Mucheol Kim

Chung-Ang University

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Co-Authors

Seungmin Rho

Sungkyul University

Jiwan Seo

Chung-Ang University

Sangyong Han

Kyungnam University

Ahyoung Kim

Leibniz Institute of Plant Biochemistry

Mincheol Shin

Chung-Ang University

Ka Lok Man

Xi’an Jiaotong-Liverpool University

JunWoo Lee

National Disaster Management Research Institute

Geunchul Park

Korea Institute of Science & Technology Information

Sang Oh Park

Chung-Ang University

Junho Kim

Chinju National University of Education

Cooperative Institutions

Chung-Ang University

Korea Advanced Institute of Science and Technology

COMSATS University Islamabad

Korea Institute of Science & Technology Information

Korea University

Sungkyunkwan University

Yonsei University

Seoul National University

University of Engineering and Technology Lahore

Electronics and Telecommunications Research Institute

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Node2bert: Only Graph Structure Learning for Robust Node Classification

Mincheol Shin Hyunwook Yu Tae-Young Choe Ajith Abraham Mucheol Kim

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

10.2139/ssrn.4781947

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Image Denoising Based on Improved Wavelet Threshold Function for Wireless Camera Networks and Transmissions

International Journal of Distributed Sensor Networks (2015)

Xiaoyu Wang Xiaoxu Ou Bo‐Wei Chen Mucheol Kim

A new wavelet threshold denoising function and an improved threshold are proposed. It not only retains the advantages of hard and soft denoising functions but also overcomes the disadvantages of the continuity problem of hard denoising function and the constant deviation of the soft denoising function in the new method. In the case of the improved threshold conditions, the new threshold function has a better performance in outstanding image details. It can adapt to different images by joining an adjusting factor. Simulation results show that the new threshold function has a better ability of performing image details and a higher peak signal to noise ratio (PSNR).

Non-Local Means

Video denoising

SIGNAL (programming language)

10.1155/2015/670216

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Citations (19)

MagSafe accessory classification using magnetometer

2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (2023)

Jaemin Choi Insu Kim Suhyeon Shin JunSeob Kim Sungrae Cho

With the integration of MagSafe into the iPhone, attaching various accessories to the back of the device has become possible. In this work, we explore the feasibility of MagSafe accessory classification with IMU sensors available on commercial smartphones. Especially, we use the magnetic features, extracted when MagSafe accessories are attached, as the main feature for the classification. Our experiments achieve high classification accuracy across a variety of accessories.

Feature (linguistics)

Statistical classification

10.1109/ictc58733.2023.10393624

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Enhancing Programming Language Models for C++ Code Clone Detection

Hyonjun Kang Haeun Chun Mucheol Kim

Code (set theory)

clone (Java method)

10.1109/platcon63925.2024.10830733

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Efficient Multimedia Contents Management System with Tag Recommendations

Lecture notes in computer science (2015)

JiSoo Park Kyeong Won Park Yeonsang Yun Mucheol Kim Seungmin Rho

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A Study on Music Distribution Framework Using Music Representation Model

Ahyoung Kim JunWoo Lee Mucheol Kim

In this paper, we propose a music management framework to manage the distribution of large volume music contents at home and abroad. In this paper, we define the music contents as an expression model that can be distributed internationally, and distribute the sound sources, analyze all the transaction information and related tasks of the distributed music, process them into various types of data. By defining standardized music expression model specific to music contents and managing music by using big data technology using proposed model, it is possible to automate all transaction information and related tasks online in the music market to provide statistical, analysis, and visualization information and proposed a music management framework to provide the sound source.

Expression (computer science)

Representation

Pop music automation

10.1109/platcon.2017.7883681

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Mixed scheduling with heterogeneous delay constraints in cyber-physical systems

Future Generation Computer Systems (2015)

Bo Shen Xingshe Zhou Mucheol Kim

Cyber-physical system

Stochastic game

10.1016/j.future.2015.10.021

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Citations (18)

Dynamic knowledge management from multiple sources in crowdsourcing environments

New Review of Hypermedia and Multimedia (2015)

Mucheol Kim Seungmin Rho

Due to the spread of smart devices and the development of network technology, a large number of people can now easily utilize the web for acquiring information and various services. Further, collective intelligence has emerged as a core player in the evolution of technology in web 2.0 generation. It means that people who are interested in a specific domain of knowledge can not only make use of the information, but they can also participate in the knowledge production processes. Since a large volume of knowledge is produced by multiple contributors, it is important to integrate and manage knowledge efficiently. In this paper, we propose a social tagging-based dynamic knowledge management system in crowdsourcing environments. The approach here is to categorize and package knowledge from multiple sources, in such a way that it easily links to target knowledge.

Crowdsourcing

Collective Intelligence

Distributed knowledge

10.1080/13614568.2015.1036133

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Citations (6)

Adaptive variable sampling model for performance analysis in high cache-performance computing environments

Heliyon (2023)

Mincheol Shin Mucheol Kim Geunchul Park Ajith Abraham

High-performance computing provides computing power for a variety of scientific disciplines, supporting advancements by offering insights beyond metacognition. Maximizing computing performance without wasting resources is a major research issue. Predicting the performance of a computer's next state is effective for scheduling. However, hardware performance monitors representing the computer's state require high expert knowledge, and there is no standardized model. In this paper, we propose an adaptive variable sampling model for performance analysis in high-performance computing environments. Our method automatically classifies the optimal variables from numerous variables related to performance prediction and predicts performance using the sampled variables. The optimal variables for performance analysis do not require expert knowledge during the sampling process. We conducted experiments in various architectures and applications to validate this method. This model performed at least 24.25% and up to 58.75% faster without any loss in accuracy.

Performance Prediction

10.1016/j.heliyon.2023.e16777

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공공데이터를 활용한 창업 장소 분석 시스템 설계

ChoYongJang Seungmin Rho Mucheol Kim

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