• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.447-450
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• 2000

컴퓨터 그래픽 기술의 발전에 따라 영화, 가상현실, 게임 등의 분야에서 캐릭터 애니메이션에 대한 연구가 활발히 이루어지고 있다 기존의 캐릭터 애니메이션 기술에서는 애니메이터가 직접 움직임을 정의하였지만, 최근에는 사용자의 환경이나 실세계 환경에 상호작용 하면서 지능적으로 동작할 수 있는 감성 캐릭터 애니메이션이 요구되고 있다. 본 논문은 사용자와 상호작용하는 과정에서 사용자의 감정상태를 파악하고, 이에 대응하는 적절한 행동을 할 수 있는 캐릭터의 생성에 대한 연구이다. 본 논문에서는 캐릭터에 감성을 부여하기 위하여 OCC모델을 바탕으로 하여 시스템을 설계하였다

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.91-101
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• 2008

For a friendly interaction between human and robot, emotional interchange has recently been more important. So many researchers who are investigating the emotion generation model tried to naturalize the robot's emotional state and to improve the usability of the model for the designer of the robot. And also the various emotion generation of the robot is needed to increase the believability of the robot. So in this paper we used the hybrid emotion generation architecture, and defined the generalized context input of emotion generation model for the designer to easily implement it to the robot. And we developed the personality and loyalty model based on the psychology for various emotion generation. Robot's personality is implemented with the emotional stability from Big-Five, and loyalty is made of familiarity generation, expression, and learning procedure which are based on the human-human social relationship such as balance theory and social exchange theory. We verify this emotion generation model by implementing it to the 'user calling and scheduling' scenario.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.208-210
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• 2006

최근 온라인 게임을 비롯하여 영화, 애니메이션 등 가상현실에서 캐릭터가 중심적인 역할을 하게 되었고 좀 더 능동적이고 사람에 가까운 캐릭터 개발이 필요하게 되었다. 이러한 요구 중에서 본 논문에서는 감성기반 캐릭터에 초점을 맞추었고 Emotion Al사의 Artificial Emotion Engine Model과 OCC Model를 바탕으로 각 캐릭터의 특성을 반영하고 캐릭터간의 상호 작용을 바탕으로 감성을 도출해 낼 수 있는 Emotion Engine의 Architecture를 제시한다.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.1-7
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• 2020

The secure access the lighting, Heating, ventilation, and air conditioning (HVAC), fire safety, and security control boxes of building facilities is the primary objective of future smart buildings. This paper proposes an authorized user access to the electrical, lighting, fire safety, and security control boxes in the smart building, by using color grid coded optical camera communication (OCC) with face recognition Technologies. The existing CCTV subsystem can be used as the face recognition security subsystem for the proposed approach. At the same time a smart device attached camera can used as an OCC receiver of color grid code for user access authentication data sent by the control boxes to proceed authorization. This proposed approach allows increasing an authorization control reliability and highly secured authentication on accessing building facility infrastructure. The result of color grid code sequence received by the unauthorized person and his face identification allows getting good results in security and gaining effectiveness of accessing building facility infrastructure. The proposed concept uses the encoded user access authentication information through control box monitor and the smart device application which detect and decode the color grid coded informations combinations and then send user through the smart building network to building management system for authentication verification in combination with the facial features that gives a high protection level. The proposed concept is implemented on testbed model and experiment results verified for the secured user authentication in real-time.

