Hyun Myung
KAIST 전기 및 전자공학부 · 공학
Hyun Myung 교수의 연구실은 구조물의 건강 상태를 실시간으로 모니터링하고 자율 주행 드론을 활용한 다리 점검 기술 개발에 초점을 맞추고 있습니다. 저비용 센서 기반의 구조변위 측정 기술과 RGB-D 센서를 활용한 SLAM 기반 자율주행 기술이 핵심 연구 분야이며, 특히 저성능 하드웨어에서도 실행 가능한 실시간 인간 자세 추정 및 제스처 인식 알고리즘 개발도 진행 중입니다. 연구는 실생활의 복잡한 환경에서의 적용 가능성을 고려해 실용성과 비용 효율성을 중시합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
The displacement measurement in structural health monitoring (SHM), though important, was not popular due to inaccessibility of the civil infrastructures and high installation cost. The currently popular approaches use accelerometer, strain gauge, PZT, GPS, etc., most of which indirectly measure the displacement and require high cost to install and maintain. Thus the development of SHM system that directly measures the displacement of the structure using low-cost sensors is urgently needed. In t
With the increasing demand for autonomous systems in the field of inspection, the use of unmanned aerial vehicles (UAVs) to replace human labor is becoming more frequent. However, the Global Positioning System (GPS) signal is usually denied in environments near or under bridges, which makes the manual operation of a UAV difficult and unreliable in these areas. This paper addresses a novel hierarchical graph-based simultaneous localization and mapping (SLAM) method for fully autonomous bridge ins
In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detec
In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from
A hybrid of evolutionary programming and a deterministic optimization procedure is applied to a series of nonlinear and quadratic optimization problems. The results indicate that the hybrid method can outperform the exclusive use of evolutionary programming when addressing constrained optimization problems with finite penalty functions. Directions for future research are outlined.
