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兰州理工大学 [19]

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期刊论文 [14]

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限定条件	专题：兰州理工大学第一署名单位第一作者单位通讯作者单位

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	Intelligent Fault Identification Method Based on Convolutional Neural Network for Imbalanced Data 期刊论文 Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2022, 卷号: 42, 期号: 2, 页码: 299-307 作者: Wu, Yaochun; Zhao, Rongzhen; Jin, Wuyin; Xing, Ziyang 收藏 \| 浏览/下载：17/0 \| 提交时间：2022/06/20 Convolutional neural networks Machinery Class spaces Convolutional neural network Fault identifications Identification method Imbalanced data Intelligent fault diagnosis Min-max Min-max objective func-tion Net work Objective functions
	Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering 期刊论文 SECURITY AND COMMUNICATION NETWORKS, 2021, 卷号: 2021 作者: Akhtar, Muhammad Shoaib; Feng, Tao 收藏 \| 浏览/下载：13/0 \| 提交时间：2022/03/01
	Remarnet: Conjoint relation and margin learning for small-sample image classification 期刊论文 IEEE Transactions on Circuits and Systems for Video Technology, 2021, 卷号: 31, 期号: 4, 页码: 1569-1579 作者: Li, Xiaoxu; Yu, Liyun; Yang, Xiaochen; Ma, Zhanyu; Xue, Jing-Hao 收藏 \| 浏览/下载：8/0 \| 提交时间：2021/06/03 Deep learning Image classification Sampling Classification mechanism Competitive performance Discriminative features Discriminative power Fully connected networks Novel neural network State-of-the-art methods State-of-the-art performance
	Fault diagnosis of rotor based on Local-Global Balanced Orthogonal Discriminant Projection 期刊论文 Measurement: Journal of the International Measurement Confederation, 2021, 卷号: 168 作者: Shi, Mingkuan; Zhao, Rongzhen; Wu, Yaochun; He, Tianjing 收藏 \| 浏览/下载：17/0 \| 提交时间：2020/11/14 Fault detection Frequency domain analysis Nearest neighbor search Rotating machinery Time domain analysis Application examples Complex characteristics Discriminant informations High dimensional feature Inter-class distance K-nearest neighbor method Structure information Time frequency domain
	CC-LOSS: CHANNEL CORRELATION LOSS FOR IMAGE CLASSIFICATION 会议论文 ELECTR NETWORK, JAN 10-15, 2021 作者: Song, Zeyu; Chang, Dongliang; Ma, Zhanyu; Li, Xiaoxu; Tan, Zheng-Hua 收藏 \| 浏览/下载：4/0 \| 提交时间：2021/12/17 Deep Learning Image Classification Loss Function Channel Attention
	ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification 期刊论文 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 4, 页码: 1569-1579 作者: Li, Xiaoxu; Yu, Liyun; Yang, Xiaochen; Ma, Zhanyu; Xue, Jing-Hao 收藏 \| 浏览/下载：6/0 \| 提交时间：2021/06/03 Training Prototypes Neural networks Feature extraction Task analysis Deep learning Adaptation models Small-sample learning deep neural network relation learning discriminative feature learning
	Application of an emotional classification model in e-commerce text based on an improved transformer model 期刊论文 PLOS ONE, 2021, 卷号: 16, 期号: 3 作者: Wang, Xuyang; Tong, Yixuan 收藏 \| 浏览/下载：3/0 \| 提交时间：2021/04/12
	Completion of multiview missing data based on multi-manifold regularised non-negative matrix factorisation 期刊论文 Artificial Intelligence Review, 2020, 卷号: 53, 期号: 7, 页码: 5411-5428 作者: Jing-Tao, Sun; Qiu-Yu, Zhang 收藏 \| 浏览/下载：17/0 \| 提交时间：2020/11/14 Classification (of information) Factorization Gaussian distribution Linear regression Predictive analytics Missing data Multi-manifold regularised Multi-view clustering Multisource data Non-negative matrix factorisation
	The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification 期刊论文 IEEE Transactions on Image Processing, 2020, 卷号: 29, 页码: 4683-4695 作者: Chang, Dongliang; Ding, Yifeng; Xie, Jiyang; Bhunia, Ayan Kumar; Li, Xiaoxu 收藏 \| 浏览/下载：3/0 \| 提交时间：2020/11/14 Complex networks Deep learning Training aircraft Attention mechanisms Feature representation Fine grained Image Categorization Loss functions mutual channel State-of-the-art performance Visual classification
	Dynamic attention loss for small-sample image classification 会议论文 Lanzhou, China, November 18, 2019 - November 21, 2019 作者: Cao, Jie; Qiu, Yinping; Chang, Dongliang; Li, Xiaoxu; Ma, Zhanyu 收藏 \| 浏览/下载：0/0 \| 提交时间：2020/11/15 Convolutional neural networks Image enhancement Cross entropy Discriminative features Loss functions Machine learning approaches Model generalization Similarity measurements Small samples Training process

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