Resource Type

Journal Article 323

Year

2023 29

2022 28

2021 23

2020 17

2019 11

2018 13

2017 19

2016 14

2015 14

2014 15

2013 14

2012 10

2011 8

2010 12

2009 23

2008 14

2007 15

2006 6

2005 7

2004 5

open ︾

Keywords

prediction 20

Machine learning 7

earthquake prediction 4

life prediction 4

machine learning 4

parameter optimization 4

ANOVA 3

Artificial intelligence 3

Deep learning 3

artificial neural network 3

concrete 3

reliability prediction 3

ANFIS 2

ANN 2

Artificial neural network 2

Electric vehicles 2

Gaussian process regression (GPR) 2

Parameter estimation 2

Wenchuan Earthquake 2

open ︾

Search scope:

排序： Display mode:

Parameter prediction in laser bending of aluminum alloy sheet

WANG Xuyue, XU Weixing, CHEN Hua, WANG Jinsong

Frontiers of Mechanical Engineering 2008, Volume 3, Issue 3,   Pages 293-298 doi: 10.1007/s11465-008-0046-x

Abstract: Experimental results indicate that the prediction allowance is controlled less than 5%–8% and can provide

Keywords： control     industry purpose     nonlinear     network     aluminum    

HTML PDF Collect

An efficient prediction framework for multi-parametric yield analysis under parameter variations Article

Xin LI,Jin SUN,Fu XIAO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1344-1359 doi: 10.1631/FITEE.1601225

Abstract: Due to continuous process scaling, process, voltage, and temperature (PVT) parameter variations havePrevious algorithms on parametric yield prediction are limited to predicting a single-parametric yieldIn this paper we suggest an efficient multi-parametric yield prediction framework, in which multipleFirst, the framework models the performance metrics in terms of PVT parameter variations by using theyield prediction problem, and to generate an accurate yield estimate.

Keywords： Yield prediction     Parameter variations     Multi-parametric yield     Performance modeling     Sparse representation    

HTML PDF Collect

Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems Research Article

Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kaien ZHANG, Liu GUO, Haiming WANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 275-288 doi: 10.1631/FITEE.2200169

Abstract: In addition, the method is introduced to support the prediction model in new propagation conditions,

Keywords： multiple-input multiple-output (MIMO) system     Channel model     Ensemble learning     Instance transfer     Parameterprediction    

HTML PDF Collect

Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0703-5

Abstract: An inverse proportional weighting method that considers the leave-one-out prediction error is presented

Keywords： laser beam welding     parameter optimization     metamodel     multi-objective    

HTML PDF Collect

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: This article examines the capability of Gaussian process regression (GPR) for prediction of effectivestress parameter ( ) of unsaturated soil.volumetric water content ( ), residual water content ( ), bubbling pressure ( ), suction ( ) and fitting parameterA sensitivity analysis has been done to determine the effect of each input parameter on .The results show that the developed GPR is reliable model for prediction of of unsaturated soil.

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network    

HTML PDF Collect

1000 MW ultra-supercritical turbine steam parameter optimization

FENG Weizhong

Frontiers in Energy 2008, Volume 2, Issue 2,   Pages 187-193 doi: 10.1007/s11708-008-0030-5

Abstract: The 2 × 1000 MW ultra-supercritical steam turbine of Shanghai Waigaoqiao Phase III project, which uses grid frequency regulation and overload control through an overload valve, is manufactured by Shanghai Turbine Company using Siemens technology. Through optimization, the steam pressure is regarded as the criterion between constant pressure and sliding pressure operation. At high circulating water temperature, the turbine overload valve is kept closed when the unit load is lower than 1000 MW while at other circulating water temperatures the turbine can run in sliding pressure operation when the unit load is higher than 1000 MW and the pressure is lower than 27 MPa This increases the unit operation efficiency. The 3D bending technology in the critical piping helps to reduce the project investment and minimize the reheat system pressure drop which improves the unit operation efficiency and safety. By choosing lower circulating water design temperature and by setting the individual Boiler Feedwater Turbine condenser to reduce the exhaust steam flow and the heat load to the main condenser, the unit average back pressure and the terminal temperature difference are minimized. Therefore, the unit heat efficiency is increased.
HTML PDF Collect

Structural parameter design method for a fast-steering mirror based on a closed-loop bandwidth

