• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.39-51
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• 2012

Cluster analysis has proven to be a useful tool for investigating the association structure among genes and samples in a microarray data set. We applied several cluster validation measures to evaluate the performance of clustering algorithms for analyzing microarray gene expression data, including hierarchical clustering, K-means, PAM, SOM and model-based clustering. The available validation measures fall into the three general categories of internal, stability and biological. The performance of clustering algorithms is evaluated using simulated and SRBCT microarray data. Our results from simulated data show that nearly every methods have good results with same result as the number of classes in the original data. For the SRBCT data the best choice for the number of clusters is less clear than the simulated data. It appeared that PAM, SOM, model-based method showed similar results to simulated data under Silhouette with of internal measure as well as PAM and model-based method under biological measure, while model-based clustering has the best value of stability measure.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.187-195
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• 2009

In a fault tree analysis, an uncertainty importance measure is often used to assess how much uncertainty of the top event probability (Q) is attributable to the uncertainty of a basic event probability ($q_i$), and thus, to identify those basic events whose uncertainties need to be reduced to effectively reduce the uncertainty of Q. For evaluating the measures suggested by many authors which assess a percentage change in the variance V of Q with respect to unit percentage change in the variance $\upsilon_i$ of $q_i$, V and ${\partial}V/{\partial}{\upsilon}_i$ need to be estimated analytically or by Monte Carlo simulation. However, it is very complicated to analytically compute V and ${\partial}V/{\partial}{\upsilon}_i$ for large-sized fault trees, and difficult to estimate them in a robust manner by Monte Carlo simulation. In this paper, we propose a method for experimentally evaluating the measure using a Taguchi orthogonal array. The proposed method is very computationally efficient compared to the method based on Monte Carlo simulation, and provides a stable uncertainty importance of each basic event.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.402-405
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• 2015

본 논문에서는 비콘이 송출한 블루투스 신호의 RSSI를 이용한 근거리 측도에서, 어떠한 조건에서 상대적으로 안정적인 양상의 거리 측도가 이뤄질 수 있는지 분석하였다. 실험 결과, 비콘 신호 송출 각도 조건이 수직 배치일 때와 Tx-Power 조건이 3.5m일 때 실제 거리와 측정 거리가 근접하고, 상대적으로 안정한 양상의 거리 측도가 이뤄졌음을 확인하였다. 본 실험 결과를 바탕으로 근거리 측도가 필요한 서비스에 적용할 수 있으며, 고정 계측 최적조건을 이용하여 향후 동적 계측 연구에 활용할 수 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.179-185
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• 2010

In a sensitivity analysis, an uncertainty importance measure is often used to assess how much uncertainty of an output is attributable to the uncertainty of an input, and thus, to identify those inputs whose uncertainties need to be reduced to effectively reduce the uncertainty of output. A function is called monotonic if the output is either increasing or decreasing with respect to any of the inputs. In this paper, for a monotonic function, we propose a method for evaluating the measure which assesses the expected percentage reduction in the variance of output due to ascertaining the value of input. The proposed method can be applied to the case that the output is expressed as linear and nonlinear monotonic functions of inputs, and that the input follows symmetric and asymmetric distributions. In addition, the proposed method provides a stable uncertainty importance of each input by discretizing the distribution of input to the discrete distribution. However, the proposed method is computationally demanding since it is based on Monte Carlo simulation.

• Geotechnical Engineering
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• v.4 no.4
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• pp.5-18
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• 1988

A series of CU triaxial compression tests were conducted to investigate the variation of -untrained shear strength of underconsolidated clay at different degrees of consolidation. The soil samples were artificially made by one-dimensional consolidation using soft Bangkok Clay. The test results showed that the undrained shear strength of clay parabolically increased convoking downward with increasing degrees of consolidation. However, all the measured shear strength were unanimously related to the effective stress. These experimental results were used in the numerical analysis. A finite element computer program was developed to investigate the stability of submarine .slope undergoing rapid deposition taking into account the variation in soil compressibility and permeability during the consolidation process. The relationships of degree of consolidation with time as a function of rate of deposition and angle of slope were established. A method of predicting the time of slope failure and the volume of moving mass of soil was also made.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.23-30
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• 2006

A clustering method for temporal data takes a model-based approach. This uses automata based model for each cluster. It is necessary to construct global models for a set of data in order to elicit individual models for the cluster. The preparation for building individual models is completed by determining the number of clusters inherent in the data set. In this paper, BIC(Bayesian Information Criterion) approximation is used to determine the number clusters and confirmed its applicability. A search technique to improve efficiency is also suggested by analyzing the relationship between data size and BIC values. A number of experiments have been performed to check its validity using artificially generated data sets. BIC approximation measure has been confirmed that it suggests best number of clusters through experiments provided that the number of data is relatively large.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.22-30
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• 2007

Most real word systems such as world economy, stock market, and medical applications, contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of the system. In this paper, we investigated methods for best clustering over time series data. As a first step for clustering, BIC (Bayesian Information Criterion) approximation is used to determine the number of clusters. A search technique to improve clustering efficiency is also suggested by analyzing the relationship between data size and BIC values. For clustering, two methods, model-based and similarity based methods, are analyzed and compared. A number of experiments have been performed to check its validity using real data(stock price). BIC approximation measure has been confirmed that it suggests best number of clusters through experiments provided that the number of data is relatively large. It is also confirmed that the model-based clustering produces more reliable clustering than similarity based ones.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.117-132
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• 2007

In a $2\times2$ table showing binary agreement between two raters, it is known that Cohen's $\kappa$, a chance-corrected measure of agreement, has two paradoxes. $\kappa$ is substantially sensitive to raters' classification probabilities(marginal probabilities) and does not satisfy conditions as a chance-corrected measure of agreement. However, $\kappa$ and other established measures have a reasonable and similar value when each marginal distribution is close to 0.5. The objectives of this paper are to present a new measure of agreement, H, which resolves paradoxes of $\kappa$ by adjusting unbalanced marginal distributions and to compare the proposed measure with established measures through some examples.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.63-70
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• 2014

Process of recognizing objects in binary images consists of image segmentation and pattern matching. If binary objects in the image are assumed to be separated, global features such as area, length of perimeter, or the ratio of the two can be used to recognize the objects in the image. However, if such an assumption is not valid, the global features can not be used but local features such as points or line segments should be used to recognize the objects. In this paper points with large curvature along the perimeter are chosen to be the feature points, and pairs of points selected from them are used as local features. Similarity of two local features are defined using elastic deformation energy for making the lengths and angles between gradient vectors at the end points same. Neighbour support value is defined and used for robust recognition of partially occluded binary objects. An experiment on Kimia-25 data showed that the proposed algorithm runs 4.5 times faster than the maximum clique algorithm with same recognition rate.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.47-54
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• 2005

In the most of speaker recognition system, speaker's characteristics is extracted from acoustic parameter by speech analysis and we make speaker's reference pattern. Parameters used in speaker recognition system are desirable expressing speaker's characteristics fully and being a few difference whenever it is spoken. Therefore we su99est following to solve this problem. This paper is proposed to use strong spectrum characteristic in non-noise circumstance and prosodic information in noise circumstance. In a stage of making code book, we make the number of data we need to combine spectrum characteristic and Prosodic information. We decide acceptance or rejection comparing test pattern and each model distance. As a result, we obtained more improved recognition rate than we use spectrum and prosodic information especially we obtained stational recognition rate in noise circumstance.