arXiv subjects

Tzu-Hsuan Chen

Publications and source records attributed to Tzu-Hsuan Chen.

2 recordsLinked to original sources

arXiv2026

Non-thermal Atmospheric Pressure Plasma Combined with Benzoyl Peroxide to Enhance the Inhibition of Bacteria

This study aims to verify whether combining non-thermal equilibrium atmospheric pressure plasma with benzoyl peroxide (BPO) can enhance the inhibition of bacteria. This work involves not only adjusting the parameters of the plasma device, such as increasing the power supply voltage, modifying the working gas flow rate, as well as incorporating a specific proportion of oxygen to the working gas, but also aims to combine plasma with BPO to inactivate bacteria. An optical emission spectroscopy was utilized to determine the optimal working gas flow rate and oxygen mixing ratio for the experimental setup. Additionally, surface temperature and ultraviolet (UV) emission measurements were also performed to characterize the physical properties of the plasma treatment. Although the precise chemical mechanisms between plasma and BPO have yet to be fully elucidated, bacterial inactivation experiments on Escherichia coli demonstrated that the addition of BPO indeed enhanced the sterilization efficacy of plasma treatment.

physics.plasm-ph↗

arXiv2022

RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds

Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. RangeSeg uses a shared encoder backbone with two range dependent decoders. A heavy decoder only computes top of a range image where the far and small objects locate to improve small object detection accuracy, and a light decoder computes whole range image for low computational cost. The results are further clustered by the DBSCAN method with a resolution weighted distance function to get instance-level segmentation results. Experiments on the KITTI dataset show that RangeSeg outperforms the state-of-the-art semantic segmentation methods with enormous speedup and improves the instance-level segmentation performance on small and far objects. The whole RangeSeg pipeline meets the real time requirement on NVIDIA\textsuperscript{\textregistered} JETSON AGX Xavier with 19 frames per second in average.

cs.CV↗