Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.

Protocol for a diagnostic accuracy study to develop diagnosis algorithm for biliary atresia using MMP-7 (DIABA-7 study): a study recruiting from Chinese Biliary Atresia Collaborative Network

IntroductionBiliary atresia is a severe liver disease in neonates, and the prognosis partially depends on the age at which infants undergo the Kasai procedure. Matrix metalloproteinase-7 (MMP-7) was confirmed to have significant value in the diagnosis of biliary atresia. However, so far, the reference range and its cut-off value for diagnosing biliary atresia have not been established yet.Methods and analysisDIagnosis Algorithm for Biliary Atresia (DIABA-7) is a prospective diagnostic test. Cholestatic infants and normal controls within 150 days of age are recruiting from the Chinese Biliary Atresia Collaborative Network. The serum samples and dried blood spot (DBS) samples are obtained to detect MMP-7 concentrations using an ELISA kit. The reference standard is the intraoperative exploration and subsequent histological examination of liver biopsies. Lambda-Mu-Sigma (LMS) method is used to calculate the normal range of serum MMP-7 of each age group. Receiver operating characteristics (ROC) curves are constructed to calculate the best cut-off point and area under the curve for the index test. The sensitivity, specificity, positive predictive value and negative predictive value are used to show the diagnostic accuracy. Pearson correlation coefficient test is applied to assess the correlation of serum MMP-7 and DBS MMP-7.Ethics and disseminationThis study was reviewed and approved by the Ethics Committee of Children’s Hospital of Fudan University (Number 2020–296). Dissemination will be guided by investigators and patients. The aim is to publish the study results in a high-quality peer-reviewed journal and present the findings at international academic meetings.Trial registration numberChiCTR2000032983.

An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%.