Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Simone Panicucci"'
Autor:
Domenico Buongiorno, Donato Caramia, Luca Di Ruscio, Nicola Longo, Simone Panicucci, Giovanni Di Stefano, Vitoantonio Bevilacqua, Antonio Brunetti
Publikováno v:
Applied Sciences, Vol 12, Iss 22, p 11581 (2022)
In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized b
Externí odkaz:
https://doaj.org/article/901d658ebd2d42bebbaf9d54cc1a0f1e
Autor:
Kosmas Alexopoulos, Salvatore Andolina, P. Greco, Enrico Macii, Nikolaos Nikolakis, Tania Cerquitelli, Simone Panicucci, Angelo Marguglio, G. Veneziano
Publikováno v:
Procedia Manufacturing. 51:1091-1097
This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the pla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cfcfa0bccfeb284515cd28a225cff4a
https://doi.org/10.1016/j.promfg.2020.10.153
https://doi.org/10.1016/j.promfg.2020.10.153
Autor:
Kosmas Alexopoulos, Salvatore Andolina, Francesco Ventura, Massimo Ippolito, A. Pagani, Enrico Macii, Simone Panicucci, Paolo Bethaz, Nikolaos Nikolakis, Angelo Marguglio, Pierluigi Petrali, Tania Cerquitelli, P. van Wilgen
Publikováno v:
IFAC-PapersOnLine. 53:179-184
In the last few years, manufacturing systems are getting gradually transformed into smart factories. In this context, an increasing number of information and communication technologies is incorporated towards facilitating management, production, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6254f76a6de2bc110a7191a91605e596
https://doi.org/10.1016/j.ifacol.2020.11.029
https://doi.org/10.1016/j.ifacol.2020.11.029
Autor:
Francesco De Pace, Andrea SANNA, FEDERICO MANURI, Damiano Oriti, Simone, Panicucci, Valerio, Belcamino
Publikováno v:
Politecnico di Torino-IRIS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::63eb28fe1d8b7ffbd270738fb7c521cc
http://hdl.handle.net/11583/2910340
http://hdl.handle.net/11583/2910340
Autor:
Massimo Ippolito, Simone Panicucci, Nikolaos Nikolakis, Chiara Napione, Lucrezia Morabito, Francesco Ventura
Publikováno v:
Information Fusion and Data Science ISBN: 9789811629396
Modern breakthroughs have enabled the massive use of automation for addressing the ever changing automation market demands. To address an ever changing market and volatile requirements, every production process should be, if possible, optimised, redu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2765fa1349873b65c6f9b7e79ebab7a5
https://doi.org/10.1007/978-981-16-2940-2_5
https://doi.org/10.1007/978-981-16-2940-2_5
Autor:
Simone Panicucci, Enrico Macii, Angelo Marguglio, Nikolaos Nikolakis, Paul Becker, David Bowden, Guido Coppo, Salvatore Andolina, Tania Cerquitelli, Alberto Macii, Lucrezia Morabito, Sven Jung, Sotiris Makris, Chiara Napione, Niamh O’Mahony
Publikováno v:
Lecture Notes in Business Information Processing ISBN: 9783030209476
CAiSE Workshops
CAiSE Workshops
Technological advances in areas such as communications, computer processing, connectivity, data management are gradually introducing the internet of things (IoT) paradigm across companies of different domain. In this context and as systems are making
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9785ec1c9e8a3891c8f709df4f5dec33
http://hdl.handle.net/11583/2786772
http://hdl.handle.net/11583/2786772
Autor:
Elena Baralis, Simone Panicucci, Stefano Proto, Enrico Macii, Daniele Apiletti, Alberto Macii, Tania Cerquitelli, Francesco Ventura
Publikováno v:
2019 IEEE International Congress on Big Data (BigDataCongress)
BigData Congress
BigData Congress
Evaluating the degradation of predictive models over time has always been a difficult task, also considering that new unseen data might not fit the training distribution. This is a well-known problem in real-world use cases, where collecting the hist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1cd5794511af5db49a031672a6b7918