arXiv subjects

Sungho Jeon

Publications and source records attributed to Sungho Jeon.

6 recordsLinked to original sources

arXiv2025

A van der Waals material exhibiting room temperature broken inversion symmetry with ferroelectricity

Since the initial synthesis of van der Waals two-dimensional indium selenide was first documented in 1957, five distinct polymorphs and their corresponding polytypes have been identified. In this study, we report a unique phase of indium selenide via Scanning Transmission Electron Microscopy (STEM) analysis in the synthesized large-area films -- which we have named the $\beta^\text{p}$ phase. The quintuple layers of the $\beta^\text{p}$ phase, characterized by a unique zigzag atomic configuration with unequal indium-selenium bond lengths from the middle selenium atom, are distinct from any other previously reported phase of indium selenide. Cross-sectional STEM analysis has revealed that the $\beta^\text{p}$ layers exhibit intralayer shifting. We found that indium selenide films with $\beta^\text{p}$ layers display electric-field-induced switchable polarization characteristic of ferroelectric materials, suggesting the breaking of the inversion symmetry. Experimental observations of nonlinear optical phenomena -- Second Harmonic Generation (SHG) responses further support this conclusion. This study reports a $\beta^\text{p}$ phase of indium selenide showing ferroelectricity over large areas at room temperature in a low-dimensional limit.

cond-mat.mtrl-sci↗

arXiv2024

REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning

Recent rehearsal-free continual learning (CL) methods guided by prompts achieve strong performance on vision tasks with non-stationary data but remain resource-intensive, hindering real-world edge deployment. We introduce resource-efficient prompting (REP), which improves the computational and memory efficiency of prompt-based rehearsal-free continual learning methods while minimizing accuracy trade-offs. Our approach employs swift prompt selection to refine input data using a carefully provisioned model and introduces adaptive token merging (AToM) and adaptive layer dropping (ALD) for efficient prompt updates. AToM and ALD selectively skip data and model layers while preserving task-specific features during the learning of new tasks. Extensive experiments on multiple image classification datasets demonstrate REP's superior resource efficiency over state-of-the-art rehearsal-free CL methods.

cs.LG↗

arXiv2023

Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency

In this paper, we show that a simple self-supervised pre-trained audio model can achieve comparable inference efficiency to more complicated pre-trained models with speech transformer encoders. These speech transformers rely on mixing convolutional modules with self-attention modules. They achieve state-of-the-art performance on ASR with top efficiency. We first show that employing these speech transformers as an encoder significantly improves the efficiency of pre-trained audio models as well. However, our study shows that we can achieve comparable efficiency with advanced self-attention solely. We demonstrate that this simpler approach is particularly beneficial with a low-bit weight quantization technique of a neural network to improve efficiency. We hypothesize that it prevents propagating the errors between different quantized modules compared to recent speech transformers mixing quantized convolution and the quantized self-attention modules.

cs.SD↗

arXiv2023

Wafer-scale growth of two-dimensional, phase-pure InSe

Two-dimensional (2D) indium monoselenide (InSe) has attracted significant attention as a III-VI two-dimensional semiconductor (2D) with a combination of favorable attributes from III-V semiconductors as well as van der Waals 2D transition metal dichalcogenides. Nevertheless, the large-area synthesis of phase-pure 2D InSe remains unattained due to the complexity of the binary In-Se system and the difficulties in promoting lateral growth. Here, we report the first polymorph-selective synthesis of epitaxial 2D InSe by metal-organic chemical deposition (MOCVD) over 2 inch diameter sapphire wafers. We achieve thickness-controlled, layer-by-layer epitaxial growth of InSe on c-plane sapphire via dynamic pulse control of Se/In flux ratio. The layer-by-layer growth allows thickness control over wafer scale with tunable optical properties comparable to bulk crystals. Finally, the gate-tunable electrical transport suggests that MOCVD-grown InSe could be a potential channel material for back-end-of-line integration in logic transistors with field-effect mobility comparable to single-crystalline flakes.

cond-mat.mtrl-sci↗

arXiv2017

Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks

This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on sound event detection. We recorded the sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental sound data to remedy the scarcity of drone sound data in diverse environments. We investigated the effectiveness of state-of-the-art event sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone sound detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time detection systems.

cs.SD↗

arXiv2016

Passive Fingerprinting of SCADA in Critical Infrastructure Network without Deep Packet Inspection

We present the first technique of passive fingerprinting for Supervisory Control And Data Acquisition (SCADA) networks without Deep Packet Inspection (DPI) and experience on real environment. Unlike existing work, our method does not rely on the functions of a specific product or DPI of the SCADA protocol. Our inference method, which is based on the intrinsic characteristics of SCADA, first identifies the network port used for the SCADA protocol, then consecutively infers the field devices and master server. We evaluated the effectiveness of our method using two network traces collected from a real environment for a month and a half, three days from different CI respectively. This confirmed the ability of our method to capture most of the SCADA with high F-score nearly 1, except for HMIs connected to master server, and demonstrated the practical applicability of the method.

cs.CR↗