• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.37-40
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• 2006

3DTV는 아나로그 TV 및 HDTV의 차세대 이슈로 부상하였다. 그러나 대부분의 컨텐츠가 2D로 획득되어 저장되어 있으므로 2D 컨텐츠의 3D로의 변화이 필수적이다. MPEG 및 JVT에서 표준화가 진행되고 있으며 이를 위해 국내외 연구소, 학교, 및 업계가 관심을 가지고 참여하고 있다. 2D/3D 변환은 오래전부터 연구되어 왔으나 실제 응용에서는 기대에 못 미치고 있다. 본 논문에서는 FPGA에 기반하고 VHDL로 코딩하여 2D/3D 변환을 위한 Convolution filter를 적용하였다. 좌우 영상을 생성하기 위하여 Convolution filter로 좌우 영상을 왜곡하였다. 필터의 사용으로 사용자의 위치나 취향에 따라서 영상의 왜곡을 달리하여 효과의 변화를 줄 수 있다.

• Honam Mathematical Journal
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• v.37 no.2
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• pp.149-185
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• 2015

Let ${\sigma}_s(N)={\sum}_{d{\mid}N}d^s$ denote the sum of sth powers of the positive divisors of N. In this article, we consider the convolution sums of four divisor functions.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.532-535
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• 2005

These days, many image processing techniques have been studied for effective image compression. Among those, 2D image filtering is widely used for 2D image processing. The 2D image filtering can be implemented by performing ID linear filtering separately in the direction of horizontal and vertical. Efficiency of image compression depends on what filtering method is used. Generally, circular convolution is widely used in the 2D image filtering for image processing. However it doesn't consider correlations at the region of image boundary, therefore filtering can not be performed effectively. To solve this problem. I proposed new convolution technique using Symmetric-Mirroring convolution, satisfying the 'alias-free' and 'error-free' requirement in the reconstructed image. This method could provide more effective performance than former compression methods. Because it used very high correlative data when performed at the boundary region. In this paper, pre-processing filtering in H.264 codec was adopted to analyze efficiency of proposed filtering technique, and the simulator developed by Matlab language was used to examine the performance of the proposed method.

• Bulletin of the Korean Mathematical Society
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• v.21 no.2
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• pp.91-95
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• 1984

Let D$^{p}$ be the space of distributions of $L^{p}$-growth and S the space of tempered destributions in $R^{n}$: D$^{p}$, 1.leq.P.leq..inf., is the dual of the space $D^{p}$ which we discribe later. We denote by O$_{c}$(S:S') the space of convolution operators in S. In [8] S. Sznajder and Z. Zielezny proved the following necessary conditions for convolution operators in O$_{c}$(S:S) to be solvable in S.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.455-457
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• 2019

In this paper we study the effect of decomposed spatiotemporal convolutions for action recognition in videos. Our motivation emerges from the empirical observation that spatial convolution applied on solo frames of the video provide good performance in action recognition. In this research we empirically show the accuracy of factorized convolution on individual frames of video for action classification. We take 3D ResNet-18 as base line model for our experiment, factorize its 3D convolution to 2D (Spatial) and 1D (Temporal) convolution. We train the model from scratch using Kinetics video dataset. We then fine-tune the model on UCF-101 dataset and evaluate the performance. Our results show good accuracy similar to that of the state of the art algorithms on Kinetics and UCF-101 datasets.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.1
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• pp.1-9
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• 2019

In this paper, we propose a method of generating a 2D gray image from LiDAR 3D reflection intensity. The proposed method uses the Fully Convolutional Network (FCN) to generate the gray image from 2D reflection intensity which is projected from LiDAR 3D intensity. Both encoder and decoder of FCN are configured with several convolution blocks in the symmetric fashion. Each convolution block consists of a convolution layer with $3{\times}3$ filter, batch normalization layer and activation function. The performance of the proposed method architecture is empirically evaluated by varying depths of convolution blocks. The well-known KITTI data set for various scenarios is used for training and performance evaluation. The simulation results show that the proposed method produces the improvements of 8.56 dB in peak signal-to-noise ratio and 0.33 in structural similarity index measure compared with conventional interpolation methods such as inverse distance weighted and nearest neighbor. The proposed method can be possibly used as an assistance tool in the night-time driving system for autonomous vehicles.

• Journal of Applied and Pure Mathematics
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• v.4 no.1_2
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• pp.63-69
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• 2022

This article is the result of calculating the convolution of Ramanujan's sum and natural number multiplied. Among these results, special values are expressed by Euler and Bernoulli functions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Journal of the Korean Mathematical Society
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• v.41 no.2
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• pp.265-294
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• 2004

In this paper, using a simple formula, we evaluate the conditional Fourier-Feynman transforms and the conditional convolution products of cylinder type functions, and show that the conditional Fourier-Feynman transform of the conditional convolution product is expressed as a product of the conditional Fourier-Feynman transforms. Also, we evaluate the conditional Fourier-Feynman transforms of the functions of the forms exp {$\int_{O}^{T}$ $\theta$(s,$\chi$(s))ds}, exp{$\int_{O}^{T}$ $\theta$(s,$\chi$(s))ds}$\Phi$($\chi$(T)), exp{$\int_{O}^{T}$ $\theta$(s,$\chi$(s))d${\zeta}$(s)}, exp{$\int_{O}^{T}$ $\theta$(s,$\chi$(s))d${\zeta}$(s)}$\Phi$($\chi$(T)) which are of interest in Feynman integration theories and quantum mechanics.