arXiv subjects

Xiaoyang Du

Publications and source records attributed to Xiaoyang Du.

3 recordsLinked to original sources

arXiv2025

Beyond Dielectrics: Interfacial Water Polarization Governs Graphene-Based Electrochemical Interfaces

Water molecules are traditionally regarded as passive dielectric media in electrochemical systems. In this work, we challenge this conventional perspective using molecular dynamics simulations and theoretical analysis. We show that interfacial water is polarized differently from bulk water and effectively screens the electrostatic potential between ions and the surface. This goes beyond the classic electric double layer (EDL) model, which treated water as merely a passive dielectric. The observed overscreening occurs because a significant portion of water polarization directly responds to the graphene surface, in addition to screening the electrostatic interactions between ions and charged surfaces. Furthermore, we reveal that this surface-induced polarization of interfacial water governs the electric potential distribution and EDL capacitance, and can even invert the electrode surface potential polarity, overriding the contribution of ions. These molecular-level insights lead to a revised EDL model that more accurately describes the electric and chemical potential distributions in the interfacial EDL regions.

physics.chem-ph↗

arXiv2024

Introducing the Brand New QiandaoEar22 Dataset for Specific Ship Identification Using Ship-Radiated Noise

Target identification of ship-radiated noise is a crucial area in underwater target recognition. However, there is currently a lack of multi-target ship datasets that accurately represent real-world underwater acoustic conditions. To ntackle this issue, we release QiandaoEar22 \textemdash an underwater acoustic multi-target dataset, which can be download on https://ieee-dataport.org/documents/qiandaoear22. This dataset encompasses 9 hours and 28 minutes of real-world ship-radiated noise data and 21 hours and 58 minutes of background noise data. We demonstrate the availability of QiandaoEar22 by conducting an experiment of identifying specific ship from the multiple targets. Taking different features as the input and six deep learning networks as classifier, we evaluate the baseline performance of different methods. The experimental results reveal that identifying the specific target of UUV from others can achieve the optimal recognition accuracy of 97.78\%, and we find using spectrum and MFCC as feature inputs and DenseNet as the classifier can achieve better recognition performance. Our work not only establishes a benchmark for the dataset but helps the further development of innovative methods for the tasks of underwater acoustic target detection (UATD) and underwater acoustic target recognition(UATR).

eess.AS↗

arXiv2024

QiandaoEar22: A high quality noise dataset for identifying specific ship from multiple underwater acoustic targets using ship-radiated noise

Target identification of ship-radiated noise is a crucial area in underwater target recognition. However, there is currently a lack of multi-target ship datasets that accurately represent real-world underwater acoustic conditions. To tackle this issue, we conducted experimental data acquisition, resulting in the release of QiandaoEar22 \textemdash a comprehensive underwater acoustic multi-target dataset. This dataset encompasses 9 hours and 28 minutes of real-world ship-radiated noise data and 21 hours and 58 minutes of background noise data. To demonstrate the availability of QiandaoEar22, we executed two experimental tasks. The first task focuses on assessing the presence of ship-radiated noise, while the second task involves identifying specific ships within the recognized targets in the multi-ship mixed data. In the latter task, we extracted eight features from the data and employed six deep learning networks for classification, aiming to evaluate and compare the performance of various features and networks. The experimental results reveal that ship-radiated noise can be successfully identified from background noise in over 99\% of cases. Additionally, for the specific identification of individual ships, the optimal recognition accuracy achieves 99.56\%. Finally, based on our findings, we provide advice on selecting appropriate features and deep learning networks, which may offer valuable insights for related research. Our work not only establishes a benchmark for algorithm evaluation but also inspires the development of innovative methods to enhance UATD and UATR systems.

eess.AS↗