https://scholar.dgist.ac.kr/handle/20.500.11750/10149 2026-04-24T11:19:49Z 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/20.500.11750/60259 Title: Robust UWB Radar Gesture Recognition Addressing Speed-Induced Scale Variations via Multi-Scale Feature Extraction Author(s): Kim, Bong-Seok; Choi, Rockhyun; Jang, Seonghyun; Kim, Sangdong Abstract: In this paper, we propose a speed-robust hand gesture recognition system incorporating a multi-scale feature extraction (MSFE) module to address the critical engineering challenge of time-frequency scale mismatch caused by varying gesture speeds in ultra-wideband (UWB) radar. Conventional convolutional neural networks (CNNs) utilizing fixed kernels fail to effectively capture features when the same gesture is performed at different speeds, leading to performance degradation. To overcome this, our MSFE is specifically designed for the range-Doppler domain and is applied to the first layer to normalize physical motion-induced scale variations early in the pipeline. Experimental results on the UWB-gestures dataset demonstrate a 98.12% accuracy, outperforming conventional CNN and LSTM-based models. Furthermore, we provide a comprehensive runtime analysis, confirming that the proposed system is computationally efficient with only a marginal 1.3% increase in parameters, making it feasible for real-time deployment on edge devices. © 2013 IEEE. 2026-02-28T15:00:00Z 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