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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph featurelearning is proposed in this paper.And the edge connections in the graph depend on the relationship between signals.On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced trainingfeature learning.

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingTherefore, a fault detection method based on self-supervised feature learning was proposed to addressThe self-supervised representation learning uses a sequence-based Triplet Loss.A comprehensive comparison study was also conducted with various feature extractors and unary classifiersmodel can detect progressive faults very quickly and achieve improved results for comparison without feature

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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Discoverymethod for distributed denial-of-service attack behavior inSDNs using a feature-pattern graphmodel Special Feature on Future Network-Research Article

Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1195-1208 doi: 10.1631/FITEE.1800436

Abstract: We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model.The feature-pattern graph model presented employs network patterns as nodes and similarity as weightedThe similarity between nodes is modeled by metric learning and the Mahalanobis distance.The proposed method can discover DDoS attacks using a graph-based neighborhood classification method;it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph

Keywords： Software-defined network     Distributed denial-of-service (DDoS)     Behavior discovery     Distance metric learning     Feature-pattern graph    

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NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 409-421 doi: 10.1631/FITEE.2000657

Abstract: Recently, graph neural networks (GNNs) have achieved remarkable performance in representation learningon graph-structured data.Many efforts have been made to improve the process of feature information aggregation from directly connectedTo alleviate oversmoothing, we propose a nested graph network (NGAT), which can work in a semi-supervised-layer NGAT uses a layer-wise aggregation strategy guided by the mechanism to selectively leverage feature

Keywords： Graph learning     Semi-supervised learning     Node classification     Attention    

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Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Frontiers in Energy 2020, Volume 14, Issue 4,   Pages 817-835 doi: 10.1007/s11708-020-0709-9

Abstract: To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from thedynamic operating data set with steep slope signals is created based on physics equations and then a featuresimilarity-based learning model with an encoder and a decoder is built and trained to achieve featureMoreover, compared with the other classical transfer learning modes, the method proposed has the bestthe hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning

Keywords： gas turbine     dynamic simulation     data-driven     transfer learning     feature similarity    

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Two-level hierarchical feature learning for image classification Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 897-906 doi: 10.1631/FITEE.1500346

Abstract: In this paper, we propose a novel two-level hierarchical feature learning framework based on the deepFirst, the deep feature extractors of different levels are trained using the transfer learning methodSecond, the general feature extracted from all the categories and the specific feature extracted fromhighly similar categories are fused into a feature vector.learning is powerful.

Keywords： Transfer learning     Feature learning     Deep convolutional neural network     Hierarchical classification    

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Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 5,   Pages 358-366 doi: 10.1631/FITEE.1400323

Abstract: In this paper, we apply several unsupervised feature learning algorithms (including -means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning

Keywords： Speech emotion recognition     Unsupervised feature learning     Neural network     Affect computing    

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Unsupervised feature selection via joint local learning and group sparse regression Regular Papers

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 538-553 doi: 10.1631/FITEE.1700804

Abstract:

Feature selection has attracted a great deal of interest over the past decades.By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improvedThe key to unsupervised feature selection is to find features that effectively reflect the underlyingTo address this issue, we propose a novel unsupervised feature selection algorithm via joint local learningJLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single

Keywords： Unsupervised     Local learning     Group sparse regression     Feature selection    

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1397-1414 doi: 10.1007/s11709-022-0860-y

Abstract: indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learningand graph embedding.element, and the state is described by integrating the joint and element features into a comprehensive featureestimation of the Q values, and handle problems with different action spaces owing to utilization of graphBesides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions Review

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1451-1478 doi: 10.1631/FITEE.2100569

Abstract:

For optimal results, retrieving a relevant feature from a has become a hot topic for researchers involveds to work on multiclass classification problems and on different ways to enhance the performance of learningway for comprehending and highlighting the multitude of challenges and issues in finding the optimal featureaccuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature

Keywords： Feature selection     High dimensionality     Learning techniques     Microarray dataset    

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Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 1, doi: 10.1007/s11465-022-0725-z

Abstract: energy operator (EO) and its variants have received considerable attention in the field of bearing fault featureaddress these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault featureSecond, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsicverifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature

Keywords： higher order energy operator     fault diagnosis     manifold learning     rolling element bearing     information    

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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,

Engineering doi: 10.1016/j.eng.2023.05.020

Abstract: GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagationand graph attention that is adaptive to the group size and dynamic interaction states.

Keywords： behavior modeling and prediction     Implicit representation of pedestrian anticipation     Group interaction     Graph    

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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

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 333-347 doi: 10.1007/s11465-017-0435-0

Abstract: Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) techniqueIt can also sparsely concentrate the feature information into a few dominant subspace coefficients.

Keywords： joint subspace learning     multiple fault diagnosis     sparse decomposition theory     coupling feature separation    

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Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management   Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to

Keywords： autonomous system     multiclass relationship     graph convolutional network     classification algorithm     Internet    

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 872-884 doi: 10.1631/FITEE.1800520

Abstract:

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-sourceFT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a featureThe feature template is used to extract local context features, and a neural network model is used to

Keywords： Network security entity     Security knowledge graph (SKG)     Entity recognition     Feature template     Neural network    

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Title Author Date Type Operation

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Journal Article

Discoverymethod for distributed denial-of-service attack behavior inSDNs using a feature-pattern graphmodel

Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN

Journal Article

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Journal Article

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Journal Article

Two-level hierarchical feature learning for image classification

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Journal Article

Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

Journal Article

Unsupervised feature selection via joint local learning and group sparse regression

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Journal Article

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Journal Article

Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Journal Article

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Journal Article

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,

Journal Article

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

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Journal Article