• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.6
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• pp.457-463
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• 2018

This paper compares the results of ${\Sigma}{\Delta}-STAP$ applied to actual radar test data and simulation data. The radar received a target signal from a virtual target generator and the clutter signal from a signal generator in an anechoic chamber. The simulation data were generated from ideal baseband radar signal modeling using the same parameter as that for the test radar. The ${\Sigma}{\Delta}-STAP$ results of the test and simulation data are similar in terms of the target signal shape and noise level. The SINR(Signal-to-Interfrence-plus-Noise Ratio) loss also had similar aspects, but the simulation result shows 1~2 dB higher SINR loss than the test result. This result verified that the simulation data can be a reasonable alternative test data when the ${\Sigma}{\Delta}-STAP$ is applied.

• Journal of Internet Computing and Services
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• v.9 no.2
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• pp.89-105
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• 2008

So far, most of research on automated test data generation(ATDG) deals with programs without pointers. Recently, few works hove been done on ATDG in the presence of pointers, but they ore path-oriented techniques which require the specification of on entire program path to be tested or a program to be executed. This paper presents a new technique for generating test data even without specifying a program path completely. The presented technique is a static technique which transforms the test data generation problem into a SAT(SATisfiability) problem and makes advantage of SAT solvers for ATDG. For the ends, we transform a program under test into Alloy which is the first-order relational logic and then produce test data via Alloy analyzer.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.1-8
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• 2018

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.

• Journal of Software Engineering Society
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• v.28 no.1
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• pp.23-28
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• 2019

Recently, in order to effectively test deep neural network model for image processing application, researches have actively conducted to automatically generate data in corner-case that is not correctly predicted by the model. This paper proposes test data generation method that selects arbitrary words from input of system and transforms them into synonyms in order to test the bug reporter automatic assignment system based on sentence classification deep neural network model. In addition, we compare and evaluate the case of using proposed test data generation and the case of using existing difference-inducing test data generations based on various neuron coverages.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.3
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• pp.167-175
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• 2007

The relations between data from test methods and conditions in structural elements are considered. NDE(Nondestructive Evaluation) methods are joint application of a test and a basis for interpretation of data obtained in the test. Correct assessments of conditions of elements depend on the inaccuracy and variability in the test data and on the uncertainty of correlations between attributes(what is measured) and conditions(what is sought in the inspection). A full description of the performance of NDE methods considers the relation of test data to condition of elements. The quality of the test data itself is important, but equally important is the interpretation that occurs after the test. To make the decision of the performance of NDE methods, this paper presents mathematical basis to measure the reliability of NDE methods.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.671-680
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• 2003

The accuracy of inflow into tunnel estimates depends largely on how well permeability is characterized. But, the average of the packer test results will always underestimate the upper end of the permeability range, and therefore underestimate the inflow. Taking an average of the test results always underestimates inflow because the average permeability does not really exist. The distribution of packer-test data may not accurately reflect permeability, however, due to the limits of the test method and the luck of the field investigation. These discrepancies may be overcome by using Raymer(2001)'s log-normal plots and Heuer(1995)'s histograms of the data to develop a permeability model that will be used in lieu of the data to calculate inflow. Furthermore, the influence on the inflow is examined by the geological characteristics based upon the hundred times of packer test OO tunnel project.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1814-1819
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• 2010

Since 1987, when statistical analyzing guide for thermal life test of Accelerated Life Test(ALT) was proposed as ANSI/IEEE Std 101, this guide has been used widely for many experiment data. Shim(2004) had done Monte Carlo simulation to compare life of two different systems or materials, based on statistic values obtained from ANSI/IEEE Std 101 data. In this study, a profile analysis is proposed for comparing life of two different systems or materials, and some examples using pre-existing data are given.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.115-122
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• 2006

A test is suggested for detecting deviations from both multivariate normality and independence. This test can be used for assessing the normality and independence of univariate time series residuals. We derive the limiting distribution of the test statistic and a simulation study is conducted to study the accuracy of the limiting distribution in finite samples. Finally, we apply our method to a real data of time series.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.707-711
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• 2007

Microarray gene expression data is believed to show the functions of living organism through the gene expression values. We have studied a method to get the informative genes from the microarray gene expression data. There are several ways for this. In recent researches to get more sophisticated and detailed results, it has used the intelligence information theory like fuzzy theory. Some methods are to add fudge factors to the significance test for more refined results. In this paper, we suggest a method to get informative genes from microarray gene expression data. We combined the difference of means between two groups and the fuzzy membership degree which reflects the variance of the gene expression data. We have called our significance test the Fuzzy Information method for Gene Expression data(FIGER). The FIGER calculates FIGER variation ratio and FIGER membership degree to show how strongly each object belongs to the each group and then it results in the significance degree of each gene. The FIGER is focused on the variation and distribution of the data set to adjust the significance level. Out simulation shows that the FIGER-test is an effective and useful significance test.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.