In this study we show that a Convolutional Neural Network (CNN) model is able to accurately discriminate between 4 different phases of neurological status in a non-Electroencephalogram (EEG) dataset recorded in an exp...
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In this study we show that a Convolutional Neural Network (CNN) model is able to accurately discriminate between 4 different phases of neurological status in a non-Electroencephalogram (EEG) dataset recorded in an experiment in which subjects are exposed to physical, cognitive and emotional stress. We demonstrate that the proposed model is able to obtain 99.99% Area Under the Curve (AUC) of Receiver Operation characteristic (ROC) and 99.82% classification accuracy on the test dataset. Furthermore, for comparison, we show that our models outperforms traditional classification methods such as SVM, and RF. Finally, we show the advantage of CNN models, in comparison to other methods, in robustness to noise by 97.46% accuracy on a noisy dataset. Copyright (C) 2020 The Authors.
With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obt...
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With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obtain more infounation beneath the displayed image. Generally, 3D reconstruction, registration, tracking and depth visualization are all important for an AR system, which directly deteunine the accuracy of the system. However, none of recent systems have achieved a perfect 3D vision for doctors. It is particularly challenging for the AR system to perform well for operation on highly defounable body tissues. Therefore, there is an urgent need to improve the 3D vision and intelligence of AR systems. In this paper, we provide a review of the recent development of augmentation technologies for surgery and medical treatment. And then, we introduce the parallel intelligence theory into AR systems, which can provide a feasible approach to enhance the efficiency. Copyright (C) 2020 The Authors.
The modelling and analysis of nonlinear cyber-physical systems is integral to applications ranging from social networks to defence strategy. However, conventional linear-quadratic methods inadequately capture the comp...
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The modelling and analysis of nonlinear cyber-physical systems is integral to applications ranging from social networks to defence strategy. However, conventional linear-quadratic methods inadequately capture the complex nonlinear behaviour of human decision-makers in these systems. Utilising game theory and multi-agent reinforcement learning (MARL), we explore dynamic interactions between humans and machines within a cyber-physical environment, using a swarmalator to represent strategic humans in a pursuit-evader game. Preliminary results indicate that MARL inadequately captures the nonlinear behaviour of human decision-making, with pursuers and evaders exhibiting human behaviours achieving the highest utilities. Copyright (c) 2024 The Authors.
Human-robotics fusion stage performance has emerged as a fledgling field with complicated interdisciplinary attributes. This paper proposes a corresponding method through literature research, induction and experiments...
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Human-robotics fusion stage performance has emerged as a fledgling field with complicated interdisciplinary attributes. This paper proposes a corresponding method through literature research, induction and experiments. We consider it as an extraordinarily forward-looking and ground-breaking approach for the following reasons. It is the first method to produce innovation led by science technology arts. On this basis, it focuses on a trinity of core semantic hub as an innovative hub mechanism, with the potential to gain access to multimodal human-robotics performance and creation in the future. It is hoped that this paper will serve as a communication platform for professionals like artists and technical personnel who are in great need of interdisciplinary collaboration, so that the creators trapped in the barriers of their respective industries will realize that emerging, interdisciplinary creation is actually possible and can be put into practice. Copyright (C) 2020 The Authors.
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