Resource Type

Journal Article 234

Conference Videos 5

Year

2023 25

2022 25

2021 23

2020 12

2019 9

2018 9

2017 17

2016 15

2015 8

2014 5

2013 13

2012 6

2011 5

2010 7

2009 16

2008 7

2007 11

2006 4

2005 3

2004 2

open ︾

Keywords

prediction 20

Machine learning 7

earthquake prediction 4

life prediction 4

machine learning 4

ANOVA 3

Artificial intelligence 3

Deep learning 3

artificial neural network 3

reliability prediction 3

ANFIS 2

ANN 2

Additive manufacturing 2

Artificial neural network 2

Controller area network 2

Electric vehicles 2

Errors 2

Feature selection 2

Wenchuan Earthquake 2

open ︾

Search scope:

排序： Display mode:

Understanding the demand predictability of bike share systems: A station-level analysis

Frontiers of Engineering Management   Pages 551-565 doi: 10.1007/s42524-023-0279-8

Abstract: While researchers have mainly focused on improving prediction accuracy and analysing demand-influencingAdditionally, to verify that these predictability measures could represent the performance of predictionmodels, we implemented two commonly used demand prediction models to compare the empirical predictionFindings from this study provide more fundamental understanding of BSS demand prediction, which can helpdecision makers and system operators anticipate diverse station-level prediction errors from their prediction

Keywords： bike share systems     demand prediction     prediction errors     machine learning     entropy    

HTML PDF Collect

Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure

Zhe SUN, Cheng ZHANG, Pingbo TANG

Frontiers of Engineering Management 2021, Volume 8, Issue 1,   Pages 109-121 doi: 10.1007/s42524-019-0084-6

Abstract: Such delay prediction capability paves the path toward predictive and resilience outage control of NPPs

Keywords： NPP outage     human error     team cognition     handoff modeling    

HTML PDF Collect

The dynamic correction of collimation errors of CT slicing pictures

LIU Ya-xiong, Sekou Sing-are, LI Di-chen, LU Bing-heng

Frontiers of Mechanical Engineering 2006, Volume 1, Issue 2,   Pages 168-172 doi: 10.1007/s11465-006-0016-0

Abstract: To eliminate the motion artifacts of CT images caused by patient motions and other related errors, twoimages, which facilitates in eliminating or decreasing the motion artifacts and correcting other static errorsand image processing errors.
HTML PDF Collect

Rotation errors in numerical manifold method and a correction based on large deformation theory

Ning ZHANG, Xu LI, Qinghui JIANG, Xingchao LIN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 5,   Pages 1036-1053 doi: 10.1007/s11709-019-0535-5

Abstract: Numerical manifold method (NMM) is an effective method for simulating block system, however, significant errorsThree kinds of errors, as volume expansion, stress vibration, and attenuation of angular velocity, wereThe first two kind errors are owing to the small deformation assumption and the last one is due to theblock rotation, beam bending, and rock falling problems and the results prove that all three kinds of errors

Keywords： numerical manifold method     rotation     large deformation     Green strain     open-close iteration    

HTML PDF Collect

Planet position errors in planetary transmission: Effect on load sharing and transmission error

Miguel IGLESIAS, Alfonso FERNáNDEZ, Ana DE-JUAN, Ramón SANCIBRIáN, Pablo GARCíA

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 1,   Pages 80-87 doi: 10.1007/s11465-013-0362-7

Abstract: transmissions developed by the authors is used to study the influence of carrier planet pin hole position errorsThe influence of carrier planet pin hole position errors on the planet load sharing is studied, and severalTangential and radial planet pin hole position errors are considered independently, and the effect of

Keywords： gear     planetary     epicyclic     transmission     load sharing     transmission error    

HTML PDF Collect

A multi-functional dynamic state estimator for error validation: measurement and parameter errors and Article

Mehdi AHMADI JIRDEHI,Reza HEMMATI,Vahid ABBASI,Hedayat SABOORI

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 11,   Pages 1218-1227 doi: 10.1631/FITEE.1500301

Abstract: We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branchparameter errors of power systems.The method uses three normalized vectors to process errors at each sampling time: normalized measurement

Keywords： Dynamic state estimation     Kalman filter     Measurement errors     Branch parameter errors     Sudden load changes    

HTML PDF Collect

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    

HTML PDF Collect

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: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A 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. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

HTML PDF Collect

Position-varying surface roughness prediction method considering compensated acceleration in milling

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

HTML PDF Collect

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-023-0961-2

Abstract: Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

Keywords： pile responses     excavation     prediction     deflection     bending moments    

HTML PDF Collect

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Frontiers in Energy 2016, Volume 10, Issue 4,   Pages 479-488 doi: 10.1007/s11708-016-0425-7

Abstract: In this paper a novel method for reliability prediction and validation of nuclear power units in serviceThe accuracy of the reliability prediction can be evaluated according to the comparison between the predictedFurthermore, the reliability prediction method is validated using the nuclear power units in North AmericanIt is found that the relative errors of the predicted equivalent availability factors for nuclear power

Keywords： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

HTML PDF Collect

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2,   Pages 171-175 doi: 10.1007/s11465-009-0091-0

Abstract: Trend prediction technology is the key technology to achieve condition-based maintenance of mechanicalTo ensure the normal operation of units and save maintenance costs, trend prediction technology is studiedThe main methods of the technology are given, the trend prediction method based on neural network isThe industrial site verification shows that the proposed trend prediction technology can reflect the

Keywords： water injection units     condition-based maintenance     trend prediction    

HTML PDF Collect

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: Developing prediction models to support drivers in performing rectifications in advance can effectivelysubsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct predictionIn addition, the effects of the activation function and input time-step length on the prediction performance

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

HTML PDF Collect

Prediction of the shear wave velocity

Amoroso SARA

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 1,   Pages 83-92 doi: 10.1007/s11709-013-0234-6

Abstract: The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

Keywords： horizontal stress index     shear wave velocity     flat dilatometer test     cone penetration test    

HTML PDF Collect

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionto simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for predictionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

HTML PDF Collect

Title Author Date Type Operation

Understanding the demand predictability of bike share systems: A station-level analysis

Journal Article

Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure

Zhe SUN, Cheng ZHANG, Pingbo TANG

Journal Article

The dynamic correction of collimation errors of CT slicing pictures

LIU Ya-xiong, Sekou Sing-are, LI Di-chen, LU Bing-heng

Journal Article

Rotation errors in numerical manifold method and a correction based on large deformation theory

Ning ZHANG, Xu LI, Qinghui JIANG, Xingchao LIN

Journal Article

Planet position errors in planetary transmission: Effect on load sharing and transmission error

Miguel IGLESIAS, Alfonso FERNáNDEZ, Ana DE-JUAN, Ramón SANCIBRIáN, Pablo GARCíA

Journal Article

A multi-functional dynamic state estimator for error validation: measurement and parameter errors and

Mehdi AHMADI JIRDEHI,Reza HEMMATI,Vahid ABBASI,Hedayat SABOORI

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

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

Journal Article

Position-varying surface roughness prediction method considering compensated acceleration in milling

Journal Article

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Prediction of the shear wave velocity

Amoroso SARA

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article