Dr. Chao Hu Profile

Dr. Chao Hu

at Univ of Connecticut

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Publications (7)

Proceedings Article | 9 May 2024 Presentation + Paper

Real-time state estimation using recurrent neural network and topological data analysis

Arman Razmarashooli, Daniel Salazar Martinez, Yang Kang Chua, Simon Laflamme, Chao Hu

Proceedings Volume 12950, 129500C (2024) https://doi.org/10.1117/12.3010900

KEYWORDS: Point clouds, Neural networks, Data analysis, Complex systems, Data modeling, Machine learning, Feature extraction

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Proceedings Article | 28 April 2023 Presentation + Paper

Hardware implementation of nonstationary structural dynamics forecasting

Puja Chowdhury, Austin R. Downey, Jason Bakos, Simon Laflamme, Chao Hu

Proceedings Volume 12483, 1248316 (2023) https://doi.org/10.1117/12.2658036

KEYWORDS: Field programmable gate arrays, Sampling rates, Vibration, Signal to noise ratio, Structural dynamics, Data acquisition, Windows, Manufacturing, Control systems, Computation time

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High-rate time series forecasting has applications in the domain of high-rate structural health monitoring and control. Hypersonic vehicles and space infrastructure are examples of structural systems that would benefit from time series forecasting on temporal data, including oscillations of control surfaces or structural response to an impact. This paper reports on the development of a software-hardware methodology for the deterministic and low-latency time series forecasting of structural vibrations. The proposed methodology is a software-hardware co-design of a fast Fourier transform (FFT) approach to time series forecasting. The FFT-based technique is implemented in a variable-length sequence configuration. The data is first de-trended, after which the time series data is translated to the frequency domain, and frequency, amplitude, and phase measurements are acquired. Next, a subset of frequency components is collected, translated back to the time domain, recombined, and the data's trend is recovered. Finally, the recombined signals are propagated into the future to the chosen forecasting horizon. The developed methodology achieves fully deterministic timing by being implemented on a Field Programmable Gate Array (FPGA). The developed methodology is experimentally validated on a Kintex-7 70T FPGA using structural vibration data obtained from a test structure with varying levels of nonstationarities. Results demonstrate that the system is capable of forecasting time series data 1 millisecond into the future. Four data acquisition sampling rates from 128 to 25600 S/s are investigated and compared. Results show that for the current hardware (Kintex-7 70T), only data sampled at 512 S/s is viable for real-time time series forecasting with a total system latency of 39.05 μs in restoring signal. In totality, this research showed that for the considered FFT-based time series algorithm the fine-tuning of hyperparameters for a specific sampling rate means that the usefulness of the algorithm is limited to a signal that does not shift considerably from the frequency information of the original signal. FPGA resource utilization, timing constraints of various aspects of the methodology, and the algorithm accuracy and limitations concerning different data are discussed.

Proceedings Article | 20 April 2022 Poster + Paper

Deterministic and low-latency time-series forecasting of nonstationary signals

Puja Chowdhury, Vahid Barzegar, Joud Satme, Austin R. Downey, Simon Laflamme, Jason Bakos, Chao Hu

Proceedings Volume 12043, 120431D (2022) https://doi.org/10.1117/12.2629025

KEYWORDS: Field programmable gate arrays, Clocks, Neurons, Signal to noise ratio, Structural health monitoring, Sensors, Detection and tracking algorithms, Data acquisition, Damage detection, Algorithm development

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Proceedings Article | 18 April 2022 Presentation

Physical Parameter Estimation with Ensemble Learning for State Estimation of High-Rate Dynamic Systems

Vahid Barzegar, Matthew Nelson, Simon Laflamme, Chao Hu, Jacob Dodson

Proceedings Volume PC12046, PC1204607 (2022) https://doi.org/10.1117/12.2613113

KEYWORDS: Systems modeling, Algorithm development, Neural networks, Dynamical systems, Structural health monitoring, Safety, Liquids

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Proceedings Article | 22 March 2021 Presentation + Paper

Long short-term memory networks with attention learning for high-rate structural health monitoring

Matthew Nelson, Vahid Barzegar, Simon Laflamme, Chao Hu, Jacob Dodson

Proceedings Volume 11591, 115910H (2021) https://doi.org/10.1117/12.2582428

KEYWORDS: Structural health monitoring, Systems modeling, Safety, Numerical simulations, Dynamical systems

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Proceedings Article | 27 March 2019 Presentation + Paper

Model-assisted validation of a strain-based dense sensor network

Jin Yan, Xiaosong Du, Simon Laflamme, Leifur Leifsson, Chao Hu, An Chen

Proceedings Volume 10970, 109702C (2019) https://doi.org/10.1117/12.2515232

KEYWORDS: Data modeling, Structural health monitoring, Sensor networks, Sensors, Damage detection, Detection and tracking algorithms, Numerical simulations, Matrices, Error analysis, Systems modeling

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Proceedings Article | 15 March 2018 Presentation + Paper

Experimental verification of tire energy harvester designed via reliability based design optimization method

Amin Toghi Eshghi, Soobum Lee, Mohammad Kazem Sadoughi, Chao Hu, Young-Cheol Kim, Jong-Ho Seo

Proceedings Volume 10595, 105951V (2018) https://doi.org/10.1117/12.2296625

KEYWORDS: Reliability, Computing systems, Ferroelectric materials, Complex systems, Complex adaptive systems, Energy harvesting, Sensors, Optimization (mathematics), Manufacturing, Smart materials

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Showing 5 of 7 publications

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