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Yi Cui

Researcher at University of Queensland

Publications -  92
Citations -  1442

Yi Cui is an academic researcher from University of Queensland. The author has contributed to research in topics: FNET & Transformer. The author has an hindex of 17, co-authored 79 publications receiving 985 citations. Previous affiliations of Yi Cui include Southwest Jiaotong University & University of Tennessee.

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Retromer has a selective function in cargo sorting via endosome transport carriers.

TL;DR: It is demonstrated that retromer is required for the maintenance of normal lysosomal morphology and function and does contribute to the retrograde trafficking of CI-M6PR required for maturation of lYSosomal hydrolases and lysOSomal function.
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A Distribution Level Wide Area Monitoring System for the Electric Power Grid–FNET/GridEye

TL;DR: The FNET/GridEye system is overviewed and its operation experiences in electric power grid wide area monitoring are presented and the implementation of a number of data analytics applications will be discussed in details.
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Impact of High PV Penetration on the Inter-Area Oscillations in the U.S. Eastern Interconnection

TL;DR: As PV increases, the damping of the dominant oscillation mode decreases monotonically and it is observed that the mode shape varies with the PV control strategy and new oscillation modes may emerge under inappropriate parameter settings in PV plant controls.
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An Updated Model to Determine the Life Remaining of Transformer Insulation

TL;DR: An algorithm is tested which takes the synergistic effect of water and oxygen on increasing the rate of paper aging into account and shows better agreement with the test results than that given from using the IEEE standard.
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Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales

TL;DR: A reference database is established for distribution-level ENF using FNET/GridEye system and an ENF identification method that combines a wavelet-based signature extraction and feedforward artificial neural network-based machine learning is presented to identify the location of unsourced ENF signals without relying on the availability of concurrent signals.