http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
김범성(BeomSeong Kim),김동엽(Dong Yeop Kim),황재필(Jae Pil Hwang),김은태(Euntai Kim),김영욱(Young-Ouk Kim) 한국지능시스템학회 2009 한국지능시스템학회 학술발표 논문집 Vol.19 No.1
SLAM에서 나오는 Data들은 모두 3차원 좌표로 존재하게 된다. 하지만 기존에 3차원 Map Building에서 주로 사용해 왔던 방법들은 속도가 느리고 높은 Hardware 사양을 요구하기 때문에 실시간으로 로봇을 구동하고 data를 처리하는 데에는 불편함이 많았다. 그래서 OpenGL을 이용하여 그래픽 API로 SLAM에 적합한 3D Map building Interface를 개발하게 되었다. 개발된 Interface는 Feature를 특성에 맞게 분류하여 표현하기 때문에 좀 더 나은 개발 환경을 제공하게 될 것이다.
Wet Area and Puddle Detection for Advanced Driver Assistance Systems (ADAS) Using a Stereo Camera
Kim, Jisu,Baek, Jeonghyun,Choi, Hyukdoo,Kim, Euntai Institute of Control, Robotics and Systems 2016 International Journal of Control, Automation, and Vol.14 No.1
Wet area or puddle detection is one of the key issues for safe driving and future Advanced Driver Assistance Systems (ADAS). A new methodology for the detection of wet areas and puddles using a stereo camera is presented in this paper. Because wet areas and puddles have different characteristics, the two areas are separately treated and different detection algorithms are proposed. For the detection of wet areas, color information is used for hypothesis generation (HG) and a support vector machine (SVM) is employed for hypothesis verification (HV). In HV, three features are proposed for classification; these are the polarization difference, graininess and gradient magnitude. For the detection of puddles, the depth map obtained by a stereo camera is used to exploit the fact that abrupt depth changes are detected around the puddles. In the experiment, it is shown that the proposed methods have a robust performance for the detection of wet areas or puddles.
Kim, Euntai,Park, Changwoo IEEE 2004 IEEE Transactions on Cybernetics Vol.34 No.3
This paper presents a new approach to robust tracking control of the nonlinear sampled systems using a discrete-time fuzzy disturbance observer (DFDO). Novel update and control laws are proposed to guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in a compact set. No persistence of excitation (PE) condition, nor the assumption on the slowness of the change of the fuzzy parameters, is required. In addition, a robustifying controller is designed to improve the tracking performance. Finally, a computer simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.
Adaptive Synchronization of Uncertain Chaotic Systems Based on T–S Fuzzy Model
Kim, Jae-Hun,Hyun, Chang-Ho,Kim, Euntai,Park, Mignon Institute of Electrical and Electronics Engineers, 2007 IEEE transactions on fuzzy systems Vol.15 No.3
<P> This paper presents an adaptive approach for synchronization of Takagi–Sugeno (T–S) fuzzy chaotic systems. T–S fuzzy model can represent a general class of nonlinear system and we employ it for fuzzy modeling of the chaotic drive system. Since the output of the drive system is only available for synchronization, the response system is designed based on fuzzy adaptive observer for uncertain parameters and parameter mismatch cases. We analyze the stability of the overall fuzzy synchronization system by applying Lyapunov stability theory and derive stability conditions by solving linear matrix inequalities (LMIs) problem. The adaptive law is derived to estimate the uncertain parameters or parameter mismatch. Numerical examples are given to demonstrate the validity of the proposed fuzzy adaptive synchronization approach. </P>
Pedestrian/Vehicle Detection Using a 2.5-D Multi-Layer Laser Scanner
Beomseong Kim,Baehoon Choi,Park, Seongkeun,Hyunju Kim,Euntai Kim IEEE 2016 IEEE SENSORS JOURNAL Vol.16 No.2
<P>Laser scanners are widely used as the primary sensor for autonomous driving. When the commercialization of autonomous driving is considered, a 2.5-D multi-layer laser scanner is one of the best sensor options. In this paper, a new method is presented to detect pedestrians and vehicles using a 2.5-D multi-layer laser scanner. The proposed method consists of three steps: 1) segmentation; 2) feature extraction; and 3) classification; this paper focuses on the last two steps. In feature extraction, new features for the multi-layer laser scanner are proposed to improve the classification performance. In classification, radial basis function additive kernel support vector machine is employed to reduce the computation time while maintaining the performance. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in many real-life situations.</P>
Hough Transform-based Road Boundary Localization
Beomseong Kim,Seongkeun Park,Euntai Kim 한국지능시스템학회 2017 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.17 No.3
The advanced driver-assistance system (ADAS) is designed to help drivers while they are driving. To help the drivers, ADAS first comprehends the situation by analyzing the data obtained from the road surroundings. In this process, the road boundary is one of the most important targets to detect for safe driving, but is frequently misdetected on crowded roads. Therefore, a new method for robustly detecting road boundaries on crowded roads is presented in this paper. First, road-boundary detection using a standard Hough transform is described, and its limitations are shown. Second, the cause of the limitations is explained by the measurement model of a laser scanner. Then, the standard Hough transform is modified to reflect the measurement model of the laser scanner; this change reduces the effect of closed obstacles. Finally, the proposed method is tested in the real-world environment, and it shows better performance than previous works in crowded environments.
김은태(Euntai Kim),신현석(Hyunseok Shin),박민용(Mignon Park) 한국정보기술학회 2003 한국정보기술학회논문지 Vol.1 No.1
This paper presents an explanation regarding on-line identification of a fuzzy system. The fuzzy system to be identified is assumed to be in the type of singleton consequent parts and be represented by a linear combination of fuzzy basis functions (FBF’)s. For on-line identification, squared-cosine (SCOS) fuzzy basis function is introduced to reduce the number of parameters to be identified and make the system consistent and differentiable. Then the parameters of the fuzzy system are identified on-line by the gradient search method. Finally, a computer simulation is performed to illustrate the validity of the suggested algorithms.