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A Federated Learning Approach For Operator Monitoring in ...
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由 S Kianoush 著作2024 — Our evaluation considers diverse and realistic scenarios where training data is collected over heterogeneous time periods, representing robotic cells with ...
A Federated Learning Approach For Operator Monitoring in ...
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由 S Kianoush 著作2024 — The results affirm the efficacy of the FL approach, particularly when utilizing heterogeneous datasets sourced from industrial robotic cells. Index Terms— ...
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A Federated Learning Approach For Operator Monitoring in ...
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An Intelligent Adaptive System (IAS) is a synergy between an intelligent interface and adaptive automation technologies capable of context sensitive interaction ...
代明军
深圳大学电子与信息工程学院
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[6] S. Kianoush, A. Minora, S. Savazzi, M. Dai, "A Federated Learning Approach For Operator Monitoring in Heterogeneous Cobot Environments," IEEE International ...
Alberto Minora
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2024年10月31日 — A Federated Learning Approach For Operator Monitoring in Heterogeneous Cobot Environments. ETFA 2024: 1-6. [i2]. view. electronic edition via ...
A. Minora's research works | Università degli Studi di ...
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A. Minora's 4 research works with 19 citations, including: A Federated Learning Approach For Operator Monitoring in Heterogeneous Cobot Environments.
On the Impact of Data Heterogeneity in Federated Learning ...
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This paper presents a comprehensive exploration of the mathematical formalization and taxonomy of heterogeneity within FL environments, focusing on the ...
Management of heterogeneous AI-based industrial ...
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Abstract. Purpose – This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial ...
Sanaz Kianoush
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2024年10月31日 — A Federated Learning Approach For Operator Monitoring in Heterogeneous Cobot Environments. ... A Carbon Tracking Model for Federated Learning ...
Search Results - collaborative robots
MEF Üniversitesi
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A Federated Learning Approach For Operator Monitoring in Heterogeneous Cobot Environments. Authors: Kianoush, S., Minora, A., Savazzi, S., Dai, Mingjun.