https://scholar.dgist.ac.kr/handle/20.500.11750/10144 2026-04-04T10:17:51Z 2026-04-04T10:17:51Z 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 김봉석 김제석 김중태 김시형 최락현 김상동 https://scholar.dgist.ac.kr/handle/20.500.11750/59385 2026-02-02T08:40:10Z 2025-11-30T15:00:00Z

Title: FFT-기반 Partition Selection을 활용한 SP-MUSIC DOA 추정 성능 개선 Author(s): 김봉석; 김제석; 김중태; 김시형; 최락현; 김상동 Abstract: 본 논문에서는 FFT 기반 Partition 선택 기법을 활용한 효율적인 도래각 (DOA) 추정 방법을 제안한다. 기존 SP-MUSIC (Spectrum Partitioning-MUltiple Signal Classification) 방식은 전체 스펙트럼을 여러 Partition으로 나누고, 각 Partition 별로 각각의 MUSIC 알고리즘을 적용할 뿐 아니라, 신호의 세기가 임계값 이하인 Partition까지 처리하기 때문에 불필요한 연산이 요구된다. 제안된 기법은 FFT를 통해 스펙트럼 에너지 분포를 먼저 분석하고, 신호가 존재하는 Partition만 선별하여 MUSIC 알고리즘을 적용한다. 이를 통해 불필요한 연산을 줄이고 노이즈 영향을 최소화함으로써, 기존 SP-MUSIC 대비 연산 효율성 및 개선된 DOA 추정 성능을 달성하였다. 시뮬레이션 결과는 제안된 기법이 감소된 복잡도에도 불구하고 기존 SP-MUSIC과 유사한 성능을 달성함을 보인다. In this paper, we propose an efficient direction estimation (DOA) method using the FFT-based partition selection technique. The conventional SP-MUSIC (Spectrum Partitioning-MUltiple Signal Classification) method divides the entire spectrum into several parts and applies the MUSIC algorithm to each partition, resulting in unnecessary operations and problems of processing even parts that do not have a signal. The proposed technique first analyzes the spectral energy distribution through FFT and applies the MUSIC algorithm by selecting only parts with a signal. Through this, the computational efficiency and DOA estimation performance compared to the conventional SP-MUSIC were improved simultaneously by reducing unnecessary operations and minimizing noise effects. Simulation results demonstrate that the proposed method achieves performance comparable to SP-MUSIC despite its reduced computational complexity.

2025-11-30T15:00:00Z Choi, Rockhyun Lee, Hyunki Kim, Bong-Seok Kim, Sangdong Kim, Min Young https://scholar.dgist.ac.kr/handle/20.500.11750/59379 2026-01-29T07:40:15Z 2025-11-30T15:00:00Z

Title: Noise-Resilient Masked Face Detection Using Quantized DnCNN and YOLO Author(s): Choi, Rockhyun; Lee, Hyunki; Kim, Bong-Seok; Kim, Sangdong; Kim, Min Young Abstract: This study presents a noise-resilient masked-face detection framework optimized for the NVIDIA Jetson AGX Orin, which improves detection precision by approximately 30% under severe Gaussian noise (variance 0.10) while reducing denoising latency by over 42% and increasing end-to-end throughput by more than 30%. The proposed system integrates a lightweight DnCNN-based denoising stage with the YOLOv11 detector, employing Quantize-Dequantize (QDQ)-based INT8 post-training quantization and a parallel CPU-GPU execution pipeline to maximize edge efficiency. The experimental results demonstrate that denoising preprocessing substantially restores detection accuracy under low signal quality. Furthermore, comparative evaluations confirm that 8-bit quantization achieves a favorable accuracy-efficiency trade-off with only minor precision degradation relative to 16-bit inference, proving the framework's robustness and practicality for real-time, resource-constrained edge AI applications.

2025-11-30T15:00:00Z