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An Estimate Method of Parametric in Reliability Engineering

Han Ming

Strategic Study of CAE 2003, Volume 5, Issue 3,   Pages 51-56

Abstract:

In this paper, the Bayesian method, an estimate method for parameter in reliability engineering isThe author gives definition of the new Bayesian estimate for failure probability and failure rate, andshows the estimate of the failure probability and the failure rate by new Bayesian method.Finally, calculations are performed regarding to practical problems, which show that the new Bayesian

Keywords： reliability engineering     parameter estimate     new Bayesian estimate     failure probability    

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Anovel approach of noise statistics estimate using H∞ filter in target tracking

Xie WANG,Mei-qin LIU,Zhen FAN,Sen-lin ZHANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 5,   Pages 449-457 doi: 10.1631/FITEE.1500262

Abstract: Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox. First, we apply the H∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost, which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

Keywords： Noise estimate     H∞     filter     Target tracking    

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Development of an analytical model to estimate the churning losses in high-speed axial piston pumps

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-021-0671-1

Abstract: design stage of high-speed axial piston pumps, but accurate analytical models are not available to estimate

Keywords： axial piston pump     rotating parts     high rotational speed     churning losses     drag torque    

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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 Bayesian

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

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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: The Lunar New Year Market is a traditional carnival event in Hong Kong held annually one week beforethe Lunar New Year.video-based observational survey to collect pedestrian flow and speed data at the Victoria Park Lunar NewYear Market on the eve of the Lunar New Year.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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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    

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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: 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    

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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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Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston

Qun CHAO, Jianfeng TAO, Junbo LEI, Xiaoliang WEI, Chengliang LIU, Yuanhang WANG, Linghui MENG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 176-185 doi: 10.1007/s11465-020-0616-0

Abstract: This paper presents a scaling law derived from an analytical cavitation model to estimate the speed limitations

Keywords： axial piston pump     cavitation     speed limitation     scaling law    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damagecomputer routine that uses the ANN as an approximation function is developed and applied to derive a new

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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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 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    

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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    

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 990-1002 doi: 10.1007/s11709-022-0864-7

Abstract: This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.

Keywords： artificial neural network     grey wolf optimize algorithm     compressive strength     self-compacting concrete    

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Data Centric Design: A New Approach to Design of Microstructural Material Systems Article

Wei Chen, Akshay Iyer, Ramin Bostanabad

Engineering 2022, Volume 10, Issue 3,   Pages 89-98 doi: 10.1016/j.eng.2021.05.022

Abstract:

Building processing, structure, and property (PSP) relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science. Recent technological advancements in data acquisition and storage, microstructure characterization and reconstruction (MCR), machine learning (ML), materials modeling and simulation, data processing, manufacturing, and experimentation have significantly advanced researchers’ abilities in building PSP relations and inverse material design. In this article, we examine these advancements from the perspective of design research. In particular, we introduce a data-centric approach whose fundamental aspects fall into three categories: design representation, design evaluation, and design synthesis. Developments in each of these aspects are guided by and benefit from domain knowledge. Hence, for each aspect, we present a wide range of computational methods whose integration realizes data-centric materials discovery and design.

Keywords： Materials informatics     Machine learning     Microstructure     Reconstruction     Bayesian optimization     Mixed-variable    

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Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German

Muhammad WASEEM, Frauke KACHHOLZ, Jens TRÄNCKNER

Frontiers of Agricultural Science and Engineering 2018, Volume 5, Issue 4,   Pages 420-431 doi: 10.15302/J-FASE-2018243

Abstract:

Various process-based models are extensively being used to analyze and forecast catchment hydrology and water quality. However, it is always important to select the appropriate hydrological and water quality modeling tools to predict and analyze the watershed and also consider their strengths and weaknesses. Different factors such as data availability, hydrological, hydraulic, and water quality processes and their desired level of complexity are crucial for selecting a plausible modeling tool. This review is focused on suitable model selection with a focus on desired hydrological, hydraulic and water quality processes (nitrogen fate and transport in surface, subsurface and groundwater bodies) by keeping in view the typical lowland catchments with intensive agricultural land use, higher groundwater tables, and decreased retention times due to the provision of artificial drainage. In this study, four different physically based, partially and fully distributed integrated water modeling tools, SWAT (soil and water assessment tool), SWIM (soil and water integrated model), HSPF (hydrological simulation program– FORTRAN) and a combination of tools from DHI (MIKE SHE coupled with MIKE 11 and ECO Lab), have been reviewed particularly for the Tollense River catchment located in North-eastern Germany. DHI combined tools and SWAT were more suitable for simulating the desired hydrological processes, but in the case of river hydraulics and water quality, the DHI family of tools has an edge due to their integrated coupling between MIKE SHE, MIKE 11 and ECO Lab. In case of SWAT, it needs to be coupled with another tool to model the hydraulics in the Tollense River as SWAT does not include backwater effects and provision of control structures. However, both SWAT and DHI tools are more data demanding in comparison to SWIM and HSPF. For studying nitrogen fate and transport in unsaturated, saturated, and river zone, HSPF was a better model to simulate the desired nitrogen transformation and transport processes. However, for nitrogen dynamics and transformations in shallow streams, ECO Lab had an edge due its flexibility for inclusion of user-desired water quality parameters and processes. In the case of SWIM, most of the input data and governing equations are similar to SWAT but it does not include water bodies (ponds and lakes), wetlands and drainage systems. In this review, only the processes that were needed to simulate the Tollense River catchment were considered, however the resulted model selection criteria can be generalized to other lowland catchments in Australia, North-western Europe and North America with similar complexity.

Keywords： diffuse pollution     ECO Lab     HSPF     lowland catchment     MIKE 11     MIKE SHE     modeling tools     SWAT     SWIM     Tollense River     water quality    

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Title Author Date Type Operation

An Estimate Method of Parametric in Reliability Engineering

Han Ming

Journal Article

Anovel approach of noise statistics estimate using H∞ filter in target tracking

Xie WANG,Mei-qin LIU,Zhen FAN,Sen-lin ZHANG

Journal Article

Development of an analytical model to estimate the churning losses in high-speed axial piston pumps

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

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

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

Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston

Qun CHAO, Jianfeng TAO, Junbo LEI, Xiaoliang WEI, Chengliang LIU, Yuanhang WANG, Linghui MENG

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

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

A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Journal Article

Data Centric Design: A New Approach to Design of Microstructural Material Systems

Wei Chen, Akshay Iyer, Ramin Bostanabad

Journal Article

Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German

Muhammad WASEEM, Frauke KACHHOLZ, Jens TRÄNCKNER

Journal Article