Jun Ma

A Deep Learning Model for Wave V Peak Detection in Auditory Brainstem Response Data

Jun Ma, Nak-Jun Sung, Sungjun Choi, Min Ji Hong et autres

In this study, we propose a YOLO-based object detection algorithm for the automated and accurate identification of the fifth wave (Wave V) in auditory brainstem response (ABR) graphs. The ABR test plays a critical role in the diagnosis of hearing disorders, with …

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XGBoost-LR: A method for network traffic anomaly detection

Tianyu Liu, Guangquan Xu, Xiao Wang, Peng Peng et autres

With the rapid evolution of internet technology, individuals are facing increasingly severe network security challenges. Confronted with diverse and sophisticated network attacks, traditional methods for network attack detection often struggle to keep pace with these evolving threats, leading to problems such as …

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Real-Time Cloth Simulation in Extended Reality: Comparative Study Between Unity Cloth Model and Position-Based Dynamics Model with GPU

Taeheon Kim, Jun Ma, Min Ji Hong

This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations …

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DSR-Net: Distinct selective rollback queries for road cracks detection with detection transformer

Yi Deng, Jun Ma, Ziyi Wu, Hai Liu

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Development and Comparison of Machine Learning and Deep Learning Models for Speech Audiometry Prediction

Jaeyoung Shin, Jun Ma, Makara Mao, Nak-Jun Sung et autres

Hearing loss significantly impacts daily communication, making accurate speech audiometry (SA) assessment essential for diagnosis and treatment. However, SA testing is time-consuming and resource-intensive, limiting its accessibility in clinical practice. This study aimed to develop a multi-class classification model that predicts SA …

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Enhancing Fabric Detection and Classification Using YOLOv5 Models

Makara Mao, Jun Ma, Ahyoung Lee, Min Ji Hong

The YOLO series is widely recognized for its efficiency in the real-time detection of objects within images and videos. Accurately identifying and classifying fabric types in the textile industry is vital to ensuring quality, managing supply, and increasing customer satisfaction. We developed …

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Real-Time Physics Simulation Method for XR Application

Nak-Jun Sung, Jun Ma, Kunthroza Hor, Taeheon Kim et autres

Real-time physics simulations are vital for creating immersive and interactive experiences in extended reality (XR) applications. Balancing computational efficiency and simulation accuracy is challenging, especially in environments with multiple deformable objects that require complex interactions. In this study, we introduce a GPU-based …

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A thermostatically controlled loads regulation method based on hybrid communication and cloud–edge–end collaboration

Liwei Zhang, Wenting Zhou, Jun Ma, Kai Li et autres

The new power system necessitates enhanced transmission and processing capacity of the communication network due to the frequent two-way interaction between the power grid and thermostatically controlled loads (TCLs). However, existing load regulation methods often assume that the communication system is in …

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Performance Comparison of Vertex Block Descent and Position Based Dynamics Algorithms Using Cloth Simulation in Unity

Jun Ma, Nak-Jun Sung, Min‐Hyung Choi, Min Ji Hong

This paper presents a comparative study of the Vertex Block Descent (VBD) and Position-Based Dynamics (PBD) algorithms, focusing on their performance in physical simulation tasks. Unity, a versatile physics engine, served as the simulation platform for the experiments. Among various types of …

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Fast BET Based on Pre-Processing Evolution Using Global Mean Inter-Vertex Distance of Deformable Surface for MRI Brain Extraction

Chang-Il Son, Jun Ma, IlRyong Bong, Nam Chol Yu

As a method based on the deformable surface evolution, brain extraction tools (BET) is widely used for brain extraction on cranial 3D magnetic resonance (MR) images. BET iteratively applies an evolution model depending on local parameters to the deformable surface until it …

Development of a Deep Learning Model for Predicting Speech Audiometry Using Pure-Tone Audiometry Data

Jaeyoung Shin, Jun Ma, Seong Jun Choi, Sungyeup Kim et autres

Speech audiometry is a vital tool in assessing an individual’s ability to perceive and comprehend speech, traditionally requiring specialized testing that can be time-consuming and resource -intensive. This paper approaches a novel use of deep learning to predict speech audiometry using pure-tone …

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Preprocessing of Pure Tone Audiometry Data and Design of Machine Learning Models for Hearing Loss Classification

Jae-Sung Shin, Jun Ma, Seong Jun Choi, Min Ji Hong

In this research, we conducted a preprocessing step on pure-tone audiometry image data to determine the presence or absence of hearing loss in patients, and designed machine learning models for hearing loss classification using the preprocessed data. The dataset utilized consisted of …

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