• Journal of Korean Society for Quality Management
• /
• v.43 no.4
• /
• pp.471-488
• /
• 2015

Purpose: Double sampling $T^2$ chart is a useful tool for detecting a relatively small shift in process mean when the process is controlled by multiple variables. This paper finds the optimal design of the double sampling $T^2$ chart in both economical and statistical sense under Weibull failure model. Methods: The expected cost function is mathematically derived using recursive equation approach. The optimal designs are found using a genetic algorithm for numerical examples and compared to those of single sampling $T^2$ chart. Sensitivity analysis is performed to see the parameter effects. Results: The proposed design outperforms the optimal design of the single sampling $T^2$ chart in terms of the expected cost per unit time and Type-I error rate for all the numerical examples considered. Conclusion: Double sampling $T^2$ chart can be designed to satisfy both economic and statistical requirements under Weibull failure model and the resulting design is better than the single sampling counterpart.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.2
• /
• pp.141-146
• /
• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• Journal of the Korea Safety Management & Science
• /
• v.5 no.4
• /
• pp.169-185
• /
• 2003

Recently many researchers contributed to the understanding of Quality Control System, but the use of economics in the design of quality assurance system is limited in treatment of the relationship between the average incoming quality level (or average process quality level) of the incoming lot and the average outgoing quality level of this lot. In this study, a traditional concept of sampling inspection plan for the quality assurance system is extended to a consideration of economic aspects in total production system by representing and analyzing the effects between proceeding and succeeding production process including inspection process. This approach recognizes that the decision at each manufacturing process (or assembly process), is to be determined not only by the cost and the average outgoing quality level of that process, but also by the input parameters of the cost and the incoming quality to the succeeding process. By analyzing the effects of the average incoming and outgoing quality, manufacturing or assembly process quality level and sampling inspection plan on the production system, mathematical models and solution technique to minimize the total production cost for a general product manufacturing system with specified average outgoing quality limit are suggested.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.29 no.4
• /
• pp.61-72
• /
• 2004

In this paper, we design sampling inspections and service capacities simultaneously for multi-products. Particularly, we extend Kim(2003) by introducing the fixed cost of providing services. We show that, due to the fixed cost considered, the cost function of a product is no longer linear or convex in terms of the level of service provision, and the total inspection is prefered to the small level of service capacity which results in high burden of the fixed cost. And we develop a simple framework to deal with this joint design problem for a product. Also we consider the problem of allocating the given number of the total service capacities among products. A dynamic programming algorithm is developed to determine the optimal allocation which minimizes the overall total cost of the system and the optimal allocation can be obtained with the considerably smaller computations than the total number of possible allocations. The results can be used to support planning decisions and to aid the joint design of inspections and service capacities for products.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.48
• /
• pp.269-277
• /
• 1998

In this study, a traditional concept of sampling inspection plan for the quality assurance system is extended to a consideration of economic aspects in total production system by representing and analyzing the effects between proceding/succeeding production process including inspection. This approach recognizes that the decision to be made at one manufacturing process (or assembly process) determine not only the cost and the average outgoing quality level of that process but also the input parameters of the cost and the incoming quality to the succeeding process. By analyzing the effects of the average incoming and outgoing quality, manufacturing/assembly quality level and sampling inspection plan on the production system, mathematical models and solution technique to minimize the total production cost for a single product manufacturing system with specified average outgoing quality limit (AOQL) are suggested.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.46
• /
• pp.119-125
• /
• 1998

In this study, a traditional concept of sampling inspection plan for the quality assurance system is extended to a consideration of economic aspects in total production system by representing and analyzing the effects between proceeding / succeeding production process including inspection. This approach recognizes that the decision to be made at one manufacturing process (or assembly process) determine not only the cost and the average outgoing quality level of that process but also the input parameters of the cost and the incoming quality to the succeeding process. By analyzing the effects of the average incoming and outgoing quality, manufacturing / assembly quality level and sampling inspection plan on the production system, mathematical models and solution technique to minimize the total production cost for a single product manufacturing system with specified average outgoing quality limit(AOQL) are suggested.

• Journal of Korean Society for Quality Management
• /
• v.30 no.2
• /
• pp.99-117
• /
• 2002

Variable sampling interval(VSI) and variable sample size(VSS) control charts vary the sampling rate for the next sample depending on the current chart statistic. This paper develops EWMA charts with the VSI and VSS features, and investigates the effectiveness of these charts in context of an economic model. The economic properties of these charts are evaluated by using Markov chain methods. The model contains cost parameters which allow the specification of the costs associated with sampling, false alarms, and operating off target. This economic model can be used to quantify the cost saving that can be obtained by using control charts with the VSI and VSS features instead of with the fixed sampling rate(FSR) feature, and can also be used to gain insight into the way that control charts with the VSI and VSS features should be designed to achieve optimal economic performance. The economic performance of X charts with the VSI and VSS features is also considered.

• The Korean Journal of Applied Statistics
• /
• v.24 no.2
• /
• pp.393-400
• /
• 2011

In a large scale survey, cluster sampling design in which a set of observation units called clusters are selected is often used to satisfy practical restrictions on time and cost. Especially, a two stage cluster sampling design is preferred when a strong intra-class correlation exists among observation units. The sample Primary Sampling Unit(PSU) and Secondary Sampling Unit(SSU) size for a two stage cluster sample is determined by the survey cost and precision of the estimator calculated. For this study, we derive the optimal sample PSU and SSU size when the population SSU size across the PSU are di erent by extending the result obtained under the assumption that all PSU have the same number of SSU. The results on the sample size are then applied to the $4^{th}$ Korea Hospital Discharge results and is compared to the conventional method. We also propose the optimal sample SSU (discharged patients) size for the $7^{th}$ Korea Hospital Discharge Survey.

• Communications for Statistical Applications and Methods
• /
• v.24 no.3
• /
• pp.241-254
• /
• 2017

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.2
• /
• pp.600-619
• /
• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.