Sunghyun Cho

Hanyang University

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i-10 index

Research Trends

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Document Type

Co-Authors

Joohan Park

Electronics and Telecommunications Research Institute

Se-young Ahn

Hanyang University

Soohyeong Kim

Hanyang University

Jaewon Noh

Hanyang University

Il Moon

Yonsei University

Jiseung Youn

Hanyang University

Jeehyeong Kim

Korea Electronics Technology Institute

Sejong Lee

Hanyang University

Yongseok Kwon

Hanyang University

Jae‐Hyun Kim

Ajou University

Cooperative Institutions

Yonsei University

Hanyang University

Seoul National University

Korea Advanced Institute of Science and Technology

Samsung (South Korea)

Weatherford College

Pohang University of Science and Technology

Korea University

Sungkyunkwan University

Electronics and Telecommunications Research Institute

Author Statistics

Papers

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Research Field

Asymptotic performance comparison of concatenated codes with different inner decoding schemes in frequency-hopping spread spectrum packet radio networks

Ye Hoon Lee Dong Ho Kim Sunghyun Cho

In this paper, we analyze the performance of a concatenated code with two different inner decoding schemes. One is the error-detecting inner decoding, and the other is the error-detecting-and-correcting inner decoding scheme. We compare the asymptotic performances of the two decoding schemes for the infinite block length when the concatenated code is applied to slow frequency-hopping spread-spectrum packet radio communication systems.

Frequency-hopping spread spectrum

Code (set theory)

10.1109/icufn.2016.7536994

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Exploiting Deblurring Networks for Radiance Fields

arXiv (Cornell University) (2025)

Haeyun Choi Heemin Yang Junyu Han Sunghyun Cho

In this paper, we propose DeepDeblurRF, a novel radiance field deblurring approach that can synthesize high-quality novel views from blurred training views with significantly reduced training time. DeepDeblurRF leverages deep neural network (DNN)-based deblurring modules to enjoy their deblurring performance and computational efficiency. To effectively combine DNN-based deblurring and radiance field construction, we propose a novel radiance field (RF)-guided deblurring and an iterative framework that performs RF-guided deblurring and radiance field construction in an alternating manner. Moreover, DeepDeblurRF is compatible with various scene representations, such as voxel grids and 3D Gaussians, expanding its applicability. We also present BlurRF-Synth, the first large-scale synthetic dataset for training radiance field deblurring frameworks. We conduct extensive experiments on both camera motion blur and defocus blur, demonstrating that DeepDeblurRF achieves state-of-the-art novel-view synthesis quality with significantly reduced training time.

Deblurring

10.48550/arxiv.2502.14454

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A downlink beamforming assisted by locally positioned communication devices

International Journal of Communication Systems (2013)

Cheolwoo You Young-Ho Jung Sunghyun Cho

Recently, multiple cell types with overlapping coverage have been deployed simultaneously to increase cellular network capacity. Cross-tier interference is one of the key technical challenges in the use of this method. A simple and practical beamforming scheme assisted by locally positioned communication devices for the downlink of a multi-cell wireless hierarchical cell structure system is proposed in this paper to maximize the capacity of the embedded small cell and simultaneously ensure minimal impact on the performance of existing macrocells. The locally positioned communication devices can be implemented with low complexity and at low cost and can continuously provide helpful and accurate information to base stations for the proper configuration of geographical cell coverage, allowing neighboring cells to cooperate effectively with each other. Simulation results verify that the proposed scheme can provide a significant gain over conventional systems not using the proposed algorithm. Copyright © 2013 John Wiley & Sons, Ltd.

Small cell

10.1002/dac.2592

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Optimization of an explosive waste incinerator via an artificial neural network surrogate model

Chemical Engineering Journal (2020)

Sunghyun Cho Minsu Kim Byeongil Lyu Il Moon

Surrogate model

Latin Hypercube Sampling

10.1016/j.cej.2020.126659

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Citations (45)

Joint hierarchical modulation and network coding for asymmetric data rate transmission over multiple-access relay channel

Dongho You Ye Hoon Lee Seung Ho Choi Dong Ho Kim Sunghyun Cho

In this paper, we consider a time-division multiple-access relay channel (MARC), in which two source nodes (SNs) transmit data with different data rate to a destination node (DN) with the help of a relay node (RN). To provide asymmetric data rates, we consider joint hierarchical 16QAM (HM 16QAM) and network coding. Simulations results show that the joint HM 16QAM and network coding has similar throughput to the conventional network coding with zero padding when the RN is closer to the DN, whereas the joint 16QAM and network coding outperforms the network coding with zero padding when the RN is closer to the SNs.

