• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.27-32
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• 2007

3D based information is demanded increasingly as cities grow three dimensionally and buildings become large and complex. The use of 3D GIS is also getting attention as fundamental data for ubiquitous computing applications such as location-based guidance, path finding and emergency escaping. However, most 3D modeling techniques are focused on the visualization of buildings or terrains and do not have topological structures required in spatial analyses. In this paper, we introduce a method to incorporate topological relationship into 3D models by combining 2D GIS layers and 3D model. We divide indoor spaces of a 3D model into discrete objects and then define the relationship with corresponding features in 2D GIS layers through database records. We also show how to construct hallways network in the 2D-3D integrated building model. Finally, we test different cases of route finding situations inside a building such as normal origin-destination path finding and emergency evacuation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.55-61
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• 2020

With the development of deep learning technology and advances in computing power, video-based research is now gaining more and more attention. Video data contains a large amount of temporal and spatial information, which is the biggest difference compared with image data. It has a larger amount of data. It has attracted intense attention in computer vision. Among them, motion recognition is one of the research focuses. However, the action recognition of human in the video is extremely complex and challenging subject. Based on many research in human beings, we have found that artificial intelligence-like attention mechanisms are an efficient model for cognition. This efficient model is ideal for processing image information and complex continuous video information. We introduce this attention mechanism into video action recognition, paying attention to human actions in video and effectively improving recognition efficiency. In this paper, we propose a new 3D residual attention network using convolutional neural network based on two attention models to identify human action behavior in the video. An evaluation result of our model showed up to 90.7% accuracy.

• The KIPS Transactions:PartA
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• v.11A no.3
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• pp.207-212
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• 2004

This paper provides the higher user QoS(Quality of Service) by analyzing media service model between server and client in single VoD(Video on Demand) environment and applying it to parallel VoD environment. Media service model is divided into the Client Pull, Server Push, and IPP(Interleaving Pull & Push) model. A server sends data based on client's request in the Client Pull Model. A server one-sidedly sends data without client's request in the Server Pull model. And the WP model unites above two models. For a parallel VoD environment, We built the PMSS system which provides the parallel media streaming services that one client is simultaneously served by several servers. In the single and parallel VoD environment, We compare and analyze the performance of service models with respect to network delay and data size in buffer. In this experiment, we found that IPP service model keeps the least network delay and stable client buffer in the parallel VoD environment. This result shows that PMSS can provide the more quality of service.

• Wind and Structures
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• v.34 no.3
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• pp.259-274
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• 2022

The present paper is focused on analyzing a set of Computational Fluid Dynamics (CFD) simulation data on reducing orthogonal peak base moment coefficients on a high-rise rectangular building with wings. The study adopts an aerodynamic optimization procedure (AOP) composed of CFD, artificial neural network (ANN), and genetic algorithm (G.A.). A parametric study is primarily accomplished by altering the wing positions with 3D transient CFD analysis using k - ε turbulence models. The CFD technique is validated by taking up a wind tunnel test. The required design parameters are obtained at each design point and used for training ANN. The trained ANN models are used as surrogates to conduct optimization studies using G.A. Two single-objective optimizations are performed to minimize the peak base moment coefficients in the individual directions. An additional multiobjective optimization is implemented with the motivation of diminishing the two orthogonal peak base moments concurrently. Pareto-optimal solutions specifying the preferred building shapes are offered.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• Wind and Structures
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• v.2 no.1
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• pp.25-40
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• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.9-14
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• 2014

The path loss data was re-calculated according to the distance between the base station and a mobile station in the mobile telecommunications network. In this paper, the averaged path loss data was plotted with the conventional path loss models(free space, plane earth, Hata model ${\ldots}$). The standard deviations for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were 2.29 dB, 3.39 dB, 4.75 dB, respectively. Additionally, the derivative values for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were evaluated to find the positions with more than 1 times or 2times of the standard deviation. The situations with the sharply fluctuated path loss were calculated to 5 positions in the 2 Km interval, to 7 positions in the 1 Km interval, to 19 positions in the 0.5 Km interval, respectively. And, the exact distances between the base station and a mobile station were found with the sharply fluctuated path loss.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.461-468
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• 2008

The needs for visualizing interactive multimedia contents on portable devices with realistic three dimensional shapes are increasing as new ubiquitous services are coming into reality. Especially in digital fashion applications with virtual reality technologies for clothes of various forms on different avatars, it is required to provide very high quality visual models over mobile networks. Due to limited network bandwidths and memory spaces of portable devices, it is very difficult to transmit visual data effectively and render realistic appearance of three dimensional images. In this thesis, we propose a compression method to reduce three dimensional data for digital fashion applications. The three dimensional model includes animation of avatar which require very large amounts of data over time. Our proposed method utilizes temporal and spatial coherence of animation data, to reduce the amount. By grouping vertices from three dimensional models, the entire animation is represented by a movement path of a few representative vertices. The existing three dimensional model compression approaches can get benefits from the proposed method by reducing the compression sources through grouping. We expect that the proposed method to be applied not only to three dimensional garment animations but also to generic deformable objects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.2
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• pp.151-157
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• 1996

This study, experimentally, aims at presenting the methodology to construct an efficient digital terrain by com-paring and analyzing the accuracy among the existing Digital Terrain Models, develope 3-D fractal terrain model-ling program by applying digital algorithm of fractal geometry and using turbo pascal, and lastly perform basic research on constructing GSIS-based 3-D fractal terrain modelling system by integrating a PC-based GSIS Pack-age and the 3-D fractal terrain modelling program developed by this paper. The results are as follows -First, the method to produce TIN(Triangulated Irregular Network) by the combination of point data and line data was showed as an alternative to construct efficient Digital Terrain Model. Second, developing GSIS-based 3-D fractal terrain modelling system, applying fractal geometry is the basic research in developing the new terrain modelling method. also, this study presented the possibility of 3-D terrain modelling with the use of fractal.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.296-305
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• 2016

This paper proposes a framework to automatically align Dental range image captured by depth sensors like the Microsoft Kinect. Aligning dental images by intraoral scanning technology is a difficult problem for applications requiring accurate model of dental-scan datasets with efficiency in computation time. The most important thing in dental scanning system is accuracy of the dental prosthesis. Previous approaches in intraoral scanning uses a Z-buffer ICP algorithm for fast registration, but it is relatively not accurate and it may cause cumulative errors. This paper proposes additional Alignment using the rough result comes after intraoral scanning alignment. It requires that Each Depth Image of the total set shares some overlap with at least one other Depth image. This research implements the automatically additional alignment system that aligns all depth images into Completed model by computing a network of pairwise registrations. The order of the each individual transformation is derived from a global network and AABB box overlap detection methods.