@article{chen_chen_hua_maier_burdette_2024, title={Experimental assessment of blowing effect on vehicle performance and aero-acoustics in small-rotor/wing interaction}, volume={3}, ISSN={["1741-2986"]}, url={https://doi.org/10.1177/10775463241236303}, DOI={10.1177/10775463241236303}, abstractNote={ The tiltrotor design for urban air mobility (UAM) combines vertical take-off and landing (VTOL) capability with fixed-wing efficiency. However, it confronts challenges posed by downward force due to rotor wake effect. Previous research has employed active flow control, such as blowing air, primarily during forward flight using a single air outlet slot. However, the implications for rotor performance and aero-acoustic characteristics have remained unexplored. The experimental approach in this study involves controlled blowing air with moderate momentum in hover mode, concentrating on rotor performance, download force, noise signature, and the influence of port patterns. It is found that the radial port pattern emerges as pivotal for mitigating downward force and augmenting rotor efficiency. The efficacy of noise reduction hinges on both receiver orientation and port pattern selection. This investigation significantly contributes to understanding the role of blowing effect in optimizing tiltrotor designs, particularly in the context of rotor wake-wing interaction. }, journal={JOURNAL OF VIBRATION AND CONTROL}, author={Chen, Mingtai and Chen, Yuhan and Hua, Jie and Maier, Nick and Burdette, Dylan}, year={2024}, month={Mar} }
 @article{soleimani_barahona_chen_bozkurt_daniele_pozdin_lobaton_2023, title={An Overview of and Advances in Modeling and Interoperability of Deep Neural Sleep Staging}, url={https://www.mdpi.com/2673-9488/4/1/1}, DOI={10.3390/physiologia4010001}, abstractNote={Sleep staging has a very important role in diagnosing patients with sleep disorders. In general, this task is very time-consuming for physicians to perform. Deep learning shows great potential to automate this process and remove physician bias from decision making. In this study, we aim to identify recent trends on performance improvement and the causes for these trends. Recent papers on sleep stage classification and interpretability are investigated to explore different modeling and data manipulation techniques, their efficiency, and recent advances. We identify an improvement in performance up to 12% on standard datasets over the last 5 years. The improvements in performance do not appear to be necessarily correlated to the size of the models, but instead seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning.}, journal={Physiologia}, author={Soleimani, Reza and Barahona, Jeffrey and Chen, Yuhan and Bozkurt, Alper and Daniele, Michael and Pozdin, Vladimir A. and Lobaton, Edgar}, year={2023}, month={Dec} }
 @article{chen_attri_barahona_hernandez_carpenter_bozkurt_lobaton_2023, title={Robust Cough Detection With Out-of-Distribution Detection}, volume={27}, ISSN={["2168-2208"]}, url={https://doi.org/10.1109/JBHI.2023.3264783}, DOI={10.1109/JBHI.2023.3264783}, abstractNote={Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way totrack potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are often clean, containing a limited set of sound categories, and thus perform poorly when they are exposed to a variety of real-world sounds which could be picked up by portable recording devices. The sounds that are not learned by the model are referred to as Out-of-Distribution (OOD) data. In this work, we propose two robust cough detection methods combined with an OOD detection module, that removes OOD data without sacrificing the cough detection performance of the original system. These methods include adding a learning confidence parameter and maximizing entropy loss. Our experiments show that 1) the OOD system can produce dependable In-Distribution (ID) and OOD results at a sampling rate above 750 Hz; 2) the OOD sample detection tends to perform better for larger audio window sizes; 3) the model's overall accuracy and precision get better as the proportion of OOD samples increase in the acoustic signals; 4) a higher percentage of OOD data is needed to realize performance gains at lower sampling rates. 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.}, number={7}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, author={Chen, Yuhan and Attri, Pankaj and Barahona, Jeffrey and Hernandez, Michelle L. and Carpenter, Delesha and Bozkurt, Alper and Lobaton, Edgar}, year={2023}, month={Jul}, pages={3210–3221} }
 @article{da silva_zhong_chen_lobaton_2022, title={Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks}, volume={13}, ISSN={["2078-2489"]}, url={https://doi.org/10.3390/info13070338}, DOI={10.3390/info13070338}, abstractNote={Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject.}, number={7}, journal={Information}, publisher={MDPI AG}, author={da Silva, Rafael Luiz and Zhong, Boxuan and Chen, Yuhan and Lobaton, Edgar}, year={2022}, month={Jul}, pages={338} }
 @article{silva_zhong_chen_lobaton_2021, title={Improving Performance and Quantifying Uncertainty of Body-Rocking Detection using Bayesian Neural Networks}, url={https://doi.org/10.36227/techrxiv.16779301.v1}, DOI={10.36227/techrxiv.16779301.v1}, abstractNote={Preprint of manuscript submitted to an IEEE Journal currently under revision.}, author={Silva, Rafael Luiz and Zhong, Boxuan and Chen, Yuhan and Lobaton, Edgar}, year={2021}, month={Oct} }
 @article{silva_zhong_chen_lobaton_2021, title={Improving Performance and Quantifying Uncertainty of Body-Rocking Detection using Bayesian Neural Networks}, url={https://doi.org/10.36227/techrxiv.16779301}, DOI={10.36227/techrxiv.16779301}, abstractNote={Preprint of manuscript submitted to an IEEE Journal currently under revision.}, author={Silva, Rafael Luiz and Zhong, Boxuan and Chen, Yuhan and Lobaton, Edgar}, year={2021}, month={Oct} }
 @article{chen_wilkins_barahona_rosenbaum_daniele_lobaton_2021, title={Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals}, ISSN={["1558-4615"]}, url={http://dx.doi.org/10.1109/embc46164.2021.9629814}, DOI={10.1109/EMBC46164.2021.9629814}, abstractNote={Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the efficacy of these networks using the raw signals and the hand-crafted energy from the signal. The results show a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat, and its performance is improved when using the energy of the Doppler signals. We further discuss issues, such as low Signal-to-Noise Ratios (SNR), present in the training of a model based on the stethoscope signals. Finally, we show that we can improve the SNR, and subsequently the performance of the stethoscope, by matching the energy from the stethoscope to that of the Doppler signal.}, journal={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, publisher={IEEE}, author={Chen, Yuhan and Wilkins, Michael D. and Barahona, Jeffrey and Rosenbaum, Alan J. and Daniele, Michael and Lobaton, Edgar}, year={2021}, pages={975–979} }