Results 31 to 40 of about 10,081 (210)

A Comparative Study of Different Multi-Fidelity Kriging Models for Reliability Analysis

Applied Sciences
The integration of active learning methods and multi-fidelity (MF) Kriging models has been demonstrated to effectively reduce the number of high-fidelity (HF) samples required for reliability analysis.
Shichao Feng, Yitao Wang, Xin Hu, Lingliang Feng, Yanqing Li, Zhengquan Wan   +5 more
doaj   +1 more source

Optimum layout of vortex generators for upper surface of wing using efficient global optimization

Nihon Kikai Gakkai ronbunshu, 2015
The “buffet” which is the shock wave vibration on the wing at transonic speed has influence to the aerodynamic performance of the civil transport. It is significant because the wing cannot maintain its aerodynamic performance and it is also led to the ...
Norikazu KOZAKAI, Hiroyuki KATO, Kazuyuki NAKAKITA, Masahiro KANAZAKI   +3 more
doaj   +1 more source

Optimization of aero-engine combustion chambers with the assistance of Hierarchical-Kriging surrogate model based on POD downscaling method

Advances in Aerodynamics, 2023
In view of the long calculation cycle, high processing test and cost of the traditional aero-engine combustion chamber design process, which restricts the engine optimization design cycle, this paper innovatively proposes a surrogate model for the ...
Shuhong Tong, Yue Ma, Mingming Guo, Ye Tian, Wenyan Song, Heng Wang, Jialing Le, Hua Zhang   +7 more
doaj   +1 more source

Implementation of four kriging models for depth inpainting

ICT Express, 2020
Image inpainting is a process of reconstructing the missing pixels by inferencing information from the known part of an image. This paper aims to increase the precision of depth inpainting by proposing four models based on kriging techniques. Our four kriging models are based on two different semivariance models (exponential model and spherical model ...
Yossawee Keaomanee, Apichat Heednacram, Prakarnkiat Youngkong   +2 more
openaire   +3 more sources

Theta-regularized Kriging: Modeling and algorithms

Applied Mathematical Modelling
To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective.
Xuelin Xie, Xiliang Lu
openaire   +2 more sources

Visual Comparison of Moving-Window Kriging Models [PDF]

Cartographica, 2011
Kriging is a spatial prediction method that is core to the geo-statistical paradigm. Commonly applied in the environmental sciences, it enables a prediction at an unsampled location coupled with a measure of confidence in its accuracy. Many variations of kriging exist, some of them complex, especially those that allow many parameters to vary spatially.
Urska Demsar, Paul Harris 0002
openaire   +2 more sources

Optimization of MOSFET Copper Clip to Enhance Thermal Management Using Kriging Surrogate Model and Genetic Algorithm

Mathematics
Metal–oxide–semiconductor field-effect transistors (MOSFETs) are critical in power electronic modules due to their high-power density and rapid switching capabilities.
Yubin Cheon, Jaehyun Jung, Daeyeon Ki, Salman Khalid, Heung Soo Kim   +4 more
doaj   +1 more source

A Kriging Surrogate Model for the Interference Reduction in the Settlement Surveillance Sensors of Steel Transmission Towers

Applied Sciences, 2019
The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly
Jiajia Shi, Liu Chu, Eduardo Souza de Cursi   +2 more
doaj   +1 more source

Improved Metaheuristic Algorithm Based Finite Element Model Updating of a Hybrid Girder Cable-Stayed Railway Bridge

Buildings, 2022
This study proposes a generally applicable improvement strategy for metaheuristic algorithms, improving the algorithm’s accuracy and local convergence in finite element (FE) model updating.
Shiqiang Qin, Yonggang Yuan, Yaowei Gan, Qiuping Wang   +3 more
doaj   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source