http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
진영훈(YoungHoon Jin),김두범(DuBeom Kim),채영호(YoungHo Chai),남상훈(SangHun Nam) 한국정보과학회 2013 정보과학회논문지 : 소프트웨어 및 응용 Vol.40 No.9
인간의 동작을 표현하는 아바타의 제작은 영화, 컴퓨터 게임 및 가상현실 분야에서 다양하게 사용되고 있다. 인간의 동작을 사실적으로 모사하기 위해서는 모션 캡처와 같은 센서로 데이터를 수집하여 데이터베이스화하고 이를 기반으로 동작을 구성한다. 방대한 양의 데이터를 통해 인간의 동작을 자연스럽게 재현할 수 있지만 동작 구성을 위해 복잡한 계산이 필요하므로 실시간으로 대화형 모바일 아바타를 구성하기가 용이하지 않다. 본 논문에서는 실시간으로 3D 모바일 아바타의 동작을 구성하기 위해 패턴매칭을 통한 직관적 분류로 데이터 셋을 구현하고, 유연하고 적응적인 계층구조 알고리즘에 의해 다양한 아바타의 동작을 사상하는 것을 제안한다. 사용자 입력의 유형을 분류하기 위해 자이로가 부착된 동작 입력센서를 사용하였고 SVM(Support Vector Machine)을 적용하여 분류하였다. Design of avatars which represents human motion is widely used in movie, computer game and virtual reality industry. In order to get the realistic human posture, motion data collection and database construction are required by using sensors such as motion capture device. Natural motion of human avatar can be achieved by using the massive amount of human posture data. However it is not easy to implement the real time interactive mobile avatar, since it requires complex calculation of those acquired posture data. This paper suggests a flexible, adaptive and hierarchical mapping of various avatar motion by using intuitive classification of posture data set through the pattern matching. Motion input sensor including gyro is used to track the user input and the SVM(support vector machine) is applied to classify the input motion.
스마트TV를 이용한 공동주택의 에너지 사용 모니터링 시스템
박성수(Sungsoo Park),진영훈(Younghoon Jin),남상훈(Sanghun Nam),채영호(Youngho Chai) (사)한국CDE학회 2013 한국CDE학회 논문집 Vol.18 No.6
This paper presents the necessary elements and data flow in developing a monitoring system of energy usage for apartment houses with a Smart TV. Energy consumption data in each home are collected and analyzed in the HUB station by way of measuring instruments. And the amount of energy usage, such as electricity, gas, hot water, heating, water and other utilities are displayed through the Smart TV application. Energy consumption Database in the HUB station are processed and displayed in the browser of a Smart TV through XML, JAVASCRIPT and Flash. Smart TV users can get the energy consumption status through the energy consumption analysis display of the Smart TV application and improve the energy efficiency by comparing the usage patterns with neighboring houses. And the application display energy usage information, consumption ranking, rates to user as well. Furthermore, usage of last month or year can be compared to help to reduce the energy usage. The proposed system can provide the information about the amount of energy use to be reduced and the warning on the waste of energy.
이지혜(JiHye Lee),진영훈(YoungHoon Jin),채영호(YoungHo Chai) (사)한국CDE학회 2014 한국CDE학회 논문집 Vol.19 No.4
In this paper, an intuitive emotional expression of the 3D avatar is presented. Using the motion selection control of 3D avatar, an easy-to-use communication which is more intuitive than emoticon is possible. 12 pieces different emotions of avatar are classified as positive emotions such as cheers, impressive, joy, welcoming, fun, pleasure and negative emotions of anger, jealousy, wrath, frustration, sadness, loneliness. The combination of lower body motion is used to represent additional emotions of amusing, joyous, surprise, enthusiasm, glad, excite, sulk, discomfort, irritation, embarrassment, anxiety, sorrow. In order to get the realistic human posture, BVH format of motion capture data are used and the synthesis of BVH file data are implemented by applying the proposed emotional expression rules of the 3D avatar.
평활도 제약과 포인트 클라우드 라이브러리를 이용한 점 군 데이터 분할
박성실(SeongSill Park),진영훈(YoungHoon Jin),채영호(YoungHo Chai) (사)한국CDE학회 2014 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2014 No.8
In this paper, the segmentation algorithm of T.Rabbani is implemented by using PCL(Point Cloud Library). The segmentation algorithm uses the smoothness constraint and is applied to divide a group of points which are belongs to the same part from the whole Point Cloud Data. Surface normal is used for searching the similar area and the neighbor points are grouped for normal estimation using the covariance matrix in either KNN(K Nearest Neighbors) or FDN(Fixed Distance Neighbor). According to the segmentation algorithm, only two variables affect the segmentation result. But regions of finding neighbors also affect the result and the different results of using KNN and FDN are presented in the paper.