• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.1
• /
• pp.1-12
• /
• 2014

This article proposes a variable sampling interval (VSI) $\bar{X}$ control chart using weighted standard deviation (WSD) method for skewed populations. The WSD method decomposes the standard deviation of a quality characteristic into upper and lower deviations and adjusts control limits and warning limits of a control chart in accordance with the direction and degree of skewness. A control chart constant is derived for estimating the standard deviation of skewed distributions with the mean of sample standard deviations. The proposed chart is compared with the conventional VSI $\bar{X}$ control chart under some skewed distributions. Simulation study shows that the proposed WSD VSI chart can control the in-control average time to signal (ATS) as an adequate level better than the conventional VSI chart, and the proposed chart can detect a decrease in the process mean of a quality characteristic following a positively skewed distribution more quickly than the standard VSI chart.

• Communications for Statistical Applications and Methods
• /
• v.16 no.3
• /
• pp.501-509
• /
• 2009

Recently the interval estimation of binomial proportions is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the well-known Wald confidence interval. Various alternatives have been proposed. Among them, the Agresti-Coull confidence interval, the Wilson confidence interval and the Bayes confidence interval resulting from the noninformative Jefferys prior were recommended by Brown et al. (2001). However, unlike the binomial distribution case, little is known about the properties of the confidence intervals in finite population sampling. In this note, the property of confidence intervals is investigated in anile population sampling.

• Communications for Statistical Applications and Methods
• /
• v.17 no.3
• /
• pp.319-326
• /
• 2010

In this paper, a new group sampling plan for the lot acceptance is proposed for the time truncated life test, which can be utilized when multi-item testers are implemented. The design parameters are found using the two-point approach such that the producer's and consumer's risks are satisfied simultaneously at the acceptable reliability level and the lot tolerance reliability level, respectively. The case of Weibull distribution is described to illustrate the procedure that can be used when the quality level is expressed by a multiple of the specified life. The advantage of the proposed plan is demonstrated by comparing with the existing plan in terms of the sample size required. The tables are constructed and some examples are given to illustrate the procedure developed here.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.19 no.6
• /
• pp.293-302
• /
• 2015

ASCE 4 requires that a hard stop be built around the seismic isolation system in nuclear power plants. In order to maintain the function of the isolation system, this hard stop is required to have clearance-to-stop, which should be no less than the 90th-percentile displacements for 150% Design Basis Earthquake (DBE) shaking. Huang et al. calculated clearance-to-stop by using a Latin Hypercube Sampling technique, without considering the rocking behavior of the isolated structure. This paper investigates the effects on estimation of clearance-to-stop due to 1) rocking behavior of the isolated structure and 2) sampling technique for considering the uncertainties of isolation system. This paper explains the simplified analysis model to consider the rocking behavior of the isolated structure, and the input earthquakes recorded at Diablo Canyon in the western United States. In order to more accurately approximate the distribution tail of the horizontal displacement in the isolated structure, a modified Latin Hypercube Sampling technique is proposed, and then this technique was applied to consider the uncertainty of the isolation system. Through the use of this technique, it was found that rocking behavior has no significant effect on horizontal displacement (and thus clearance-to-stop) of the isolated structure, and the modified Latin Hypercube Sampling technique more accurately approximates the distribution tail of the horizontal displacement than the existing Latin Hypercube Sampling technique.

• The Korean Journal of Applied Statistics
• /
• v.27 no.3
• /
• pp.357-371
• /
• 2014

There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations.

• Journal of Distribution Science
• /
• v.22 no.4
• /
• pp.23-35
• /
• 2024

This study investigates the organizational factors including firm size, adaptive capability, absorptive capability, innovative capability, and executive support to determine internet of things, production and services, and organizational performance. Research design, data, and methodology: A quantitative methodology was employed, involving the distribution of surveys to 460 employees occupying managerial and strategic roles. These individuals have accrued a minimum of one year of experience within 20 leading manufacturing and distribution companies in Thailand, each boasting a workforce exceeding 250 employees. Sampling techniques utilized encompass judgmental, quota, and snowball sampling. Furthermore, analysis of the data was conducted through Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The findings indicate that factors such as firm size, adaptive capability, absorptive capability, and innovative capability exert significant influence on the Internet of Things (IoT). In addition, IoT significantly impacts both production and services. Furthermore, the study highlights the significant influence of production and services on organizational performance. However, the anticipated relationship between executive support and IoT lacks support according to the results. Conclusions: This study highlights the transformative potential of IoT for the manufacturing and distribution sector, paving the way for enhanced efficiency, competitiveness, and sustainability in a rapidly evolving business landscape.

• Journal of Communications and Networks
• /
• v.18 no.5
• /
• pp.818-825
• /
• 2016

Network management and anomaly detection are challenges in high-speed networks due to the high volume of packets that has to be analysed. Flow-based analysis is a scalable method which reduces the high volume of network traffic by dividing it into flows. As sampling methods are extensively used in flow generators such as NetFlow, the impact of sampling on the performance of flow-based analysis needs to be investigated. Monitoring using sampled traffic is a well-studied research area, however, the impact of sampling on flow-based anomaly detection is a poorly researched area. This paper investigates flow sampling methods and shows that these methods have negative impact on flow-based anomaly detection. Therefore, we propose an efficient probabilistic flow sampling method that can preserve flow traffic distribution. The proposed sampling method takes into account two flow features: Destination IP address and octet. The destination IP addresses are sampled based on the number of received bytes. Our method provides efficient sampled traffic which has the required traffic features for both flow-based anomaly detection and monitoring. The proposed sampling method is evaluated using a number of generated flow-based datasets. The results show improvement in preserved malicious flows.

• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.557-573
• /
• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.

• Journal of Korean Institute of Industrial Engineers
• /
• v.5 no.1
• /
• pp.53-57
• /
• 1979

The concept of AOQL in designing Dodge's continuous sampling plans is modified to include probabilistic consideration reflecting the prior knowledge about the process average fraction defectives, and a new design criterion called AOQL, which eliminates some of the drawbacks of the AOQL criterion is proposed. AOQL, approach provides more economical sampling plans in many cases, and can be used even when only limited amount of prior information is available.

• The Korean Journal of Applied Statistics
• /
• v.7 no.1
• /
• pp.1-17
• /
• 1994

This article basically considers the calibration problem with censored data from the Bayesian point of view. The Gibbs sampling method is discussed to solve the difficulty encountered in computing the posterior distribution. Also presented is an approach for impementing the Gibbs sampling in actual data situation with the estimation procedures.