• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.62-65
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• 2003

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. The second is global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.121-124
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing Information from these detectors. The second is hybrid step, we Identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.356-358
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• 1994

In this paper, to alleviate the effect of approximation error and discontinuous variation of the controller parameters, the variable structure control scheme using neural networks is presented. In the proposed method, the variable structure control rules for each local linear models are designed to reject the effect of linearization error caused by linearization of the nonlinear system. And neural network infer approximate controller gains from combination of local linear control gains. The proposed control methods can be used to control nonlinear systems and it has robust characteristic against system parameter variations and external disturbances.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.221-224
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. The second is global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.155-178
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• 2024

Regression discontinuity (RD) design is one of the most widely used methods in causal inference for estimation of treatment effect when the treatment is created by a cutpoint from the covariate of interest. There has been little attention to RD design, although it provides a very useful tool for analysis of treatment effect for censored data. In this paper, we define the causal effect for survival function in RD design when the treatment is assigned deterministically by the covariate of interest. We propose estimators of this causal effect for survival data by using transformation, which leads unbiased estimator of the survival function with local linear regression. Simulation studies show the validity of our approach. We also illustrate our proposed method using the prostate, lung, colorectal and ovarian (PLCO) dataset.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.193-200
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• 2021

In this study, an analysis technique using static condensation is proposed for an efficient local nonlinear static analysis. The static condensation method is a model reduction method based on the degrees of freedom, and the analysis model is divided into a target part and a condensed part to be omitted. In this study, the nonlinear and linear parts were designated to the target and the omitted parts, respectively, and both the stiffness matrix and load vector corresponding to the linear part were condensed into the nonlinear part. After model condensation, the reduced model comprising the stiffness matrix and the load vector for the nonlinear part is constructed, and only this reduced model was updated through the Newton-Raphson iteration for an efficient nonlinear analysis. Finally, the efficiency and reliability of the proposed analysis technique were presented by applying it to various numerical examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2068-2086
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• 2011

Based on the quincunx sub-sampling grid, the New Interleaved Hierarchical INTerpolation (NIHINT) method is recognized as a superior pyramid data structure for the lossless and progressive coding of natural images. In this paper, we propose a new image interpolation algorithm, Edge Adaptive Hierarchical INTerpolation (EAHINT), for a further reduction in the entropy of interpolation errors. We compute the local variance of the causal context to model the strength of a local edge around a target pixel and then apply three statistical decision rules to classify the local edge into a strong edge, a weak edge, or a medium edge. According to these local edge types, we apply an interpolation method to the target pixel using a one-directional interpolator for a strong edge, a multi-directional adaptive weighting interpolator for a medium edge, or a non-directional static weighting linear interpolator for a weak edge. Experimental results show that the proposed algorithm achieves a better compression bit rate than the NIHINT method for lossless image coding. It is shown that the compression bit rate is much better for images that are rich in directional edges and textures. Our algorithm also shows better rate-distortion performance and visual quality for progressive image transmission.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.794-814
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• 2012

We propose a novel method for improving Wi-Fi positioning accuracy while reducing the energy consumption of mobile devices. Our method presents three contributions. First, we jointly and intelligently select the optimal subset of access points for positioning via maximum mutual information criterion. Second, we further propose local discriminant embedding algorithm for nonlinear discriminative feature extraction, a process that cannot be effectively handled by existing linear techniques. Third, to reduce complexity and make input signal space more compact, we incorporate clustering analysis to localize the positioning model. Experiments in realistic environments demonstrate that the proposed method can lower energy consumption while achieving higher accuracy compared with previous methods. The improvement can be attributed to the capability of our method to extract the most discriminative features for positioning as well as require smaller computation cost and shorter sensing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.4
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• pp.629-640
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• 2022

This study analyzed the relationship between local government size and regional economic growth using regional data from 2002 to 2020. By dividing local government expenditure into social development expenditure and economic development expenditure, economic growth and the inverted U-shaped Armey curve were verified, and the optimal size of local government expenditure was examined. In particular, the AMG estimation method considering the cross-sectional dependence and regional heterogeneity existing in the panel data was utilized. As a result of the analysis, it was found that there was an inverted U-shaped relationship between local fiscal expenditure and regional economic growth. When the proportion of total local fiscal expenditure is 7.63% of GRDP and social development expenditure is 3.45%, it is found that the optimal size of expenditure can maximize the regional economic growth rate. Local governments should increase the effectiveness of public expenditure policies by considering these points.