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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning
《机械工程前沿(英文)》 2021年 第16卷 第4期 页码 829-839 doi: 10.1007/s11465-021-0652-4
关键词: imbalanced fault diagnosis graph feature learning rotating machinery autoencoder
《能源前沿(英文)》 2023年 第17卷 第4期 页码 527-544 doi: 10.1007/s11708-023-0880-x
关键词: fault detection unary classification self-supervised representation learning multivariate nonlinear time series
基于特征-模式图的SDN下分布式拒绝服务攻击发现方法 Special Feature on Future Network-Research Article
Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN
《信息与电子工程前沿(英文)》 2019年 第20卷 第9期 页码 1195-1208 doi: 10.1631/FITEE.1800436
NGAT:基于广度和深度探索注意力机制的半监督图表示学习 Research Articles
胡荐苛,张引
《信息与电子工程前沿(英文)》 2022年 第23卷 第3期 页码 409-421 doi: 10.1631/FITEE.2000657
关键词: 图学习;半监督学习;节点分类;注意力机制
Dynamic simulation of gas turbines via feature similarity-based transfer learning
Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG
《能源前沿(英文)》 2020年 第14卷 第4期 页码 817-835 doi: 10.1007/s11708-020-0709-9
关键词: gas turbine dynamic simulation data-driven transfer learning feature similarity
基于两级层次特征学习的图像分类方法 Article
Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE
《信息与电子工程前沿(英文)》 2016年 第17卷 第9期 页码 897-906 doi: 10.1631/FITEE.1500346
Speech emotion recognitionwith unsupervised feature learning
Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO
《信息与电子工程前沿(英文)》 2015年 第16卷 第5期 页码 358-366 doi: 10.1631/FITEE.1400323
关键词: Speech emotion recognition Unsupervised feature learning Neural network Affect computing
联合局部学习和组稀疏回归的无监督特征选择 Regular Papers
Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU
《信息与电子工程前沿(英文)》 2019年 第20卷 第4期 页码 538-553 doi: 10.1631/FITEE.1700804
关键词: 无监督;局部学习;组稀疏回归;特征选择
Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO
《结构与土木工程前沿(英文)》 2022年 第16卷 第11期 页码 1397-1414 doi: 10.1007/s11709-022-0860-y
关键词: progressive collapse alternate load path demolition planning reinforcement learning graph embedding
微阵列数据集的特征选择技术:综合评述、分类和未来方向 Review
Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI
《信息与电子工程前沿(英文)》 2022年 第23卷 第10期 页码 1451-1478 doi: 10.1631/FITEE.2100569
为获得最佳结果,从微阵列数据集中检索相关特征已成为特征选择(FS)技术的研究热点。本综述旨在全面阐述各种最新特征选择技术,同时介绍了基于微阵列数据集的处理多类分类问题的技术以及提高学习算法性能的不同方法。我们试图理解和解决数据集不平衡问题,以证实研究人员在微阵列数据集上的工作。对文献的分析为理解和强调在通过各种特征选择技术寻找最佳特征子集时存在的众多挑战和问题铺平了道路。同时提供了一个案例说明该方法的实施过程,该方法使用3个微阵列癌症数据集评估一些包装方法和混合方法的分类精度和收敛能力,以确认最优特征子集。
关键词: 特征选择;高维;学习技术;微阵列数据集
Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature
《机械工程前沿(英文)》 2023年 第18卷 第1期 doi: 10.1007/s11465-022-0725-z
关键词: higher order energy operator fault diagnosis manifold learning rolling element bearing information fusion
The Group Interaction Field for Learning and Explaining Pedestrian Anticipation
Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang,
《工程(英文)》 doi: 10.1016/j.eng.2023.05.020
关键词: Human behavior modeling and prediction Implicit representation of pedestrian anticipation Group interaction Graph neural network
Multiple fault separation and detection by joint subspace learning for the health assessment of wind
Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN
《机械工程前沿(英文)》 2017年 第12卷 第3期 页码 333-347 doi: 10.1007/s11465-017-0435-0
The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.
关键词: joint subspace learning multiple fault diagnosis sparse decomposition theory coupling feature separation wind turbine gearbox
Classifying multiclass relationships between ASes using graph convolutional network
《工程管理前沿(英文)》 页码 653-667 doi: 10.1007/s42524-022-0217-1
关键词: autonomous system multiclass relationship graph convolutional network classification algorithm Internet topology
一种基于特征模板和CNN-BiLSTM-CRF的网络安全实体识别方法 Research Papers
Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN
《信息与电子工程前沿(英文)》 2019年 第20卷 第6期 页码 872-884 doi: 10.1631/FITEE.1800520
标题 作者 时间 类型 操作
Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning
期刊论文
Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and
期刊论文
Dynamic simulation of gas turbines via feature similarity-based transfer learning
Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG
期刊论文
Speech emotion recognitionwith unsupervised feature learning
Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO
期刊论文
Deep reinforcement learning-based critical element identification and demolition planning of frame structures
Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO
期刊论文
The Group Interaction Field for Learning and Explaining Pedestrian Anticipation
Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang,
期刊论文
Multiple fault separation and detection by joint subspace learning for the health assessment of wind
Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN
期刊论文