Szczegóły publikacji
Opis bibliograficzny
Human-in-the-loop anomaly detection in industrial data streams / Jakub JAKUBOWSKI, Szymon Bobek, Grzegorz J. Nalepa // W: CHItaly 2023 [Dokument elektroniczny] : proceedings of the 15th biannual conference of the Italian SIGCHI Chapter : crossing HCI and AI : Torino, Italy, September 20-22, 2023 / eds. Cristina Gena, [et al.]. — Wersja do Windows. — Dane tekstowe. — New York : ACM, cop. 2023. — e-ISBN: 979-8-4007-0806-0. — S. [372–373]. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dl.acm.org/doi/pdf/10.1145/3605390 [2023-10-06]. — Bibliogr. s. [373], Abstr.
Autorzy (3)
- AGHJakubowski Jakub
- Bobek Szymon
- Nalepa Grzegorz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 149281 |
|---|---|
| Data dodania do BaDAP | 2023-11-10 |
| DOI | 10.1145/3605390.3610830 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
Abstract
The detection of anomalies in an industrial setting remains an important and open challenge for most manufacturing companies. The potential benefits from the utilization of an anomaly detection system are substantial, as deviations from normal operating conditions can cause downtimes, quailty issues or safety hazards. The main requirements for an anomaly detection system include the selection of the machine learning model applicable to streaming data, providing the explanations of the model’s decision and participation of human operator in the learning process of the model. We have proposed the anomaly detection system, which addresses the above challenges and is applicable in industrial environment.
