Zhenyu Kong

Professor

Virginia Tech

United States

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Professor Zhenyu Kong is a faculty member at Virginia Tech in the United States. His research areas include machine learning applications in fault diagnosis, anomaly detection, and real-time process monitoring. He has contributed to advancements in fields such as biosensors, 3D printing, and COVID-19 diagnostics through various innovative methodologies.

Articles (10)

Year:

2024

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2023

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2023

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2023

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2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Collaborators (5)

Rongxuan Wang

Assistant Professor

Auburn University

UNITED STATES

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Maren Roman

Associate Professor

Virginia Tech

UNITED STATES

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Vikram Deshpande

Professor

University of Cambridge

UNITED KINGDOM

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Zhenpeng Xu

University of California, Berkeley

UNITED STATES

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Bo Shen

Assistant Professor

New Jersey Institute of Technology

UNITED STATES

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Zhenyu Kong

Research Interests

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About

Articles (10)

A novel sparse Bayesian learning and its application to fault diagnosis for multistation assembly systems

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning

Real-time process monitoring and closed-loop control on laser power via a customized laser powder bed fusion platform

Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning

Active defect discovery: A human-in-the-loop learning method

Anomaly detection in additive manufacturing processes using supervised classification with imbalanced sensor data based on generative adversarial network

Process parameter optimization for reproducible fabrication of layer porosity quality of 3D-printed tissue scaffold

A mask-guided attention deep learning model for COVID-19 diagnosis based on an integrated CT scan images database

Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning

Collaborators (5)

Rongxuan Wang

Maren Roman

Vikram Deshpande

Zhenpeng Xu

Bo Shen

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