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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0736-9

Abstract: First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition resultsSecond, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault

Keywords： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy doi: 10.1007/s11708-023-0891-7

Abstract: AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural networkReports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has theby, first, utilizing more field data to play a more practical role in health feature screening and model

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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State of the Art of Compartment Fire Modeling

Zheng Xin,Yuan Hongyong

Strategic Study of CAE 2004, Volume 6, Issue 3,   Pages 68-74

Abstract: The relevant underlying physical assumptions are presented first and the conventional model performance

Keywords： compartment     field model     zone model     network model     FZN (field     zone and network) model     empirical model    

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A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1066-1082 doi: 10.1007/s11709-020-0651-2

Abstract: A constrained back propagation neural network (C-BPNN) model for standard penetration test based soilThe C-BPNN model design procedure for liquefaction assessment is established by considering appropriatefines content adjustment are shown to be able to improve the prediction success rate of the neural networkmodel, and are thus adopted as constraints for the C-BPNN model.The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability

Keywords： soil liquefaction assessment     case history dataset     constrained neural network model     existing knowledge    

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A novel flow-resistor network model for characterizing enhanced geothermal system heat reservoir

Jian GUO, Wenjiong CAO, Yiwei WANG, Fangming JIANG

Frontiers in Energy 2019, Volume 13, Issue 1,   Pages 99-106 doi: 10.1007/s11708-018-0555-1

Abstract: This paper presents the development of a novel flow-resistor network model to describe the hydraulicfractures in the reservoir are simplified by using flow resistors and the typically complicated fracture networkof the heat reservoir is converted into a flow-resistor network with a reasonably simple pattern.

Keywords： enhanced geothermal systems     flow-resistor network model     fracture characteristics     heat reservoir    

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: This article intends to model the multiscale constitution using feedforward neural network (FNN) andrecurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3,   Pages 674-685 doi: 10.1007/s11709-018-0505-3

Abstract: M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches wereM5 model tree provides simple linear relation which can be used for the prediction of oblique load forModel developed using RF regression approach with smooth pile group data was found to be in good agreement

Keywords： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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Multi-class dynamic network traffic flow propagation model with physical queues

Yanfeng LI, Jun LI

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 399-407 doi: 10.15302/J-FEM-2017041

Abstract: This paper proposes an improved multi-class dynamic network traffic flow propagation model with a considerationTo characterize this phenomenon by numerical methods, the improved model is directly formulated in discreteNumerical examples are developed to illustrate the unrealistic flows of the existing model and the performanceof the improved model.

Keywords： first-in-first-out (FIFO)     multi-class traffic     physical queues     traffic flow modeling    

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Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1213-1232 doi: 10.1007/s11709-022-0880-7

Abstract: The present study describes a reliability analysis of the strength model for predicting concrete columnsinfluence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Networkmodel (artificial neural network (ANN) with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters, i.e.The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive

Keywords： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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Sustainability performance analysis of environment innovation systems using a two-stage network DEA model

Frontiers of Engineering Management   Pages 425-438 doi: 10.1007/s42524-022-0205-5

Abstract: The term environmental innovation system refers to an innovation network composed of enterprises, universitiesA two-stage data envelopment analysis (DEA) model is developed in this study to analyze the efficiency

Keywords： data envelopment analysis     environmental efficiency     environmental innovation system     shared resources     two-stage structure    

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Hydraulic model for multi-sources reclaimed water pipe network based on EPANET and its applications in

JIA Haifeng, WEI Wei, XIN Kunlun

Frontiers of Environmental Science & Engineering 2008, Volume 2, Issue 1,   Pages 57-62 doi: 10.1007/s11783-008-0013-0

Abstract: In order to support the plan, the integrated hydraulic model of planning pipe network was developed basedThe complicated pipe network was divided into four weak conjunction subzones according to the distributionIt could provide a better solution for the problem of overhigh pressure in several regions of the networkThrough the scenarios analysis in different subzones, some of the initial diameter of pipes in the networkAt last the pipe network planning scheme of reclaimed water was proposed.

Keywords： diameter     Beijing municipal     reclaimed     planning     elevation    

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An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1299-1315 doi: 10.1007/s11709-020-0712-6

Abstract: This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC.This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23In the model, three methods to process output features (no-processed, mid-processed, and processed) areMoreover, a traditional equation-based model is also established and compared with the ANN model.The results show that the ANN model has a better prediction than the equation-based model in terms of

Keywords： artificial neural network     hybrid fiber reinforced concrete     tensile behavior     sensitivity analysis     stress-strain    

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A modified zone model for estimating equivalent room thermal capacity

Hua CHEN, Xiaolin WANG

Frontiers in Energy 2013, Volume 7, Issue 3,   Pages 351-357 doi: 10.1007/s11708-013-0254-x

Abstract: The zone model has been widely applied in control analysis of heating, ventilation and air conditioningThis paper proposed a modified zone model which is much simpler in the HVAC system simulation and hasthe similar accuracy to the complicated simulation model.The thermal admittance for the building enclosure was developed based on the building thermal networkThe efficacy of the proposed model was demonstrated by comparing it with the complicated model — heat

Keywords： room model     thermal network analysis     transfer function     heating     ventilation and air conditioning (HVAC)    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fractureA total of 147 raw input features are considered in our model.The presented model is compared with several benchmarks based on various metrics to prove its effectiveness

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Elevator Configuration Based on the Markov Network Queuing Model

Zong Qun,Cheng Yiju,Song Junyuan

Strategic Study of CAE 2003, Volume 5, Issue 10,   Pages 69-72

Abstract:

The article applies the Markov network theory to build the elevator traffic model.Based on the model elevator configuration parameters of the serving stations are calculated, comparing

Keywords： Markov network queuing theory     elevator traffic model     optimizing elevator configuration    

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Title Author Date Type Operation

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article

State of the Art of Compartment Fire Modeling

Zheng Xin,Yuan Hongyong

Journal Article

A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

Journal Article

A novel flow-resistor network model for characterizing enhanced geothermal system heat reservoir

Jian GUO, Wenjiong CAO, Yiwei WANG, Fangming JIANG

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Journal Article

Multi-class dynamic network traffic flow propagation model with physical queues

Yanfeng LI, Jun LI

Journal Article

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Journal Article

Sustainability performance analysis of environment innovation systems using a two-stage network DEA model

Journal Article

Hydraulic model for multi-sources reclaimed water pipe network based on EPANET and its applications in

JIA Haifeng, WEI Wei, XIN Kunlun

Journal Article

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

Journal Article

A modified zone model for estimating equivalent room thermal capacity

Hua CHEN, Xiaolin WANG

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Elevator Configuration Based on the Markov Network Queuing Model

Zong Qun,Cheng Yiju,Song Junyuan

Journal Article