2022
DOI: 10.31763/ijrcs.v3i1.822
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Deep Learning-based Attack Detector for Bilateral Teleoperation Systems

Abstract: A teleoperation system is referred to as a plant that is controlled remotely, and it is often composed of a human operator, a local master manipulator, and a remote slave manipulator, all connected by a communication network. Bilateral teleoperation systems (BTOS) include transmissions in both the forward and backward directions between the master and slave. This paper discusses a class of (BTOS) focusing on the security of the system after modeling the master and slave robots mathematically. The false data in… Show more

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(1 citation statement)
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“…In this work, we propose an operating room air quality monitoring estimating system based on a deep-learning technique. Due to the unclear and fuzzy nature of ORs' data, as well as the relationships that exist in the decision support system models, the FIS and ANFIS are the most effective technique under deep learning-base systems [22]- [24] particularly used in the healthcare context [25][26]. According to the case study situation, the main challenge was the lack of integrity between input data due to the nature of data and the lack of a predetermined model to estimate the air condition and monitor the results during surgeries in the ORs.…”
Section: Introductionmentioning
confidence: 99%
“…In this work, we propose an operating room air quality monitoring estimating system based on a deep-learning technique. Due to the unclear and fuzzy nature of ORs' data, as well as the relationships that exist in the decision support system models, the FIS and ANFIS are the most effective technique under deep learning-base systems [22]- [24] particularly used in the healthcare context [25][26]. According to the case study situation, the main challenge was the lack of integrity between input data due to the nature of data and the lack of a predetermined model to estimate the air condition and monitor the results during surgeries in the ORs.…”
Section: Introductionmentioning
confidence: 99%