Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Massimo Pacella"'
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3217 (2024)
The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high va
Externí odkaz:
https://doaj.org/article/254c02558b574bb382500443c3520222
Publikováno v:
Algorithms, Vol 16, Iss 11, p 524 (2023)
Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-
Externí odkaz:
https://doaj.org/article/2e5f4f1121f24ff3bd83c8cf65270d17
Publikováno v:
Algorithms, Vol 16, Iss 2, p 94 (2023)
The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain repor
Externí odkaz:
https://doaj.org/article/30dbd53027a74db5b4e18710949c05c4
Publikováno v:
Algorithms, Vol 15, Iss 6, p 204 (2022)
This paper focuses on the automatic analysis of conversation transcriptions in the call center of a customer care service. The goal is to recognize topics related to problems and complaints discussed in several dialogues between customers and agents.
Externí odkaz:
https://doaj.org/article/c7d1878bbc7d4093b07119b4da26cfa0
Autor:
Massimo Pacella, Gabriele Papadia
Publikováno v:
Computation, Vol 10, Iss 2, p 23 (2022)
The focus of the present paper is on clustering, namely the problem of finding distinct groups in a dataset so that each group consists of similar observations. We consider the finite mixtures of regression models, given their flexibility in modeling
Externí odkaz:
https://doaj.org/article/2a0019b5204c4473a75085c64f61676c
Autor:
Massimo Pacella, Gabriele Papadia
Publikováno v:
Sensors, Vol 20, Iss 24, p 7065 (2020)
This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosi
Externí odkaz:
https://doaj.org/article/76c6ad9bf1e0476ebadcf44932b18b53
Publikováno v:
IISE Transactions. :1-11
Publikováno v:
Journal of Quality Technology. 54:503-516
Faults occurred during the operational lifetime of photovoltaic (PV) systems can cause energy loss, system shutdown, as well as possible fire risks. Therefore, it is crucial to detect anomalies and faults to control the system’s performance and ens
Publikováno v:
Procedia CIRP. 104:588-593
In the last few decades, thanks to the interest of industry and academia, production planning (PP) models have shown significant growth. Several structured literature reviews highlighted the evolution of PP and guided the work of scholars providing i
Publikováno v:
Procedia CIRP. 99:598-603
Since 2015 developments such as Industry 4.0 and cyber-physical production systems on the technology side, and approaches such as flexible and smart manufacturing systems hold great potential. These in turn give rise to special requirements that the