• Cartoon and Animation Studies
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• s.20
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• pp.1-14
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• 2010

The success of a three-dimensional blockbuster movie, AVARTA, brought an public attention on the expansion of three-dimensional computer applications, and it allows experts predict further hardware technology developments to support the such applications. Futhermore, an internet based infra structure and three-dimensional structure, third generation network community, advanced computer networks have influenced advancement in computer technology within the 3D game industry and the spread of 2D computer animation technologies. This advancement of computer technologies allow the industry to overcome a limitation of generating cultural design contexts existed within 2D network community. However, despite of the expansion of 2D and 3D computer technologies, a limitation of analysing designers' intentions on morphology of digital contents and user interface still exists. Therefore, the purpose of this study is to analyze (1) present conditions of the 3D industry and (2) protocols of designers' cognitive design processes based on their design communication, contents, and tools. Analysis was conducted based on literature reviews and case precedent analyses. For the analysis, a 2D Avarta sketch character was designed and then applied into a 3D game system. Observations how designers solve cultural problem within the structure via Avarta were conducted. Outcomes were then coded for further analysis.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.39-46
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• 2019

In this paper, combining features is proposed as a way to enhance the classification accuracy of sounds under noisy environments using the CNN (Convolutional Neural Network) structure. A robust log Mel-filter bank using Wiener filter and PNCCs (Power Normalized Cepstral Coefficients) are extracted to form a 2-dimensional feature that is used as input to the CNN structure. An ebird database is used to classify 43 types of bird species in their natural environment. To evaluate the performance of the combined features under noisy environments, the database is augmented with 3 types of noise under 4 different SNRs (Signal to Noise Ratios) (20 dB, 10 dB, 5 dB, 0 dB). The combined feature is compared to the log Mel-filter bank with and without incorporating the Wiener filter and the PNCCs. The combined feature is shown to outperform the other mentioned features under clean environments with a 1.34 % increase in overall average accuracy. Additionally, the accuracy under noisy environments at the 4 SNR levels is increased by 1.06 % and 0.65 % for shop and schoolyard noise backgrounds, respectively.

• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.65-74
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• 2021

3D reconstruction means that reconstructing the 3D shape of the object in an image and a video. We proposed a progressive occupancy network architecture that can recover not only the overall shape of the object but also the local details. Unlike the original occupancy network, which uses a feature vector embedding information of the whole image, we extract and utilize the different levels of image features depending on the receptive field size. We also propose a novel network architecture that applies the image features sequentially to the decoder blocks in the decoder and improves the quality of the reconstructed 3D shape progressively. In addition, we design a novel decoder block structure that combines the different levels of image features properly and uses them for updating the input point feature. We trained our progressive occupancy network with ShapeNet. We compare its representation power with two prior methods, including prior occupancy network(ONet) and the recent work(DISN) that used different levels of image features like ours. From the perspective of evaluation metrics, our network shows better performance than ONet for all the metrics, and it achieved a little better or a compatible score with DISN. For visualization results, we found that our method successfully reconstructs the local details that ONet misses. Also, compare with DISN that fails to reconstruct the thin parts or occluded parts of the object, our progressive occupancy network successfully catches the parts. These results validate the usefulness of the proposed network architecture.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10C
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• pp.956-963
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• 2006

This research studies multi participant consistency and dynamic data shared through 3D scenes in virtual network environments. In a distributed virtual environment of client-server structure, consistency is maintained by the static information exchange; as jerks occur by packet delay when updating messages of dynamic data exchanges are broadcasted frequence, the network bottleneck is reduced by predicting the movement path by using the Dead-reckoning algorithm. In Dynamic data path prediction, the tests the location prediction error between Dead-reckoning convergence interval and error of prediction and actual condition one time above threshold it interpolates a previously location. The shared dynamic data of the 3D virtual environment is implementation using the VRML EAI.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.387-392
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• 2006

3D-based information is needed increasingly as well as 2D Information as cities grow and buildings become large and complex, and use of 3D-models is getting attention to handle such problems. However, there are limitations in using 3D-models because most applications and research efforts using them have been for visual analysis. This study presents a method to find optimal paths in indoor spaces as an illustration for using 3D-models in spatial analysis. We modeled rooms, paths and other facilities in a building as individual 3D objects. We made it possible to find paths based on network structure by integrating the vector-based networks of 2D-GIS and 3D-model.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.71-79
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• 1998

In this paper, we propose a new mART(modified ART) neural network by combining the winner neuron definition method of SOM(self-organizing map) and the real-time adaptive clustering function of ART(adaptive resonance theory) and construct it in a modular structure, for the purpose of organizing the feature maps of three dimensional targets. Being constructed in a modular structure, the proposed modular mART can effectively prevent the clusters from representing multiple classes and can be trained to organze two dimensional distortion invariant feature maps so as to recognize targets with three dimensional distortion. We also present the recognition result and self-organization perfdormance of the proposed modular mART neural network after carried out some experiments with 14 tank and fighter target models.

• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1021-1025
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• 2009

A novel supramolecular network containing binuclear copper unit $[Cu(phen)_{2}({\mu}-ID\;A)Cu(phen){\cdot}(NO_{3})](NO_{3}){\cdot}4(H_{2}O)$ (1) was synthesized through the self-assembly of iminodiacetic acid ($H_2IDA$) and 1,10-phenanthroline (phen) in the condition of pH = 6. It has been characterized by the infrared (IR) spectroscopy, elemental analysis, single crystal X-ray diffraction, and thermogravimetric analysis (TGA). 1 shows a 2-D supramolecular structure assembled through strong and unique $\pi-\pi$ packing interactions. Density functional theory (DFT) calculations show that theoretical optimized structures can well reproduce the experimental structure. The TGA and powder X-ray diffraction (PXRD) curves indicate that the complex 1 can maintain the structural integrity even at the loss of free water molecules. The magnetic property is also reported in this paper.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.325-331
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• 2004

The multiobjective optimization (MO) problem usually includes the conflicting objectives and the use of conventional optimization algorithms for MO problem does not so good approach to obtain an effective optimal solution. In this paper, genetic algorithm (GA) as an effective method is used to solve such MO problem of brushless DC motor (BLDCM). 3D equivalent magnetic circuit network (EMCN) method which enables us to reduce the computational burden is also used to consider the 3D structure of BLDCM. In order to effectively obtain a set of Pareto optimal solutions in MO problem, ranking method proposed by Fonseca is applied. The objective functions are decrease of cogging torque and increase of torque respectively. The airgap length, teeth width and magnetization angle of PM are selected for the design variables. The experimental results are also shown to confirm the validity of the optimization results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.3 no.3
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• pp.13-22
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• 1980

Research and Development has a property that involves uncertainity and risk in itself. Therefore, in order to scheduling of R & D activity, it Is needed of a certain probabilistic network technique with due regard to feedback process used to occur in the R & D proceeding. It is GERT that was developed as the need arises . In this study, the network structure of GERT-I and GERT-II was combined and then simulation analysis was used to it. According to that analysis , an advanced GERT model which covers the following stochastic problems was examined. 1 Evaluating success feasibility under the complex condition (time and cost). 2 Selecting acceptance range for the worst. 3. Selecting optimum path on basis of time, cost and success. 4. Evaluating project utility among the project alternatives. It is for managing R&D projects more effectively.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.