Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Antonio Acernese"'
Autor:
Kisan Sarda, Antonio Acernese, Vittorio Nole, Leonardo Manfredi, Luca Greco, Luigi Glielmo, Carmen Del Vecchio
Publikováno v:
IEEE Access, Vol 9, Pp 53827-53837 (2021)
Steel making industries exhibit extreme working conditions characterized by high temperature, pressure, and production speed as well as intense throughput. Due to high economic and energy investments of the overall production process, an intense and
Externí odkaz:
https://doaj.org/article/6d3ee2e89271401a9d067e7057ea9621
Publikováno v:
IEEE Access, Vol 8, Pp 199254-199265 (2020)
In this article, a reinforcement learning (RL)-based scalable technique is presented to control the probabilistic Boolean control networks (PBCNs). In particular, a double deep-Q network (DDQN) approach is firstly proposed to address the output track
Externí odkaz:
https://doaj.org/article/f2ee796bd12a47a2941bb0d6a6f53541
Publikováno v:
ACC
In this letter, a model-free co-design scheme of triggering-driven controller is proposed for probabilistic Boolean control networks (PBCNs) in order to achieve feedback stabilization with minimum controller efforts. Specifically, $Q$ -learning ( $Q\
Publikováno v:
2022 American Control Conference (ACC).
Autor:
Mirko Mazzoleni, Kisan Sarda, Antonio Acernese, Luigi Russo, Leonardo Manfredi, Luigi Glielmo, Carmen Del Vecchio
Since the introduction of the industry 4.0 paradigm, manufacturing companies are investing in the development of algorithmic diagnostic solutions for their industrial equipment, relying on measured data and process models. However, process and fault
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::263a694908615b270fe067411325ab91
https://hdl.handle.net/11588/910708
https://hdl.handle.net/11588/910708
Publikováno v:
2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
Complexity of manufacturing systems and variability in anomalous operations make fault detection and diagnosis in industrial systems a challenging task. In steel industries characterized by high temperatures and pressures, elevated production speeds,
Autor:
Kisan Sarda, Vittorio Nole, Antonio Acernese, Leonardo Manfredi, Luigi Glielmo, Carmen Del Vecchio, Luca Greco
Publikováno v:
MED
This paper reports the outcome of an industrial research project on data-based anomaly detection in a steel making production process. Namely, the study aims to assess a fault detection strategy for rotating machines in the hot rolling mill line. Due
Publikováno v:
IECON
Steel-working industries are characterized by high temperatures and pressures, elevated production speeds, and intense throughput, so that their sudden interruption leads to great money losses. Undoubtedly, they would extremely benefit from Industry
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57965fee6f1de29897875a3e63ec8623
http://hdl.handle.net/10446/198059
http://hdl.handle.net/10446/198059
Autor:
Vittorio Nole, Leonardo Manfredi, Kisan Sarda, Carmen Del Vecchio, Luigi Glielmo, Luca Greco, Antonio Acernese
Publikováno v:
IEEE Access, Vol 9, Pp 53827-53837 (2021)
Steel making industries exhibit extreme working conditions characterized by high temperature, pressure, and production speed as well as intense throughput. Due to high economic and energy investments of the overall production process, an intense and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c30af125c7c04506db5aeb72b12f86ab
https://hdl.handle.net/11588/910665
https://hdl.handle.net/11588/910665
PurposeThe purpose of this paper is to describe a model for the design and development of a condition-based maintenance (CBM) strategy for the cutting group of a labeling machine. The CBM aims to ensure the quality of labels' cut and overall machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d187f1db74611008f9fd93447fbd7c3
https://hdl.handle.net/11588/910710
https://hdl.handle.net/11588/910710