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		题名	作者	年代	出处	被引量
	1	Effects of Cr interlayer on mechanical and tribological properties of Cr-Al-Si-N nanocomposite coating显示文摘Cr-Al-Si-N coatings were deposited on SUS 304 substrate by a hybrid coating system. A Cr interlayer was introduced between Cr-Al-Si-N coating and SUS 304 substrate to improve the coating adherence. The effects of Cr interlayer on the microhardness, adhesion, and tribological behavior of Cr-Al-Si-N coatings were systematically investigated. The results indicate that the microhardness of the Cr-Al-Si-N coatings gradually deceases with increasing thickness of Cr interlayers. The adhesion between Cr-Al-Si-N and SUS 304 substrate is improved by addition of the Cr interlayers. A peak critical load of ~50 N is observed for the coating containing Cr interlayer of 60 nm as compared ~ 20 N for the coating without Cr interlayer. The thicker Cr interlayers result in reduced critical load values. Moreover, the wear resistance of the Cr-Al-Si-N coatings is greatly enhanced by introducing the Cr interlayer with thickness of 60 nm in spite of the decreased microhardness. The friction coefficient of the coating system is also moderately reduced.	Young Su HONG Se Hun KWON Tiegang WANG Doo-In KIM Jihwan CHOI Kwang Ho KIM	2011	Transactions of Nonferrous Metals Society of China2011，21，A01：	6
	2	Diagnosis of Middle Ear Diseases Based on Convolutional Neural Network显示文摘An otoscope is traditionally used to examine the eardrum and ear canal.A diagnosis of otitis media(OM)relies on the experience of clinicians.If an examiner lacks experience,the examination may be difficult and time-consuming.This paper presents an ear disease classification method using middle ear images based on a convolutional neural network(CNN).Especially the segmentation and classification networks are used to classify an otoscopic image into six classes:normal,acute otitis media(AOM),otitis media with effusion(OME),chronic otitis media(COM),congenital cholesteatoma(CC)and traumatic perforations(TMPs).The Mask R-CNN is utilized for the segmentation network to extract the region of interest(ROI)from otoscopic images.The extracted ROIs are used as guiding features for the classification.The classification is based on transfer learning with an ensemble of two CNN classifiers:EfficientNetB0 and Inception-V3.The proposed model was trained with a 5-fold cross-validation technique.The proposed method was evaluated and achieved a classification accuracy of 97.29%.	Yunyoung Nam Seong Jun Choi Jihwan Shin Jinseok Lee	2023	Computer Systems Science & Engineering2023，46，8：	0

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