• Convergence Security Journal
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• v.6 no.3
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• pp.145-153
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• 2006

Infinite failure NHPP models for a record value satisfies mode proposed in the literature exhibit either monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, propose comparative study of software reliability model using Erlang distribution, Rayleigh and Gumbel distribution. Equations to estimate the parameters using maximum likelihood estimation of infinite failure NHPP model based on failure data collected in the form of interfailure times are developed. For the sake of proposing distribution, we used to the special pattern. Analysis of failure data set using arithmetic and Laplace trend tests, goodness-of-fit test, bias tests is presented.

• Convergence Security Journal
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• v.7 no.3
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• pp.7-13
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• 2007

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. Accurate predictions of software release times, and estimation of the reliability and availability of a software product require quantification of a critical element of the software testing process : test coverage. This model called Enhanced non-homogeneous poission process (ENHPP). In this paper, exponential coverage and S-shaped model was reviewed, proposes the superposition model, which maked out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE statistics for the sake of efficient model, was employed.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3821-3828
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• 2000

Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.61-73
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• 2009

Many software reliability growth models(SRGM) have been proposed since the software reliability issue was raised in 1972. The technology to estimate and grow the reliability of developing S/W to target value during testing phase were developed using them. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or Logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. I propose the methology to evaluate the SRGM using least square estimater and maximum likelihood estimater for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• Computers and Concrete
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• v.16 no.6
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• pp.847-864
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• 2015

This study aimed to develop models of sulfate diffusion and ettringite content profile in cement paste for the predication of the damage behavior in cement paste subject to external sulfate. In the models, multiphase reaction equilibrium between ions in pore solution and solid calcium aluminates phases and the microstructure changes in different positions of cement paste were taken into account. The distributions of expansive volume strain and expansion stress in cement paste were calculated based on the ettringite content profile model. In addition, more sulfate diffusion tests and SEM analyses were determined to verify the reliability and veracity of the models. As the results shown, there was a good correlation between the numerical simulation results and experimental evidences. The results indicated that the water to cement ratio (w/c) had a significant influence on the diffusion of sulfate ions, ettringite concentration profile and expansion properties in cement paste specimens. The cracking points caused by ettringite growth in cement paste specimens were predicted through numerical methods. According to the simulation results, the fracture of cement paste would be accelerated when the specimens were prepared with higher w/c or when they were exposed to sulfate solution with higher concentration.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.109-125
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• 2014

Suitable climate condition is essential for stable growth of crops which directly leads to an increase in crop production. Preceding domestic researches mostly used crop models to predict grain or crop yield in relation to climate change. However, the use of various models and input data based on foreign background lowered the reliability for result. Therefore in this study, we evaluated domestic applicability by comparing and analyzing various crop models developed abroad. In addition, we selected models based on the possibility of acquiring input data and suggested domestic applicability.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.9-18
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• 2006

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the exponentiated exponential distribution reliability model, which maked out efficiency substituted for gamma and Weibull model(2 parameter shape illustrated by Gupta and Kundu(2001) Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE, AIC statistics and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using NTDS data set for the sake of proposing shape parameter of the exponentiated exponential distribution was employed. This analysis of failure data compared with the exponentiated exponential distribution model and the existing model (using arithmetic and Laplace trend tests, bias tests) is presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.514-522
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• 2007

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this parer, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the Burr distribution reliability model, which making out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE, AIC statistics and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using real data set for the sake of proposing shape parameter of the Burr distribution was employed. This analysis of failure data compared with the Burr distribution model and the existing model(using arithmetic and Laplace trend tests, bias tests) is presented.

• The Korean Journal of Food & Health Convergence
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• v.3 no.1
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• pp.1-7
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• 2017

The study has made theoretical system by thesis journals at home and abroad, material of the Association of Convenience Store Industry of Korea, materials of Statistics Korea and reference books. First, the study examined convenience store's brand image, cognized value, quality level, reliability upon brand, structural relation of purchasing intention, and designed models and theses. Second, the first questionnaire consisted of test items of precedent study, and inspected in advance to inspect reliability and appropriateness, and made correction and supplemented problems. Third, SPSS and AMOS statistics were used to conduct empirical analysis and to find out conclusion and implications. In this study, the subject was consumers who visited local convenience stores such as GS25, Seven Eleven and CU last 3 months to buy PB food. Consumers' consumption pattern and culture made change to be reasonable and practical owing to aging society, nuclear family, women's entering into the society, one person household consumer, and convenient and prompt buying to make different consumption pattern and to make change of commodity distribution pattern. Convenience store took action against change of the trend to expand business area by new distribution channels to get another growth source. Private brand (PB) food that was distributor's brand was expanded. The PB product has been competitiveness of food service industry.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.