The relationship between age and the central nervous system (CNS) in humans has been a classical issue that has aroused extensive attention. Especially for individuals, it is of far greater importance to clarify the mechanisms between CNS and age. The primary goal of existing methods is to use MR images to derive high-accuracy predictions for age or degenerative diseases. However, the associated mechanisms between the images and the age have rarely been investigated. In this paper, we address the correlation between gray matter volume (GMV) and age, both in terms of gray matter themselves and their interaction network, using interpretable machine learning models for individuals. Our goal is not only to predict age accurately but more importantly, to explore the relationship between GMV and age. In addition to targeting each individual, we also investigate the dynamic properties of gray matter and their interaction network with individual age. The results show that the mean absolute error (MAE) of age prediction is 7.95 years. More notably, specific locations of gray matter and their interactions play different roles in age, and these roles change dynamically with age. The proposed method is a data-driven approach, which provides a new way to study aging mechanisms and even to diagnose degenerative brain diseases.
This paper deals with the problem of disturbance rejection for uncertain LTI SISO systems perturbed by an unmeasurable external disturbance under the framework of output regulation. The system is assumed to be minimum phase and internally stable, but the model parameters are completely unknown. In addition, no knowledge of the external disturbance, including frequency, amplitude and phase is required to be known in advance. A novel high-order sliding modebased Unknown Input Observer(UIO) is developed to stabilize the system and reconstruct the external disturbance. The main feature distinguishing the proposed method from the existing ones is that we do not need to integrate a frequency estimator into the adaptive controller or update the frequency estimation in a hybrid manner. Instead, the disturbance is directly duplicated by the aforementioned unknown input observer. The boundedness of states and asymptotic convergence properties are rigorously proved. Finally, the effectiveness of the proposed technique is illustrated by a numerical example.
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