• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.125-134
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• 2006

This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.105-109
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• 2013

There are the free space model, the direct-path and ground reflected model, Egli model, Okumura-Hata model in the representative propagational models. The measured results at the area of PNG area were used as the experimental data in this paper. The new proposed partial interval analysis method is applied on the measured propagation data in the cellular band. The interval for the analysis is divided from the entire 30 Km distance to 5 Km, and next to 1 Km. The best-fit propagation models are chosen on all partial intervals. The means and standard deviations are calculated for the differences between the measured data and all partial interval models. By using the 5 Km- or 1 Km- partial interval analysis, the standard deviation between the measured data and the partial propagation models was improved more than 1.7 dB.

• Journal of Internet Computing and Services
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• v.4 no.4
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• pp.45-51
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• 2003

In general, the values for attribute appearing in fuzzy object-oriented data models are represented by the fuzzy sets. If it can allow the attribute values in the fuzzy object-oriented data models to be represented by the interval-valued fuzzy sets, then it can allow the fuzzy object-oriented data models to represent the attribute values in more flexible manner. The attribute values of frames appearing in the inheritance structure of the fuzzy object-oriented data models are calculated by a prloritized conjunction operation using interval-valued fuzzy sets. This approach can be applied to knowledge and information processing in which degree of membership is represented as not the conventional fuzzy sets but the interval-valued fuzzy sets.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.217-223
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• 2010

The research proposes the complementary methodology using integrated hypothesis testing and confidence interval models that can be identified the statistical difference and practical equivalence. The models developed in this study can be used in the quality improvement processes such as QC story 15 steps. For the expressions of CI4LSD(Confidence Interval for Least Significant Difference) and CI4TOST(Confidence Interval for Two One-Sided Tests) are simple, quality practioners can efficiently handle them. CI4TOST models as a complement can be applied when CI4LSD models are influenced by sample size and precision.

• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.1-10
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• 2003

We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

• MALSORI
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• no.56
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• pp.147-158
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• 2005

In this paper, we are focused to develop the simplified speaker verification algorithm without background speaker models, which will be adopted in the portable speaker verification system equipped in portable terminals such as mobile phone and PMP. According to the tolerance interval analysis, the population of someone's speaker model can be represented by a suitable number of selected independent samples of speaker model. So we can make the representative speaker model and threshold under the specified confidence level and coverage. Using proposed algorithm with the number of samples is 40, the experiments show that the false rejection rate is $3.0\%$ and the false acceptance rate $4.3\%$, worth comparing to conventional method's results, $5.4\%\;and\;5.5\%$, respectively. Next step of research will be on the suitable adaptation methods to overcome speech variation problems due to aging effect and operating environments.

• Environmental Engineering Research
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• v.24 no.2
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• pp.298-308
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• 2019

This work deals with the membrane fouling mode and the unclogging in seawater ultrafiltration process. The identification of the fouling mechanism by modeling the experimental flux decline was performed using both the classical models of Hermia and the combined models of Bolton. The results show that Bolton models did not bring more precise information than the Hermia's and the flux decline can be described by one of the four Hermia's models since the backwash interval is ${\leq}60$ min. An experimental screening study has been then conducted to choose among 5 parameters (backwash interval, duration, pulses and the flow-rate or injected hypochlorite concentration) those that are the most influential on the fouling and the net water production. It has emerged that fouling is mainly affected by the backwash interval; its prolongation from 30 to 60 min engenders an increase in the reversible fouling and a decrease in the irreversible fouling. This later is also significantly reduced when the hypochlorite concentration increases from 4.5 to 10 ppm. Moreover, the net water production significantly increases with increasing the filtration duration up to 60 min and decreases with decreasing the backwash duration and backwash flow-rate from 10 to 40 s and from 15 to ${\geq}20L.min^{-1}$, respectively.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.643-653
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• 2008

Application of the bootstrap to problems in multistage inference procedures are discussed in normal and other related models. After a general introduction to these procedures, here we explore in multistage fixed precision inference in models. We present numerical comparisons of these procedures based on bootstrap critical points for small and moderate sample sizes obtained via extensive sets of simulated experiments. It is expected that the procedure based on bootstrap leads to better results.

• Journal of Applied Reliability
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• v.1 no.2
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• pp.175-181
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• 2001

Recently, the quality of software has become a major issue. The statistical models used in making predictions about the quality of software are termed software reliability growth models (SRGM). However, the existing SRGMs have not been satisfactory in predicting software reliability behavior (Keiller and Miller(1991), Keiller and Littlewood(1984), Musa(1987)). In this paper, we present a fuzzy-based interval estimation of software errors (failures).