• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.579-586
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• 2003

In this paper, we propose a novel approach to employ emotional features to document retrieval systems. Fine emotional features, such as HAPPY, SAD, ANGRY, FEAR, and DISGUST, have been used to represent Korean document. Users are allowed to use these features for retrieving their documents. Next, retrieved documents are learned by classification methods like cohesion factor, naive Bayesian, and, k-nearest neighbor approaches. In order to combine various approaches, voting method has been used. In addition, k-means clustering has been used for our experimentation. The performance of our approach proved to be better in accuracy than other methods, and be better in short texts rather than large documents.

• Journal of Korea Game Society
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• v.20 no.5
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• pp.53-64
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• 2020

Supporting NNS method in KD-Tree algorithm is essential in multidimensional data applications. In this paper, we propose HD-Tree, a high-performance Lock-Free KD-Tree that supports NNS in situations where reads and writes occurs concurrently. HD-Tree reduced the number of synchronization nodes used in NNS and requires less atomic operations during Lock-Free method execution. Comparing with existing algorithms, in a multi-core system with 8 core 16 thread, HD-Tree's performance has improved up to 95% on NNS and 15% on modifying in oversubscription situation.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.39-47
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• 2010

Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.82.2-82.2
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• 2012

We present an investigation of X-ray hot gas halo in 21 early-type galaxies(ETGs) from cross-matched sample of XMM-Newton(2XMM-DR3 catalog) and SDSS DR 7(0.025 < z < 0.085 and Mr <-19.5). It has been controversial whether or not the environment affects X-ray luminosity of ETGs. In this research, we mainly considered how dense the surrounding galaxies of the target galaxy are and how isolated the target galaxy is from the nearest neighboring galaxy. It appears that the second environmental factor has more effects on X-ray luminosity (0.5-2 keV) of hot gas halo than the first one. We found that the closer a galaxy is to the nearest neighboring galaxy, the brighter it is when the galaxy is located within the neighbor galaxy's virial radius. However, when a galaxy is located outside the neighbor's virial radius, the luminosity does not show any trend. In this poster, we report preliminary results from our study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.611-620
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• 2021

The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.547-557
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• 2012

This study purposed to estimate the forest biomass using k-Nearest Neighbor (k-NN) algorithm. Multiple data sources were used for the analysis such as forest type map, field survey data and Landsat TM data. The accuracy of forest biomass was evaluated with the forest stratification, horizontal reference area (HRA) and spatial filtering. Forests were divided into 3 types such as conifers, broadleaved, and Korean pine (Pinus koriansis) forests. The applied radii of HRA were 4 km, 5 km and 10 km, respectively. The estimated biomass and mean bias for conifers forest was 222 t/ha and 1.8 t/ha when the value of k=8, the radius of HRA was 4 km, and $5{\times}5$ modal was filtered. The estimated forest biomass of Korean pine was 245 t/ha when the value of k=8, the radius of HRA was 4km. The estimated mean biomass and mean bias for broadleaved forests were 251 t/ha and -1.6 t/ha, respectively, when the value of k=6, the radius of HRA was 10 km. The estimated total forest biomass by k-NN method was 799,000t and 237 t/ha. The estimated mean biomass by ${\kappa}NN$method was about 1t/ha more than that of filed survey data.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.374-380
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• 2012

The k-Nearest Neighbor (kNN) technique is popularly applied to assess forest resources at the county level and to provide its spatial information by combining large area forest inventory data and remote sensing data. In this study, two approaches such as distance-weighting and stratification of training dataset, were compared to improve kNN-based forest growing stock estimates. When compared with five distance weights (0 to 2 by 0.5), the accuracy of kNN-based estimates was very similar ranged ${\pm}0.6m^3/ha$ in mean deviation. The training dataset were stratified by horizontal reference area (HRA) and forest cover type, which were applied by separately and combined. Even though the accuracy of estimates by combining forest cover type and HRA- 100 km was slightly improved, that by forest cover type was more efficient with sufficient number of training data. The mean of forest growing stock based kNN with HRA-100 and stratification by forest cover type when k=7 were somewhat underestimated ($5m^3/ha$) compared to statistical yearbook of forestry at 2011.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.52.2-52.2
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• 2013

We explore the relative importance of the role of small-scale environment and large-scale environment in triggering nuclear activity of the local galaxies using a volume-limited sample with $M_r$ < -19.5 and 0.02 < z < 0.0685 selected from the Sloan Digital Sky Survey Data Release 7. The active galactic nuclei (AGN) host sample is composed of Type II AGNs identified with flux ratios of narrow emission lines with S/N > 6 and the central velocity dispersion of the sample galaxies is limited to have a narrow range between 130 < ${\sigma}$ < 200($km\;s^{-1}$), corresponding to 7.4 < $log(M_{BH}/M_{\odot})$ < 8.1 in order to fix the mass of the supermassive black hole at the center of its host galaxy. In this study, we find that the AGN fraction ($f_{AGN}$) of late-type galaxies are larger than of early-type galaxies and that for target galaxy with late-type nearest neighbor, $f_{AGN}$ starts to increase as the target galaxy approaches the virial radius of the nearest neighbor (about a few hundred kpc scale). The latter result may support the idea that the hydrodynamic interaction with the nearest neighbor as well as tidal interaction and merger also plays an important role in triggering the nuclear activity of galaxy. We also find that early-type cluster galaxies show decline of AGN activity compared to ones in lower density regions, whereas the direction of dependence of AGN activity for late-type galaxies is opposite.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.372-378
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• 2010

This paper propose the fast motion estimation algorithm with adaptive search range adjustment using motion activities of temporal and spatial neighbor blocks. The existing fast motion estimation algorithms with adaptive search range adjustment use the maximum motion vector of all blocks in the reference frame. So these algorithms may not control a optimum search range for slow moving block in current frame. The proposed algorithm use the maximum motion vector of neighbor blocks in the reference frame to control a optimum search range for slow moving block. So the proposed algorithm can reduce computation time for motion estimation. The experiment results show that the proposed algorithm can reduce the number of search points about 15% more than Simple Dynamic Search Range(SDSR) algorithm while maintaining almost the same bit-rate and motion estimation error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.812-817
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• 2018

Blockchain technology removes the central system and connects each block throughout every person. This paper proposes new method to exploit the blockchain feature to apply existing donation system. In the current system, the donator does not donate to poor neighbor which means donator must have to donate some donate organization. The donator does not recognize how the donation has been spent after donation. In this case, we can solve that problem by using the blockchain where the donator can watch how the money works and everyone can browse the account history. To make a virtual fundraising box based on blockchain technology, donator sends some money at virtual account and exchanges the money to crypto-currency. Donator can use crypto-currency which is donate to poor neighbor that poor neighbor can exchanges crypto-currency to the money. Through this proposed system, the donation is managed by secure and transparent blockchain system.