21C의 디자이너는 인간의 생존과 삶의 질적 향상을 위한 미적 노력을 하고 있다. 20C의 대중성, 경제성, 합리성 중심에서 소외되었던 개인과 소수 중심에 경험을 중심으로 하는 다양한 가치에 대한 요구를 해결하여야만 한다. 가치는 기존의 가치를 조합(Value Organizing)하는 능력이 더욱 중요시되고, 조합의 능력은 융합(Convergence)으로 학계에서는 다양한 시도를 하고 있다. 무사시노미술대학의 학과 횡단적 교육, 울름조형대학의 사회적, 문화적인 책임을 강조, NASAD의 복합적 커리큘럼을 통하여 가치와 융합의 중요성을 확인하였다. 그러므로 가치 인식과 표현을 위한 디자인 중심에 관련 전문가의 제안을 수용적 자세로 들어서 하나의 비전을 제시하는 가치융합디자인을 제안하였다. 가치융합디자인 사례를 함께 제안함으로서 디자인 방법론으로서 가능성을 구체화하였다.

While displacement is valuable information regarding the behavior of structures, measuring displacement from large civil structures is often challenging and costly. This study develops an indirect displacement estimation method based on the multimetric data (i.e., acceleration and strain) that can estimate static as well as dynamic displacements. The approach is numerically validated on a simple-beam model with moving force

The purpose of this paper is to improve scientific attitude and interest of the elementary school students by using STEAM-based robots in education. In this work, we analyzed PDIE model that describes methodology for generating STEAM integrated education materials and then we also developed teaching materials utilizing STEAM-based robots. The effect of the STEM-based robots in education was analyzed through a test class which demonstrated the robots indeed help improving scientific attitude and interest of students. During the class, individual knowledge and experience of the students were integrated into the STEAM-based robots which then provide customized help for each student so that they have better understanding and interest in science, technology, and engineering field.

Common feature of displacement-based sensing is that the high-frequency resolution is limited, and often relatively low sampling rates are used. Another problem is that integration of accelerometer data that causes low-frequency noise amplification, and potentially more problematic differentiation of displacement measurements which amplify high-frequency noise. A multi-rate Kalman filtering approach is proposed to solve these problems. This method yields highly accurate motion data.

Common feature of displacement-based sensing is that the high-frequency resolution is limited, and often relatively low sampling rates are used. Another problem is that integration of accelerometer data that causes low-frequency noise amplification, and potentially more problematic differentiation of displacement measurements which amplify high-frequency noise. A multi-rate Kalman filtering approach is proposed to solve these problems. This method yields highly accurate motion data.

It is very important for a mobile robot to recognize and model its environments for navigation. However, the grid map constructed by sonar sensors cannot accurately represent the environment, especially the narrow environment, due to the angular uncertainty of sonar data. Therefore, we propose a map building scheme which combines sonar sensors and IR sensors. The maps built by sonar sensors and IR sensors are combined with different weights which are determined by the degree of translational and rotational motion of a robot. To increase the effectiveness of sensor fusion, we also propose optimal sensor arrangement through various experiments. The experimental results show that the proposed method can represent the environment such as narrow corridor and open door more accurately than conventional sonar sensor-based map building methods.

Finding a head of a person in a scene is very important for taking a well composed picture by a robot photographer because it depends on the position of the head. So in this paper, we propose a robust head tracking algorithm using a hybrid of an omega shape tracker and local binary pattern (LBP) AdaBoost face detector for the robot photographer to take a fine picture automatically. Face detection algorithms have good performance in terms of finding frontal faces, but it is not the same for rotated faces. In addition, when the face is occluded by a hat or hands, it has a hard time finding the face. In order to solve this problem, the omega shape tracker based on active shape model (ASM) is presented. The omega shape tracker is robust to occlusion and illumination change. However, when the environment is dynamic, such as when people move fast and when there is a complex background, its performance is unsatisfactory. Therefore, a method combining the face detection algorithm and the omega shape tracker by probabilistic method using histograms of oriented gradient (HOG) descriptor is proposed in this paper, in order to robustly find human head. A robot photographer was also implemented to abide by the 'rule of thirds' and to take photos when people smile.

현재의 교실 수업은 입시중심 교실 수업, 교과 간 단절 상태의 단일 교과 중심 교실 수업, 학생과의 소통이 대체로 적은 교사 측의 일 방향 진행 수업의 형태로 특징지을 수 있으며, 이러한 형태의 수업으로는 학생들의 창의성 신장을 기대하는 데에 많은 한계가 있다. 창의성은 교과 간의 융합적 수업을 통해서도 신장시킬 수 있을 것으로 기대된다. 이 사례 연구의 목적은 교과융합 프로그램을 개발하고 실행함으로써 학생들의 창의성 신장을 도모하기 위함이다. 이 연구에서는 창의성 신장을 위한 교과융합 프로그램을 개발하였고, 홍익대학교 사범대학 부속여자중학교 전교생을 대상으로 2009학년도 1년 동안 교과융합 프로그램을 실행하였다. 프로그램 실행 결과에 대해서는 외부 평가위원들이 개발한 평가 지표를 이용하여 평가를 실시하였다. ‘공간’과 ‘인물’이라는 공통 주제를 가지고 10개 교과의 교과융합이 가능하도록 교과융합 프로그램을 개발하였고, 개발된 교과융합 프로그램 실시 결과 학생들의 창의적인 교육 성과물이 산출되어 교과융합 프로그램을 통하여 창의적인 학습 성과를 얻을 수 있음을 확인하였다.

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinates using extrinsic calibration matrixes of a camera-LRF ( ) and a camera calibration matrix ( ). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

Protoplasts of Panax ginseng C. A. Meyer and Aralia continentalis K. (Araliaceae) were isolated from callus cells and mesophyll cells, respectively. The maximum yield of protoplasts isolated from callus cells of P. ginseng were obtained by incubation for 3 hrs in the enzyme mixture of 0.5% macerozyme, 1.5% cellulase, and 0.5 M mannitol as an osmoticum. In the case of mesophyll cells of A. continentalis, the highest yield of protoplasts were obtained by incubation for 5 hrs in the enzyme mixture of 1% macerozyme, 2% cellulase, and 0.6 M mannitol. A polyethylene glycol (PEG) treatment induced an intergeneric fusion of the protoplasts. The fusion products, that is, heterokaryocytes were obtained by treatment of 50% PEG containing 0.05 M Ca salts.

We propose a optimal fusion method for localization of multiple robots utilizing correlation between GPS on each robot in common workspace. Each mobile robot in group collects position data from each odometer and GPS receiver and shares the position data with other robots. Then each robot utilizes position data of other robot for obtaining more precise estimation of own position. Because GPS data errors in common workspace have a close correlation, they contribute to improve localization accuracy of all robots in group. In this paper, we simulate proposed optimal fusion method of odometer and GPS through virtual robots and position data.

This paper presents a new sensor system. CALOS, for motion estimation and 3D reconstruction. The 2D laser sensor provides accurate depth information of a plane, not the whole 3D structure. On the contrary, the CCD cameras provide the projected image of whole 3D scene, not the depth of the scene. To overcome the limitations, we combine these two types of sensors, the laser sensor and the CCD cameras. We develop a motion estimation scheme appropriate for this sensor system.In the proposed scheme, the motion between two frames is estimated by using three points among the scan data and their corresponding image points, and refined by non-linear optimization. We validate the accuracy of the proposed method by 3D reconstruction using real images. The results show that the proposed system can be a practical solution for motion estimation as well as for 3D reconstruction.