Alejandro Blanco-M.; Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE
Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Jordi Cusidó; +1 AuthorsJordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Alejandro Blanco-M.; Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Jordi Cusidó; Jordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Jordi Solé-Casals; Jordi Solé-Casals
Harvested from ORCID Public Data FileJordi Solé-Casals in OpenAIREDetecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en12101982&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Alejandro Blanco-M.;
Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Jordi Cusidó; +1 AuthorsJordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Alejandro Blanco-M.; Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Jordi Cusidó; Jordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Jordi Solé-Casals; Jordi Solé-Casals
Harvested from ORCID Public Data FileJordi Solé-Casals in OpenAIREDetecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en12101982&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Gibert, Karina;
Gibert, Karina
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGibert, Karina in OpenAIRE Sànchez-Marrè, Miquel; Jadoon, Muhammad Awais; Grabiec, Natalia; +4 AuthorsSànchez-Marrè, Miquel
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSànchez-Marrè, Miquel in OpenAIRE Gibert, Karina; Gibert, Karina
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGibert, Karina in OpenAIRE Sànchez-Marrè, Miquel; Jadoon, Muhammad Awais; Grabiec, Natalia; Lovagnini, Greta; Perriard, Arnaud; Sharma, Sumit; Wargocki, Piotr;Sànchez-Marrè, Miquel
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSànchez-Marrè, Miquel in OpenAIREThe paper presents a preliminary analysis of some intelligent decision support systems for sustainability from the point of view of the end user experience, by comparing up to four available systems. The role that these kind of ICT tools play on the improvement of several sustainability goals in different scenarios is analyzed as well. The results of this research are the outcome of an innovative methodology for teaching Intelligent Decision Support Systems to students of a master on Sustainability given at the UPC. The interest of the proposal from the educational point of view is that in short time it empowers students not necessarily skilled in ICT to get a useful overview of what intelligent decision support systems are and how they can be used in their professional practices. Peer Reviewed
0 citations 0 popularity Average influence Average impulse Average 
Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2117/176299&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Gibert, Karina;
Gibert, Karina
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGibert, Karina in OpenAIRE Sànchez-Marrè, Miquel; Jadoon, Muhammad Awais; Grabiec, Natalia; +4 AuthorsSànchez-Marrè, Miquel
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSànchez-Marrè, Miquel in OpenAIRE Gibert, Karina; Gibert, Karina
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGibert, Karina in OpenAIRE Sànchez-Marrè, Miquel; Jadoon, Muhammad Awais; Grabiec, Natalia; Lovagnini, Greta; Perriard, Arnaud; Sharma, Sumit; Wargocki, Piotr;Sànchez-Marrè, Miquel
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSànchez-Marrè, Miquel in OpenAIREThe paper presents a preliminary analysis of some intelligent decision support systems for sustainability from the point of view of the end user experience, by comparing up to four available systems. The role that these kind of ICT tools play on the improvement of several sustainability goals in different scenarios is analyzed as well. The results of this research are the outcome of an innovative methodology for teaching Intelligent Decision Support Systems to students of a master on Sustainability given at the UPC. The interest of the proposal from the educational point of view is that in short time it empowers students not necessarily skilled in ICT to get a useful overview of what intelligent decision support systems are and how they can be used in their professional practices. Peer Reviewed
0 citations 0 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2117/176299&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Aida Valls;
Aida Valls
Harvested from ORCID Public Data FileAida Valls in OpenAIRE Karina Gibert; Karina Gibert
Harvested from ORCID Public Data FileKarina Gibert in OpenAIREArtificial Intelligence (AI) research has expanded very quickly in recent years due to the increase in data and resources, along with the engagement of companies in proposing many challenging applications [...]
Access Routesgold 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12199639&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Aida Valls;
Aida Valls
Harvested from ORCID Public Data FileAida Valls in OpenAIRE Karina Gibert; Karina Gibert
Harvested from ORCID Public Data FileKarina Gibert in OpenAIREArtificial Intelligence (AI) research has expanded very quickly in recent years due to the increase in data and resources, along with the engagement of companies in proposing many challenging applications [...]
