Yuhan Chen

Works (7)

Updated: November 29th, 2024 05:02

2024 article

Experimental assessment of blowing effect on vehicle performance and aero-acoustics in small-rotor/wing interaction

Chen, M., Chen, Y., Hua, J., Maier, N., & Burdette, D. (2024, March 7). JOURNAL OF VIBRATION AND CONTROL, Vol. 3.

By: M. Chen n , Y. Chen* , J. Hua *, N. Maier n & D. Burdette n

author keywords: Small urban air mobility tiltrotor; blow effect; active flow control; noise reduction; vehicle performance
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 8, 2024

2023 review

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

[Review of ]. PHYSIOLOGIA, 4(1), 1–42.

By: R. Soleimani n, J. Barahona n, Y. Chen n , A. Bozkurt n, M. Daniele n , V. Pozdin*, E. Lobaton n 

author keywords: sleep stage classification; EEG; ECG; deep learning; contrastive learning; transfer learning; data augmentation; interpretability
TL;DR: An improvement in performance up to 12% on standard datasets over the last 5 years is identified and the causes for these trends seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: February 1, 2024

2023 journal article

Robust Cough Detection With Out-of-Distribution Detection

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 27(7), 3210–3221.

By: Y. Chen n , P. Attri  n, J. Barahona n, M. Hernandez *, D. Carpenter *, A. Bozkurt n, E. Lobaton n 

author keywords: Cough detection; audio classification; out-of-distribution; bio-signal processing; machine learning
TL;DR: The incorporation of OOD detection techniques improves cough detection performance by a significant margin and provides a valuable solution to real-world acoustic cough detection problems. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: April 6, 2023

2022 journal article

Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks

Information, 13(7), 338.

By: R. da Silva n, B. Zhong n , Y. Chen n  & E. Lobaton n 

author keywords: Bayesian Neural Networks; uncertainty quantification; stereotypical motor movement; body rocking
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: July 13, 2022

2021 article

Improving Performance and Quantifying Uncertainty of Body-Rocking Detection using Bayesian Neural Networks

Silva, R. L., Zhong, B., Chen, Y., & Lobaton, E. (2021, October 14).

By: R. Silva, B. Zhong, Y. Chen*  & E. Lobaton

Source: ORCID
Added: September 15, 2024

2021 article

Improving Performance and Quantifying Uncertainty of Body-Rocking Detection using Bayesian Neural Networks

Silva, R. L., Zhong, B., Chen, Y., & Lobaton, E. (2021, October 14).

By: R. Silva, B. Zhong, Y. Chen*  & E. Lobaton

Source: ORCID
Added: September 15, 2024

2021 journal article

Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 975–979.

By: Y. Chen n , M. Wilkins n, J. Barahona n, A. Rosenbaum n, M. Daniele n  & E. Lobaton n 

Event: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) at Mexico on November 1-5, 2021

MeSH headings : Female; Fetal Monitoring; Heart Rate; Humans; Neural Networks, Computer; Pregnancy; Signal Processing, Computer-Assisted; Stethoscopes
TL;DR: This work implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope, and shows a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: April 25, 2022

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