• Journal of Electrical Engineering and Technology
• /
• v.7 no.4
• /
• pp.606-614
• /
• 2012

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2012.11a
• /
• pp.141-142
• /
• 2012

Estimating the productivity of labor has relied on the experience and intuition of an estimator, or productivity data, such as standard in practical estimation. However, when new technologies and new construction methods employed in the construction industry, Dependence of a quantity surveyor's intuition is increased. This is because of the lack of a systematic methodology which models, estimates, and measures the labor productivity, Due to the historical productivity data is unavailable. Even thought project history data contains information that is important to predict the performance of a project, It can not provide valuable information to model, estimate, and measure the labor productivity. This is because 1) the absence of the productivity measurement system with consistency, 2) the difficulty of reusability of historical data collected. Therefor, this study suggests a methodology which build a productivity model by measuring the productivity of labor and collecting the historical data systematically. In addition, this methology is applied to develop a productivity model of shop-drawing and manufacturing process using descrete event simulation.

• Proceedings of the KIEE Conference
• /
• 1995.11a
• /
• pp.175-177
• /
• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• Proceedings of the Korean Association for Survey Research Conference
• /
• 2006.06a
• /
• pp.131-159
• /
• 2006

This paper consider multinomial group testing which is concerned with classification each of N given units into one of k disjoint categories. In this paper, we propose exact Bayesian, approximate Bayesian, bootstrap methods for estimating individual category proportions using the multinomial group testing model proposed by Bar-Lev et al (2005). By the comparison of Mcan Squre Error (MSE), it is shown that the exact Bayesian method has a bettor efficiency and consistency than maximum likelihood method. We suggest an approximate Bayesian approach using Markov Chain Monte Carlo (MCMC) for posterior computation. We derive exact credible intervals based on the exact Bayesian estimators and present confidence intervals using the bootstrap and MCMC. These intervals arc shown to often have better coverage properties and similar mean lengths to maximum likelihood method already available. Furthermore the proposed models are illustrated using data from a HIV blooding test study throughout California, 2000.

• Journal of Korean Society for Quality Management
• /
• v.32 no.4
• /
• pp.229-241
• /
• 2004

Process capability index is used to determine whether a production process is capable of producing items within a specified tolerance. The index $C_{pmk}$ is the third generation process capability index. This index is more powerful than two useful indices $C_p$ and $C_{pk}$. Whether a process distribution is clearly normal or nonnormal, there may be some questions as to which any process index is valid or should even be calculated. As far as we know, yet there is no result for statistical inference with process capability index $C_{pmk}$. However, asymptotic method and bootstrap could be studied for good statistical inference. In this paper, we propose various bootstrap confidence limits for our process capability Index $C_{pmk}$. First, we derive bootstrap asymptotic distribution of plug-in estimator $C_{pmk}$ of our capability index $C_{pmk}$. And then we construct various bootstrap confidence limits of our capability index $C_{pmk}$ for more useful process capability analysis.

• Korean Journal of Mathematics
• /
• v.21 no.4
• /
• pp.429-437
• /
• 2013

Let $X_1$, $X_2$, ${\cdots}$, $X_n$ be independent and identically distributed random variables having common exponential density with unknown mean ${\mu}$. In the sequential confidence interval estimation for the exponential hazard rate ${\theta}=1/{\mu}$, when the loss function is strictly convex, the following stopping rule is proposed with the half length d of prescribed confidence interval $I_n$ for the parameter ${\theta}$; ${\tau}$ = smallest integer n such that $n{\geq}z^2_{{\alpha}/2}\hat{\theta}^2/d^2+2$, where $\hat{\theta}=(n-1)\bar{X}{_n}^{-1}/n$ is the minimum risk estimator for ${\theta}$ and $z_{{\alpha}/2}$ is defined by $P({\mid}Z{\mid}{\leq}{\alpha}/2)=1-{\alpha}({\alpha}{\in}(0,1))$ Z ~ N(0, 1). For the confidence intervals $I_n$ which is required to satisfy $P({\theta}{\in}I_n){\geq}1-{\alpha}$. These estimated intervals $I_{\tau}$ have the asymptotic consistency of the sequential procedure; $$\lim_{d{\rightarrow}0}P({\theta}{\in}I_{\tau})=1-{\alpha}$$, where ${\alpha}{\in}(0,1)$ is given.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.1
• /
• pp.151-157
• /
• 2019

