Xiaodong JIA

Assistant Professor - Research

University of Cincinnati

United States

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Dr. Xiaodong Jia is an Assistant Professor in the Research department at the University of Cincinnati, USA. His research interests encompass semiconductor virtual metrology, intracranial pressure analysis, and advanced methodologies for sensor applications. Recent publications highlight his work in physics-informed modeling, early detection systems for mechanical components, and intelligent maintenance solutions for electric vehicle battery systems.

Articles (18)

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2025

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2020

Collaborators (4)

Jianshe Feng

University of Cincinnati

UNITED STATES

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Jay Lee

Professor

University of Maryland at College Park

UNITED STATES

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Armando Walter Colombo

GERMANY

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PRADEEP KUNDU

Assistant Professor

KU Leuven

BELGIUM

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Xiaodong JIA

Research Interests

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Articles (18)

A Comparative Study of Semiconductor Virtual Metrology Methods and Novel Algorithmic Framework for Dynamic Sampling

A Novel Methodology for Intracranial Pressure Subpeak Identification Enabling Morphological Feature Analysis

A Novel Methodology for Incipient Ball Screw Backlash Measurement Using Capacitive Sensor

Effective prediction of drug transport in a partially liquefied vitreous humor: Physics-informed neural network modeling for irregular liquefaction geometry

Robust feature design for early detection of ball screw preload loss

Detection of inception of preload loss and remaining life prediction for ball screw considering change in dynamics due to worktable position

Alignment of the IEEE Industrial Agents Recommended Practice Standard With the Reference Architectures RAMI4.0, IIRA, and SGAM

Intelligent Maintenance of Electric Vehicle Battery Charging Systems and Networks

Cross-Chamber Data Transferability Evaluation for Fault Detection and Classification in Semiconductor Manufacturing

A Novel Methodology for Lens Matching in Compact Lens Module Assembly

Stream-of-Quality methodology for industrial Internet-based manufacturing system

Evaluating Feature Selection and Anomaly Detection Methods of Hard Drive Failure Prediction

A Markov model for short term wind speed prediction by integrating the wind acceleration information

Intelligent Diagnostics for Ball Screw Fault Through Indirect Sensing Using Deep Domain Adaptation

Gaussian Process Regression for numerical wind speed prediction enhancement

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

A novel scalable method for machine degradation assessment using deep convolutional neural network

A similarity based methodology for machine prognostics by using kernel two sample test

Collaborators (4)

Jianshe Feng

Jay Lee

Armando Walter Colombo

PRADEEP KUNDU

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