• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.349-352
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• 2004

In this literature, the selection of data set among the universe set is carried out with the fuzzy entropy function. By the definition of fuzzy entropy, we have proposed the fuzzy entropy function and the proposed fuzzy entropy function is proved through the definition. The proposed fuzzy entropy function calculate the certainty or uncertainty value of data set, hence we can choose the data set that satisfying certain bound or reference. Therefore the reliable data set can be obtained by the proposed fuzzy entropy function. With the simple example we verify that the proposed fuzzy entropy function select reliable data set.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.655-659
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• 2004

In this literature, the selection of data set among the universe set is carried out with the fuzzy entropy function. By the definition of fuzzy entropy, the fuzzy entropy function is proposed and the proposed fuzzy entropy function is proved through the definition. The proposed fuzzy entropy function calculate the certainty or uncertainty value of data set, hence we can choose the data set that satisfying certain bound or reference. Therefore the reliable data set can be obtained by the proposed fuzzy entropy function. With the simple example we verify that the proposed fuzzy entropy function select reliable data set.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.419-423
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• 1997

This paper is concerned about the classification of objects together with muti-attributes such as remote sensing image data by using tolerance rough set. To produce more reliable relations from given attributes in the data, we define new similarity measures by using scaling. Our Method will be applied to classify multi-spectral image data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1020-1025
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• 2007

EMS(energy management system) and SCADA(supervisory control and data acquisition) systems are used for reliable and efficient operation of electrical power systems. Various functions in EMS such as power flow, contingency analysis, security analysis essentially need accurate data set for reliable operation. State estimation can be a tool for providing these data. In this paper, programs for observability analysis and bad data processing are developed. Fundamental algorithms are introduced and validity of the proposed techniques is inspected with test cases.

• Journal of the Korean Geotechnical Society
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• v.15 no.2
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• pp.95-104
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• 1999

The bearing capacity of piles driven in soils showing set-up tendency increases with time. Though WEAP is an excellent tool for evaluating the driveability of driven pile, it has some limitations to predict reliable bearing capacity of pile after driving. It is because the existing WEAP method cannot take into account time-dependent soil properties after driving. The set-up effect should be accounted for to obtain a reliable bearing capacity by the WEAP. Unfortunately, there are no sufficient methods to take the set-up effect into consideration in wave equation analysis. This paper suggests an alternative to consider time effect in wave equation analysis through statistical analysis of dynamic load test data both at the end of driving and in the beginning of restrike. It is shown that the suggested parameters(quake and damping) would be more reliable than the existing one for the wave equation analysis of driven piles.

• Genomics & Informatics
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• v.10 no.3
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• pp.194-199
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• 2012

In addition to single-nucleotide polymorphisms (SNP), copy number variation (CNV) is a major component of human genetic diversity. Among many whole-genome analysis platforms, SNP arrays have been commonly used for genomewide CNV discovery. Recently, a number of CNV defining algorithms from SNP genotyping data have been developed; however, due to the fundamental limitation of SNP genotyping data for the measurement of signal intensity, there are still concerns regarding the possibility of false discovery or low sensitivity for detecting CNVs. In this study, we aimed to verify the effect of combining multiple CNV calling algorithms and set up the most reliable pipeline for CNV calling with Affymetrix Genomewide SNP 5.0 data. For this purpose, we selected the 3 most commonly used algorithms for CNV segmentation from SNP genotyping data, PennCNV, QuantiSNP; and BirdSuite. After defining the CNV loci using the 3 different algorithms, we assessed how many of them overlapped with each other, and we also validated the CNVs by genomic quantitative PCR. Through this analysis, we proposed that for reliable CNV-based genomewide association study using SNP array data, CNV calls must be performed with at least 3 different algorithms and that the CNVs consistently called from more than 2 algorithms must be used for association analysis, because they are more reliable than the CNVs called from a single algorithm. Our result will be helpful to set up the CNV analysis protocols for Affymetrix Genomewide SNP 5.0 genotyping data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5B
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• pp.274-286
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• 2005

In this paper, we propose a new power-efficient and reliable connected dominating set based routing protocol in the mobile ad hoc networks. Gateway nodes must be elected in consideration of residual energy and mobility because frequent reconstruction of connected dominating set result in transmission error for route losses. If node density is high, it results in a lot of contentions and more delays for network congestion. Therefore, in this paper, we propose a new construction method of connected dominating set that supports reliable and efficient data transmission through minimizing reconstruction of connected dominating set by delaying neighbor set advertisement message broadcast in proportion to weighted sum of residual energy, mobility, and the number of neighbor nodes. The performance of the proposed protocol is proved by simulation of various conditions.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.149-158
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• 2009

It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.3
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• pp.130-135
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• 2001

In this paper, the distribution characteristics of salt contaminants with the distance from sea in East coast, from Sokcho to Pusan of Korea peninsula were investigated to evaluate the design standard of KEPCO. To get the equivalent salt deposit density(ESDD), conventional brush wiping method was used. As the measuring period is comparatively short, and the measuring interval is long to check the maximum value, acquired ESDD data is very lower than the recommended value in the standard. The measured data didn't follow normal distribution, so it should take the statistical treatment. Through normalizing method, we could get a reliable probability data. In the past investigation, the accumulation characteristics of Japan is consulted to set the criterion, but the climatic condition of Korea is different to Japan. With the comparison of precipitation data and some measured data for long tern accumulation, we could set appropriate accumulation factor.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1024-1027
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• 2009

In this letter, we evaluate the classification performance of mixed numeric and categorical data for comparing the efficiency of feature filtering and feature wrapping. Because the mixed data is composed of numeric and categorical features, the feature selection method was applied to data set after discretizing the numeric features in the given data set. In this study, we choose the feature subset for improving the classification performance of the data set after preprocessing. The experimental result of comparing the classification performance show that the feature wrapping method is more reliable than feature filtering method in the aspect of classification accuracy.