Uncertainty identification method using kriging surrogate model and Akaike information criterion for industrial electromagnetic device

Kim, Saekyeol; Lee, Soo-Gyung; Kim, Jimin; Lee, Tae Hee; Lim, Myung-Seop

doi:10.1049/iet-smt.2019.0349

Cited by 11 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The models were trained using eight (8) different MV (independent variables); however, the AIC was used to assess multiple feasible models and find which one suited best. It should be noted that the AIC assists in comparing multiple alternative models [48], therefore incorporating diverse independent variables to choose the best. The study evaluated many model combinations, which are reflected in the AIC chart (ref.…”

Section: Analysis Of the Aicmentioning

confidence: 99%

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Afrifa

Zhang

Xin

et al. 2023

IET Signal Processing

View full text Add to dashboard Cite

One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.

show abstract

Section: Analysis Of the Aicmentioning

confidence: 99%

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Afrifa

Zhang

Xin

et al. 2023

IET Signal Processing

View full text Add to dashboard Cite

show abstract

“…Another widely used approach, especially in the presence of the prototype electrical machine, is to use a test pod for direct measurement [22]. A typical test bench for cogging torque measurement is shown in Figure 6.…”

Section: Cogging Torque Modeling Approachesmentioning

confidence: 99%

Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems

Dini

Saponara

2022

Energies

View full text Add to dashboard Cite

This review of the state of the art aims to collect the description and main research results in the field of development and validation of control algorithms with the main purpose to solve the problem of cogging torque and main sources of electromagnetic torque ripple. In particular, we focus on electric drives for advanced and modern mechatronic applications such as industrial automation, robotics, and automotive applications, with special emphasis on work that exploits model-based design. A great added value of this paper is to explicitly show the operational steps required for the model-based design design of optimized control algorithms for electric drives where it is necessary to make up for electromagnetic torque oscillations due to the main sources of ripple, particularly cogging torque. The ultimate goal of this paper is to provide researchers approaching this particular problem with a comprehensive collection of the most effective solutions reported in the state of the art and also a summary for effectively applying the model-based design methodology.

show abstract

“…The Kriging surrogate model covers the global trend and local nonlinearity of the response, and especially has high accuracy for predicting nonlinear response. It can analyze the trend and dynamic characteristics of known information [24][25][26].…”

Section: Kriging Surrogate Modelmentioning

confidence: 99%

Multi-Objective Reliability-Based Optimization of Control Arm Using MCS and NSGA-II Coupled with Entropy Weighted GRA

et al. 2021

View full text Add to dashboard Cite

Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At the same time, the fatigue life of automobile parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with the NSGA-II algorithm coupled with entropy weighted grey relational analysis (GRA) for lightweight design of the lower control arm of automobile Macpherson suspension. The dynamic load histories of the control arm were extracted through dynamic simulations of a rigid-flexible coupling vehicle model on virtual proving ground. Then, the nominal stress method was used to predict its fatigue life. Six design variables were defined to describe the geometric dimension of the control arm, while mass and fatigue life were taken as optimization objectives. The multi-objective optimization design of the control arm was carried out based on the Kriging surrogate model and NSGA-II algorithm. Aiming at the uncertainty of design variables, the reliability constraint was added to the multi-objective optimization to improve the reliability of the fatigue life of the control arm. The optimal design of the control arm was determined from Pareto solutions by entropy weighted grey relational analysis (GRA). The optimization results show that the mass of the control arm was reduced by 4.1% and the fatigue life was increased by 215.8% while its reliability increased by 7.8%. The proposed multi-objective reliability optimization method proved to be feasible and effective for lightweight design of a suspension control arm.

show abstract

Uncertainty identification method using kriging surrogate model and Akaike information criterion for industrial electromagnetic device

Cited by 11 publications

References 34 publications

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems

Multi-Objective Reliability-Based Optimization of Control Arm Using MCS and NSGA-II Coupled with Entropy Weighted GRA

Contact Info

Product

Resources

About