Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

Ganoderma lucidum polysaccharide inhibits LPS-induced inflammatory injury to mammary epithelial cells

This study sought to investigate whether Ganoderma lucidum polysaccharide(GLP)has a protective effect on lipopolysaccharide(LPS)-induced inflammatory injury to mammary epithelial HC-11 cells and to characterize the mechanism involved.Cell viability was assessed using the cell counting kit 8(CCK-8)method,tumor necrosis factor-α(TNF-α),interleukin-6(IL-6)and IL-1βlevels were measured by enzyme linked immunosorbent assay(ELISA),and IκBa,p65 NF-κB and STAT3 mRNA were determined using quantitative reverse transcription PCR(qRT-PCR),p65 and STAT3 protein expression were determined using Western blotting,respectively.GLP was shown to inhibit LPS-induced TNF-α,IL-6,and IL-1βproduction(P<0.01 or P<0.05),GLP was also shown to increase IκBαmRNA expression(P<0.01),decrease p65 and STAT3 mRNA expression(P<0.01 or P<0.05),and decrease p-p65,p65,p-STAT3,and STAT3 protein expression in breast epithelial cells(P<0.01 or P<0.05).The findings suggest that GLP inhibits nuclear factor kappa-B(NF-κB)and signal transducers and activators of transcription(STAT)signaling by preventing IκBαdegradation and p65 and STAT3 phosphorylation.This results in lower LPS-induced TNF-α,IL-6,and IL-1βproduction and prevents inflammatory cell injury.

Ship detection and classification from optical remote sensing images: A survey

Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.