Liu, Zheng

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Research Interests

• Intelligent Sensing, Measurement, and Instrumentation
• Diagnostics, Prognostics, and Health Management
• Predictive Maintenance
• Digital Twin
• Computational Intelligence and Data/Information Fusion
• Non-Destructive Testing & Evaluation
• Machine/Computer Vision
• Data Analytics and Machine Learning

Research Projects

• Thermal Image Translation for Enhanced Environmental Perception at Night: Humans have poor night vision compared to many animals, partly because human eyes lack tapetum lucidum. This biological deficiency may lead to several undesirable fatalities. Hence, context enhancement plays a critical role in many night vision applications. In the night situation, the visible camera does not function properly, but the Infrared (IR) thermal camera works well and can highlight the objects with emitted energy. Theoretically, the useful semantic information of an image to the human visual system (HVS) includes contour, texture, and color. However, the IR image only has the contour information. In this research project, we aim to develop a framework to translate the IR image at night to the color visible image with rich semantic information for enhanced environmental perception at night.
• Deep Multi-Modal Image Fusion for Enhanced Situation Awareness: Automated situation awareness in complex and dynamic environments is a challenging task. An accurate perception of the target is critical to completing a mission. In this research project, the objective is to develop a deep learning multi-modal image fusion algorithm for enhanced situation awareness and toward the preservation of soldier safety in operations, the achievement of threat identification and possible avoidance, the minimization of collateral damages, and the achievement of improved speed, accuracy, confidence, assurance, and precision of impact as part of the operations decision-action cycle.
• Data-driven Predictive Analytics for Infrastructure Management: Canadian municipalities have reported that 59% of the water systems needed repair, and the status of 43% of these systems is unacceptable. Thus, it is important to have an integrated asset management system to optimize the rehabilitation process. Integrated infrastructure management consists of several components: asset condition monitoring and evaluation, pipe failure consequence, and risk analysis. Our study aims to provide an integrated decision-support framework for asset management by developing a general ensemble learning framework for pipe performance prediction and a weighted-score system for pipe risk analysis.