• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.1-13
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• 2022

A weighted linear combination of seismic fragility models previously developed for cut-and-cover railway tunnels was presented and the appropriateness of the combined model was evaluated. The seismic fragility function is expressed in the form of a cumulative probability function of the lognormal distribution based on the peak ground acceleration. The model uncertainty can be reduced by combining models independently developed. Equal weight is applied to four models. The new seismic fragility function was developed for each damage level by determining the median and standard deviation, which are model metrics. Comparing fragility curves developed for other bored tunnels, cut-and-cover tunnels for high-speed railway system have a similar level of fragility. We postulated that this is due to the high seismic design standard for high-speed railway tunnel.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.87-97
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• 2004

In this paper, we propose an algorithm for interlaced-to-progressive conversion by the weighted summation of the information collected from spatial do-interlacing method, in which the weighted edge based line average is applied, and the temporal method in which the motion compensation is employed by using hi-directional BMA (block matching algorithm). We employed time-recursive and motion adaptive processing as motion detection is involved. Also, a median filter is used to deal with limitation of the linear summation in which only an intermediate of values being involved is determined. The main goal of the approach is to overcome the shortcomings of each of the do-interlacing techniques without significant increment of the computational complexity, and the proposed method is apt to implement in hardware for real-time processing.

• 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.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.375-387
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• 2013

Unit non-response of surveys reduces the efficiency of the estimates and also causes non-response bias especially when there is large difference between respondents and non-respondents. Non-response weighting adjustments have usually been used to compensate for non-response. It is not easy to examine the non-response bias as well as to obtain information on the non-respondents in sample surveys. A household panel survey, called The Survey of Household Finances, was conducted in both 2010 and 2011. In this paper, we assume that non-response households in Wave 2 have strong non-response (non-cooperative) tendency. We classify those households into non-response households in Wave 1. Under this assumption, the characteristics of non-response households, the non-response bias and the effect of non-response adjustments are investigated.

• Journal of Internet Computing and Services
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• v.8 no.2
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• pp.105-115
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• 2007

The up-to-moment dataset is built by combining the past dataset and the recent dataset. The proposal is to compute association rules in real time. This study proposed the model, $EM_{past'}$ and algorithm that is sensitive to time. It can be utilized in real time by applying partitioned combination law after dividing the past dataset into(k-1). Also, we suggested $EM^{ES}_{past}$ applying the exponential smoothing method to $EM^p_{past'}$ When the association rules of $EM_{past'}\;EM^w_{past'\;and\;EM^{ES}_{past}$ were compared, The simulation results showed that $EM^{ES}_{past}$ is most accurate for testing dataset than $EM_{past}$ and $EM^w_{past}$ in huge dataset.

• Survey Research
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• v.6 no.1
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• pp.83-98
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• 2005

Large scale survey database may contain some erroneous data or missing data. Incomplete or erroneous data may be produced in the process of data collection or data capture. Since erroneous data can cause some bias and inconsistency, data editing, which is the procedure for detecting and adjusting individual errors in data records, is a very important work in statistical survey. In this paper, we introduce an editing process for the housing price survey to enhance discussions on that topic. We explain how to decide some appropriate edit rules and show some related data. Furthermore, we describe input editing procedures which is appropriate for on-line survey and how to find and eliminate erroneous data through output editing.

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.309-315
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• 2009

Integrated interpretation of multi-geophysical data has been continuously used in terms that it has provided more confident information than the result from single-geophysical data. Especially, geostatistical integration has its own superiority that it is possible to deal with spatial characteristics as well as physical properties of survey data and the process of integration is clear. This paper further extends the previous work of geostatistical inversion for integrated interpretation. In this paper, we propose a new way of dealing with the case that the multi-geophysical data do not share the measurement range. According to the geostatistical kriging, the closer between the measurement points, the smaller kriging variance we get, and vice versa. We used this spatial properties as a weighting value to the process of geostatistical inversion for the geophysical data integration. An objective way to integrate different kinds of geophysical data measured at different ranges is provided with this algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.544-550
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• 2015

The opinion mining technique which analogize significant information from SNS message is increasingly important because opinions communicated through SNS are increasing. This paper propose SEIAS(SNS Emotional Information Analysis Strategy) based on opinion mining that analogize emotional information from SNS setting a different weight according to position of antonym and adverb. Firstly, the proposed SEIAS constructs a emotion dictionary for opinion mining analysis, Secondly, it collects SNS data on real time, compare it with emotion dictionary and calculates opinion value of SNS data. Specially, it increases the precision of opinion analysis result compared to the existing SO-PMI because it sets up the different value according to the position of antonym and adverb when it calculates the opinion value of data.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.739-748
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• 2012

In this paper we propose a mixed effects least squares support vector machine (LS-SVM) for the censored data which are observed from different groups. We use weights by which the randomly right censoring is taken into account in the nonlinear regression. The weights are formed with Kaplan-Meier estimates of censoring distribution. In the proposed model a random effects term representing inter-group variation is included. Furthermore generalized cross validation function is proposed for the selection of the optimal values of hyper-parameters. Experimental results are then presented which indicate the performance of the proposed LS-SVM by comparing with a standard LS-SVM for the censored data.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.991-1001
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• 2015

Digital images acquired by digital devices are used in many fields. Applying statistical methods to the processing of images will increase speed and efficiency. Methods to remove noise and image quality have been researched as a basic operation of image processing. This paper proposes a novel reduction method that considers the direction and magnitude of the edge to remove image noise effectively using statistical methods. The proposed method estimates the brightness of pixels relative to pixels in the same direction based on a robust regression model. An estimate of pixel brightness is obtained by weighting the magnitude of the edge that improves the performance of the average filter. As a result of the simulation study, the proposed method retains pixels that are well-characterized and confirms that noise reduction performance is improved over conventional methods.