• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.8-14
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• 2022

With the development of deep learning, super-resolution (SR) methods have tried to use deep learning mechanism, instead of using simple interpolation. SR methods using deep learning is generally based on convolutional neural networks (CNN), but recently, SR researches using attention mechanism have been actively conducted. In this paper, we propose an approach of improving SR performance using one of the attention mechanisms, non-local sparse attention (NLSA). Through experiments, we confirmed that the performance of the existing SR models, IMDN, CARN, and OISR-LF-s can be improved by using NLSA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2473-2489
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• 2022

Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.503-510
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• 2018

Restoring of damaged images is a fundamental problem that was attempted before digital image processing technology appeared. Various algorithms for reconstructing damaged images have been introduced. However, the results show inferior restoration results compared with manual restoration. Recent developments of DNN (Deep Neural Network) have introduced various studies that apply it to image restoration. However, if the wide area is damaged, it can not be solved by a general interpolation method. In this case, it is necessary to reconstruct the damaged area through contextual information of surrounding images. In this paper, we propose an image restoration network using a generative adversarial network (GAN). The proposed system consists of image generation network and discriminator network. The proposed network is verified through experiments that it is possible to recover not only the natural image but also the texture of the original image through the inference of the damaged area in restoring various types of images.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.460-464
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• 1990

Texture provides an important source of information about the local orientation of visible surfaces. An important task that arises in many computer vision systems is the reconstruction of three-dimensional depth information from two-dimensional images. The surface orientation of texel is classified by the Artificial Neural Network. The classification method to recognize the shape of 3D object with artificial neural network requires less developing time comparing to conventional method. The segmentation problem is assumed to be solved. The surface in view is smooth and is covered with repeated texture elements. In this study, 3D shape reconstruct using interpolation method.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.2
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• pp.30-41
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, It needs a robust performance which can get over the nonlinear characteristics due to hull shape. Second, It needs an accurate performance which has the small overshoot phenomenon and steady state error to avoid colliding with ground surface and obstacles. Third, It needs a continuous control input to reduce the acoustic noise. Finally, It needs an effective interpolation method which can reduce the dependency of control parameters on speed. To solve these problems, we propose a Intelligence depth control method using Fuzzy Sliding Mode Controller and Neural Network Interpolator. Simulation results show the proposed control scheme has robust and accurate performance by continuous control input and has no speed dependency problem.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2531-2533
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• 2005

The proposed neural network technique is the real time computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objects during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of objects. All of therm request much memory space and time. Therefore the most reliable neural-network algorithm is drived for real-time matching of obejects, which is composed of a dynamic programming algorithm based on sequence matching techniques in moving objects.

• ETRI Journal
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• v.29 no.6
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• pp.823-825
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• 2007

In this letter, we propose a novel algorithm to predict MPEG-coded real-time variable bit rate (VBR) video traffic. From the frame size measurement, the algorithm extracts the statistical property of video traffic and utilizes it for the prediction of the next frame for I-, P-, and B- frames. The simulation results conducted with real-world MPEG-4 VBR video traces show that the proposed algorithm is capable of providing more accurate prediction than those in the research literature.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.2-176
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over nonlinear characteristics. Second, it needs accurate performance which have small overshoot phenomenon and steady state error. Third, it needs continuous control input. Finally, it needs interpolation method which can solve the speed dependency problem of controller parameters. To solve these problems, we propose adepth control method using Fuzzy Sliding Mode Controller and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.814-817
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• 2002

High-dimensional data with two or more attributes are considered. A typical example of such data is face images of various individuals and expressions. In these cases, collecting a complete data set is often difficult since the number of combinations can be large. In the present study, we propose a method to interpolate data of missing combinations from other data. If this becomes possible, robust recognition of multiple attributes is expectable. The key of this subject is appropriate extraction of the similarity that the face images of same individual or same expression have. Bilinear model ［1］has been proposed as a solution of this subjcet. However, experiments on application of bilinear model to classification of face images resulted in low performance ［2］. In order to overcome the limit of bilinear model, in this research, a nonlinear model on a neural network is adopted and usefulness of this model is experimentally confirmed.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2006.04a
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• pp.391-393
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• 2006

In this study, the performance of artificial neural network (ANN) models for estimating spatial distribution of water quality was evaluated using electric conductivity (EC) values in landfill site. For the ANN model development, feedforward neural networks and backpropagation algorithm with gradient descent method were used. In Test 1, the interpolation ability of the ANN model was evaluated. Results of the ANN model were more precise than those of the Kriging model. In Test 2, spatial distributions of EC values were predicted using precipitation data. Results seemed to be reasonable, however, they showed a limitation of ANN models in extrapolations.