• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.213-217
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• 2007

소프트웨어 테스팅은 소프트웨어의 개발 과정에 있어서 가장 중요하고 많은 비용이 드는 부분이다. 소프트웨어 테스팅을 수동으로 행하는 것은 많은 문제를 발생시킬 수 있다. 소프트웨어 자동 테스팅을 하기 위해서 최근 활발히 연구되고 있는 부분이 모델 기반 소프트웨어 자동 테스팅 기법이다. 본 논문에서는 UML 모델 기반 테스트 케이스 자동 생성 기법을 제안한다. UML state chart 로 모델링 된 테스트 대상 소프트웨어를 제안된 자료구조에 저장 한 후, 이를 flow graph 로 변환한다. 최종적으로 변환된 flow graph 에서 테스트 케이스를 생성한다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.126-128
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• 2012

소프트웨어 시스템의 안전성을 보장하기 위해 여러 안전성 분석 기법들이 사용되고 있으며, 안전성 분석의 결과인 안전성 요구사항을 시스템이 만족하고 있는지 확인하는지 확인하는 안전성 평가과정이 수행되고 있다. 안전성 평가는 많은 시간과 비용이 드는 문제점을 가지고 있다. 본 논문은 안전성 평가에 드는 시간과 비용을 줄이기 위해 테스트 케이스를 이용하여 안전성 요구사항을 소프트웨어 개발의 가장 기본적인 검증기법인 테스팅을 이용하여 시스템이 만족하는지 확인하는 방법을 제안하고 핸드폰의 카메라 컨트롤러 예제를 이용한 사례 연구를 진행하였으며 이를 지원 위한 도구인 TC2SMV를 개발하였다. TC2SMV는 UML의 state chart diagram에서 생성된 test case들을 SMV 입력 프로그램으로 자동으로 변환하여 사용자가 SMV 모델체킹을 수행할 수 있는 환경을 제공한다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.4
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• pp.127-137
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• 1989

This paper describes hardware flow-chart and SDL-II, which are register-transfer level, to automate logic design. Hardware flow-chart specifies behavioral and structural charaterstics of generalized FSMs (Finite State Machine) usin the modified ASM (Algorithmic State Machnine) design techniques. SDL-II describes the hardware flow-chat which specifies the control and the data path of ASIC(Application Specific IC). Also many examples are enumerated to illustrate the features of hardware flow-chart and SDL-II.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1045-1053
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• 2003

Markovian EWMA control chart for simultaneously monitoring mean vector of the several correlated quality characteristics is investigated. Properties of multivariate Shewhart chart and EWMA chart are evaluated for matched FSI (fixed sampling interval) and VSI(variable sampling interval) scheme. We obtained VSI EWMA chart is more efficient than Shewhart chart for small or moderate shifts. And, we obtained stablized numerical results with Markov chain method when the number of transient state is greater than 100.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.50-60
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• 1998

This paper is concerned with the design of two residual control charts for real-time monitoring of the continuous flow processes. Two different control charts are designed under the situation that observations are correlated each other. Kalman-Filter based model estimation is employed when the process model is known. A black-box approach, based on Back-Propagation Neural Network, is also applied for the design of control chart when there is no prior information of process model. Performance of the designed control charts and traditional control charts is evaluated. Average run length(ARL) is adopted as a criterion for comparison. Experimental results show that the designed control chart using the Neural Network's modeling has shorter ARL than that of the other control charts when process mean is shifted. This means that the designed control chart detects the out-of-control state of the process faster than the others. The designed control chart using the Kalman-Filter based model estimation also has better performance than traditional control chart when process is out-of-control state.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.1
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• pp.38-43
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• 2012

For safe and stable operations of power plants, it is essential to monitor closely crucial measurement values related to power plant trips. In this paper, an intelligent power plant operating state monitoring technique enabling the operating crew member to monitor conveniently the status of the important measurement values and to perceive almost instantly the significance of the implications of those measurement values is developed. The proposed technique is called a "POST(Plant Operating State Tracking) Chart" technique and provides the foundations in developing an intelligent and integrated power plant operating state monitoring support system called the "P-OASIS"(Plant Operation Assessment and Support Intelligent System). The P-OASIS is applied to a thermal power plant of 500[MW] capacity and exhibited impressive performances.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.139-147
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• 2009

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.65-71
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• 1998

Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, a robust control chart for variables is proposed, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a stable level and high power. To compare the performances of the proposed control chart with the classical (equation omitted), some Monte Carlo simulations are performed. The simulation results show that the robust control chart has good performance.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.233-241
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• 1999

Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, we propose a robust EWMA(exponentially weighted moving averages) control chart for variables, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a s table level and high power. To compare the performances of the proposed control chart with other charts, some Monte Carlo simulations we performed. The simulation results show that the robust EWMA control chart has good performance.

• Journal of Korean Society for Quality Management
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• v.28 no.3
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• pp.44-52
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• 2000

In this paper we apply VSS X chart to the case when two assignable causes effect the process mean shift. We compare the case when two compound assignable causes effect the process mean with another one which effects individually. For the practical use of VSS $\bar{X}$ chart we propose the sample size which minimizes the average number of samples until the signal is given in out-of-control state.