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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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Finite element model updating of a large structure using multi-setup stochastic subspace identification

Reza KHADEMI-ZAHEDI, Pouyan ALIMOURI

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 965-980 doi: 10.1007/s11709-019-0530-x

Abstract: The natural frequencies of the solar power plant structure are estimated by multi-setup stochastic subspace

Keywords： operational modal analysis     solar power plant structure     multi-setup stochastic subspace     bees optimization    

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An improved subspace weighting method using random matrix theory Research Articles

Yu-meng Gao, Jiang-hui Li, Ye-chao Bai, Qiong Wang, Xing-gan Zhang,ychbai@nju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 9,   Pages 1302-1307 doi: 10.1631/FITEE.1900463

Abstract: The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (

Keywords： Direction of arrival     Signal subspace     Random matrix theory    

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Improving the Performance of OFDM Channel Estimation through Subspace Projecting and Tracking

Dong Liang,Cao Xiuying,Bi Guangguo

Strategic Study of CAE 2006, Volume 8, Issue 11,   Pages 86-93

Abstract: observation of the true channel frequency response, so the noise component can be compressed through subspaceIn this paper, the substance of the improvement of LS channel estimation is analyzed when subspace projectingis enforced, and the general framework to perform subspace projecting in the context of OFDM channelWhen signal subspace varies with time, subspace tracking should be performed to maintain a good estimationof signal subspace.

Keywords： OFDM     subspace     projecting     tracking     channel estimation    

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Subspace-based identification of discrete time-delay system Article

Qiang LIU,Jia-chen MA

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 6,   Pages 566-575 doi: 10.1631/FITEE.1500358

Abstract: Then a conventional subspace identification method is used to estimate augmented system matrices and

Keywords： Identification problems     Time-delay systems     Subspace identification method     Alternate convex search    

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A two-stage parametric subspace model for efficient contrast-preserving decolorization Article

Hong-yang LU, Qie-gen LIU, Yu-hao WANG, Xiao-hua DENG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1874-1882 doi: 10.1631/FITEE.1600017

Abstract: We show that the first subspace in the two-order model is the most important and the second one can bethe first stage of our model, the gradient correlation similarity (Gcs) measure is used on the first subspace, Gcs is applied again to select the optimal result from the immediate grayed image plus the second subspace-induced

Keywords： Color-to-gray conversion     Subspace modeling     Two-order polynomial model     Gradient correlation similarity    

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

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Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

Xin Peng, Yang Tang, Wenli Du, Feng Qian

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 429-439 doi: 10.1007/s11705-017-1675-6

Abstract: In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving (MSAGL) projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methods.

Keywords： non-Gaussian processes     subspace projection     independent component analysis     locality preserving projection    

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Machine learning for fault diagnosis of high-speed train traction systems: A review

Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2

Abstract: In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstratedMachine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensiveThis paper primarily aims to review the research and application of machine learning in the field ofThen, the research and application of machine learning in traction system fault diagnosis are comprehensivelydiagnosis under actual operating conditions are revealed, and the future research trends of machine learning

Keywords： high-speed train     traction systems     machine learning     fault diagnosis    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predictdecision support for moving trajectory control and serve as a foundation for the application of deep learning

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Frontiers of Medicine 2023, Volume 17, Issue 4,   Pages 768-780 doi: 10.1007/s11684-023-0982-1

Abstract: illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learningMachine learning modeling based on personal whole-exome data identified 46 genes with mutation burden

Keywords： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

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Title Author Date Type Operation

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

Finite element model updating of a large structure using multi-setup stochastic subspace identification

Reza KHADEMI-ZAHEDI, Pouyan ALIMOURI

Journal Article

An improved subspace weighting method using random matrix theory

Yu-meng Gao, Jiang-hui Li, Ye-chao Bai, Qiong Wang, Xing-gan Zhang,ychbai@nju.edu.cn

Journal Article

Improving the Performance of OFDM Channel Estimation through Subspace Projecting and Tracking

Dong Liang,Cao Xiuying,Bi Guangguo

Journal Article

Subspace-based identification of discrete time-delay system

Qiang LIU,Jia-chen MA

Journal Article

A two-stage parametric subspace model for efficient contrast-preserving decolorization

Hong-yang LU, Qie-gen LIU, Yu-hao WANG, Xiao-hua DENG

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

Xin Peng, Yang Tang, Wenli Du, Feng Qian

Journal Article

Machine learning for fault diagnosis of high-speed train traction systems: A review

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Journal Article