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A pixel-level deep segmentation network for automatic defect detection
期刊论文
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 卷号: 215, 页码: 11
Authors:
Yang, Lei
;
Xu, Shuai
;
Fan, Junfeng
;
Li, En
;
Liu, Yanhong
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View/Download:43/0
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Submit date:2023/02/22
Defect detection
Deep convolutional neural network
U-shape network
ConvLSTM network
Meta-Energy: When Integrated Energy Internet Meets Metaverse
期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 3, 页码: 580-583
Authors:
Chenghui Zhang
;
Shuai Liu
Adobe PDF(1476Kb)
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View/Download:37/1
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Submit date:2023/03/02
Vision-Based Power Line Segmentation With an Attention Fusion Network
期刊论文
IEEE SENSORS JOURNAL, 2022, 卷号: 22, 期号: 8, 页码: 8196-8205
Authors:
Yang, Lei
;
Fan, Junfeng
;
Xu, Shuai
;
Li, En
;
Liu, Yanhong
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View/Download:63/0
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Submit date:2022/07/25
Power transmission lines
Inspection
Feature extraction
Sensors
Image segmentation
Navigation
Vision sensors
Power line extraction
deep network architecture
image segmentation
multi-scale feature fusion
attention module
Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study
期刊论文
LANCET DIGITAL HEALTH, 2022, 卷号: 4, 期号: 1, 页码: E8-E17
Authors:
Feng, Lili
;
Liu, Zhenyu
;
Li, Chaofeng
;
Li, Zhenhui
;
Lou, Xiaoying
;
Shao, Lizhi
;
Wang, Yunlong
;
Huang, Yan
;
Chen, Haiyang
;
Pang, Xiaolin
;
Liu, Shuai
;
He, Fang
;
Zheng, Jian
;
Meng, Xiaochun
;
Xie, Peiyi
;
Yang, Guanyu
;
Ding, Yi
;
Wei, Mingbiao
;
Yun, Jingping
;
Hung, Mien-Chie
;
Zhou, Weihua
;
Wahl, Dantel R.
;
Lan, Ping
;
Tian, Jie
;
Wan, Xiangbo
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View/Download:88/0
  |  
Submit date:2022/02/16
Soft Contrastive Learning with Q-irrelevance Abstraction for Reinforcement Learning
期刊论文
IEEE Transactions on Cognitive and Developmental Systems, 2022, 页码: doi={10.1109/TCDS.2022.3218940}
Authors:
Minsong Liu
;
Luntong Li
;
Shuai Hao
;
Yuanheng Zhu
;
Dongbin Zhao
Adobe PDF(12013Kb)
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View/Download:6/0
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Submit date:2023/04/26
Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
期刊论文
NPJ PRECISION ONCOLOGY, 2021, 卷号: 5, 期号: 1, 页码: 9
Authors:
Yan, Jing
;
Zhang, Bin
;
Zhang, Shuaitong
;
Cheng, Jingliang
;
Liu, Xianzhi
;
Wang, Weiwei
;
Dong, Yuhao
;
Zhang, Lu
;
Mo, Xiaokai
;
Chen, Qiuying
;
Fang, Jin
;
Wang, Fei
;
Tian, Jie
;
Zhang, Shuixing
;
Zhang, Zhenyu
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View/Download:90/0
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Submit date:2021/11/02
Exploiting the directional coherence function for multichannel source extraction
期刊论文
SPEECH COMMUNICATION, 2021, 卷号: 128, 页码: 1-14
Authors:
Liang, Shan
;
Li, Guanjun
;
Nie, Shuai
;
Yang, ZhanLei
;
Liu, WenJu
;
Tao, Jianhua
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View/Download:95/0
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Submit date:2021/05/06
Directional coherence function
Coherent-to-Diffuse Ratio
General sidelobe canceller
Desired Speech Presence Probability
Trip Purposes Mining From Mobile Signaling Data
期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 99, 期号: 99, 页码: 13
Authors:
Li, Zhishuai
;
Xiong, Gang
;
Wei, Zebing
;
Zhang, Yu
;
Zheng, Meng
;
Liu, Xiaoli
;
Tarkoma, Sasu
;
Huang, Min
;
Lv, Yisheng
;
Wu, Chuheng
Adobe PDF(3962Kb)
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View/Download:116/17
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Submit date:2022/01/27
Cellular networks
Trajectory
Semantics
Unsupervised learning
Supervised learning
Resource management
Public transportation
Trip purpose inference
cellular network data
latent Dirichlet allocation
travel behavior
big data
An incrementally cascaded broad learning framework to facial landmark tracking
期刊论文
NEUROCOMPUTING, 2020, 卷号: 410, 页码: 125-137
Authors:
Liu, Caifeng
;
Feng, Lin
;
Guo, Shuai
;
Wang, Huibing
;
Liu, Shenglan
;
Qiao, Hong
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  |  
View/Download:71/0
  |  
Submit date:2021/01/07
Facial landmark tracking
Face alignment
Cascade regression
Incremental learning
Convolutional prototype learning for zero-shot recognition
期刊论文
IMAGE AND VISION COMPUTING, 2020, 卷号: 98, 页码: 8
Authors:
Liu, Zhizhe
;
Zhang, Xingxing
;
Zhu, Zhenfeng
;
Zheng, Shuai
;
Zhao, Yao
;
Cheng, Jian
Favorite
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View/Download:118/0
  |  
Submit date:2020/06/22
Zero-shot recognition
Prototype learning
Image recognition
Deep learning