Guozhen CHEN, Pinkuan LIU, Han DING

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 1,   Pages 55-65 doi: 10.1007/s11465-019-0545-y

Abstract: This study proposes a structural parameter design approach for an FSM system based on the quantitative

Keywords： fast-steering mirror     structural parameter     PI controller     bandwidth     notch filter    

HTML PDF Collect

Energy efficient cutting parameter optimization

Xingzheng CHEN, Congbo LI, Ying TANG, Li LI, Hongcheng LI

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 221-248 doi: 10.1007/s11465-020-0627-x

Abstract: Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimizationThis paper conducts a comprehensive literature review of current studies on energy efficient cutting parameterCurrent studies on energy efficient cutting parameter optimization by using experimental design methodCombined with the current status, future research directions of energy efficient cutting parameter optimization

Keywords： energy efficiency     cutting parameter     optimization     machining process    

HTML PDF Collect

Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

Xueping PAN, Ping JU, Feng WU, Yuqing JIN

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 367-376 doi: 10.1007/s11465-017-0429-y

Abstract:

A new hierarchical parameter estimation method for doubly fed induction generator (DFIG) and drive

Keywords： wind turbine generator     DFIG     drive train system     hierarchical parameter estimation method     trajectory sensitivity    

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) isconducted to demonstrate the efficiency and highlight the superiority of the proposed method in modal parameterThe proposed method is proved to be efficient and accurate in modal parameter identification for bothlinear and nonlinear civil structures, including structures with closely spaced modes, sudden modal parameter

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

HTML PDF Collect

Shallow foundation response variability due to soil and model parameter uncertainty

Prishati RAYCHOWDHURY,Sumit JINDAL

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 3,   Pages 237-251 doi: 10.1007/s11709-014-0242-1

Abstract: Geotechnical uncertainties may play crucial role in response prediction of a structure with substantialapproach and the parameters required for the modeling are also critically important for the response predictionThe present work intends to investigate the effect of soil and model parameter uncertainty on the response

Keywords： shallow foun dation     sensitivity analysis     centrifuge data     first-order-second-moment (FOSM) method     parameter    

HTML PDF Collect

Parameter studies on impact in a lap joint

Amir M. RAHMANI,Elizabeth K. ERVIN

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 1,   Pages 64-77 doi: 10.1007/s11465-014-0322-x

Abstract: Each parameter’s effect on system response is presented including unstable regions, unique trend

Keywords： impact mechanics     contact     joint behaviour     modal analysis     parameter study    

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

Learning-based parameter prediction for quality control in three-dimensional medical image compression Research Articles

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1169-1178 doi: 10.1631/FITEE.2000234

Abstract: In , regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominantIn this paper we propose a parameter prediction scheme to achieve efficient .

Keywords： 医学图像压缩；高效视频编码（HEVC）；质量控制；基于学习方法    

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

Title Author Date Type Operation

Parameter prediction in laser bending of aluminum alloy sheet

WANG Xuyue, XU Weixing, CHEN Hua, WANG Jinsong

Journal Article

An efficient prediction framework for multi-parametric yield analysis under parameter variations

Xin LI,Jin SUN,Fu XIAO

Journal Article

Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems

Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kaien ZHANG, Liu GUO, Haiming WANG

Journal Article

Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Journal Article

1000 MW ultra-supercritical turbine steam parameter optimization

FENG Weizhong

Journal Article

Structural parameter design method for a fast-steering mirror based on a closed-loop bandwidth

Guozhen CHEN, Pinkuan LIU, Han DING

Journal Article

Energy efficient cutting parameter optimization

Xingzheng CHEN, Congbo LI, Ying TANG, Li LI, Hongcheng LI

Journal Article

Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

Xueping PAN, Ping JU, Feng WU, Yuqing JIN

Journal Article

Variational mode decomposition based modal parameter identification in civil engineering

Mingjie ZHANG, Fuyou XU

Journal Article

Shallow foundation response variability due to soil and model parameter uncertainty

Prishati RAYCHOWDHURY,Sumit JINDAL

Journal Article

Parameter studies on impact in a lap joint

Amir M. RAHMANI,Elizabeth K. ERVIN

Journal Article

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

Journal Article

Learning-based parameter prediction for quality control in three-dimensional medical image compression

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Journal Article

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

Journal Article