• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.448-449
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• 2022

In the image upscaling field, the method using deep learning is showing better results than using the interpolation method. And for hologram upscaling, using deep learning is showing better results than general interpolation. In this paper, the network structure and learning results are analyzed. The learning results are compared by adjusting the depth of the network and the number of channels at the same weight.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.124-131
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• 2000

In this study, the NEURAL NETWORK (hereinafter referred to as NN) was applied to control of the nonlinear factors for turning movement of the crawler vehicle and experiment was carried out using a small model of crawler vehicle in order to inspect an application of NN. Furthermore, CHAOS NEURAL NETWORK (hereinafter referred to as CNN) was also applied to this control so as to compare with conventional NN. CNN is especially effective for plane in many variables with local minimum which conventional NN is apt to fall into, and it is relatively useful to nonlinear factors. Experiment of turning on the slope of crawler vehicle was performed in order to estimate an adaptability of nonlinear problems by NN and CNN. The inclination angles of the road surface which the vehicles travel on, were respectively 4deg, 8deg, 12deg. These field conditions were selected by the object for changing nonlinear magnitude in turning phenomenon of vehicle. Learning of NN and CNN was carried out by referring to positioning data obtained from measurement at every 15deg in turning. After learning, the sampling data at every 15deg were interpolated based on the constructed learning system of NN and CNN. Learning and simulation programs of NN and CNN were made by C language ("Association of research for algorithm of calculating machine (1992)"). As a result, conventional NN and CNN were available for interpolation of sampling data. Moreover, when nonlinear intensity is not so large under the field condition of small slope, interpolation performance of CNN was a little not so better than NN. However, when nonlinear intensity is large under the field condition of large slope, interpolation performance of CNN was relatively better than NN.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.128-133
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• 1997

This paper examines the relation between multidimensional linear interpolation ( MDI ) and regularization networks, and shows that and MDI is a special form of regularization networks. For this purpose we propose a triangular basis function ( TBF ) network. Also we verified the condition when our proposed TBF becomes a well-known radial basis function ( RBF ).

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.29-41
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• 2010

In this study, the prediction errors of various spatial interpolation methods used to model values at unmeasured locations was compared and the accuracy of these predictions was evaluated. The root mean square (RMS) was calculated by processing different parameters associated with spatial interpolation by using techniques such as inverse distance weighting, kriging, local polynomial interpolation and radial basis function to known elevation data of the east coastal area under the same condition. As a result, a circular model of simple kriging reached the smallest RMS value. Prediction map using the multiquadric method of a radial basis function was coincident with the spatial distribution obtained by constructing a triangulated irregular network of the study area through the raster mathematics. In addition, better interpolation results can be obtained by setting the optimal power value provided under the selected condition.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.375-383
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• 2018

In order to apply the Network RTK (real time kinematics) technology, which has been used for positioning of stationary points, to the navigation of vehicles, its infrastructure should provide correction data with a quality indicator that can show the expected accuracy in real time. In this paper, we analyzed various indicator generation algorithms such as I95 (ionospheric index 95) / G95 (geodetic index 95), SBI (semivariance based index) and RIU (residual interpolation uncertainty). We also applied them to the raw observables from the reference stations of National Geographic Information Institute and VRS (virtual reference station) users, and then examined its feasibility to be used as a real-time performance index of the Network RTK rover. 24 hour data analysis shows that the RIU index, which can represent the non-linearty of the correction, has the strongest correlation with the Network RTK rover accuracy. Therefore, RIU index is expected to be used as a real-time performance index of the Network RTK rover.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.205-208
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• 2008

In numerical analysis numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But most of the thermodynamic properties of the steam table are determined by experiment. Therefore they are supposed to have measurement errors. In order to make noised thermodynamic properties corresponding to errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. the neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure. It was proved that the neural networks give smaller percentage error compared with quadratic spline interpolation. From this fact it was confirmed that the neural networks trace the original values of thermodynamic properties better than the quadratic interpolation method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.246-249
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• 2007

In numerical analysis for phase change material, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be used without modelling. In this study applicability of neural networks in modelling superheated vapor region of water was examined by comparing with the quadratic spline. neural network consists of an input layer with 2 nodes, two hidden layers and an output layer with 3 nodes. Quadratic spline interpoation method was also applied for comparison. Neural network model revealed smaller percentage error to quadratic spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the superheated range of the steam table.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.189-198
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• 2024

Lipreading is one of the important parts of speech recognition, and several studies have been conducted to improve the performance of lipreading in lipreading systems for speech recognition. Recent studies have used method to modify the model architecture of lipreading system to improve recognition performance. Unlike previous research that improve recognition performance by modifying model architecture, we aim to improve recognition performance without any change in model architecture. In order to improve the recognition performance without modifying the model architecture, we refer to the cues used in human lipreading and set other regions such as chin and cheeks as regions of interest along with the lip region, which is the existing region of interest of lipreading systems, and compare the recognition rate of each region of interest to propose the highest performing region of interest In addition, assuming that the difference in normalization results caused by the difference in interpolation method during the process of normalizing the size of the region of interest affects the recognition performance, we interpolate the same region of interest using nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, and compare the recognition rate of each interpolation method to propose the best performing interpolation method. Each region of interest was detected by training an object detection neural network, and dynamic time warping templates were generated by normalizing each region of interest, extracting and combining features, and mapping the dimensionality reduction of the combined features into a low-dimensional space. The recognition rate was evaluated by comparing the distance between the generated dynamic time warping templates and the data mapped to the low-dimensional space. In the comparison of regions of interest, the result of the region of interest containing only the lip region showed an average recognition rate of 97.36%, which is 3.44% higher than the average recognition rate of 93.92% in the previous study, and in the comparison of interpolation methods, the bilinear interpolation method performed 97.36%, which is 14.65% higher than the nearest neighbor interpolation method and 5.55% higher than the bicubic interpolation method. The code used in this study can be found a https://github.com/haraisi2/Lipreading-Systems.

• Journal of KIISE:Information Networking
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• v.34 no.6
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• pp.446-457
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• 2007

In wireless sensor network, to provide the proper responses quickly for diverse events, wireless sensor nodes have to cooperate with each other. For successful cooperation, the time synchronization among sensor nodes is an important requirement for application execution. In the wireless sensor network, the message packets including time information are used for the time synchronization. However, the transmission of many message packets will exhaust the battery of wireless sensor nodes. Since wireless sensor nodes works on the limited battery capacity, the excessive transmission of message packets has an negative impact upon their lifetime. In this paper, the Reference Interpolation Protocol (RIP) is proposed to reduce the number of message packets for the time synchronization. The proposed method performs the time interpolation between the reference packet's time and the global time of the base station. The proposed method completes the synchronization operation with only 2 message packets when compared to the previous Reference Broadcast Synchronization (RBS) technique. Due to the simple synchronization procedure, our method greatly reduces the number of synchronization messages and showed the 12.7 times less power consumption than the RBS method. From the decrease in the transmission of message packets, the convergence time among wireless sensor nodes is shortened and the lifetime of wireless sensor nodes is also prolonged as much as the amount of saved battery energy.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.192-207
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• 2020

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.