledge management practices are the main enablers of continuous software engineering adoption and success.Item Challenges in the implementation of responsible research and innovation across Horizon 2020(2022-07-13) Tabarés, Raúl; Loeber, Anne; Nieminen, Mika; Bernstein, Michael J.; Griessler, Erich; Blok, Vincent; Cohen, Joshua; Hönigmayer, Helmut; Wunderle, Ulrike; Frankus, Elisabeth; BIGDATAIn the last decade, the European Commission (EC) developed an ambitious strategy to promote RRI across the Horizon 2020 Framework Programme for Research and Innovation (H2020). This effort resulted in a significant number of European-funded projects that substantially expanded the available knowledge of the theory, methods and implementation of RRI. However, various evaluations and studies revealed a limited and diffuse implementation of the concept. In this article, we aim to shed some light on this matter with a study covering eight programme lines of H2020 (ERC, MSCA, LEIT, FOOD, ENV, SEC, WIDENING and EURATOM). We employ an extensive policy document analysis and 112 semi-structured interviews carried out with various stakeholders. We argue that the limited implementation of RRI in H2020 is the result of conflicts with existing values, science cultures, economic objectives, restricted resources for its implementation and a lack of clarification around what RRI means.Item Collaborative platform based on standard services for the semi-automated generation of the 3d city model on the cloud(CRC Press/Balkema, 2018) Prieto, I.; Izkara, J. L.; Mediavilla, A.; Arambarri, J.; Arroyo, A.; Karlshøj, Jan; Scherer, Raimar; Tecnalia Research & Innovation; LABORATORIO DE TRANSFORMACIÓN URBANA; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNThe urban 3D model is increasingly recognized as the adequate support to integrate, harmonize and store the information of a city and make it accessible to all stakeholders (citizens, city managers, companies or researchers). These models facilitate the cooperation of experts in different areas, contributing their knowledge in the generation of a single model. Urban 3D models will allow transforming urban management processes. One example of such processes is the Special Interior Reform Plans (PERI). These urban management processes are developed by the administration, but require the collaboration of different agents. In addition, they represent a clear example of the need to advance in the integration of heterogeneous data at different scales (building and city). The solution proposed in this article presents a platform based on web services for the collaborative generation of urban 3D models. The platform is composed of an information system based on standard data models (IFC and CityGML) and a web services infrastructure that manages the information and relationships stored in the information system at different levels. Besides, a prototype of 3 applications based on the service infrastructure and developed to support the urban management process is presented. The platform is used and tested in the collaborative generation process of the 3D model of the historic district of the city of Vitoria-Gasteiz (Spain) during the definition, editing and monitoring of the PERI of the historic center of the city.Item Combined model-based and machine learning approach for damage identification in bridge type structures(2021) Fernández-Navamuel, Ana; Zamora-Sánchez, Diego; Armijo-Prieto, Alberto; Varona-Poncela, Tomás; García-Sánchez, David; García-Villena, Francisco; Ruiz-Cuenca, Francisco; E&I SEGURAS Y RESILIENTES; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN; Tecnalia Research & InnovationIn this work, we propose a combined approach of model-based and machine learning techniques for damage identification in bridge structures. First, a finite element model is calibrated with real data from experimental vibration modes for the undamaged or baseline state. Second, generic synthetic damage scenarios based on modal parameters are automatically generated with the model to train machine learning algorithms for damage classification (Support Vector Machine, SVM) and damage location and quantification (Neural Network, NN). For an initial validation of the method we use a lab scale truss bridge model, proving that specific damage scenarios can be assessed by the Supervised Machine Learning algorithms trained with generic damage scenarios including a certain variability. The NN provides an assessment in terms of damage location and quantification, whereas the SVM provides a damage severity classification with graphical indication of the damage location and quantification through a reduced dimension plot.Item Conceptualising a Benchmarking Platform for Embedded Devices(Institute of Electrical and Electronics Engineers Inc., 2024) Garcia-Perez, Asier; Miñón, Raúl; Torre-Bastida, Ana I.; Diaz-De-Arcaya, Josu; Zulueta-Guerrero, Ekaitz; Solic, Petar; Nizetic, Sandro; Rodrigues, Joel J. P. C.; Rodrigues, Joel J.P.C.; Gonzalez-de-Artaza, Diego