• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.435-443
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• 2010

When testing systems that incorporate probabilistic behavior, it is necessary to apply test inputs a number of times in order to give a test verdict. Interval estimation can be used to assert the correctness of probabilities where the selection of confidence interval is one of the important issues for quality of testing. The Wald interval has been widely accepted for interval estimation. In this paper, we compare the Wald interval and the Agresti-Coull interval for various sizes of samples. The comparison is carried out based on the test pass probability of correct implementations and the test fail probability of incorrect implementations when these confidence intervals are used for probability testing. We consider two-sided confidence intervals to check if the probability is close to a given value. Also one-sided confidence intervals are considered in the comparison in order to check if the probability is not less than a given value. When testing probabilities using two-sided confidence intervals, we recommend the Agresti-Coull interval. For one-sided confidence intervals, the Agresti-Coull interval is recommended when the size of samples is large while either one of two confidence intervals can be used for small size samples.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1195-1200
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• 2019

This paper proposes a detection algorithm for group testing. Group testing is a problem of finding a very small number of defect samples out of a large number of samples, which is similar to the problem of Compressed Sensing. In this paper, we define a noiseless group testing and propose a probabilistic algorithm for detection of defective samples. The proposed algorithm is constructed such that the extrinsic probabilities between the input and output signals exchange with each other so that the posterior probability of the output signal is maximized. Then, defective samples are found in the group testing problem through a simulation on the detection algorithm. The simulation results for this study are compared with the lower bound in the information theory to see how much difference in failure probability over the input and output signal sizes.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.102-110
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• 1994

We investigate the small-sample behavior of the probability of rejection curves and its performance for a equivalence testing procedure based on confidence intervals which was developed with a motivation from bioequivalence studies. This type of equivalence studies are conducted frequently in pharmaceutical industries to compare the relative bioavailabilty of two formulations of a drug and can be applied various fields where assurance of quality equivalence is needed. From the Monte-Carlo simulation results we suggest proper sample sizes for the equivalence testing procedure.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.688-693
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• 2002

Adhesive bonding is becoming one of the popular joining techniques in metal industries, since it has some advantages over other techniques such as welding and diffusion bonding, e.g., any dissimilar metals are easily adhesive-bonded together. In this study, the experiments were carried out in order to provide the statistical data with strength evaluation methods: tension, shear and four-point bending tests for thermoplastic epoxy resin based adhesive-bonded metal joints. We should certificate on the probability of the adhesive strength that has the tendency of brittle fracture, the adhesive bonding strength between metals with thermoplastic adhesive has the best probability at four-point bending test. The strength testing method that has higher probability is four-point bending test, shear test and tensile test in order.

• Journal of Veterinary Clinics
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• v.27 no.4
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• pp.421-426
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• 2010

In view of free from bluetongue (BT) in the domestic cattle population in Korea, the key of quarantine testing for BT virus (BTV) infection is detection of cattle previously exposed to the virus. The objective of this study was to estimate the probability of detecting a cattle infected with BTV using a stochastic modeling analysis of existing quarantine testing data. Three testing scenarios were considered in this study: serological testing of all animals in all imported lots (scenario 1), serological testing of a sample of cattle from all imported lots (scenario 2), and serological testing of 50% of imported lots (scenario 3). In scenario 2 and 3, it was assumed that cattle were sampled (sample size) within each lot to detect 5% of the cattle in each lot with a 95% confidence, taking into account diagnostic sensitivity of the ELISA (enzyme-linked immunosorbent assay). The model output was the total number of BTV-infected cattle and the prevalence of BTV infection in imported cattle from the US, Australia, Canada and Japan. Compared to the scenario 1, the probability of detecting a BTV-infected cattle was estimated to be 19% and 1.6% in scenario 2 and 3, respectively. Furthermore, the analyses showed a 95% confidence that BTV prevalence was less or equal to $9.7{\times}10^{-4}$ (median = $1.5{\times}10^{-5}$), indicating that, for the scenario 2 and 3 with serological testing for a sample of cattle, the risk of introducing an exotic strain of BTV into Korea through the importation of live cattle would not be acceptable.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.95-101
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• 2020

In this paper, we consider a group testing (GT) framework which is to find a set of defective samples out of a large number of samples. To handle this framework, we propose a maximum product detection algorithm (MPDA) which is based on maximum a posteriori probability (MAP). The key idea of this algorithm exploits iterative detection to propagate belief to neighbor samples by exchanging marginal probabilities between samples and output results. The belief propagation algorithm as a conventional approach has been used to detect defective samples, but it has computational complexity to obtain the marginal probability in the output nodes which combine other marginal probabilities from the sample nodes. We show that the our proposed MPDA provides a benefit to reduce computational complexity up to 12% in runtime, while its performance is only slightly degraded compared to the belief propagation algorithm. And we verify the simulations to compare the difference of performance.

• Journal of Korean Society for Quality Management
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• v.30 no.3
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• pp.186-194
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• 2002

In this paper we introduce formulae for estimating the failure rate of a large scaled software by using the Bayesian rule when a black-box random testing which selects an element(test case) at random with equally likely probability, is performed. A program or software can be treated as a mathematical function with a well-defined (input)domain and range. For a large scaled software, their input domains can be partitioned into multiple subdomains and exhaustive testing is not generally practical. Testing is proceeding with selecting a subdomain, and then picking a test case from within the selected subdomain. Whether or not the proportion of selecting one of the subdomains is assumed probability, we developed the formulae either case by using Bayesian rule with gamma distribution as a prior distribution.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.572-578
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• 2018

This paper considers Group Testing as one of combinatorial problems. The group testing first began to inspect soldier's syphilis infection during World War II and have long established an academic basis. Recently, there has been much interest in related areas because of the rediscovery of the value of the group testing. The group testing is the same as finding a few defect samples out of a large number of samples, which is similar to the inverse problem of Compressed Sensing. In this paper, we introduce the definition of the group testing, and specify the classes of the group testing and the bounds on performance of the group testing. In addition, we show a lower bound for the number of tests required to find defective samples using the theoretical theorem which is mainly used for relationship between conditional entropy and the probability of error in the information theory. We see how our result can be different from other related results.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1391-1396
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• 2008

In this paper, we consider two components system which the lifetimes have Marshall and Olkin's bivariate exponential model with bivariate type I censored data. We propose a Bayesian independent test procedure for above model using fractional Bayes factor method by O'Hagan based on improper prior distributions. And we compute the fractional Bayes factor and the posterior probabilities for the hypotheses, respectively. Also we select a hypothesis which has the largest posterior probability. Finally a numerical example is given to illustrate our Bayesian testing procedure.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.4
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• pp.1160-1171
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• 2015

The purpose of this study is to design and develop a computer based learning and test system, which supports not only testing learner's ability but also learning contents with giving feedback and hint. In order to design and develop a computer based learning and test system, Visual Basic dot Net software is used. The system works in three stages: sequential problem solving stage, randomized problem solving stage, and the challenge stage of pass/fail. The results of this study are as follows: (a) We propose the context of design for the computer based learning and test system. (b) We design and develop items display function with sequential and random algorithm in this system. (c) We design and develop pass/fail function by applying SPRT(Sequential Probability Ratio Testing) algorithm in the computer based learning and test system.