Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

In this paper,a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network.The sensor fault and the system input uncertainty are assumed to be unknown but bounded.The radial basis function (RBF) neural network is used to approximate the sensor fault.Based on the output of the RBF neural network,the sliding mode observer is presented.Using the Lyapunov method,a criterion for stability is given in terms of matrix inequality.Finally,an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

Design of Time-Resolved Shifted Dual Transmission Grating Spectrometer for the X-Ray Spectrum Diagnostics

A new time-resolved shifted dual transmission grating spectrometer （SDTGS） is designed and fabricated in this work. This SDTGS uses a new shifted dual transmission grating （SDTG） as its dispersive component, which has two sub transmission gratings with different line densities, of 2000 lines/mm and 5000 lines/mm. The axes of the two sub transmission gratings in SDTG are horizontally and vertically shifted a certain distance to measure a broad range of 0.1-5 keV time-resolved X-ray spectra. The SDTG has been calibrated with a soft X-ray beam of the synchrotron radiation facility and its diffraction efficiency is also measured. The designed SDTGS can take full use of the space on a record panel and improve the precision for measuring spatial and temporal spectrum simultaneously. It will be a promising application for accurate diagnosis of the soft X-ray spectrum in inertial confinement fusion.

Design of Distributed Fault-Tolerant Industrial Network

A token-bus-based design method of the distributedfault-tolerant industrial network is presented in this pa-per.The dual-link network is of hot-redundancy.The performance of the network is also discussed.

A brain structural connectivity biomarker for autism spectrum disorder diagnosis in early childhood

Background:Autism spectrum disorder(ASD)is associated with altered brain development,but it is unclear which specific structural changes may serve as potential diagnostic markers,particularly in young children at the age when symptoms become fully estab-lished.Furthermore,such brain markers need to meet the requirements of precision medicine and be accurate in aiding diagnosis at an individual rather than only a group level.Objective:This study aimed to identify and model brain-wide differences in structural connectivity using diffusion tensor imaging(DTI)in young ASD and typically developing(TD)children.Methods:A discovery cohort including 93 ASD and 26 TD children and two independent validation cohorts including 12 ASD and 9 TD children from three different cities in China were included.Brain-wide(294 regions)structural connectivity was measured using DTI(fractional anisotropy,FA)together with symptom severity and cognitive development.A connection matrix was constructed for each child for comparisons between ASD and TD groups.Pattern classification was performed on the discovery dataset and the resulting model was tested on the two independent validation datasets.Results:Thirty-three structural connections showed increased FA in ASD compared to TD children and associated with both autistic symptom severity and impaired general cognitive development.The majority(29/33)involved the frontal lobe and comprised five different networks with functional relevance to default mode,motor control,social recognition,language and reward.Overall,clas-sification achieved very high accuracy of 96.77%in the discovery dataset,and 91.67%and 88.89%in the two independent validation datasets.Conclusions:Identified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish novel individual ASD from TD children and may therefore represent a robust early brain biomarker which can address the requirements of precision medicine.

Can Biomarkers Help the Early Diagnosis of Parkinson's Disease?

Parkinson＇s disease （PD） is a complex neurode- generative disease with progressive loss of dopamine neurons. PD patients usually manifest a series of motor and non-motor symptoms. In order to provide better early diagnosis and subsequent disease-modifying therapies for PD patients, there is an urgent need to identify sensitive and specific biomarkers. Biomarkers can be divided into four categories： clinical, imaging, biochemical, and genetic. Ideal biomarkers not only improve our under- standing of PD pathogenesis and progression, but also provide benefits for early risk evaluation and clinical diagnosis of PD. Although many efforts have been made and several biomarkers have been extensively investigated, few if any have been found useful for early diagnosis. Here, we summarize recent developments in the discovered biomarkers of PD and discuss their merits and limitations for the early diagnosis of PD.