• Korean Management Science Review
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• v.31 no.3
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• pp.27-39
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• 2014

We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.154-166
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• 2006

Product Line Engineering(PLE) is a software reuse paradigm that core assets are defined using common features in a domain and are instantiated in various applications. To apply the core asset effectively, variants which satisfy application requirements are extracted and the core asset should be also instantiated based on the variants. For the work, variability on architecture and components should be extracted exactly and an instantiation process and guidelines should be defined based on this variability In this paper, we define variability types depending on core assets elements and describe artifact templates related to tile variability. We also propose a systematic process which uses defined core assets including variability and verify practicability of the proposed process and variability expression through doing ease study. If utilizing with the proposed process in PLE, it can be feasible to model concrete core asset and variability and to utilize practical application engineering.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.791-800
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• 2009

Recently, the business process family model (BPFM), which is new approachfor assuring businessflexibility and enhancing reuse in application development with service oriented architecture (SOA), was proposed. The BPFM is a model which can explicitly represent the variabilities in business process family by using the variability analysis method of software product line. Many business process models (BPM) can be generated automatically through decision and pruning processes from BPFM. At this time, the variabilities tend to have inclusive or exclusive dependencies between them. This affects the decision and pruning processes. So far, little attention has been given to the binding information of variability dependency in the BPFM. In this paper, we propose an approach for analyzing various types of dependency relationships between variabilities and representing the variability and their relationships as a dependency analysis model. Additionally, a method which can trace the variabilities affected by a decision on the dependency analysis model is presented. The case study shows that the proposed approach helps to reduce the number of variability decision and to solve a disagreement of functions in BPM produced by incorrectly deciding the variability.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.1-10
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• 2011

In queueing network analysis, arrival processes are usually modeled as renewal processes by matching mean and variance. The renewal approximation simplifies the analysis and provides reasonably good estimate for the performance measures of the queueing systems under moderate conditions. However, high variability in arrival process or in service process requires more sophisticated approximation procedures for the variability parameter of departure/arrival processes. In this paper, we propose an heuristic approach to refine Whitt's variability function with the k-interval squared coefficient of variation also known as the index of dispersion for intervals(IDI). Regression analysis is used to establish an empirical relationships between the IDI of arrival process and the IDI of departure process of a queueing system.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.381-399
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• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.57.2-57.2
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• 2021

Variability in Young Stellar Objects (YSOs) can be caused by time-dependent accretion rates, geometric changes in the circumstellar disks, the stochastic hydromagnetic interactions between stellar surfaces and inner disk edges, reconnections within the stellar magnetosphere, and hot/cold spots on stellar surfaces. We uncover ~1400 variables from a sample of ~5300 YSOs in nearby low-mass star-forming regions using mid-IR light curves obtained from the 5.5-years NEOWISE All Sky Survey. The mid-IR variability traces a wide range of dynamical, physical, and geometrical phenomenon. We classify six types of YSO variability based on their light curves: secular variability (Linear, Curved, Periodic) and stochastic variability (Burst, Drop, Irregular). YSOs in earlier evolutionary stages have higher fractions of variables at all types and higher amplitudes for the variability. Along with brightness variability, we also find a diverse range of secular color variations, which can be attributed to a competitive interplay between the variable accretion luminosity of the central source and the variable extinction by material associated with the accretion process. We compare the variability of known FUors/EXors and VeLLOs/LLSs, which represent two extreme ends (burst versus quiescent) of the episodic accretion process; FUors/EXors have a higher fraction of variables (65%) than VeLLOs/LLSs (41%). Short-term (few day) and long-term (decades) variability, as well as possible AGB contamination in the YSO catalogues, are also discussed.molecules become more complex by surface chemistry induced directly by high energy photons or by the thermal energy diffused over heated grain surface. Therefore, the ice composition is an

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.227-240
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• 2000

The research of the variability gains more gravity in component-based software development, because it helps to extend the reusability of the component. A domain-specific component supports the more variability, the wider scope that the component can be applied to. However, the more variability included in a component, It makes the size of a component bigger, and the cost to construct the component is rises. As a result, this disturbs making an optimized system. In this paper, we classify the variability into 3 types, according to their features. And we propose some implementation techniques for each type based COM. Moreover, we also propose a process to analysis and design the variability with their artifacts, which includes some tasks from variability extraction to implementation of it. This proposed process can be applied as a part of the component developing process.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.211-216
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• 2006

Monitoring variability is a vital part of modem statistical process control. The conventional Shewhart Rand S charts address the setting where the in-control process readings have a constant variance. In some settings, however, it is the coefficient of variation, rather than the variance, that should be constant. This paper develops a chart, equivalent to the S chart, for monitoring the coefficient of variation using rational groups of observations.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.2
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• pp.101-107
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• 2016

Generally, the shipbuilding industry has finite resources and limited workspace. Due to finite resources, limited workspace and state of block preparation, erection process in shipbuilding industry is frequently delayed than erection process scheduling which is planned at long-term plan stage. In this study, considering variability of block reserve ratio, the degree of delay in real erection process is measured and compared to scheduling which is planned at long-term plan stage in shipbuilding industry including finite capacity and variative lead time. Also, the erection process scheduling which has minimum lead time can be checked through simulation. The results of this study could be improved the accuracy of erection process scheduling by checking the main event compliance ratio by block reserve ratio and calculating the optimum erection pitch for the main event compliance.

• Ocean and Polar Research
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• v.23 no.4
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• pp.433-440
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• 2001

Marine organisms in Antarctica live in an environment which exhibits variability in physical processes over a wide range of temporal scales, from seconds to millennia. This time scale tends to be correlated with the spatial scale over which a given process operates, though this relationship is influenced by biology. The way organisms respond to variability in the physical environment depends on the time-scale of that variability in relation to life-span. Short-term variations are perceived largely as noise and probably have little direct impact on ecology. Of much greater importance to organisms in Antarctica are seasonal and decadal variations. Although seasonality has long been recognised as a key feature of polar environments, the realization that decadal scale variability is important is relatively recent. Long-term change has always been a feature of polar environments and may be a key factor in the evolution of the communities we see today.