Over the past years, interest in classifying drivers’ behavior from data has surged. Such interest is particularly relevant for car insurance companies who, due to privacy constraints, often only have access to data ...
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Tremor is a very common motor disorder, mainly manifested as involuntary, periodic and rhythmic movement in any part of the body, especially in hands and upper-limbs, which seriously affects the life quality of patien...
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Tremor is a very common motor disorder, mainly manifested as involuntary, periodic and rhythmic movement in any part of the body, especially in hands and upper-limbs, which seriously affects the life quality of patients. Functional electrical stimulation (FES) has been shown a promising technique to suppress tremor. Most existing FES based design methods assume tremor is a single frequency signal which however is a highly idealized simplification of the real case which contains multiple-frequency or even a frequency band, therefore limiting their practical performance. To address this problem, this paper proposes a controller design method based on multi-periodic repetitive control that is capable of suppressing tremor signal with multiple frequencies. Simulation and experimental results verify the effectiveness of the proposed method.
The analysis of the photospheric velocity field is essential for understanding plasma turbulence in the solar surface, which may be responsible for driving processes such as magnetic reconnection, flares, wave propaga...
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Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applicati...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169279
Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.
The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous naviga...
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This paper is concerned with the network-theoretic properties of so-called k-nearest neighbor intelligent vehicular platoons, where each vehicle communicates with k vehicles, both in front and behind. The network-theo...
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This paper focuses on the stability analysis of a formation shape displayed by a team of mobile robots that uses heterogeneous sensing mechanism. Depending on the convenience and reliability of the local information, ...
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