Access Routesgold 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12199639&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Alejandro Blanco-M.;
Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE Jordi Cusidó; +1 AuthorsJordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Alejandro Blanco-M.; Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE Jordi Cusidó; Jordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Jordi Solé-Casals; Jordi Solé-Casals
Harvested from ORCID Public Data FileJordi Solé-Casals in OpenAIREBackground: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en11040723&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Alejandro Blanco-M.;
Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE Jordi Cusidó; +1 AuthorsJordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Alejandro Blanco-M.; Alejandro Blanco-M.
Harvested from ORCID Public Data FileAlejandro Blanco-M. in OpenAIRE Karina Gibert; Karina Gibert
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesKarina Gibert in OpenAIRE Pere Marti-Puig; Pere Marti-Puig
Harvested from ORCID Public Data FilePere Marti-Puig in OpenAIRE Jordi Cusidó; Jordi Cusidó
Harvested from ORCID Public Data FileJordi Cusidó in OpenAIRE Jordi Solé-Casals; Jordi Solé-Casals
Harvested from ORCID Public Data FileJordi Solé-Casals in OpenAIREBackground: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en11040723&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| SHAREBOX Gatzioura, Anna;
Gatzioura, Anna
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGatzioura, Anna in OpenAIRE Sànchez-Marrè, Miquel; Sànchez-Marrè, Miquel
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSànchez-Marrè, Miquel in OpenAIRE Gibert, Karina; Gibert, Karina
Harvested from ORCID Public Data FileGibert, Karina in OpenAIRERecently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without taking the possible alternatives into account, usually due to the difficulty of properly evaluating them. Therefore, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find resources able to cover their needs, is still of high importance both for the companies and the whole ecosystem. In this work, we present a hybrid recommender system to support users in properly identifying the symbiotic relationships that might provide them an improved performance. This recommender combines a graph-based model for resource similarities, while it follows the basic case-based reasoning processes to generate resource recommendations. Several criteria, apart from resource similarity, are taken into account to generate, each time, the list of the most suitable solutions. As highlighted through a use case scenario, the proposed system could play a key role in the emerging industrial symbiotic platforms, as the majority of them still do not incorporate automatic decision support mechanisms.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en12183546&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| SHAREBOX Gatzioura, Anna;
Gatzioura, Anna
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGatzioura, Anna in OpenAIRE Sànchez-Marrè, Miquel; Sànchez-Marrè, Miquel
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSànchez-Marrè, Miquel in OpenAIRE Gibert, Karina; Gibert, Karina
Harvested from ORCID Public Data FileGibert, Karina in OpenAIRERecently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without taking the possible alternatives into account, usually due to the difficulty of properly evaluating them. Therefore, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find resources able to cover their needs, is still of high importance both for the companies and the whole ecosystem. In this work, we present a hybrid recommender system to support users in properly identifying the symbiotic relationships that might provide them an improved performance. This recommender combines a graph-based model for resource similarities, while it follows the basic case-based reasoning processes to generate resource recommendations. Several criteria, apart from resource similarity, are taken into account to generate, each time, the list of the most suitable solutions. As highlighted through a use case scenario, the proposed system could play a key role in the emerging industrial symbiotic platforms, as the majority of them still do not incorporate automatic decision support mechanisms.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en12183546&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Shikha Suman; Ashutosh Karna;
Ashutosh Karna
Harvested from ORCID Public Data FileAshutosh Karna in OpenAIRE Karina Gibert; Karina Gibert
Harvested from ORCID Public Data FileKarina Gibert in OpenAIREThe agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12042191&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Shikha Suman; Ashutosh Karna;
Ashutosh Karna
Harvested from ORCID Public Data FileAshutosh Karna in OpenAIRE Karina Gibert; Karina Gibert
Harvested from ORCID Public Data FileKarina Gibert in OpenAIREThe agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app12042191&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Sánchez Carracedo, Fermín;
