• 한국정밀공학회지
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• 제1권1호
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• pp.34-49
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• 1984

The stability of machine tool systems is analyzed by considering the machining process as a stochastic process without decomposing into machine tool structural dynamics and cutting processes. In doing so the time series analysis technique developed by Wu and Pandit is applied systematically to the relative vibration between cutting tool and work- piece measured under actual working conditions. Various characteristic properties derived from the fitted ARMA(Autoregressive Moving Average) Models and those from raw data directly are investigated in relation with the system stability. Both damping ratio and absolute value of the characteristic roots of the AR part of the most significant dynamic mode are preferred as stability indicating factors to the other pro-perties such as theoretical variance .gamma. (o) or absolute power of the most dominant dynamic mode. Maximum aplitude during a certain interval and variance estimated from raw data are shown to be very sensi- tive to the type of the signal and the location of measurement point although they can be obtained rather easily. The relative vibration signal is also analyzed by FFT(Fast Fourier Transform) Analyzer for the purpose of comparison with the spectrums derived from the fitted ARMA models.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• 한국습지학회지
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• 제18권3호
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• pp.313-323
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• 2016

수온은 하천의 물리적 생물학적 과정에 지대한 영향을 미치는 인자로서 어류를 비롯한 수생생태계에 대한 제약조건으로 작용한다. 기후변화로 인하여 실질적인 환경의 변화가 나타나고 있는 현실에서 수온 변화에 대한 예측은 필수적이라 하겠다. 본 연구의 목적은 자연 소하천을 대상으로 하천 수온을 모의 및 그 효율을 비교 분석하고, 향후 기후변화로 인한 하천 수온의 변동을 고찰하는 것이다. 이를 위하여 본 연구에서는 캐나다 동북부의 Fourchue 강을 대상으로 하여 2011년부터 2014년까지의 하천수온을 측정하고 결정론적, 확률론적, 비선형 수온모형을 적용하여 각각의 방법론에 따른 효율성을 비교 분석하여 미래 수온 모의를 위한 모형으로 결정론적 모형인 CEQUEAU 모형을 선정하였다. 또한, 선정된 모형을 기반으로 하여 CMIP5 기후모형과 RCP 2.6, 4.5, 8.5 기후변화 시나리오를 이용하여 해당 소하천 유역의 미래 수온 변동성을 예측하고 분석하였다. 연구결과, Fourchue 강의 수온은 6월 중 평균 수온은 $0.2{\sim}0.7^{\circ}C$가 상승하고, 9월은 $0.2{\sim}1.1^{\circ}C$가 감소하는 것으로 나타나 실질적인 수온환경의 변화가 발생하는 것으로 나타나서 이에 대한 주의가 요구된다. 또한, 해당 수역에 서식하고 있는 연어류의 치사상한수온을 넘는 경우도 발생하여 이에 대한 대책이 시급한 것으로 판단된다.