https://scholar.dgist.ac.kr/handle/20.500.11750/15995 2026-04-11T02:17:47Z 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/20.500.11750/58758 Title: METHOD FOR CORRECTING ROBOT CONTROL SIGNAL AND ELECTRONIC DEVICE THEREFOR Author(s): 김현; 황민호; 백현우; 장성혁; 김창순; 권동수; 뚜아니데사로사; 김덕상; 강동훈; 이동호; 박준현; 양운제; 설세민 Abstract: According to the present disclosure, disclosed is a method for correcting a robot control signal, the method comprising the steps of: for each of at least one driving axis of a robot, obtaining, on the basis of an inverse driving model of the robot, an initial input rotation angle with respect to a target rotation angle; obtaining, on the basis of a driving model of the robot, a predicted rotation angle with respect to the initial input rotation angle; and correcting the initial input rotation angle on the basis of an error between the predicted rotation angle and the target rotation angle. 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