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Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo +3 more
wiley +1 more source
Self‐Sensing Artificial‐Muscle‐Empowered Humanlike Perception, Interaction, and Positioning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The proposed self‐sensorized artificial muscle (SSAM) can sense its length change as small as 0.01 mm via a seamlessly integrated multi‐segment induction coil. The SSAM provides accurate length information regardless of its loadings, driving pressure, or muscle design, adequate for robust data‐driven feedback control.
Houping Wu +6 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai +3 more
wiley +1 more source
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Quantum Driven Machine Learning
International Journal of Theoretical Physics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shivani Saini +3 more
openaire +1 more source
2021
In this chapter and the next one, we will explore the exciting areas of quantum machine learning and quantum deep learning. Machine learning and deep learning have seen great success in recent years because of the increase in the computational power at our disposal and because of the high-end research in these fields.
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In this chapter and the next one, we will explore the exciting areas of quantum machine learning and quantum deep learning. Machine learning and deep learning have seen great success in recent years because of the increase in the computational power at our disposal and because of the high-end research in these fields.
openaire +2 more sources
SOFT MEASUREMENTS AND COMPUTING, 2022
The article is devoted to the issues of quantum computing and algorithms aimed at implementing quantum machine learning both on separate quantum processors and in hybrid circuits using TPU and CPU.
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The article is devoted to the issues of quantum computing and algorithms aimed at implementing quantum machine learning both on separate quantum processors and in hybrid circuits using TPU and CPU.
openaire +2 more sources
Distributed secure quantum machine learning
Science Bulletin, 2017Distributed secure quantum machine learning (DSQML) enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server ...
Yu-Bo, Sheng, Lan, Zhou
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Machine learning and quantum physics
Science, 2017Many-Body Physics Elucidating the behavior of quantum interacting systems of many particles remains one of the biggest challenges in physics. Traditional numerical methods often work well, but some of the most interesting problems leave them stumped.
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