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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learningmethod, named the Bayesian multiple kernel learning (BMKL) model.

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 8,   Pages 617-633 doi: 10.1631/FITEE.1400263

Abstract: A local discriminant regularized soft -means (LDRSKM) method with Bayesian inference is proposed forWith the data partition obtained, kernel support vector data description (KSVDD) is used to establishTwo Bayesian inference based global fault detection indicators are then developed using the local monitoringBased on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode

Keywords： Multimode process monitoring     Local discriminant regularized soft k-means clustering     Kernel supportvector data description     Bayesian inference     Tennessee Eastman process    

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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: The concurrence of multiple faults in gearbox components is a common phenomenon due to fault inductionHowever, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriateThus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) techniqueIts main advantage is its capability to learn multiple fault subspaces directly from the observationThe superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated

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

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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 490-505 doi: 10.1007/s11709-020-0669-5

Abstract: This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-inducedliquefaction potential of soil based on the cone penetration test field case history records using the Bayesianbelief network (BBN) learning software Netica.climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning

Keywords： seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structurallearning    

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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers None

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 639-650 doi: 10.1631/FITEE.1700007

Abstract: We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrainedThe learning problem is to maximize the margin under the constraint that each training pattern is classifiedWe applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models,

Keywords： Discriminative learning     Statistical modeling     Bayesian pattern classifiers     Gaussian mixture models     UCI    

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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: We propose a fully automatic brain tumor segmentation method based on kernel sparse coding.It is validated with 3D multiple-modality magnetic resonance imaging (MRI).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.In the end, morphological filtering is used to fill in the area among multiple connected components to

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs Article

Junling Fang, Bin Gong, Jef Caers

Engineering 2022, Volume 18, Issue 11,   Pages 116-128 doi: 10.1016/j.eng.2022.04.015

Abstract: Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulationThe application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic casesIn field applications, however, one of the main problems is that the Bayesian prior is falsified, becauseWe then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate

Keywords： Bayesian evidential learning     Falsification     Fractured reservoir     Random forest     Approximate Bayesian computation    

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IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 428-436 doi: 10.15302/J-FEM-2017048

Abstract: sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning

Keywords： railway     intelligent asset management     collaborative learning     big data     hybrid cloud     Bayesian    

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Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 80-98 doi: 10.1007/s11709-021-0682-3

Abstract: a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesianpredictive performance results of the proposed BBN models are compared with those of frequently used multiple

Keywords： Bayesian belief network     seismically induced soil liquefaction     interpretive structural modeling     lateral    

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Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember

Jing LI,Xiao-run LI,Li-jiao WANG,Liao-ying ZHAO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 3,   Pages 250-257 doi: 10.1631/FITEE.1500244

Abstract: The kernel new simplex growing algorithm (KNSGA), recently developed as a nonlinear alternative to the

Keywords： Modified Cholesky factorization     Spatial pixel purity index (SPPI)     New simplex growing algorithm (NSGA)     Kernel    

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A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 483-489 doi: 10.15302/J-FEM-2017023

Abstract: Bayesian inference was employed to calibrate the model by using prior data obtained from a previous controlled

Keywords： pedestrian flow model     bi-directional interactions     empirical studies     Bayesian inference    

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Development of soft kernel durum wheat

Craig F. MORRIS

Frontiers of Agricultural Science and Engineering 2019, Volume 6, Issue 3,   Pages 273-278 doi: 10.15302/J-FASE-2019259

Abstract:

Kernel texture (grain hardness) is a fundamental and determining factor related to wheat ( spp.) millingThere are three kernel texture classes in wheat: soft and hard hexaploid ( ), and very hard durum ( subspPhenotypically, the easiest means of quantifying kernel texture is with the Single Kernel CharacterizationSoft kernel durum wheat was created via homeologous recombination using the mutation, which facilitatedExpression of the puroindoline genes in durum grain resulted in kernel texture and flour milling characteristics

Keywords： soft durum wheat     grain hardness     puroindolines     milling     baking     pasta     noodles    

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Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! Perspective

Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem

Engineering 2023, Volume 27, Issue 8,   Pages 23-30 doi: 10.1016/j.eng.2023.02.019

Abstract:

By combining machine learning with the design of experiments, thereby achieving so-called active machinelearning, more efficient and cheaper research can be conducted.While active machine learning algorithms are maturing, their applications are falling behind.In this article, three types of challenges presented by active machine learning—namely, convincingA bright future lies ahead for active machine learning in chemical engineering, thanks to increasing

Keywords： Active machine learning     Active learning     Bayesian optimization     Chemical engineering     Design of experiments    

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Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1685-1

Abstract:

● A hydrodynamic-Bayesian inference model was developed for water

Keywords： Identification of pollution sources     Water quality restoration     Bayesian inference     Hydrodynamic model    

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Bayesian Optimization for Field-Scale Geological Carbon Storage

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

Engineering 2022, Volume 18, Issue 11,   Pages 96-104 doi: 10.1016/j.eng.2022.06.011

Abstract:

We present a framework that couples a high-fidelity compositional reservoir simulator with BayesianFurthermore, IPARS is coupled to the International Business Machines (IBM) Corporation BayesianBO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm

Keywords： Compositional flow     Bayesian optimization     Geological carbon storage     CCUS     Machine learning     AI for    

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Title Author Date Type Operation

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

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

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Journal Article

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

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

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang, Bin Gong, Jef Caers

Journal Article

IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

Journal Article

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Journal Article

Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember

Jing LI,Xiao-run LI,Li-jiao WANG,Liao-ying ZHAO

Journal Article

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Journal Article

Development of soft kernel durum wheat

Craig F. MORRIS

Journal Article

Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!

Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem

Journal Article

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

Journal Article

Bayesian Optimization for Field-Scale Geological Carbon Storage

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

Journal Article