• The Korean Journal of Nuclear Medicine
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• v.31 no.1
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• pp.73-82
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• 1997

Regional myocardial blood flow (rMBF) can be noninvasively quantified using N-13 ammonia and dynamic positron emission tomography (PET). The quantitative accuracy of the rMBF values, however, is affected by the distortion of myocardial PET images caused by finite PET image resolution and cardiac motion. Although different methods have been developed to correct the distortion typically classified as partial volume effect and spillover, the methods are too complex to employ in a routine clinical environment. We have developed a refined method incorporating a geometric model of the volume representation of a region-of-interest (ROI) into the two-compartment N-13 ammonia model. In the refined model, partial volume effect and spillover are conveniently corrected by an additional parameter in the mathematical model. To examine the accuracy of this approach, studies were performed in 9 coronary artery disease patients. Dynamic transaxial images (16 frames) were acquired with a GE $Advance^{TM}$ PET scanner simultaneous with intravenous injection of 20 mCi N-13 ammonia. rMBF was examined at rest and during pharmacologically (dipyridamole) induced coronary hyperemia. Three sectorial myocardium (septum, anterior wall and lateral wall) and blood pool time-activity curves were generated using dynamic images from manually drawn ROIs. The accuracy of rMBF values estimated by the refined method was examined by comparing to the values estimated using the conventional two-compartment model without partial volume effect correction rMBF values obtained by the refined method linearly correlated with rMBF values obtained by the conventional method (108 myocardial segments, correlation coefficient (r)=0.88). Additionally, underestimated rMBF values by the conventional method due to partial volume effect were corrected by theoretically predicted amount in the refined method (slope(m)=1.57). Spillover fraction estimated by the two methods agreed well (r=1.00, m=0.98). In conclusion, accurate rMBF values can be efficiently quantified by the refined method incorporating myocardium geometric information into the two-compartment model using N-13 ammonia and PET.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.395-398
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• 2007

본 논문에서는 사람이 생각하고 표현하는데 영향을 주는 감성 요인을 사용하여 컴퓨터가 다양한 환경에 적응하며 지능을 표현할 수 있는 에이전트 모델을 제안한다. 감성은 사람이 생각하고 판단하는데 중요한 요소가 되고, 이러한 감성을 에이전트에 표현하면 사람의 추론하는 과정을 효과적으로 표현할 수 있다. 이에 사람의 추론 과정을 표현하기에 적합한 한 에이전트 모델인 BDI(Belief, Desire, Intention) 에이전트 모델을 감성과 결합하여 에이전트들의 행동을 빠르게 결정할 수 있는 ExMEBDI(Expanded Multi Emotional BDI) 에이전트 모델을 제안한다. 또 기존의 사람의 감성을 모델로 구성된 OCC모델을 기반으로 ExMEBDI 모델의 감성 추출 방법인 GEM(Generated EMotion)을 제안하였다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.267-273
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• 2005

Stereo image analysis has been an important tool for reconstructing 3D terrain. By In its nature, occlusion is one of difficulties Ive cannot avoid in stereo matching. This paper presents a study on occlusion detection by employing LRC(Left-Right Check) and OCC(Occlusion Constraint) and how we can improve the accuracy of DEM(Digital Elevation Model) y using interpolated data into the detected occluded area. Experimental results show that these method can effectively detect occluded regions and improve the accuarcy of DEM using the occlusion detection.

• Coupled systems mechanics
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• v.12 no.4
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• pp.315-335
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• 2023

The building sector has seen a huge increase in the use of lightweight concrete recently, which might result in saving in both cost and time. As a result, the study has been done on various types of concrete, including lightweight (LC), heavyweight (HC), and ordinary concrete (OC), to understand how they react to earthquake loads. The comparisons between their responses have also been taken into account in order to acquire the optimal reaction for various materials in building work. The findings demonstrate that LWC building models are more earthquake-resistant than the other varieties due to the reduction in building weight which can be a curial factor in the resistance of earthquake forces. Another crucial factor that was taken into study is the combination of various types of concrete [HC, LC, and OC] in the structural components. On the other hand, the bending moments and shear forces of LC had reduced to 17% and 19%, respectively, when compared to OC. Otherwise, the bending moment and shear force demand responses in the HC model reach their maximum values by more than 34% compared to the reference model OC. In addition, the results show that the LCC-OCR (light concrete column and ordinary concrete roof) and OCC-LCR (ordinary concrete for the column and light concrete for the roof) models' responses have fewer values than the other types.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.