• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.262-265
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• 2007

Metallic sandwich plates constructed of two face sheets and low relative density cores have lightweight characteristics and various static and dynamic load bearing functions. To predict the formability and performance of these structured materials, a computationally efficient FE-analysis method incorporating virtual equivalent projected model has been newly introduced for analysis of metallic sandwich plates. Two dimensional models using the projected shapes of 3D structures have the same equivalent elastic-plastic properties with original geometries including anisotropic stiffness, yield strength and linear hardening function. The projected shapes and virtual properties of the virtual equivalent projected model have been estimated analytically with the same equivalent properties and face buckling strength of 3D pyramidal truss core.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.129-135
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• 2022

Real human digital models appear more and more frequently in VR/AR application scenarios, in which real-time markerless face capture animation of personalized virtual human faces is an important research topic. The traditional way to achieve personalized real human facial animation requires multiple mature animation staff, and in practice, the complex process and difficult technology may bring obstacles to inexperienced users. This paper proposes a new process to solve this kind of work, which has the advantages of low cost and less time than the traditional production method. For the personalized real human face model obtained by 3D reconstruction technology, first, use R3ds Wrap to topology the model, then use Avatary to make 52 Blend-Shape model files suitable for AR-Kit, and finally realize real-time markerless face capture 3D real human on the UE4 platform facial motion capture, this study makes rational use of the advantages of software and proposes a more efficient workflow for real-time markerless facial motion capture of personalized 3D real human models, The process ideas proposed in this paper can be helpful for other scholars who study this kind of work.

• Journal of Korea Multimedia Society
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• v.24 no.9
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• pp.1279-1284
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• 2021

This paper addresses the task of transferring facial blendshape models to an arbitrary target face. Blendshape is a common method for the facial rig; however, production of blendshape rig is a time-consuming process in the current facial animation pipeline. We propose automatic blendshape facial rigging based on our blendshape transfer method. Our method computes the difference between source and target facial model and then transfers the source blendshape to the target face based on a deformation transfer algorithm. Our automatic method provides efficient production of a controllable digital human face; the results can be applied to various applications such as games, VR chating, and AI agent services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.490-493
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• 2021

This paper proposes an embedded system that detects mask and face recognition based on a microprocessor instead of Nvidia Jetson Board what is popular development kit. We use a class of efficient models called Mobilenets for mobile and embedded vision applications. MobileNets are based on a streamlined architechture that uses depthwise separable convolutions to build light weight deep neural networks. The device used a Maix development board with CNN hardware acceleration function, and the training model used MobileNet_V2 based SSD(Single Shot Multibox Detector) optimized for mobile devices. To make training model, 7553 face data from Kaggle are used. As a result of test dataset, the AUC (Area Under The Curve) value is as high as 0.98.

• Korean Journal of Computational Design and Engineering
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• v.5 no.3
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• pp.242-254
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• 2000

This paper describes an efficient solid modeling method for thin-walled plastic or sheet metal parts, based on the non-manifold offsetting operations. Since the previous methods for modeling and converting a sheet into a solid have adopted the boundary representations for solid object as their topological framework, it is difficult to represent the exact adjacency relationship between topological entities of a sheet model and a mixture of wireframe and sheet models that can appear in the meantime of modeling procedure, and it is hard to implement topological operations for sheet modeling and transformation of a sheet into a solid. To solve these problems, we introduce a non-manifold B-rep and propose a sheet conversion method based on a non-manifold offset algorithm. Because the non-manifold offset aigorithm based on mathematical definitions results in an offset solid with tubular and spherical thickness-faces we modify it to generate the ruled or planar thickness-faces that are mostly shown in actual plastic or sheet metal parts. In addition, in order to accelerate the Boolean operations used the offset algorithm, we also develope an efficient face-face intersection algorithm using topological adjacency information.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.342-347
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• 2008

Face tracking and recognition are difficult problems because the face is a non-rigid object. If the system tries to track or recognize the unknown face continuously, it can be more hard problems. In this paper, we propose the method to track and to recognize the face of the unknown person on video sequences using linear combination of nonlinear manifold models that is constructed in the system. The arbitrary input face has different similarities with different persons in system according to its shape or pose. Do we can approximate the new nonlinear manifold model for the input face by estimating the similarities with other faces statistically. The approximated model is updated at each frame for the input face. Our experimental results show that the proposed method is efficient to track and recognize for the arbitrary person.

• Fourth Industrial Review
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• v.2 no.2
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• pp.1-9
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• 2022

Purpose - The purpose is to present an efficient library-use education model in the form of flip learning, reflecting traditional teaching methods and gamification elements even in such non-face-to-face and face-to-face situations after COVID-19. Research design, data, and methodology - Research on library use education, research on ubiquitous environment and gamification instructional design, flip the learning, and gamification elements are classified, compared, and analyzed to present educational models for library education, COVID-19 Pandemic situation, and subsequent library use education. Result - We propose an e-learning content development strategy for flipped learning-based library education. First, benchmark and use the existing educational contents. Second, a user-friendly interface is configured so that learners can flexibly organize their learning contents. Third, it allows learners to experience it directly or indirectly in a virtual space. Conclusion - If the e-learning environment can be standardized to the level of schools or educational institutions, a good educational model that can be used not only in library user education but also in other fields will be possible.

• Wind and Structures
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• v.35 no.3
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• pp.193-211
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• 2022

The present study focuses on aerodynamic parameters behaviors and control on the single and double side setback building models at the buildings mid-height. The study is conducted by computational fluid dynamics (CFD) simulation. This study estimates the face wise pressure coefficient on single side setback buildings with a setback range of 20%-50% and double side setback buildings with setbacks ranging from 10%-25%. The polynomial fitted graphs from CFD data predict the Cp on different setback model faces within permissible limit ±13% error. The efficient model obtained according to the minimum drag, lift, and moment consideration for along and across wind conditions. The study guides the building tributary area doesn't control the drag, lift, and moment on setback type buildings. The setback distance takes a crucial role in that. The 20% double side setback model is highly efficient to regulate the moment for both along and across wind conditions. It reduces 17.5% compared to the 20% single side setback and 14% moment compared to the 10% double side setback models. The double side setback building is more efficient to control 4.2% moment than the single side setback building

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• IE interfaces
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• v.23 no.2
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• pp.118-125
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• 2010

The determination of a regression model is important in using statistical designs of experiments. Generally, the exact regression model is not known, and experimenters suppose that a certain model form will be fit. Then an experimental design suitable for that predetermined model form is selected and the experiment is conducted. However, the initially chosen regression model may not be correct, and this can result in undesirable statistical properties. We develop model-robust experimental designs that have stable prediction variance for a family of candidate regression models over a cuboidal region by using genetic algorithms and the desirability function method. We then compare the stability of prediction variance of model-robust experimental designs with those of the 3-level face centered cube. These model-robust experimental designs have moderately high G-efficiencies for all candidate models that the experimenter may potentially wish to fit, and outperform the cuboidal design for the second-order model. The G-efficiencies are provided for the model-robust experimental designs and the face centered cube.