Sánchez Carracedo, Fermín
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSánchez Carracedo, Fermín in OpenAIRE Sabaté i Garriga, Ferran; Sabaté i Garriga, Ferran
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSabaté i Garriga, Ferran in OpenAIRE Gibert, Karina; Gibert, Karina
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGibert, Karina in OpenAIREThis paper presents a methodology to find out whether or not engineering students perceive sustainability learning throughout their studies at the university. The methodology is applied to students from Barcelona School of Informatics (FIB). The sustainability questionnaire of the EDINSOST project is used as a tool to determine student perception about their sustainability learning. The questionnaire contains 34 questions related to four sustainability competencies: (C1) Critical contextualization of knowledge establishing interrelations with social, economic and environmental, local and / or global problems, (C2) Sustainable use of resources and prevention of negative impacts on the natural and social environment, (C3) Participation in community processes that promote sustainability, and (C4) Application of ethical principles related to the values of sustainability in personal and professional behaviors. The questionnaire was submitted to the students of two first-year subjects and those who complete the Bachelor Thesis, with the aim of determining the initial level of sustainability that students have at the beginning of their studies with regard to that perceived when they graduate. The results show that students declare an improvement in the sustainability learning in the 34 questions analyzed, and that the competency of which they perceive to learn the most is ‘‘participation in community processes that promote sustainability’’. On the other hand, the competency in which they perceive themselves less prepared is the ‘‘application of ethical principles related to the values of sustainability in personal and professional behaviors’’. This research was funded by the Spanish Ministerio de Economía y Competitividad, from study design to submission, grant number EDU2015-65574-R’’, and by the Spanish ‘‘Ministerio de Ciencia, Innovación y Universidades’’, the Spanish ‘‘Agencia Estatal de Investigación’’ (AEI), and the ‘‘Fondo Europeo de Desarrollo Regional’’ (FEDER), from study design to submission, under grant number RTI2018- 094982-B-I00. Peer Reviewed
0 citations 0 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2117/353759&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Sánchez Carracedo, Fermín;
Sánchez Carracedo, Fermín
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSánchez Carracedo, Fermín in OpenAIRE Sabaté i Garriga, Ferran; Sabaté i Garriga, Ferran
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSabaté i Garriga, Ferran in OpenAIRE Gibert, Karina; Gibert, Karina
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGibert, Karina in OpenAIREThis paper presents a methodology to find out whether or not engineering students perceive sustainability learning throughout their studies at the university. The methodology is applied to students from Barcelona School of Informatics (FIB). The sustainability questionnaire of the EDINSOST project is used as a tool to determine student perception about their sustainability learning. The questionnaire contains 34 questions related to four sustainability competencies: (C1) Critical contextualization of knowledge establishing interrelations with social, economic and environmental, local and / or global problems, (C2) Sustainable use of resources and prevention of negative impacts on the natural and social environment, (C3) Participation in community processes that promote sustainability, and (C4) Application of ethical principles related to the values of sustainability in personal and professional behaviors. The questionnaire was submitted to the students of two first-year subjects and those who complete the Bachelor Thesis, with the aim of determining the initial level of sustainability that students have at the beginning of their studies with regard to that perceived when they graduate. The results show that students declare an improvement in the sustainability learning in the 34 questions analyzed, and that the competency of which they perceive to learn the most is ‘‘participation in community processes that promote sustainability’’. On the other hand, the competency in which they perceive themselves less prepared is the ‘‘application of ethical principles related to the values of sustainability in personal and professional behaviors’’. This research was funded by the Spanish Ministerio de Economía y Competitividad, from study design to submission, grant number EDU2015-65574-R’’, and by the Spanish ‘‘Ministerio de Ciencia, Innovación y Universidades’’, the Spanish ‘‘Agencia Estatal de Investigación’’ (AEI), and the ‘‘Fondo Europeo de Desarrollo Regional’’ (FEDER), from study design to submission, under grant number RTI2018- 094982-B-I00. Peer Reviewed
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