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A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Engineering 2021, Volume 7, Issue 9,   Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028

Abstract: data and online testing data that is induced by changeable operation environments, a robust transfer dictionarylearning (RTDL) algorithm is proposed in this paper for industrial process monitoring.The RTDL is a synergy of representative learning and domain adaptive transfer learning.In this way, a robust dictionary can be learned even if the characteristics of the source domain andSuch a dictionary can effectively improve the performance of process monitoring and mode classification

Keywords： Process monitoring     Multimode process     Dictionary learning     Transfer learning    

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Laplacian sparse dictionary learning for image classification based on sparse representation Article

Fang LI, Jia SHENG, San-yuan ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1795-1805 doi: 10.1631/FITEE.1600039

Abstract: proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learningAs one of the building blocks of the sparse representation method, dictionary learning plays an importantLaplacian sparse dictionary (LSD) learning.Our method is based on manifold learning and double sparsity.learn a smaller dictionary for each class.

Keywords： Sparse representation     Laplacian regularizer     Dictionary learning     Double sparsity     Manifold    

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Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based

Min YUAN,Bing-xin YANG,Yi-de MA,Jiu-wen ZHANG,Fu-xiang LU,Tong-feng ZHANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1069-1087 doi: 10.1631/FITEE.1400423

Abstract: Recently, dictionary learning (DL) based methods have been introduced to compressed sensing magneticHowever, single-scale trained dictionary directly from image patches is incapable of representing imageEach sub-dictionary of different sub-bands is trained independently to form the multi-scale dictionaries

Keywords： sensing (CS)     Magnetic resonance imaging (MRI)     Uniform discrete curvelet transform (UDCT)     Multi-scale dictionarylearning (MSDL)     Patch-based constraint splitting augmented Lagrangian shrinkage algorithm (PB C-SALSA    

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scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionarylearning for post-processing Article

Yong DING, Tuo HU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 2001-2008 doi: 10.1631/FITEE.1700287

Abstract: we propose a promising reconstruction scheme which combines total-variation minimization and sparse dictionarylearning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration

Keywords： Low-dose computed tomography (CT)     CT imaging     Total variation     Sparse dictionary learning    

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Entity and relation extraction with rule-guided dictionary as domain knowledge

Frontiers of Engineering Management   Pages 610-622 doi: 10.1007/s42524-022-0226-0

Abstract: Entity and relation extraction is an indispensable part of domain knowledge graph construction, which can serve relevant knowledge needs in a specific domain, such as providing support for product research, sales, risk control, and domain hotspot analysis. The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets. However, the performance of these methods degrades when they face domain-specific datasets. Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains. Relation extraction is based on the hypothesis that the relations hidden in sentences are unified, thereby neglecting that relations may be diverse in different entity tuples. To address the problems above, this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’ dependence. Second, domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction. Finally, experiments were designed to verify the effectiveness of our proposed methods. Experimental results on two domains, including laser industry and unmanned ship, showed the superiority of our methods. The F1 value on laser industry entity, unmanned ship entity, laser industry relation, and unmanned ship relation datasets is improved by +1%, +6%, +2%, and +1%, respectively. In addition, the extraction accuracy of entity relation triplet reaches 83% and 76% on laser industry entity pair and unmanned ship entity pair datasets, respectively.

Keywords： entity extraction     relation extraction     prior knowledge     domain rule    

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Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 471-480 doi: 10.1631/FITEE.1620342

Abstract: In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learningA kernel-clustering algorithm based on dictionary learning is developed to code the voxels.

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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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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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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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Title Author Date Type Operation

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Journal Article

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

Journal Article

Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based

Min YUAN,Bing-xin YANG,Yi-de MA,Jiu-wen ZHANG,Fu-xiang LU,Tong-feng ZHANG

Journal Article

scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionarylearning for post-processing

Yong DING, Tuo HU

Journal Article

Entity and relation extraction with rule-guided dictionary as domain knowledge

Journal Article

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

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

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

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article