• Nuclear Engineering and Technology
• /
• v.46 no.1
• /
• pp.55-62
• /
• 2014

A method for estimating software reliability for nuclear safety software is proposed in this paper. This method is based on the software reliability growth model (SRGM), where the behavior of software failure is assumed to follow a non-homogeneous Poisson process. Two types of modeling schemes based on a particular underlying method are proposed in order to more precisely estimate and predict the number of software defects based on very rare software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating software test cases as a covariate into the model. It was identified that these models are capable of reasonably estimating the remaining number of software defects which directly affects the reactor trip functions. The software reliability might be estimated from these modeling equations, and one approach of obtaining software reliability value is proposed in this paper.

• Communications for Statistical Applications and Methods
• /
• v.29 no.2
• /
• pp.203-223
• /
• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.10 no.3
• /
• pp.127-136
• /
• 2008

The main focus of the study resides in antecedents of price acceptability. Levels of acceptable price may be related to the consumers' perception on reasonable or expected price. Price acceptability is known to have several psychological antecedents. One of the antecedents to price acceptability reported by prior researches is price-quality inference, a tendency to correlate high price to excellence in quality. In addition, price-conscious consumers are likely to show lower level of price acceptability level. Another well-known antecedent is sale proneness. Sales-prone consumers may relate price of apparel products to product quality information. Moreover, it was reported that involved consumers should be more concerned with the products to its price and thus should have higher levels of price acceptability. A conceptual model with price consciousness, sale proneness and product involvement as the exogenous variable, price-quality inference and price acceptability as the endogenous variable was developed for the empirical study. Measures of research variables were developed based on previous studies. Questionuaires from 298 respondents were analyzed for the study. The average age of respondents was 27. About 60% of the respondents were married and about 65% of them had college degrees. Empirical results supported all of the hypothesized relationships. Price consciousness had significant negative influence on price-quality inference and price acceptability. Sale proneness significantly influenced price-quality inference, while apparel involvement had significant impact on price-quality inference and price acceptability. Price-quality affected price acceptability significantly. This study generated a framework to help scholars understand antecedents of price acceptability of apparel products. Price has been shown to playa dual role in consumer's perceptions, either positively or negatively. Price consciousness played a negative role, and product involvement had a positive role in evoking higher level of price acceptability. This study also suggests additional source of positive, yet indirect role of price, sale proneness. This study also affirmed the importance of price-quality inference in arousing higher level of price acceptability.

• Journal of Korea Water Resources Association
• /
• v.44 no.7
• /
• pp.523-536
• /
• 2011

The objective of this study is to develop the data driven model for the flood forecasting that are improved the problems of the existing hydrological model for flood forecasting in medium and small streams. Neuro-Fuzzy flood forecasting model which linked the Takagi-Sugeno fuzzy inference theory with neural network, that can forecast flood only by using the rainfall and flood level and discharge data without using lots of physical data that are necessary in existing hydrological rainfall-runoff model is established. The accuracy of flood forecasting using this model is determined by temporal distribution and number of used rainfall and water level as input data. So first of all, the various combinations of input data were constructed by using rainfall and water level to select optimal input data combination for applying Neuro-Fuzzy flood forecasting model. The forecasting results of each combination are compared and optimal input data combination for real-time flood forecasting is determined.

• The Korean Journal of Applied Statistics
• /
• v.35 no.2
• /
• pp.311-325
• /
• 2022

For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.

• Journal of KIISE:Software and Applications
• /
• v.27 no.4
• /
• pp.412-421
• /
• 2000

In spite that the main contents of mathematical and scientific learning are understanding principles and their applications, most of existing educational softwares are based on rote learning, thus resulting in limited educational effects. In the artificial intelligence research, some progress has been made in developing automatic tutors based on proving and simulation, by adapting the techniques of knowledge representation, search and inference to the design of tutors. However, these tutors still fall short of being practical and the turor, even a prototype model, for learning problem solving is yet to come out. The geometry problem-solving tutor proposed by this research involves dynamic inference performed in parallel with learning. As an ontology for composing the problem space within a real-time setting, we have employed the notions of propositions, hypotheses and operators. Then we investigated the mechanism of interactive learning of problem solving in which the main target of inference involves the generation and the test of these components. Major accomplishment from this research is a practical model of a problem tutor embedded with a series of inference techniques for algebraic manipulation, which is indispensable in geometry problem solving but overlooked by previous research. The proposed model is expected to be applicable to the design of problem tutors in other scientific areas such as physics and electric circuitry.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4D
• /
• pp.573-580
• /
• 2006

The Saturation headway is a major parameter in estimating the intersection capacity and setting the signal timing. But Existing algorithms are still far from being robust in dealing with factors related to the variation of saturation headways at signalized intersections. So this study apply the fuzzy inference system using ANFIS. The ANFIS provides a method for the fuzzy modeling procedure to learn information about a data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data. The climate conditions and the degree of brightness were chosen as the input variables when the rate of heavy vehicles is 10-25 %. These factors have the uncertain nature in quantification, which is the reason why these are chosen as the fuzzy variables. A neuro-fuzzy inference model to estimate saturation headways at signalized intersections was constructed in this study. Evaluating the model using the statistics of $R^2$, MAE and MSE, it was shown that the explainability of the model was very high, the values of the statistics being 0.993, 0.0289, 0.0173 respectively.

• Journal of Korea Water Resources Association
• /
• v.44 no.7
• /
• pp.537-551
• /
• 2011

Based on optimal input data combination selected in the earlier study, Neuro-Fuzzy flood forecasting model linked Takagi-Sugeno fuzzy inference theory with neural network in Wangsukcheon and Gabcheon is established. The established model was applied to Wangsukcheon and Gabcheon and water levels for lead time of 0.5 hr, 1 hr, 1.5 hr, 2.0 hr, 2.5 hr, 3.0 hr are forecasted. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The forecasted results have shown good agreements with observed data. Additionally to evaluate quantitatively for applicability of the model, various statistical errors such as Root Mean Square Error are calculated. As a result of the flood forecasting can be simulated successfully without large errors in all statistical error. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.17 no.2
• /
• pp.1-9
• /
• 2009

A parking-assist system is defined that a driver adjusts vehicle velocity through brake pedal operation and parking-assist system controls the motion of the vehicle to follow a collision-free path. In this study, a motion control algorithm using Fuzzy inference is proposed to track a maneuvering clothoid parallel path. Simulations are performed under SIMULINK environments using MATLAB and CarSim for a vehicle model. As the vehicle model in MATLAB a bicycle model is used including lateral dynamics. The simulation results show that the path tracking performance is satisfactory under various driving and initial conditions.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1865-1869
• /
• 2004

A new model for the propagation prediction for mobile communication network inside building is presented in this paper. The model is based on the determination of the dominant paths between the transmitter and the receiver. The field strength is predicted with adaptive neuro - fuzzy inference systems (ANFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved.