https://scholar.dgist.ac.kr/handle/20.500.11750/15997 2026-04-11T05:17:38Z https://scholar.dgist.ac.kr/handle/20.500.11750/60047 Title: Vibration-Assisted Hysteresis Mitigation for Achieving High Compensation Efficiency Author(s): Park, Myeongbo; An, Chunggil; Park, Junhyun; Kang, Jonghyun; Hwang, Minho Abstract: Tendon-sheath mechanisms (TSMs) are widely used in minimally invasive surgical (MIS) applications, but their inherent hysteresis—caused by friction, backlash, and tendon elongation—leads to significant tracking errors. Conventional modeling and compensation methods struggle with these non-linearities and require extensive parameter tuning. To address this, we propose a vibration-assisted hysteresis compensation approach, where controlled vibrational motion is applied along the tendon’s movement direction to mitigate friction and reduce dead zones. Experimental results demonstrate that the exerted vibration consistently reduces hysteresis across all tested frequencies, decreasing RMSE by up to 23.41% (from 2.2345 mm to 1.7113 mm) and improving correlation, leading to more accurate trajectory tracking. When combined with a Temporal Convolutional Network (TCN)-based compensation model, vibration further enhances performance, achieving an 85.2% reduction in MAE (from 1.334 mm to 0.1969 mm). Without vibration, the TCN-based approach still reduces MAE by 72.3% (from 1.334 mm to 0.370 mm) under the same parameter settings. These findings confirm that vibration effectively mitigates hysteresis, improving trajectory accuracy and enabling more efficient compensation models with fewer trainable parameters. This approach provides a scalable and practical solution for TSM-based robotic applications, particularly in MIS. 2025-10-22T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59144 Title: OFF-CLIP: Improving Normal Detection Confidence in Radiology CLIP with Simple Off-Diagonal Term Auto-adjustment Author(s): Park, Junhyun; Moon, Chanyu; Lee, Donghwan; Kim, Kyung Su; Hwang, Minho Abstract: Contrastive Language-Image Pre-Training (CLIP) based models enable zero-shot classification in radiology but often struggle with detecting normal cases due to rigid intra-sample alignment, which leads to poor feature clustering and increased false positive and false negative rates. We propose OFF-CLIP, a simple and effective refinement that introduces an off-diagonal loss term to promote the clustering of normal samples explicitly. In addition, it applies sentence-level filtering to remove typical normal phrases embedded within abnormal reports. OFF-CLIP does not require architectural changes and does not compromise abnormal classification performance. In the VinDr-CXR dataset, normal classification shows a notable 0.61 AUC improvement over the state-of-the-art baseline CARZero. It also improves zero-shot grounding performance by increasing pointing game accuracy and providing more reliable and precise anomaly localization. These results clearly demonstrate that OFF-CLIP serves as an efficient plug-and-play enhancement to existing medical vision-language models. The code and pre-trained models are publicly available at https://github.com/Junhyun-Park01/OFF-CLIP. 2025-09-23T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57865 Title: Optimizing Base Placement of Surgical Robot: Kinematics Data-Driven Approach by Analyzing Working Pattern Author(s): Yoon, Jeonghyeon; Park, Junhyun; Park, Hyojae; Lee, Hakyoon; Lee, Sangwon; Hwang, Minho Abstract: In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Conventional base placement relies on the experience of trained medical staff. This study proposes a novel method for determining the optimal base pose based on the surgeon's working pattern. The proposed method analyzes recorded end-effector poses using a machine learning-based clustering technique to identify key positions and orientations preferred by the surgeon. We introduce two scoring metrics to address the joint limit and singularity issues: joint margin and manipulability scores. We then train a multi-layer perceptron regressor to predict the optimal base pose based on these scores. Evaluation in a simulated environment using the da Vinci Research Kit shows unique base pose score maps for four volunteers, highlighting the individuality of the working patterns. Results comparing with 20,000 randomly selected base poses suggest that the score obtained using the proposed method is 28.2% higher than that obtained by random base placement. These results emphasize the need for operator-specific optimization during base placement in RAMIS. © 2024 IEEE. 2024-10-13T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/46780 Title: Design of a Multi-bending Flexible Manipulator for Gastrointestinal Surgery Author(s): Park, Hyojae; Hwang, Minho Abstract: Unlike the existing rigid surgical robots, flexible endoscopic robotic platforms have been actively researched owing to the advantages of high lesion accessibility and scar-free. However, there still exists difficult-to-access areas such as cardia and fundus in the gastrointestinal tract due to the limited Degrees of Freedom (DoFs) of the manipulator. In this study, we propose a multi-bending endoscopic manipulator to improve accessibility to the gastric region in any position and orientation. We perform mechanism design, kinematics, and cable-control analysis of the proposed manipulator. Finally, we verify the multi-bending motion in the simulated flexible pathway. © 2022 ICROS. 2022-11-29T15:00:00Z