김태성 교수

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise rout

Our work suggests that denoising via NLM could be a key preprocessing method for clinical DXA imaging.

In this study, the authors propose a novel three‐dimensional (3D) convolutional neural network for shape reconstruction via a trilateral convolutional neural network (Tri‐CNN) from a single depth view. The proposed approach produces a 3D voxel representation of an object, derived from a partial object surface in a single depth image. The proposed Tri‐CNN combines three dilated convolutions in 3D to expand the convolutional receptive field more efficiently to learn shape reconstructions. To evalu

Abstract Three‐dimensional (3D) shape reconstruction of objects requires multiple scans and complex reconstruction algorithms. An alternative approach is to infer the 3D shape of an object from a single depth image (i.e. single depth view). This study presents such a 3D shape reconstructor based on U‐Net 3D‐convolutional neural network (3D‐CNN) with bottle‐neck skipped connection blocks (U‐Net BNSC 3D‐CNN) to infer the 3D shapes of objects from only a single depth view. The BNSC block is a fully

Dexterous object manipulation using anthropomorphic robot hands is of great interest for natural object manipulations across the areas of healthcare, smart homes, and smart factories. Deep reinforcement learning (DRL) is a particularly promising approach to solving dexterous manipulation tasks with five-fingered robot hands. Yet, controlling an anthropomorphic robot hand via DRL in order to obtain natural, human-like object manipulation with high dexterity remains a challenging task in the curre

Autonomous object manipulation is a challenging task in robotics because it requires an essential understanding of the object’s parameters such as position, 3D shape, grasping (i.e., touching) areas, and orientation. This work presents an autonomous object manipulation system using an anthropomorphic soft robot hand with deep learning (DL) vision intelligence for object detection, 3D shape reconstruction, and object grasping area generation. Object detection is performed using Faster-RCNN and an

본 논문은 역할 기반 뷰잉이라는 방법을 기반으로 하여 협업 설계에서의 정보 보호에 대한 기본 구조를 제안한다. 역할 기반 뷰잉은 다중 해상 기하 모델과 보안 모델을 조합하여 달성된다. 주어진 3차원 모델은 기하적으로 분할되며, 분할된 각 모델을 이용하여 다중 해상 메쉬 계층 구조가 생성된다. 협업 설계 환경에서 각 디자이너의 접근 권한에 알맞은 모델의 생성은 접근 통제 방법에 의해 이루어진다. This paper provides a framework for information assurance within collaborative design, based on a technique we call role-based viewing. Such role-based viewing is achieved through integration of multi-resolution geometry and security models. 3D models are geometrically partitio