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Tingli Xie
Researcher at Georgia Institute of Technology
Publications - 16
Citations - 234
Tingli Xie is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Kriging & Robust optimization. The author has an hindex of 6, co-authored 13 publications receiving 89 citations. Previous affiliations of Tingli Xie include Huazhong University of Science and Technology.
Papers
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Deep Transfer Convolutional Neural Network and Extreme Learning Machine for lung nodule diagnosis on CT images
TL;DR: A novel diagnosis method based on Deep Transfer Convolutional Neural Network (DTCNN) and Extreme Learning Machine (ELM) is explored, which merges the synergy of two algorithms to deal with benign–malignant nodules classification.
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Intelligent Mechanical Fault Diagnosis Using Multi-Sensor Fusion and Convolution Neural Network
TL;DR: In this article, a novel intelligent diagnosis method based on multisensor fusion (MSF) and convolutional neural network (CNN) is explored and shows that the proposed method outperforms other DL-based methods in terms of accuracy.
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An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging
TL;DR: An adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model and shows that it provides a more accurate metamodel at the same simulation cost, which is very important in metAModel-based engineering design problems.
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A model validation framework based on parameter calibration under aleatory and epistemic uncertainty
TL;DR: Results show that the proposed framework can identify the most appropriate parameters to calibrate the simulation model and provide a correct judgment about the validity of the candidate model, which is useful for the validation of simulation models in practical engineering design.
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Advanced Multi-Objective Robust Optimization under Interval Uncertainty Using Kriging and Support Vector Machine
TL;DR: An advanced multi-objective robust optimization approach based on Kriging model and support vector machine (MORO-KS) is proposed in this work, which is tested on two numerical examples and the design optimization of a micro-aerial vehicle fuselage.