• Journal of Industrial Technology
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• v.28 no.A
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• pp.27-34
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• 2008

The purpose of this paper is an adaptive image interpolation using parametric cubic convolution. Proposed method derive parameter of adapting the frequency from adjacent values. The parameter optimize the interpolation kernel of cubic convolution. Simulation results show that the proposed method is superior to the conventional method in terms of the subjective and objective image quality.

• Structural Engineering and Mechanics
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• v.29 no.3
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• pp.279-288
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• 2008

In the present study, the discrete singular convolution (DSC) method is developed for buckling analysis of columns and thin plates having different geometries. Regularized Shannon's delta (RSD) kernel is selected as singular convolution to illustrate the present algorithm. In the proposed approach, the derivatives in both the governing equations and the boundary conditions are discretized by the method of DSC. The results obtained by DSC method were compared with those obtained by the other numerical and analytical methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1037-1041
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• 2007

With the advent of sound field simulator, many sound fields have been reproduced by obtaining the impulse responses of specific acoustic spaces like famous concert hall, opera house. This sound field reproduction has been done by the linear convolution operation between the sound input signal and the impulse response of certain acoustic space. However, the conventional finite impulse response based linear convolution operation always makes real-time implementation of sound field generator impossible due to the large amount of computational burden. This paper introduces the fast convolution method using perceptual redundancy in the processed signals, input audio signal and room impulse response. Temporal and spectral psycho-acoustic filters considering masking effects are implemented in the proposed convolution structure. It reduces the computational burden of convolution methods for realtime implementation of a sound field generator. The conventional convolutions are compared with the proposed one in views of computational burden and sound quality. In the proposed method, a considerable reduction in the computational burden was realized with acceptable changes in sound quality.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.2
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• pp.231-237
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• 2008

With the advent of sound field simulator, many sound fields have been reproduced by obtaining the impulse responses of specific acoustic spaces like famous concert hall, opera house. This sound field reproduction has been done by the linear convolution operation between the sound input signal and the impulse response of certain acoustic space. However, the conventional finite impulse response based linear convolution operation always makes real-time implementation of sound field generator impossible due to the large amount of computational burden. This paper introduces the fast convolution method using perceptual redundancy in the processed signals, input audio signal and room impulse response. Temporal and spectral real-time masking blocks are implemented in the proposed convolution structure. It reduces the computational burden of convolution methods for real-time implementation of a sound field generator. The conventional convolutions are compared with the proposed one in views of computational burden and sound quality. In the proposed method, a considerable reduction in the computational burden was realized with acceptable changes in sound quality.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.838-845
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• 2007

In this paper, we propose a modified cubic convolution scaler for the enlargement or reduction of digital images. The proposed method has less computational complexity than the cubic convolution method. In order to reduce the computational complexity, we use the linear function of the cubic convolution and the difference value of adjacent pixels for selecting interpolation methods. We employ adders and barrel shifts to calculate weights of the proposed method. The proposed method is compared with the conventional one for the computational complexity and the image quality. It has been designed and verified by HDL(Hardware Description Language), and synthesized using Xilinx Virtex FPGA.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.1259-1262
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• 2006

In this paper, we propose a modified cubic convolution interpolation for the enlargement or reduction of digital images using a pixel difference value. The proposed method has a low complexity: the number of multiplier of weighted value to calculate one pixel of a scaled image has seven less than that of cubic convolution interpolation has sixteen. We use the linear function of the cubic convolution and the difference pixel value for selecting interpolation methods. The proposed method is compared with the conventional one for the computational complexity and the image quality. The simulation results show that the proposed method has less computational complexity than one of the cubic convolution interpolation.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.299-307
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• 1999

In order to fast predict the wind-driven current in a small bay, a convolution method in which the wind-driven current can be generated only with the local wind is developed and applied in the idealized bay and the idealized Sachon Bay. The accuracy of the convlution method is assessed through a series of the numerical experiements carried out in the jidealized bay and the idealized Sachon Bay. The optimum response function for the convolution method is obtained by minimizing the root man square (rms) difference between the current given by the numerical model and the current given by the convolution method. The north-south component of the response function shows simultaneous fluctuations in the wind and wind-driven current at marginal region while it shows "sea-saw" fluctuations (in which the wind and wind-driven current have opposite direction) at the central region in the idealized Sachon Bay. The present wind is strong enough to influence on the wind-driven current especially in the idealized Sachon Bay. The spatial average of the rms ratio defined as the ratio of the rms error to the rms speed is 0.05 in the idealized bay and 0.26 in the idealized Sachon Bay. The recover rate of kinetic energy(rrke) is 99% in the idealized bay and 94% in the idealized Sachon Bay. Thus, the predicted wind-driven current by the convolution model is in a good agreement with the computed one by the numerical model in the idealized bay and the idealized Sachon Bay.achon Bay.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.1-8
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• 2016

Convolutional neural network (CNN) consists of a few pairs of both convolution layer and subsampling layer. Thus it has more hidden layers than multi-layer perceptron. With the increased layers, the size of convolution mask ultimately determines the total number of weights in CNN because the mask is shared among input images. It also is an important learning factor which makes or breaks CNN's learning. Therefore, this paper proposes the best method to choose the convolution size and the number of layers for learning CNN successfully. Through our face recognition with vast learning examples, we found that the best size of convolution mask is 5 by 5 and 7 by 7, regardless of the number of layers. In addition, the CNN with two pairs of both convolution and subsampling layer is found to make the best performance as if the multi-layer perceptron having two hidden layers does.

• 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.