• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.313-321
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• 2012

Korea has been continuously affected by landslides, as 70% of the land is covered by mountains and most of annual rainfall concentrates between June and September. Recently, abrupt climate change affects the increase of landslide occurrence. Gangwon region is especially suffered by landslide damages, because the most of the part is mountainous, steep, and having shallow soil. In this study, a landslide risk assessment model was developed by applying logistic regression to the various data of Duksan-ri, Inje-eup, Inje-gun, Gangwon-do, which has suffered massive landslide triggered by heavy rain in July 2006. The information collected from field investigation and aerial photos right after the landslide of study area were stored in GIS DB for analysis. Slope gradient entered in two ways-as categorical variable and as linear variable. Error matrix for each case was made, and developed model showed the classification accuracy of 81.4% and 81.9%, respectively.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

• Journal for History of Mathematics
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• v.21 no.4
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• pp.169-182
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• 2008

This study has been searching for reasoning process solving the problem effectively in activities related to meaningful classification of pieces and geometric properties with tangram. In activities using some pieces of tangram, we systematically came up with every solution in classifying properties of pieces and combining selected pieces. It is very difficult for regular students to do this tangram. In order to solve this problem effectively, we need to show that there are activities using the idea acquired in reasoning process. Through this process, we do not simply use tangram to understand he concept and play for interest but to use it more meaningfully. And the best solution an not be found by a process of trial and error but must be given by experience to look or it systematically and methods to reason it logically.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.74-84
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• 2015

If a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures. Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident management strategies. Therefore, support systems that can quickly provide this kind of information will be very useful when operators try to manage severe accidents. In this research, the occurrence times of several events that could happen during a severe accident were predicted using support vector machines with short time variations of plant status variables inputs. For the preliminary step, the break location and size of a loss of coolant accident (LOCA) were identified. Training and testing data sets were obtained using the MAAP5 code. The results show that the proposed algorithm can correctly classify the break location of the LOCA and can estimate the break size of the LOCA very accurately. In addition, the occurrence times of severe accident major events were predicted under various severe accident paths, with reasonable error. With these results, it is expected that it will be possible to apply the proposed algorithm to real NPPs because the algorithm uses only the early phase data after the reactor SCRAM, which can be obtained accurately for accident simulations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.153-158
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• 2018

At the Bank of Korea, capital stock statistics were created by the PIM (perpetual inventory method) with fixed capital formation data. Asset classifications also included 2 categories in residential buildings, 4 non-residential buildings, 14 constructions, 9 transportation equipment, 28 machinery, and 2 intangible fixed assets. It is the Korean government accounting system which is developed much with the field of the national accounts including the valuation, but until 2008 it was consistent with single-entry bookkeeping. Many countries, including Korea, were single-entry bookkeeping, not double-entry bookkeeping which can be aggregated by government accounting standard account. There was no distinction in journaling between revenue and capital expenditure when it was consistent with single-entry bookkeeping. For example, we would like to appropriately divide the past budget accounts and the settlement accounts data that have been spent on dredging into capital expenditure and revenue expenditure. It, then, tries to add the capital expenditure calculated to FCF (fixed capital formation), because revenue expenditure is cost for maintenance etc. This could be a new direction, especially, in the estimation of capital stock by the perpetual inventory method for infrastructure (SOC, social overhead capital). It should also be noted that there are differences not only between capital and income expenditure but also by other factors. How long will this difference be covered by the difference between the 'new series' and 'old series' methodologies? In addition, there is no large difference between two series by the major asset classification level. If this is treated as a round-off error, this is a problem.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.993-1000
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• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.1630-1637
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• 2008

Jeju-do is a island formed by the volcanic activity and has more than 360 volcanic cones distributed widely along the long axis of the elliptically shaped island. The volcanic cones consist mainly of scoria, so called "Song-I" in the local dialect. In this study the chemical and soil mechanical properties of scoria being very different from those of the inland were investigated with the various tests. In the sieve-passing test the particle size of scoria had more than 10 of uniformity coefficient and gradation coefficient of 1 ~ 3, showing relatively homogenous distribution. Based on the uniformity classification, scoria was assorted into GW. In the large scale direct shear tested for measuring the mechanical strength of scoria the internal friction angle of red scoria was $37^{\circ}$ and that of black scoria was $36^{\circ}$. This indicated that there was no difference in the mechanical strength between two types of scoria. On the other hand, red and black scoria had $1.24{\times}10^{-3}$ to $3.55{\times}10^{-2}$ cm/sec of k values for the static water level permeability, thus being classified into a coarse or fine sand as compared with that representing the saturated soil. They also had 1.411 to $1.477\;g/cm^3$ of notably low $r_{dmax}$ values for the compaction test as compared with common soil, which was considered to be due to their low specific gravity and high porosity. In conclusion, the soil mechanic properties of scoria obtained from this study are thought to be very helpful for reducing lots of trial and error happening in the civil engineering construction.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.261-266
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• 2017

In this paper, we propose a vocal and nonvocal separation method which uses a combination of kernel model and LSTM (Long-Short Term Memory) networks. Conventional vocal and nonvocal separation methods estimate the vocal component even in sections where only non-vocal components exist. This causes a problem of the source estimation error. Therefore we combine the existing kernel based separation method with the vocal/nonvocal classification based on LSTM networks in order to overcome the limitation of the existing separation methods. We propose a parallel combined separation algorithm and series combined separation algorithm as combination structures. The experimental results verify that the proposed method achieves better separation performance than the conventional approaches.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.190-200
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• 2014

The video-based system of the broadcasters and the video-related institutions have shifted from analogical to digital in worldwide. This migration process can generate a defect, digital dropout, in the quality of the contents. Moreover, there are limited researches focused on these kind of defects and those related have limitations. For that reason, we are proposing a new method for feature extraction emphasizing in the peculiar block pattern of digital dropout based on discrete cosine transform (DCT). For classification of error block, we utilize support vector machine (SVM) which can manage feature vectors efficiently. Further, the proposed method overcome the limitation of the previous one using continuity of frame by frame. It is using only the information of a single frame and works better even in the presence of fast moving objects, without the necessity of specific model or parameter estimation. Therefore, this approach is capable of detecting digital dropout only with minimal complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.156-163
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• 2013

The use of an error-correcting code is essential in communication systems where the channel is noisy. When channel coding parameters are unknown at a receiver side, decoding becomes difficult. To perform decoding without the channel coding information, we should estimate the parameters. In this paper, we introduce a method to reconstruct the generator polynomial of BCH(Bose-Chaudhuri-Hocquenghem) codes based on the idea that the generator polynomial is compose of minimal polynomials and BCH code is cyclic code. We present a probability compensation method to improve the reconstruction performance. This is based on the concept that a random data pattern can also be divisible by a minimal polynomial of the generator polynomial. And we confirm the performance improvement through an intensive computer simulation.