https://scholar.dgist.ac.kr/handle/20.500.11750/15726 2026-04-05T09:40:56Z 2026-04-05T09:40:56Z Cho, A. Young Yoon, Ji Hyun Lee, Sangwoo Yun, Heeseo Ma, Joonhee Park, Jun-Young Kim, Soo Young Lee, Jonghun Choi, Taekjib https://scholar.dgist.ac.kr/handle/20.500.11750/59897 2026-03-03T05:40:13Z 2026-01-31T15:00:00Z

Title: Enhancing photoelectrochemical CO2 reduction with CuBi2O4-cellulose nanofiber hybrid photocathodes Author(s): Cho, A. Young; Yoon, Ji Hyun; Lee, Sangwoo; Yun, Heeseo; Ma, Joonhee; Park, Jun-Young; Kim, Soo Young; Lee, Jonghun; Choi, Taekjib Abstract: The photoelectrochemical (PEC) conversion of carbon dioxide (CO2) into valuable chemicals and fuels offers a promising strategy to address global challenges such as climate change and glacier retreat. However, developing high-performance photocathodes for the CO2 reduction reaction (CO2RR) is challenging, particularly in optimizing the surface morphology and active site distribution of the electrodes. In this study, we propose a CuBi2O4 (CBO)-based photocathode capable of gas-phase CO2RR through hybridization with cellulose nanofiber (CNF). Our results reveal that the CBO-CNF membrane exhibits inherent hydrophilicity and significantly larger active sites compared to a CBO film prepared with a Nafion binder, leading to reduced charge transfer resistance on the photocathode surface. Moreover, the simultaneous hydrothermal synthesis of the CBO-CNF composite precursor solution effectively inhibits the formation of undesirable CuO nanoparticles on the surface, which would otherwise increase charge transport resistance within the photocathode bulk. Consequently, the CBO-CNF membrane demonstrates superior PEC activities for CO2RR, achieving a photocurrent density of - 5.69 mA/cm2 at - 0.4 VRHE and an onset potential of 0.015 VRHE. Furthermore, the incorporation of CNF improves the long-term PEC stability of the photocathode by promoting charge carrier participation in CO2RR rather than undesired self-reduction reaction. This enhanced stability, coupled with the improved PEC performance, highlights the potential of CNF to replace existing polymer binder materials. These results suggest the feasibility of developing a new type of CBO photocathode with a porous membrane structure suitable for gas-phase PEC cells, marking a significant step forward in PEC technology for CO2 conversion.

2026-01-31T15:00:00Z Son, Eungang Song, Seungeon Kim, Bong-Seok Kim, Sangdong Lee, Jonghun https://scholar.dgist.ac.kr/handle/20.500.11750/59387 2026-02-19T04:40:11Z 2025-10-31T15:00:00Z

Title: Radar Foot Gesture Recognition with Hybrid Pruned Lightweight Deep Models Author(s): Son, Eungang; Song, Seungeon; Kim, Bong-Seok; Kim, Sangdong; Lee, Jonghun Abstract: Foot gesture recognition using a continuous-wave (CW) radar requires implementation on edge hardware with strict latency and memory budgets. Existing structured and unstructured pruning pipelines rely on iterative training–pruning–retraining cycles, increasing search costs and making them significantly time-consuming. We propose a NAS-guided bisection hybrid pruning framework on foot gesture recognition from a continuous-wave (CW) radar, which employs a weighted shared supernet encompassing both block and channel options. The method consists of three major steps. In the bisection-guided NAS structured pruning stage, the algorithm identifies the minimum number of retained blocks—or equivalently, the maximum achievable sparsity—that satisfies the target accuracy under specified FLOPs and latency constraints. Next, during the hybrid compression phase, a global L1 percentile-based unstructured pruning and channel repacking are applied to further reduce memory usage. Finally, in the low-cost decision protocol stage, each pruning decision is evaluated using short fine-tuning (1–3 epochs) and partial validation (10–30% of dataset) to avoid repeated full retraining. We further provide a unified theory for hybrid pruning—formulating a resource-aware objective, a logit-perturbation invariance bound for unstructured pruning/INT8/repacking, a Hoeffding-based bisection decision margin, and a compression (code-length) generalization bound—explaining when the compressed models match baseline accuracy while meeting edge budgets. Radar return signals are processed with a short-time Fourier transform (STFT) to generate unique time–frequency spectrograms for each gesture (kick, swing, slide, tap). The proposed pruning method achieves 20–57% reductions in floating-point operations (FLOPs) and approximately 86% reductions in parameters, while preserving equivalent recognition accuracy. Experimental results demonstrate that the pruned model maintains high gesture recognition performance with substantially lower computational cost, making it suitable for real-time deployment on edge devices.

