Resource Type

Journal Article 38

Year

2023 5

2022 7

2021 3

2020 2

2019 2

2018 1

2017 7

2015 2

2014 2

2013 1

2009 1

2008 1

2007 2

2006 1

2003 1

open ︾

Keywords

Bayesian inference 4

Bayesian belief network 3

Bayesian optimization 3

Machine learning 2

cone penetration test 2

In situ database 1

AI for science 1

ANN 1

Active learning 1

Active machine learning 1

Approximate Bayesian computation 1

Artificial intelligence 1

Autonomous driving 1

Bayesian 1

Bayesian analysis 1

Bayesian bootstrap 1

Bayesian decision 1

Bayesian evidential learning 1

Bayesian filtering 1

open ︾

Search scope:

排序： Display mode:

Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity Research Article

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 530-541 doi: 10.1631/FITEE.2000575

Abstract: To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact method. First, a multi-area damage mining model, which can describe damages in different spatial layers, is built based on an infrared thermal image sequence. Subsequently, to identify different impact damage types from infrared image data effectively, the inference is used to solve for the parameters in the model. Then, an image-processing framework is proposed to eliminate errors and compare locations of different damage types. It includes an image segmentation algorithm with an energy function and an image fusion method with . In the experiment, the proposed method is used to evaluate the complex damages caused by the impact of the secondary debris cloud on the rear wall of the typical Whipple shield configuration. Experimental results show that it can effectively identify and evaluate the complex damage caused by , including surface and internal defects.

Keywords： Hypervelocity impact     Variational Bayesian     Sparse representation     Damage assessment    

HTML PDF Collect

Variational mode decomposition based modal parameter identification in civil engineering

Mingjie ZHANG, Fuyou XU

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 5,   Pages 1082-1094 doi: 10.1007/s11709-019-0537-3

Abstract: An out-put only modal parameter identification method based on variational mode decomposition (VMD) is

Keywords： modal parameter identification     variational mode decomposition     civil structure     nonlinear system     closely    

HTML PDF Collect

Stability analysis of slopes with planar failure using variational calculus and numerical methods

Norly BELANDRIA, Roberto ÚCAR, Francisco M. LEÓN, Ferri HASSANI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1262-1273 doi: 10.1007/s11709-020-0657-9

Abstract: This study investigates the technique of variational calculus applied to estimate the slope stability

Keywords： slopes stability     planar failure     variational calculus     numerical methods    

HTML PDF Collect

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 Bayesian

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

HTML PDF Collect

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    

HTML PDF Collect

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: values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian

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

HTML PDF Collect

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    

HTML PDF Collect

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 forTwo 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： monitoring     Local discriminant regularized soft k-means clustering     Kernel support vector data description     Bayesian    

HTML PDF Collect

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 414-428 doi: 10.1007/s11705-017-1663-x

Abstract: In view of this, a method based on Fuzzy-Bayesian network to construct multi-source knowledge solidificationcause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian

Keywords： abnormal aluminum electrolysis cell condition     Fuzzy-Bayesian network     multi-source knowledge solidification    

HTML PDF Collect

Study on variational principles of smoke particle shape and dimension with water mist appling

Fang Yudong

Strategic Study of CAE 2014, Volume 16, Issue 2,   Pages 93-100

Abstract:

The micrographs of diesel oil soot particles were achieved by transmission scanning electron microscope with and without water mist applied. It was seen by statistical analysis that the average diameter of soot particle increased and the surface density of soot particle decreased with water mist applied. The experimental results show that water mist washes out smoke mainly by dynamics effect，cloud physics effect and transportation mechanism of Steffen flow. This paper provides scientific references for water mist technology using in smoke scrubbing of computer room fires.

Keywords： water mist     smoke particle     surface density     average diameter    

HTML PDF Collect

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    

HTML PDF Collect

long-term nutrient removal in a full-scale closed-loop bioreactor for sewage treatment: an example of Bayesian

Zheng LI,Rong QI,Wei AN,Takashi MINO,Tadashi SHOJI,Willy VERSTRAETE,Jian GU,Shengtao LI,Shiwei XU,Min YANG

Frontiers of Environmental Science & Engineering 2015, Volume 9, Issue 3,   Pages 534-544 doi: 10.1007/s11783-014-0660-2

Abstract: To overcome the identifiability problem, the classic Bayesian inference approach was utilized for parameterlong-term trend of nutrient removal and short-term variations of the process performance, showing that the Bayesian

Keywords： activated sludge model     Bayesian inference     biological nutrient removal     closed-loop bioreactor     oxidation    

HTML PDF Collect

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 ‘constrainedWe 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    

HTML PDF Collect

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1688-y

Abstract:

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

Keywords： Water quality prediction     Grasshopper optimization algorithm     Variational mode decomposition     Long short-term    

HTML PDF Collect

A FEniCS implementation of the phase field method for quasi-static brittle fracture

HIRSHIKESH, Sundararajan NATARAJAN, Ratna Kumar ANNABATTULA

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 2,   Pages 380-396 doi: 10.1007/s11709-018-0471-9

Abstract: In the recent years, the phase field method for simulating fracture problems has received considerable attention. This is due to the salient features of the method: 1) it can be incorporated into any conventional finite element software; 2) has a scalar damage variable is used to represent the discontinuous surface implicitly and 3) the crack initiation and subsequent propagation and branching are treated with less complexity. Within this framework, the linear momentum equations are coupled with the diffusion type equation, which describes the evolution of the damage variable. The coupled nonlinear system of partial differential equations are solved in a ‘staggered’ approach. The present work discusses the implementation of the phase field method for brittle fracture within the open-source finite element software, FEniCS. The FEniCS provides a framework for the automated solutions of the partial differential equations. The details of the implementation which forms the core of the analysis are presented. The implementation is validated by solving a few benchmark problems and comparing the results with the open literature.

Keywords： phase field method     FEniCS     brittle fracture     crack propagation     variational theory of fracture    

HTML PDF Collect

Title Author Date Type Operation

Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Journal Article

Variational mode decomposition based modal parameter identification in civil engineering

Mingjie ZHANG, Fuyou XU

Journal Article

Stability analysis of slopes with planar failure using variational calculus and numerical methods

Norly BELANDRIA, Roberto ÚCAR, Francisco M. LEÓN, Ferri HASSANI

Journal Article

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

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

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

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

Journal Article

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

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

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

Journal Article

Study on variational principles of smoke particle shape and dimension with water mist appling

Fang Yudong

Journal Article

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang, Bin Gong, Jef Caers

Journal Article

long-term nutrient removal in a full-scale closed-loop bioreactor for sewage treatment: an example of Bayesian

Zheng LI,Rong QI,Wei AN,Takashi MINO,Tadashi SHOJI,Willy VERSTRAETE,Jian GU,Shengtao LI,Shiwei XU,Min YANG

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

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Journal Article

A FEniCS implementation of the phase field method for quasi-static brittle fracture

HIRSHIKESH, Sundararajan NATARAJAN, Ratna Kumar ANNABATTULA

Journal Article