No abstract

Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer's disease (AD) is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine (ELM)-based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. First, features were extracted from magnetic resonance (MR) images, and useful features were selected using a feature selection method. Second, multiple modalities of MCI subjects, including MRI, positron emission tomography, cerebrospinal fluid biomarkers, and gene data, were individually graded using the ELM method. Finally, these grading scores calculated from different modalities were fed into a classifier to discriminate subjects with progressive MCI from those with stable MCI. The proposed approach has been validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and an accuracy of 84.7% was achieved for an AD prediction within 3 years. Experiments on predicting AD conversion from MCI within different periods showed similar results with the 3-year prediction. The experimental results demonstrate that the proposed approach benefits from the efficient fusion of four modalities, resulting in an accurate prediction of MCI-to-AD conversion.

We investigate light localization at a single phase-slip defect in one-dimensional photonic lattices, both numerically and experimentally. We demonstrate the existence of various robust linear and nonlinear localized modes in lithium niobate waveguide arrays exhibiting saturable defocusing nonlinearity.

In this paper, in order to handle the high nonlinearity and the sophisticated disturbance in marine engines, a variable sampling rate based active disturbance rejection controller is developed for engine speed control. In the proposed method, the Active Disturbance Rejection Control (ADRC) is designed with the consideration of the practical application in engine speed control that is known as the Crank-angle (CA) based or event-based sampling and control, which means the sampling interval varies with the engine speed. Such a problem has not been discussed in any previous study regarding the application of ADRC in engine control. To this end, this paper discusses the convergence of the variable sampling rate based Extended State Observer (ESO), as well as its parameters that guarantee stability. To verify the proposed control scheme more properly, a cycle-detailed hybrid nonlinear engine model is employed. Finally, simulations are carried out on the Hardware-in-the-loop (HIL) system to assess the superiority of the proposed strategy. The comparative results with a Fuzzy-Proportional-Integral-Derivative (PID) controller demonstrate that the proposed control scheme has better adaptation to engine speed, load disturbances, and stronger robustness towards model uncertainties, which indicates a promising reduction of time and burden for calibrating the controller. It also proved that the proposed CA based ADRC by variable sampling rate method outperforms the general fixed sampling rate ADRC, which is widely used in previous works. Moreover, the successful application of the proposed algorithm via CA based strategy in a real Engine Control Unit (ECU) indicates its huge potential in practical engine control.

This paper proposes a disturbance observer-based discrete sliding-mode control scheme with the variable sampling rate control for the marine diesel engine speed control in the presence of system uncertainties and disturbances. Initially, a sliding-mode controller based on the fast power reaching law is employed, which has a good dynamic quality of the arrival stage and can suppress chattering. To satisfy the practical requirements in the digital controller and the crank angle-based fuel injection in engine speed control, the proposed method is discretized under the variable sampling rate condition. A disturbance observer based on the second-order sliding-mode control is designed to compensate the system uncertainties and disturbances, by doing such the requirement of the parameters of the sliding-mode controller to be reduced significantly. In addition, a cylinder-by-cylinder mean value engine model (MVEM) is built by restructuring the combustion torque model, based on which numerical simulations are carried out by comparing the proposed method with PID and the extended state observer (ESO)-based sliding mode controllers. The common operation situations of the marine diesel engines are taken into account, including starting process, acceleration and deceleration, load variation, and varied propulsion system parameters. The results demonstrate that the proposed disturbance observer-based sliding-mode controller has prominent control performance and strong robustness.