This study is about the process capability index (PCI). In this study, we introduce several indices including the index $C_{PR}$ and present the characteristics of the $C_{PR}$ as well as its validity. The difference between the other indices and the $C_{PR}$ is the way we use to estimate the standard deviation. Calculating the index, most indices use sample standard deviation while the index $C_{PR}$ uses range R. The sample standard deviation is generally a better estimator than the range R. But in the case of the panel process, the $C_{PR}$ has more consistency than the other indices at the point of non-conforming ratio which is an important term in quality control. The reason why the $C_{PR}$ using the range has better consistency is explained by introducing the concept of 'flatness ratio'. At least one million cells are present in one panel, so we can't inspect all of them. In estimating the PCI, it is necessary to consider the inspection cost together with the consistency. Even though we want smaller sample size at the point of inspection cost, the small sample size makes the PCI unreliable. There is 'trade off' between the inspection cost and the accuracy of the PCI. Therefore, we should obtain as large a sample size as possible under the allowed inspection cost. In order for $C_{PR}$ to be used throughout the industry, it is necessary to analyze the characteristics of the $C_{PR}$. Because the $C_{PR}$ is a kind of index including subgroup concept, the analysis should be done at the point of sample size of the subgroup. We present numerical analysis results of $C_{PR}$ by the data from the random number generating method. In this study, we also show the difference between the $C_{PR}$ using the range and the $C_P$ which is a representative index using the sample standard deviation. Regression analysis was used for the numerical analysis of the sample data. In addition, residual analysis and equal variance analysis was also conducted.

• The Korean Journal of Applied Statistics
• /
• v.34 no.1
• /
• pp.1-8
• /
• 2021

Abstract concepts in social research must use measurement tools that are assured of validity and reliability. Observation score derived by a measurement tool can be divided into a valid observation score, a biased observation score, and an error. The presence or absence of a biased value is associated with validity, and the presence or absence of an error value is associated with reliability. There are many techniques for seeing whether a measurement tool is valid and reliable. For example, there are construct validity using factor analysis and internal consistency based on the Cronbach alpha. In this study, the calculation of the Cronbach alpha is derived through a sample, so we suggest an estimator of the Cronbach alpha under complex sample design and nonresponse. In a simulation, the proposed method is compared with many other existing estimators of Cronbach alpha under a multivariate normal distribution.

• The Korean Journal of Applied Statistics
• /
• v.26 no.3
• /
• pp.431-440
• /
• 2013

We consider the problem of estimating the parameters of heavy tailed distributions. In general, maximum likelihood estimation(MLE) is the most preferred method of parameter estimation because it has good properties such as asymptotic consistency, normality and efficiency. However, MLE is not always the best solution because MLE is unstable or does not exist in some cases. This paper proposes another parameter estimation method, non-linear least squares(NLS) and compares its performance to MLE. The NLS estimator is achieved by minimizing sum of squared difference between empirical cumulative distribution function(CDF) and a theoretical distribution function. In this article, we compare the NLS method to MLE using simulated data from heavy tailed distributions. The NLS method is shown to perform better than MLE in Burr distribution when the sample size is small; in addition, it performs well in a Frechet distribution.

• Korean Journal of Psychosomatic Medicine
• /
• v.27 no.2
• /
• pp.77-84
• /
• 2019

Objectives : The Illness Intrusiveness Rating Scale (IIRS) is a well-validated self-report instrument for assessing negative impact of chronic illness and/or adverse effects of its treatment on everyday life domains. Although extensive literature probed its psychometric properties in medical illness, little attention was paid for its validity for psychiatric population. This study aimed to test factorial structure of the Korean Version of the IIRS (IIRS-K) in a consecutive sample of psychiatric outpatients. Methods : Data set of 307 first-visit patients of psychiatric clinic at Guri Hanyang univ. Hospital were used. Exploratory and confirmatory factor analysis, internal consistency were tested in IIRS-K. We also checked Spearman's correlation analysis between IIRS-K, Zung's self-report anxiety scale and Zung's self-report depression scale. Results : 76.9% of the patients were with anxiety disorder and depressive disorder. The principal component factor analysis of the IIRS-K extracted three-factor structure accounted for 63.2% of total variance that was contextually similar to the original English version. This three-factor solution showed the best fit when tested confirmatory factor analysis compared to the original IIRS, two-factor model of IIRS-K suggested from medical outpatients, and one-factor solution. The IIRS-K also showed good internal consistency (Cronbach's α=0.90) and good convergent validity with anxiety and depression scales. Conclusions : The IIRS-K showed the three-factor structure that was similar but not identical to original version. Overall, this study proved factorial validity of the IIRS-K and it can be used for Korean clinical population.