2025-10-31T15:00:00Z 양경택 송승언 류정탁 이종훈 https://scholar.dgist.ac.kr/handle/20.500.11750/58247 2025-07-25T03:30:49Z 2025-01-31T15:00:00Z

Title: 건물 붕괴 모델에서 GPR의 교차피크 신호 검출기반 시간 지연 추정기법 제안 Author(s): 양경택; 송승언; 류정탁; 이종훈 Abstract: 본 논문은 지표 투과 레이다(Ground Penetrating Radar, GPR)에서 반사 신호의 불규칙한 변화로 인해 기존 상관 시간 지연 추정기술의 정확도가 저하되는 한계를 해결하기 위해 비상관 시간 지연 추정기법을 제안한다. 공동을 포함한 건물 붕괴 모델을 gprMax를 활용해 구현하고, A-scan 결과를 분석하여 매질의 전파전송시간을 추정하였다. 제안된 교차피크 신호검출방식을 이용한 시간 지연 추정기술은 기존 시간 지연 추정기법보다 정확도 측면에서 백분율 오차율이 대략 90 % 이상의 개선율을 확보할 수 있었다. This paper proposes an Alternative Peak Time Delay Estimation(APT-DE) technique to overcome the limitations of conventional Time Delay Estimation (CTDE) in Ground Penetrating Radar (GPR), caused by irregular changes in reflection signals. A collapsed building model including voids was created using gprMax, and A-scan results were analyzed to estimate Propagation Transit Time (PTT). The proposed APT-DE technique significantly improved PTT percentage errors, achieving more than 90 % of improvement in estimation error performance compared to CTDE.

2025-01-31T15:00:00Z Kim, Bong-Seok Jin, Youngseok Lee, Jonghun Kim, Sangdong Narayanan, Ram M. https://scholar.dgist.ac.kr/handle/20.500.11750/58129 2025-07-25T04:26:05Z 2024-12-31T15:00:00Z

Title: Super-Resolution Angle Estimation Algorithm using Low Complexity MUSIC-Based RELAX for MIMO FMCW Radar Author(s): Kim, Bong-Seok; Jin, Youngseok; Lee, Jonghun; Kim, Sangdong; Narayanan, Ram M. Abstract: A novel super-resolution angle estimation algorithm using low complexity-multiple signal classification (LC-MUSIC)-based relaxation (RELAX) for multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar is proposed in this paper. FMCW radar is widely used to estimate target location information in various fields, including autonomous driving, defense, and robotics. However, FMCW radar struggles to provide high-resolution direction of arrival (DOA) data. To obtain precise target positions, especially for multiple targets, FMCW radar with high-resolution DOA capability is needed. Conventional FMCW radar employs MIMO-based technology to improve angular resolution because of its low complexity and ease of implementation, but MIMO-based radar still struggles to provide precise angular resolution. To improve the angle resolution, we propose a novel super-resolution DOA algorithm in a MIMO scheme. The new method consists of RELAX-based CLEAN and LC-MUSIC. Combining the MIMO technique with the new super-resolution algorithm enables FMCW radar with high angular resolution to be obtained. To analyze the performance of the proposed estimation, Monte Carlo simulations are performed, and root-mean-square error results are analyzed. Using the FMCW radar module, experiments comparing the proposed and conventional algorithms were performed in an indoor environment. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. © Copyright The Korean Institute of Electromagnetic Engineering and Science.

2024-12-31T15:00:00Z