Lopez-de-Ipina; Perkovic, Toni; Catarinucci, Luca; Patrono, Luigi; HPAIn the current era, a notable presence of data-generating devices is being witnessed which spans across various sectors like industries, healthcare or smart cities. The diversity of options in the market pose a significant challenge for companies facing the task of selecting the most suitable embedded devices for their specific needs in their particular environments. Several benchmarking solutions have been conducted to overcome this barrier. Nevertheless, generally, they are ad-hoc solutions for a given experiment, which makes it impossible to extrapolate the results to future scenarios. To address this uncertainty and facilitate informed decision-making, this paper presents the conceptualization of a modular and extensible benchmarking platform. This platform has been designed to adapt to the changing dynamics of the market and the specific needs of users. Its modular and extensible approach allows companies to assess and select integrated devices more efficiently, while providing the necessary flexibility to address new and diverse situations.Item Control Strategies for Dual Arm Co-Manipulation of Flexible Objects in Industrial Environments(Institute of Electrical and Electronics Engineers Inc., 2020-06-10) Ibarguren, Aitor; Daelman, Paul; Prada, Miguel; ROBOTICA_AUTOMA; Tecnalia Research & Innovation; Robótica MédicaThe introduction of collaborative robots had a great impact in the development of robotic solutions for cooperative tasks typically performed by humans, specially in industrial environments where robots can act as assistants of operators. Even so, the coordinated manipulation of large and deformable parts between dual arm robots and humans rises many technical challenges, ranging from the coordination of both robotic arms to the detection of the forces applied by the operator. This paper presents a novel control architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop. The obtained results demonstrate the validity of the implemented control architecture as well as its suitability for the implementation of collaborative cyber-physical systems.Item 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; IATunnel 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.Item 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; SWTPublic 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.Item 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; QuantumIn 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.Item Dilated LSTM Networks for Short-Term Traffic Forecasting using Network-Wide Vehicle Trajectory Data(Institute of Electrical and Electronics Engineers Inc., 2020-09-20) Fafoutellis, Panagiotis; Vlahogianni, Eleni I.; Del Ser, Javier; IAShort-term traffic forecasting is anticipated as an always evolving research topic, boosted by the tremendous recent advances of Machine Learning and Deep Learning, as well as computational power of modern PCs. In this paper, the Dilated Recurrent Neural Networks are introduced in traffic forecasting. Their architecture promotes the deployment of long-term relations and prevents common issues of RNNs, such as exploding and vanishing gradients. The Dilated LSTM Network is exploited to perform traffic conditions forecasting using network-wide data. The data consist of GPS trajectories of ride-hailing company DiDi's vehicles from November of 2016. After preprocessing the data and organizing them into section's travel speed of five-minute time resolution timeseries for each one of the 498 road sections of the road network of Xi'an, China, we fed them to the Dilated LSTM Network. The model consists of four hidden layers, each of them implementing an LSTM Network with one, two and four-step dilation correspondingly. The model achieves 85% accuracy, which is improved over a classic LSTM structure, trained on the same data.Item Discrete part manufacturing energy efficiency improvements with modelling and simulation(Springer New York LLC, 2013) Heilala, Juhani; Paju, Marja; Montonen, Jari; Ruusu, Reino; Sorli, Mikel; Armijo, Alberto; Bermell-Garcia, Pablo; Astwood, Simon; Quintana, Santiago; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNEnergy efficiency has become a key concern in industry due to increased energy cost and associated environmental impacts. It is as well factor on marketing and reputation. Customers require information on the ecological performance of products and the process to build that product. Therefore ecoefficient manufacturing is in our days a matter of competitiveness and economic success. This paper presents industrial driven research and the key findings from production eco and energy efficiency analysis and development projects. Both static and dynamic multi-level modelling and simulation is covered with examples. The use of Value Stream Mapping and Discrete Event Simulation with life cycle inventory data for production eco efficiency analysis is explained. Generic developement steps for process, machine