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Tonghao Zhang

Researcher at University of Illinois at Chicago

Publications -  14
Citations -  31

Tonghao Zhang is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Engineering & Computer science. The author has an hindex of 2, co-authored 4 publications receiving 10 citations.

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Phased acoustic emission sensor array for localizing radial and axial positions of defects in hollow structures

TL;DR: In this paper, a phase array acoustic emission (AE) localization strategy is introduced to localize both radial and axial positions of defects by considering the actual trajectory of propagating elastic waves.
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Nondestructive assessment of cross-laminated timber using non-contact transverse vibration and ultrasonic testing

TL;DR: In this paper, the free vibration response triggered by the impulse excitation is captured using contact accelerometer and non-contact laser Doppler vibrometer (LDV) measurement is proven to respond similar to conventional accelerometers but more convenient as a rapid assessment method.
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Effect of modified washing process on water usage, composition and gelling properties of grass carp surimi.

TL;DR: In this paper , the authors investigated the composition, structure, and gelling properties of the grass carp surimi prepared with modified washing process (MWP) between two washing cycles and found that water usage and wastewater discharge were reduced significantly by 33% and 38%, respectively, when MWP was applied.
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A multiscale indentation-based technique to correlate acoustic emission with deformation mechanisms in complex alloys

TL;DR: In this paper, the authors demonstrate a multiscale indentation-based technique to isolate dislocation and martensitic transformation sources in the acoustic emission data of a complex alloy, which facilitates the detection of shared deformation mechanisms across different tests.
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Disentangling the Influence of Data Contamination in Growth Curve Modeling: A Median Based Bayesian Approach

TL;DR: A robust double medians growth curve modeling approach (DOME GCM) is proposed to thoroughly disentangle the influence of data contamination on model estimation and inferences, where two conditional medians are employed for the distributions of the within-subject measurement errors and of random effects, respectively.