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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.effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph featurelearning.

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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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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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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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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A software defect prediction method with metric compensation based on feature selection and transferlearning Research Article

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 715-731 doi: 10.1631/FITEE.2100468

Abstract: Cross-project software solves the problem of insufficient training data for traditional , and overcomes the challenge of applying models learned from multiple different source projects to target project. At the same time, two new problems emerge: (1) too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metric values will vary greatly from project to project due to the development environment and other factors, resulting in lower prediction accuracy when the model achieves cross-project prediction. In the proposed method, the Pearson method is introduced to address data redundancy, and the based technique is used to address the problem of large differences in data distribution between the source project and target project. In this paper, we propose a software method with based on and . The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric.

Keywords： Defect prediction     Feature selection     Transfer learning     Metric compensation    

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Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Frontiers in Energy 2017, Volume 11, Issue 2,   Pages 175-183 doi: 10.1007/s11708-017-0471-9

Abstract: The high-dimensional feature sets with redundant information are frequently encountered when dealingIn this paper, two kinds of feature set construction methods are proposed which can achieve the properfeature set either by selecting the subsets or by transforming the original variables with specificA locally weighted learning method is also proposed to utilize the processed feature set to produce the

Keywords： regional wind power forecasting     feature set     minimal-redundancy-maximal-relevance (mRMR)     principal componentanalysis (PCA)     locally weighted learning model    

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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.

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

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Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis Article

Yesheng Xu, Ming Kong, Wenjia Xie, Runping Duan, Zhengqing Fang, Yuxiao Lin, Qiang Zhu, Siliang Tang, Fei Wu, Yu-Feng Yao

Engineering 2021, Volume 7, Issue 7,   Pages 1002-1010 doi: 10.1016/j.eng.2020.04.012

Abstract:

Infectious keratitis is the most common condition of corneal diseases in which a pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues. Infectious keratitis is a medical emergency for which a rapid and accurate diagnosis is needed to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage; otherwise, it may develop a sight threatening and even eye-globe-threatening condition. In this paper, we propose a sequentiallevel deep model to effectively discriminate infectious corneal disease via the classification of clinical images. In this approach, we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis. In a comparison, the performance of the proposed sequential-level deep model achieved 80% diagnostic accuracy, far better than the 49.27% ± 11.5% diagnostic accuracy achieved by 421 ophthalmologists over 120 test images.

Keywords： Deep learning     Corneal disease     Sequential features     Machine learning     Long short-term memory    

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Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation None

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1352-1361 doi: 10.1631/FITEE.1601512

Abstract: from both the time and frequency domains, by preserving the diffusion distances within the intrinsic featurecoupling the features to a discriminant kernel to refine the information from the high-dimensional featureThe proposed DDMA method consists of three main steps: (1) signal processing and feature extraction;(2) intrinsic dimensionality estimation; (3) feature fusion implementation through feature space mapping

Keywords： Tool condition monitoring     Manifold learning     Dimensionality reduction     Diffusion mapping analysis     Intrinsicfeature extraction    

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Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0737-8

Abstract: This study proposes a method to determine the build orientation of multi-feature mechanical parts (MFMPsThe weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchyThe measured average sampling surface roughness of the most crucial feature of the bracket in the original

Keywords： selective laser melting (SLM)     build orientation determination     multi-feature mechanical part (MFMP)    

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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

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

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

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

A software defect prediction method with metric compensation based on feature selection and transferlearning

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Journal Article

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

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

Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis

Yesheng Xu, Ming Kong, Wenjia Xie, Runping Duan, Zhengqing Fang, Yuxiao Lin, Qiang Zhu, Siliang Tang, Fei Wu, Yu-Feng Yao

Journal Article

Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Journal Article

Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

Journal Article