Junyong Shin

Pohang University of Science and Technology

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Yo–Seb Jeon

Pohang University of Science and Technology

Yujin Kang

Guangxi University

Seong-Mok Jeong

National Fisheries Research and Development Institute

Junghyun Yoon

Pusan National University

M. D. Harsh

Ashland (United States)

Jinsung Park

Pohang University of Science and Technology

Seunghyun Jeon

C. M. Kim

Kwonse Kim

Kongju National University

H. Vincent Poor

Princeton University

Cooperative Institutions

Yonsei University

Pohang University of Science and Technology

National Fisheries Research and Development Institute

Pusan National University

Jeonbuk National University

Princeton University

Korea Telecom (South Korea)

Kookmin University

Korea Automotive Technology Institute

The National Institute for Strategic Studies

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Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems

IEEE Wireless Communications Letters (2024)

Junyong Shin Yujin Kang Yo–Seb Jeon

Learning vector quantization

10.1109/lwc.2024.3415633

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MIMO Detection under Hardware Impairments: Data Augmentation With Boosting

arXiv (Cornell University) (2024)

Yujin Kang Seunghyun Jeon Junyong Shin Yo–Seb Jeon H. Vincent Poor

This paper addresses a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop novel likelihood function (LF) estimation methods based on data augmentation and boosting. The core idea of our methods is to generate multiple augmented datasets by injecting noise with various distributions into seed data consisting of online received signals. We then estimate the LF using each augmented dataset based on either the expectation maximization (EM) algorithm or the kernel density estimation (KDE) method. Inspired by boosting, we further refine the estimated LF by linearly combining the multiple LF estimates obtained from the augmented datasets. To determine the weights for this linear combination, we develop three methods that take different approaches to measure the reliability of the estimated LFs. Simulation results demonstrate that both the EM- and KDE-based LF estimation methods offer significant performance gains over existing LF estimation methods. Our results also show that the effectiveness of the proposed methods improves as the size of the augmented data increases.

Boosting

10.48550/arxiv.2412.06049

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A STUDY ON THE CHARACTERISTICS AND CHANGES IN SPEED IN RELATION TO THE INCREASE OF PLUNGER HOLE IN HYDROGEN INJECTORS

JP Journal of Heat and Mass Transfer (2023)

Junyong Shin Kwonse Kim

The purpose of this study is to investigate a method that can improve the velocity characteristics of hydrogen injectors. Specifically, we aim to analyze the physical changes in velocity characteristics as the plunger center within the injector changes and bypass holes are increased. The method of analysis used in this research involves setting conditions based on a mesh matrix control, such as inlet pressure, outlet pressure, and flow direction, to analyze the plunger within the hydrogen injector. Regarding the analytic model, we set the method of heat transfer, where the temperature inside and outside is set at 30°C, turbulence within the flow layer is k-epsilon, and mass and momentum are in a stable condition of total pressure. As a result, the velocity within the plunger designed with four bypass hole can be increased in pressure uniformity at the plunger center as the stroke increases. Received: April 24, 2023Accepted: June 1, 2023

Momentum (technical analysis)

Plunger pump

10.17654/0973576323032

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Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems

arXiv (Cornell University) (2024)

Junyong Shin Yujin Kang Yo–Seb Jeon

This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook. A multi-rate codebook design strategy is also developed by introducing a codeword selection rule for a nested codebook along with the design of a loss function. Simulation results demonstrate that the proposed method reduces the computational complexity associated with VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.

Learning vector quantization

10.48550/arxiv.2403.07355

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A standard test case for a low speed, turbulent junction vortex flow

Flint Pierce M. D. Harsh C. M. Kim S. K. Nath Junyong Shin

Source

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Development of the New Aquaculture Fishcage for Open Ocean Areas

Seong-Mok Jeong Junyong Shin J. Y. Lee Junghyun Yoon

Until now, Korean aquaculture industry has been grown largely in quality and quantity. However, an increase of quantity has caused lots of problems like sea pollution lately. Nearly 82% of Korean aquaculture farms had been installed mainly at inshore of the southern sea, so, sewage, factory wastes, high density of farms at the limited area and autoinfection have deteriorated inshore fisheries. Now, the open sea has been on the rise for the solution to these problems since it has clear seawater that will expect to decrease the environmental pollution with much current. As a matter of fact, it is impossible to move the existing fish cages to the open sea due to the rough environmental conditions such as higher waves, strong currents etc.; therefore, it is necessary to develop new fish cage equipment that will fully meet the open sea conditions. In this research, the theoretical study and experimental analysis have been carried out for the structural fish cage design which is feasible for the open sea.

Open sea

Factory (object-oriented programming)

10.1115/omae2006-92534

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Entropy-Constrained VQ-VAE for Deep-Learning-Based CSI Feedback

IEEE Transactions on Vehicular Technology (2025)

Junyong Shin Jinsung Park Yo–Seb Jeon

10.1109/tvt.2025.3542267

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Error-Robust Deep Learning-Based CSI Feedback in Massive MIMO Systems: A Multi-Rate Vector Quantization Approach