and production system model with environmantal aspects is shown. Development continues in EPES "Eco Process Engineering System for Composition of Services to Optimise Product Life-Cycle"- project.Item Domain generalized person reidentification based on skewness regularity of higher-order statistics(2024-10-09) Xiong, Mingfu; Xu, Yang; Hu, Ruimin; Wang, Zhongyuan; Del Ser, Javier; Muhammad, Khan; Xiong, Zixiang; IAThe goal of domain-generalized person reidentification (DG-ReID) is to train a model in the source domain and apply it directly to unknown target domains for specific pedestrian retrieval. Existing methods rely primarily on low-order statistics (such as the mean, standard deviation, or variance), thereby ensuring the stability of the source domain data distribution for model training. However, such methods underperform when the data follow a non-Gaussian distribution, thereby reducing the generalization ability of the model on unseen target domains. To address this issue, this study proposes an instance normalization-based skewness regularity (INSR) framework that uses high-order statistics (skewness and high-order moments) to measure the skewness and regularity of the data distribution. Such measures allow further learning of the morphological features (skewness degree, trait of data near the mean, etc.) of complex data distributions for DG-ReID. Specifically, the proposed framework first extracts the skewness and third-order moments from the source domains, which provide more features (high-order moments, variance, etc.) to characterize the data distribution. Subsequently, a batch normalization-like operation was implemented to project the data into a new feature space with zero mean and unit variance, enhancing model adaption and accuracy. Extensive experiments were conducted on small-scale (VIPeR, PRID, GRID, and i-LIDS) and large-scale (Market-1501, DukeMTMC-reID, CUHK03, MSMT17) public datasets using two different protocols, demonstrating that the proposed INSR framework significantly outperforms other state-of-the-art counterparts for DG-ReID.Item Dynamic Risk Assessment and Certification in the Power Grid: A Collaborative Approach(Institute of Electrical and Electronics Engineers Inc., 2022) Liatifis, Athanasios; Alcazar, Pedro Ruzafa; Grammatikis, Panagiotis Radoglou; Papamartzivanos, Dimitris; Menesidou, Sofianna; Krousarlis, Thomas; Alberto, Molinuevo Martin; Angulo, Inaki; Sarigiannidis, Antonios; Lagkas, Thomas; Argyriou, Vasileios; Skarmeta, Antonio; Sarigiannidis, Panagiotis; Clemm, Alexander; Maier, Guido; Machuca, Carmen Mas; Ramakrishnan, K.K.; Risso, Fulvio; Chemouil, Prosper; Limam, Noura; SWT; DIGITAL ENERGYThe digitisation of the typical electrical grid introduces valuable services, such as pervasive control, remote monitoring and self-healing. However, despite the benefits, cybersecurity and privacy issues can result in devastating effects or even fatal accidents, given the interdependence between the energy sector and other critical infrastructures. Large-scale cyber attacks, such as Indostroyer and DragonFly have already demonstrated the weaknesses of the current electrical grid with disastrous consequences. Based on the aforementioned remarks, both academia and industry have already designed various cybersecurity standards, such as IEC 62351. However, dynamic risk assessment and certification remain crucial aspects, given the sensitive nature of the electrical grid. On the one hand, dynamic risk assessment intends to re-compute the risk value of the affected assets and their relationships in a dynamic manner based on the relevant security events and alarms. On the other hand, based on the certification process, new approach for the dynamic management of the security need to be defined in order to provide adaptive reaction to new threats. This paper presents a combined approach, showing how both aspects can be applied in a collaborative manner in the smart electrical grid.Item E-Business in the construction sector: A service oriented approach(Springer Verlag, 2009) Sánchez, Valentín; Angulo, Iñaki; Bilbao, Sonia; HPA; DIGITAL ENERGY; BIGDATAThe e-NVISION project (www.e-nvision.org) aims to develop and validate an innovative e-Business platform for the SMEs allowing them: to model and adapt in their organizations particular business scenarios requested by their customers and suppliers; to integrate all their enterprise applications following a service-oriented architecture; and to incorporate legal, economical and social services offered by external organizations. This paper provides an overview of the results of the project regarding the definition, implementation and validation of external and integration services within the e-Business platform. The overview includes details on the construction sector e-Business scenarios, the service-oriented techniques used, open issues surrounding actual implementations and applications, and the lessons learned in the field.
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