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- W4313304941 abstract "Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as illness up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume." @default.
- W4313304941 created "2023-01-06" @default.
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- W4313304941 date "2022-12-27" @default.
- W4313304941 modified "2023-09-23" @default.
- W4313304941 title "Anomaly detection in laser-guided vehicles' batteries: a case study" @default.
- W4313304941 doi "https://doi.org/10.48550/arxiv.2212.13513" @default.
- W4313304941 hasPublicationYear "2022" @default.
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