• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.109-141
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• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.63-70
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• 2006

This study aims to development of web-based process management system for spatial data construction. For developing this system work classification of basic surveying was standardized and quality management method for spatial data was established. Production process and work classification system for basic surveying such as control point surveying using GPS, leveling, aerial photographing, digital mapping, topographic mapping, digital elevation modeling, aerial photographic DB construction and digital orthophotomap was standardized. The status of the output and quality inspection for basic surveying project were analyzed, and the elements of quality inspection and data format for the type of outputs were analyzed. Based on standardized and analyzed contents, web-based process management system was developed after database and process was designed. The process management system consisted of process management, quality control, metadata management, and system management.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.37-45
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• 2016

Control chart is representative tools of statistical process control (SPC). It is a graph that plotting the characteristic values from the process. It has two steps (or Phase). First step is a procedure for finding a process parameters. It is called Phase I. This step is to find the process parameters by using data obtained from in-controlled process. It is a step that the standard value was not determined. Another step is monitoring process by already known process parameters from Phase I. It is called Phase II. These control chart is the process quality characteristic value for management, which is plotted dot whether the existence within the control limit or not. But, this is not given information about the economic loss that occurs when a product characteristic value does not match the target value. In order to meet the customer needs, company not only consider stability of the process variation but also produce the product that is meet the target value. Taguchi's quadratic loss function is include information about economic loss that occurred by the mismatch the target value. However, Taguchi's quadratic loss function is very simple quadratic curve. It is difficult to realistically reflect the increased amount of loss that due to a deviation from the target value. Also, it can be well explained by only on condition that the normal process. Spiring proposed an alternative loss function that called reflected normal loss function (RNLF). In this paper, we design a new control chart for overcome these disadvantage by using the Spiring's RNLF. And we demonstrate effectiveness of new control chart by comparing its average run length (ARL) with ${\bar{x}}-R$ control chart and expected loss control chart (ELCC).

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.1-14
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• 2002

Traditional SPC techniques are looking out variation of process by fixed sampling interval and fixed sample size about every hour, the process of in-control or out-of-control couldn't be detected actually when the sample points are plotted near control limits, and it takes no notice of expense concerned with such sample points. In this paper, to overcome that, consider VSI(variable sampling interval) EWMA control charts which VSI method is applied. The VSI control charts use a short sampling internal if previous sample points are plotted near control limits, then the process has high probability of out-of-control. But it uses a long sampling interval if they are plotted near centerline of the control chart, since process has high possibility of in-control. And then a comparison and analysis between FSI(fixed sampling interval) and VSI EWMA in the statistical aspect and economic aspect is studied. Finally, we show that VSI EWMA control chart is more efficient than FSI EWMA control chart in the both aspects.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.385-389
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• 2012

With the development of computer storage and the rapidly growing ability to process large amounts of data, the multivariate control charts have received an increasing attention. The existing univariate and multivariate control charts are a single hypothesis testing approach to process mean or variance by using a single statistic plot. This paper proposes a multiple hypothesis approach to developing a new multivariate control scheme. Plotted Hotelling's $T^2$ statistics are used for computing the corresponding p-values and the procedure for controlling the false discovery rate in multiple hypothesis testing is applied to the proposed control scheme. Some numerical simulations were carried out to compare the performance of the proposed control scheme with the ordinary multivariate Shewhart chart in terms of the average run length. The results show that the proposed control scheme outperforms the existing multivariate Shewhart chart for all mean shifts.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.39-56
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• 2000

${\overline{X}}$ control chart has proven to be an effective tool to improve the product quality. Shewhart charts assume that the observations are independent and normally distributed. Under the presence of positive autocorrelation and severe skewness, the control limits are not accurate because assumptions are violated- Autocorrelation in process measurements results in frequent false alarms when standard control chats are applied in process monitoring. In this paper, Threshold Bootstrap and Moving Block Bootstrap are used for constructing a confidence interval of correlated observations. Monte Carlo simulation studies are conducted to compare the performance of the bootstrap methods and that of standard method for constructing control charts under several conditions.

• Journal of the Korean Society of Systems Engineering
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• v.7 no.1
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• pp.53-56
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• 2011

Configuration management plays a key role in systems engineering process for any project from earlier stage of development. It consists of five major activities, ie., configuration management planing, configuration identification, configuration control, configuration status accounting and configuration verification and audit, and is essential to control system design, development and operation throughout entire life cycle of the system development. And it is directly associated with other part of systems engineering management process, ie., technical data management which provides traceability of important decisions and changes during development. In this paper, we describe how to apply CASE(Computer-aided Systems Engineering) tool-Cradle for the configuration management to achieve effectiveness of Technical Management process.

• Journal of Korean Society for Quality Management
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• v.31 no.3
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• pp.126-135
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• 2003

A basic assumption in standard applications of control charts is that the observations are statistically independent. However, this assumption is often violated from processes in many industries. The presence of autocorrelation has a serious impact on the performance of control charts, causing a dramatic increase in the frequency of false alarms. This paper considers a process in which the observations can be modeled as a first order autoregressive(AR(1)) process, and develops (equation omitted) charts with the variable sample size(VSS) scheme for monitoring the mean of this process.

• IE interfaces
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• v.6 no.2
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• pp.171-181
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• 1993

In this study, a process management system is designed that can automatically control the heat treating atmosphere, and a managment software is developed to monitor and control continously the heat treating process using a n interface device. Especially, a communication protocol is developed to control and monitor atmosphere condition, temperature, surrounding gas, and time. The developed interface device, called COMPORT SELECTOR is to send and receive information from PID controllers and PLC via RS-232C communication. This system will reduce manufacturing cost and cycle time, and improve the effectiveness of working process and quality.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.487-494
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• 2013

Purpose: In this study, we propose the precision guided munitions verification methodology using the statistical analysis method has been proposed. and it can be applied to the precision guided munitions quality assurance work. Methods: This modeling is based on Failure Mode and Effects Analysis, Statistical Process Control, Defense Quality Managerment System, Production Readiness Review, Manufacturing Readiness Assesment and so on. Results: The Process Control Modeling that has the following procedures ; searching the critical to quality, statistical analysis by process, verify process. Moreover, the effectiveness of the methodology is verified by applying to the precision guided munitions. Conclusion: To achieve a analysis methods of statistical process control and verify process for precision guided munitions.