Linear network coding

Padding

QAM

10.1109/icufn.2016.7536991

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On Achievable Sum Rates of A Multiuser MIMO Relay Channel

Taiwen Tang Chan‐Byoung Chae Robert W. Heath Sunghyun Cho

In this paper, we investigate a multiple input multiple output (MIMO) multiuser relay channel, where a source with multiple antennas sends data to multiple users via a relay with multiple antennas. The relay applies linear processing to the received signal and forwards the processed signal to multiple users. In our system model, the direct links from the source to the users are neglected. We propose algorithms to compute achievable sum rates of this system based on dirty paper coding. An achievable sum rate defines a sum rate that can be achieved in the MIMO multiuser relay channel with zero error probability for any user, hence it is also a lower bound of the capacity of this channel. These algorithms also produce coefficients of the precoder at the source node and the coefficients of the linear processing unit at the relay. Simulations show that the proposed system architecture and algorithms achieve sum rate performance that is close to the derived performance upper bound

Dirty paper coding

10.1109/isit.2006.261883

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Citations (32)

Power saving mechanism with network coding in the bottleneck zone of multimedia sensor networks

Computer Networks (2015)

Kyu Hwan Lee Jae‐Hyun Kim Sunghyun Cho

Linear network coding

Packet forwarding

Packet loss

10.1016/j.comnet.2015.07.005

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Citations (9)

Multi-objective optimization of an explosive waste incineration process considering nitrogen oxides emission and process cost by using artificial neural network surrogate models

Process Safety and Environmental Protection (2022)

Sunghyun Cho Young‐Jin Kim Minsu Kim Hyungtae Cho Il Moon

Fluidized bed incinerators are more efficient and safe for treating explosive waste than previous methods because they can emit nitrogen oxide (NOx) concentrations below the standard value (90 ppm). However, a limitation is that they have only focused on optimizing the operating conditions to minimize NOx emission concentrations till now. In this situation, it is crucial to balance NOx and process costs. Therefore, this study designed an explosive waste incineration process and performed multi-objective optimization. An artificial neural network surrogate modeling method is vital to reduce optimization time. Therefore, surrogate models with 95% and 99% accuracies were obtained, reducing the calculation time by 90%. Furthermore, an index combining NOx emission concentrations and process costs was proposed to obtain an optimal balanced operating condition of the process. By optimizing the process index, a new operating condition was obtained that could reduce 20% of the process costs while maintaining NOx emission concentrations within the standard limit. The proposed operating condition and data, such as from sensitivity analysis, would provide a valuable guideline for operating the abovementioned process associated with NOx emission standards.

Operating cost

Nitrogen oxides

10.1016/j.psep.2022.04.062

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Citations (10)

Design and optimization of energy supplying system for electric vehicles by mobile charge stations

Journal of Industrial and Engineering Chemistry (2024)

Sunghyun Cho Jong Hun Lim Wangyun Won Junghwan Kim Seongbin Ga

10.1016/j.jiec.2024.04.027

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Citations (2)

DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

Seunghun Lee Sunghyun Cho Sunghoon Im

In this paper, we present DRANet, a network architecture that disentangles image representations and transfers the visual attributes in a latent space for unsupervised cross-domain adaptation. Unlike the existing domain adaptation methods that learn associated features sharing a domain, DRANet preserves the distinctiveness of each domain’s characteristics. Our model encodes individual representations of content (scene structure) and style (artistic appearance) from both source and target images. Then, it adapts the domain by incorporating the transferred style factor into the content factor along with learnable weights specified for each domain. This learning framework allows bi/multi-directional domain adaptation with a single encoder-decoder network and aligns their domain shift. Additionally, we propose a content-adaptive domain transfer module that helps retain scene structure while transferring style. Extensive experiments show our model successfully separates content-style factors and synthesizes visually pleasing domain-transferred images. The proposed method demonstrates state-of-the-art performance on standard digit classification tasks as well as semantic segmentation tasks.

Representation

Optimal distinctiveness theory

Factor (programming language)

Domain Adaptation

10.1109/cvpr46437.2021.01500

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Citations (44)