Cone-shaped corneas are blinding eye diseases characterised by dilated and thinning corneal tissue and forward conical protrusions, most often in adolescents. Early detection and intervention can prevent further dilatation of the cornea. The prevailing examination methods and techniques are not difficult to diagnose clinical cone corneas, but there are limitations in the diagnosis of early cone corneas (static cone corneas and subclinical cone corneas). In this study, we investigated the diagnostic value of the combination of these two instruments in the diagnosis of clinical cone corneas and subclinical cone corneas by performing the Pentacam and Corvis ST examinations in healthy eyes and dystrophic cone corneas. This study provides a theoretical basis for early cone cornea screening and diagnosis. The analysis revealed that only TP, SPA1, ARTh, and bIOP were normally distributed among all included parameters, and only bIOP was normally distributed with equal variance. TBI and CBI indices have a certain sensitivity and specificity for the diagnosis of subclinical cone corneas, with a strong diagnostic ability, and can be used for screening and early diagnosis of cone corneas.

No abstract

Most of the existing hydraulic cylinder internal leakage detection methods are laboratory testing methods, mainly the pressure-holding method, measurement of hydraulic cylinder settlement method, and measuring cup measurement method. The internal leakage of the hydraulic cylinder affects the damping characteristics of the control system. Therefore, real-time internal leakage detection plays an important role in the control characteristics. This paper first proposes a wavelet analysis-based extraction of hydraulic cylinder internal leakage fault eigenvalues for analysis, that is, data processing. A convolutional neural network-based detection method is also proposed, in which the pressure signal of a chamber of a hydraulic cylinder is first obtained through simulation under four operating conditions: no leakage, small leakage, medium leakage, and large leakage. Compared to traditional modelling methods, the method overcomes the difficulties in modelling nonlinear hydraulic systems, requires only pressure signal acquisition, is simple and reliable, and is compared with traditional BP neural networks to demonstrate its superiority.