• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.48-57
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• 1997

This paper presents the automatic construction and parameter optimization technique for fuzzy logic controllers using genetic algorithm. In general. the design of fuzzy logic controllers has difficulties in the acq~lisition of expert's knowledge and relies to a great extent on empirical and heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controllers c:an be degraded in the case of plant parameter variations or unpredictable incident which a designer may have ignored, and the parameters of fuzzy logic controllers obtained by expert's control action may not be optirnal. Some of these problems can be resolved by the use of genetic algorithm. The proposed method can tune the parameters of fuzzy logic controllers including scaling factors and determine: the appropriate number of fuzzy rulcs systematically. Finally, we provides the second order dead time plant to evaluate the feasibility and generality of the proposed method. Comparison shows that the proposed method can produce fuzzy logic controllers with higher accuracy and a smaller number of fuzzy rules than manually tuned fuzzy logic controllers.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1125-1132
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• 2017

In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.

• Journal of Korean Society of Steel Construction
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• v.11 no.4 s.41
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• pp.373-384
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• 1999

The shell structures with composite materials have the advantages in strength, corrosion resistance, and weight reduction. The objective of this study is to analyze anisotropic composite circular cylindrical shells with shear deformation theory. In applying numerical methods to solve differential equations of anisotropic shells, this paper use finite difference method. The accuracy of the numerical method can be improved by taking higher order of interval ${\Delta}$ to reduce error. This study compares the results of finite difference method with the results of ANSYS based on finite element method. Several numerical examples show the advantages of the stiffness increasement when the composite materials aroused. Therefore, it is expected that results of this study give various guides for change of the subtended angles, load cases, boundary conditions, and side-to-thickness ratio.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.475-487
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• 2010

Sea surface wind field was retrieved from high-resolution SIR-C SAR data by using CMOD algorithms off the east coast of Korea. In order to extract wind direction information from SAR data, a two-dimensional spectral analysis method was applied to the normalized radar cross section of the image. An $180^{\circ}$-ambiguity problem in the determination of wind direction was solved by selecting a direction nearest to the wind vector of the ECMWF reanalysis data. Comparison of the wind retrieval patterns with the ECMWF and NCEP/NCAR dataset showed RMS errors in the range of 1.30 to $1.72\;ms^{-1}$. In contrast, comparison of wind directions revealed large errors of greater than $60^{\circ}$, which is enormously higher than the permitted limit of about $20^{\circ}$ for satellite scatterometer winds. Compared with wind speed results from different algorithms, wind vectors based on commonly-used CMOD4 algorithm showed good agreement with those derived by other algorithms such as CMOD_IFR2 and CMOD5, particularly at medium winds from 4 to $8\;ms^{-1}$. However, apparent discrepancy appeared at low winds (< $4\;ms^{-1}$). This study also addressed an importance of accurate wind direction data to improve the accuracy of wind speed retrieval and discussed potential causes of wind retrieval errors from SAR data.

• Journal of Ocean Engineering and Technology
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• v.21 no.3 s.76
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• pp.52-57
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• 2007

Composite material is one of the strongest candidates for deep see pressure hulls. Research regarding composite cylinders, subjected to hydrostatic pressure, has been ongoing for a couple of decades, abroad, but domestic research is very new. Experimental investigations seem necessary, in order to understand their structural behavior not only up to the ultimate limit state, but in the post-ultimate regime. That experimental information will be very helpful in the development of any theoretical methods or to substantiate any commercial numerical packages for structural analyses. In this study, ultimate strength tests on seven composite cylinders subjected to hydrostatic pressure are reported, which includes the fabrication method of models, mechanical properties of the material, initial shape imperfection measurements, test procedure, and strain and axial shortening measurements during the tests. The ultimate strengths of the models were compared with predictions of numerical analyses. The numerical predictions are higher than the test results. It seems necessary to improve the accuracy of the numerical predictions by considering the initial shape and material imperfections.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.31-40
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• 2013

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.401-410
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• 2017

Estimation of snow depth using optical image is conducted by using correlation with Snow Cover Fraction (SCF). Various algorithms have been proposed for the estimation of snow cover fraction based on Normalized Difference Snow Index (NDSI). In this study we tested linear, quadratic, and exponential equations for the generation of snow cover fraction maps using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite in order to evaluate their applicability to the complex terrain of South Korea and to search for improvements to the estimation of snow depth on this landscape. The results were validated by comparison with in-situ snowfall data from weather stations, with Root Mean Square Error (RMSE) calculated as 3.43, 2.37, and 3.99 cm for the linear, quadratic, and exponential approaches, respectively. Although quadratic results showed the best RMSE, this was due to the limitations of the data used in the study; there are few number of in-situ data recorded on the station at the time of image acquisition and even the data is mostly recorded on low snowfall. So, we conclude that linear-based algorithms are better suited for use in South Korea. However, in the case of using the linear equation, the SCF with a negative value can be calculated, so it should be corrected. Since the coefficients of the equation are not optimized for this area, further regression analysis is needed. In addition, if more variables such as Normalized Difference Vegetation Index (NDVI), land cover, etc. are considered, it could be possible that estimation of national-scale snow depth with higher accuracy.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.2
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• pp.114-121
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• 2009

Ultrasonic guided waves have been widely utilized for long range inspection of structures. Especially, development of array guided waves techniques and its application for long range gas pipe lines(length of from hundreds meters to few km) were getting increased. In this study, focusing algorithm for array guided waves was developed in order to improve long range inspectability and accuracy of the array guided waves techniques for long range inspection of gas pipes, and performance of the developed techniques was verified by experiments using the developed array guided wave system. As a result, S/N ratio of array guided wave signals obtained with the focusing algorithm was increased higher than that of signals without focusing algorithm.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.310-319
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• 2016

This paper proposes an effective FRUC (Frame Rate Up-Conversion) technique, which is based on ROI (Region Of Interest) separations and adaptive motion vector refinement. In this paper, in order to overcome the weakness of the EBME (Extended Bi-lateral Motion Estimation) algorithm, which is widely known in FRUC techniques, first, the proposed algorithm performs a bi-directional motion estimation for the complementary asymmetric region. Then, the proposed algorithm classifies each block into ROI or non-ROI block and refine motion vectors in accordance with their block characteristics to have a higher accuracy than the conventional EBME algorithm, specially, for the occlusion regions. The experimental results show that the proposed algorithm can improves 0.59dB on average PSNR as compared to the conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.275-285
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• 2003

The now video coding standard H.264 employs variable block size motion compensation, multiple references, and quarter-pel motion vector accuracy. These techniques are key features to accomplish higher coding gain, however, at the same time main factors that increase overall computational complexity. Therefore, in order to apply H.264 to many applications, key techniques are requested to improve their speed. For this reason, we propose a fast motion estimation which is suited for variable block size motion communication. In addition, we propose a fast mode decision method to choose the best mode at early stage. Experimental results show the reduction of the number of SAT SATD calculations by a factor of 4.5 and 2.6 times respectively, when we compare the proposed fast motion estimation and the conventional MVFAS $T^{［8-10］}$. Besides, the number of RDcost computations is reduced by about 45%. Therefore, the proposed methods reduces significantly its computational complexity without noticeable coding loss.