Dr. Yingjun Zhao Profile

Dr. Yingjun Zhao

Postdoc Fellow at Univ of California San Diego

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Publications (4)

Proceedings Article | 27 March 2018 Presentation + Paper

Updating the finite element model for electrical impedance tomography using self-organizing map

Yingjun Zhao, Martin Schagerl, Christoph Kralovec

Proceedings Volume 10598, 1059832 (2018) https://doi.org/10.1117/12.2296894

KEYWORDS: Finite element methods, Tomography

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Proceedings Article | 19 April 2017 Presentation + Paper

Characterization of the spatial elastoresistivity of inkjet-printed carbon nanotube thin films for strain-state sensing

Patrick Gruener, Yingjun Zhao, Martin Schagerl

Proceedings Volume 10169, 101690F (2017) https://doi.org/10.1117/12.2260890

KEYWORDS: Thin films, Carbon nanotubes, Tomography, Sensors, Polymers, Resistance, Reconstruction algorithms, Intelligence systems, Intelligent sensors, Intelligent sensor systems, Electrodes, Structural health monitoring, Aerospace engineering, Analytical research

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Proceedings Article | 19 April 2017 Presentation + Paper

Comparison of electrical impedance tomography inverse solver approaches for damage sensing

Yingjun Zhao, Long Wang, Sumit Gupta, Kenneth Loh, Martin Schagerl

Proceedings Volume 10169, 1016915 (2017) https://doi.org/10.1117/12.2260880

KEYWORDS: Tomography, Sensors, Inverse problems, Structural health monitoring, Carbon nanotubes, Thin films, Compressed sensing, Reconstruction algorithms, Spatial resolution, Intelligence systems, Electrodes, Inspection, Signal to noise ratio, Nondestructive evaluation, Transducers, Visualization

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Proceedings Article | 10 April 2014 Paper

Operational model updating of spinning finite element models for HAWT blades

Antonio Velazquez, R. Andrew Swartz, Kenneth Loh, Yingjun Zhao, Valeria La Saponara, Robert Kamisky, Cornelis van Dam

Proceedings Volume 9061, 90610U (2014) https://doi.org/10.1117/12.2046434

KEYWORDS: Data modeling, Structural health monitoring, Algorithms, Stochastic processes, Matrices, Systems modeling, System identification, Wind energy, Process modeling, Wind turbine technology

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