• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.19-28
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• 2011

Adaptive Random Testing (ART) aims to enhance the performance of pure random testing by detecting failure region in a software. The ART algorithm generates effective test cases which requires less number of test cases than that of pure random testing. However, all ART algorithms currently proposed are designed for the tests of monolithic system or unit level. In case of integrated system tests, ART approaches do not achieve same performances as those of ARTs applied to the unit or monolithic system. In this paper, we propose an extended ART algorithm which can be applied to the integrated system testing environment without degradation of performance. The proposed approach investigates an input distribution of the unit under a test with limited number of seed input data and generates information to be used to resizing input domain partitions. The simulation results show that our approach in an integration environment could achieve similar level of performance as an ART is applied to a unit testing. Results also show resilient effectiveness for various failure rates.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.371-377
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• 2010

In this study, we propose two informal hypothesis tests which may be useful in determining the number of trees in a random forest for use in classification. The first test declares that a case is 'easy' if the hypothesis of the equality of probabilities of two most popular classes is rejected. The second test declares that a case is 'hard' if the hypothesis that the relative difference or the margin of victory between the probabilities of two most popular classes is greater than or equal to some small number, say 0.05, is rejected. We propose to continue generating trees until all (or all but a small fraction) of the training cases are declared easy or hard. The advantage of combining the second test along with the first test is that the number of trees required to stop becomes much smaller than the first test only, where all (or all but a small fraction) of the training cases should be declared easy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.747-750
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• 2004

After launching, spacecraft is exposed to extreme environments. So spacecraft should be tested after design/manufacture to verify whether components can be operated functionally. Acceleration transferred from launch vehicle to spacecraft produces quasi-static load, sine vibration and random vibration. Random vibration is also induced by acoustic vibrations transferred by surface of spacecraft. And shock vibration is produced when spacecraft is separated from launch vehicle. To verify operation of spacecraft under these launch environments, separation shock test, sine vibration test, acoustic vibration test and random vibration test should be performed. This paper describes these launch environment test requirements.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.224-227
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• 2007

This paper introduces the results of acoustic loads test conducted on the upper stage assembly of KSLV-I, which is the first Korea space launch vehicle. A launch vehicle and its payloads are subjected to severe acoustic pressure loading when they lift off and ascent during the transonic periods. Acoustic loadings are spreaded out broad frequncy-spectrum up to 10,000Hz. Acoustic loads are a primary source of structural random vibration of the upper stage and payloads. Therefore, in order to verify the structural integrity of the upper stage assembly of KSLV-I and the survivability of its components under severe random vibration environment, acoustic loads test is conducted in the high intensity acoustic chamber with 142dB (overall SPL). The results show the structural design and component random vibration specifications well meet with the environmental requirements.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.789-797
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• 2003

In this paper, we consider two components system which have bivariate weibull model with bivariate random censored data. We proposed large sample test for independence based on maximum likelihood estimator and relative frequency estimator, respectively. Also we derive asymptotic properties for the large sample tests and present a numerical study.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.8
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• pp.610-618
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• 2003

Recently, many deterministic built-in self-test schemes to reduce test time have been researched. These schemes can achieve a good quality test by shortening the whole test process, but require complex algorithms or much hardware. In this paper, a new deterministic BIST scheme is provided that reduces the additional hardware requirements, as well as keeping test time to a minimum. The proposed BIST (Built-In Self-Test) methodology brings about the reduction of the hardware requirements for pseudo-random tests as well. Theoretical study demonstrates the possibility of reducing the hardware requirements for both pseudo-random and deterministic tests, with some explanations and examples. Experimental results show that in the proposed test scheme the hardware requirements for the pseudo-random test and deterministic test are less than in previous research.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.865-868
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• 1993

This paper describes a simple random signal generator employing by CMOS analog technology in current mode. The system is a nonlinear dynamical system described by a difference equation, such as x(t＋1) = f(x(t)) , t = 0,1,2, ... , where f($.$) is a nonlinear function of x(f). The tent map is used as a nonlinear function to produce the random signals with the uniform distribution. The prototype is implemented by using transistor array devices fabricated in a mass product line. It can be easily realized on a chip. Uniform randomness of the signal is examined by the serial correlation test and the $\chi$2 test.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.7
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• pp.526-531
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• 2003

Recently, many BIST(Built-in Self Test) schemes have been researched to reduce test time and hardware. But, most BIST schemes about pattern generation are for deterministic pattern generation. In this paper a new pseudo-random BIST scheme is provided to reduce the existing test hardware and keep a reasonable length of test time. Theoretical study demonstrates the possibility of the reduction of the hardware for pseudo-random test with some explanations and examples. Also the experimental results show that in the proposed test scheme the hardware for the pseudo-random test is much less than in the previous scheme and provide comparison of test time between the proposed scheme and the current one.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.283-288
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• 2005

Test case prioritization methods schedule test cases for execution when we can not practically run all test cases for regression testing. We proposed a new prioritization method that is based on historical execution and mr detection data. And we conducted an experiment to compare the proposed method with existing Random and LRU methods using the fault age under the long run environment as criterion. The experiment shows several interesting results. First, our results show that they are complementary. Random method shows good performance for programs that have many error-detectable test cases and HED is more effective for the programs that can be detected by very small amount of test cases. But LRU is more effective for the programs that have relatively medium amount of error detectable test cases. Next, the performance of prioritization method is affected by the size of test suites. Two experiments that have different size of test suites show considerably different fault ages and performance order. And lastly, the $20\%$ of test cases shows considerably good performance compared to the execution result of the full test suite.