Master Student
About me
My research studies structural health monitoring (SHM) for civil infrastructures. I am interested in developing methods that learn structural behavior/performance as inverse problems. Also, I pay close attention to improve monitoring systems’ reliability.
I graduated from HNUST, I completed my bachelor’s degree there as well. Now, I am working at Kunming University of Science and Technology, where is in my hometown!
I want to be a passionate Master student.
Papers
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An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference (Appl Sci-Basel) Link
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Data Anomaly Detection for Structural Health Monitoring by Multi-View Representation Based on Local Binary Patterns (Measurement) Preprint Link
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A data-driven multi-scale constitutive model of concrete material based on polynomial chaos expansion and stochastic damage model (Constr Build Mater) Link
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Machine-learning-based methods for output-only structural modal identification (Struct Control Hlth) Preprint Link
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Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring (Struct Health Monit) Link
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Deep reinforcement learning-based sampling method for structural reliability assessment (Reliab Eng Syst Safe) Link
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Clarifying and quantifying the geometric correlation for probability distributions of inter-sensor monitoring data: A functional data analytic methodology (Mech Syst Signal Pr) Link
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The State of the Art of Data Science and Engineering in Structural Health Monitoring (Engineering) Link
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Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach (Struct Health Monit) Preprint Link
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Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring (Struct Control Hlth) Link
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Computer vision and deep learning–based data anomaly detection method for structural health monitoring (Struct Health Monit) Link
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