https://scholar.dgist.ac.kr/handle/20.500.11750/10149 2026-08-23T22:33:24Z https://scholar.dgist.ac.kr/handle/20.500.11750/60268 Title: Tag interference based mobile object tracking with passive UHF RFID system Author(s): Choi, Jae Sung; Kang, Won Seok; Son, Chan Sik; Son, Byung Rak; Lee, Dong Ha Abstract: This paper proposes a novel method that enables location sensing for a mobile object by utilizing deployed passive UHF Radio Frequency Identification (RFID) tags and a stationary RFID reader. In order to estimate the mobile object location, the proposed method utilizes the second order under damped system based tag to tag interference model. The empirical study using RFID systems and a mobile robot verifies the effectiveness and performance of the proposed method. © Springer-Verlag Berlin Heidelberg 2015. 2014-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60267 Title: Stable path planning algorithm for avoidance of dynamic obstacles Author(s): Kang, Won-Seok; Yun, Sanghun; Kwon, Hyung-Oh; Choi, Rock Hyun; Son, Chang-Sik; Lee, Dong Ha Abstract: Previous research of path planning has focused mainly on finding shortest paths or smallest movements. These methods, however, have poor stability characteristics when dynamic obstacles are considered on real-life or in-body map's environments. In this paper, we suggest a stable path planning algorithm for avoidance of dynamic obstacles. The proposed method makes the movement of a mobile robot more stable in a dynamic environment. Our focus is based on finding optimal movements for stability rather than finding shortest paths or smallest movements. The algorithm is based on Genetic Algorithm (GA) and uses k-means clustering to recognize the distribution of dynamics obstacles in various mobile space. Simulation results confirm this method can determine stable paths through environments involving dynamic obstacles. In order to validate our results, we compared the dynamic k values used in k-means clustering and grid-based dynamic cell sizes from several test sets. © 2015 IEEE. 2014-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59405 Title: A Novel Multi-parametric H∞ Filter Design Method for Imperfectly Reconstructed Lateral Vehicle Dynamics Author(s): Jin, Yongsik; Han, Seungyong Abstract: This paper proposes novel multi-parametric filtering problems for imperfectly reconstructed lateral dynamics of autonomous driving vehicles in the presence of disturbances. The primary objective of this study is to provide a theoretical filter design criterion for lateral vehicle dynamics where cornering stiffness is estimated. To achieve this goal, we establish a new condition to define stable regions for the cornering stiffness estimation error and formulate a multi-parametric filtering error system using a polytopic approach. Then, we present a new robust filter design condition in terms of linear matrix inequalities (LMIs), and it provides globally optimized solutions. In this formulation, the cornering stiffness estimation error is incorporated into the convex optimization problem by adding a constraint that ensures the stability criteria are satisfied. Finally, we demonstrate the effectiveness of the proposed approach by simulating a lateral vehicle dynamics model. 2025-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59400 Title: MOSInversion: Knowledge distillation-based incremental learning in organ segmentation using DeepInversion Author(s): Kim, Jihyeon; Lee, Gyeongmin; Shin, Seung Yeon; Kim, Soopil; Park, Sang Hyun Abstract: Despite recent advancements in multi-organ segmentation (MOS) of medical images, existing models are limited in terms of extending their capability to unseen classes. Incremental learning has been proposed to enable models to learn new classes progressively, possibly using multiple datasets from different institutions. In this setting, models easily experience performance degradation on previously learned classes i.e., catastrophic forgetting. Although many methods have been proposed to mitigate this issue, applying them to medical imaging applications like multi-organ segmentation is not easy due to the large memory requirement when used for 3D medical data such as CT scans or the need for additional training of a generator for image synthesis. In this paper, we propose an incremental learning framework that leverages diverse synthetic images to retain the knowledge learned from previously seen data. We design MOSInversion to generate the synthetic images by utilizing a pre-trained model from the previous step. MOSInversion generates diverse images by using segmentation masks so that we can manipulate the shape, location, and size of organs. We evaluate our proposed method using three abdominal CT datasets (FLARE21, MSD, and KiTS19) and achieve state-of-the-art accuracy. 2025-11-30T15:00:00Z