• International journal of advanced smart convergence
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• 제4권2호
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• pp.46-53
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• 2015

This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.

• 전기학회논문지
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• 제63권4호
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• pp.451-454
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• 2014

Short-term load forecasting(STLF) for electric power demand is essential for stable power system operation and efficient power market operation. We improved STLF method by using hourly temperature as an input data. In order to using hourly temperature to STLF algorithm, we calculated temperature-electric power demand sensitivity through past actual data and combined this sensitivity to exponential smoothing method which is one of the STLF method. The proposed method is verified by case study for a week. The result of case study shows that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1991년도 학술발표회 논문집
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• pp.7-10
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• 1991

In a text-to-speech system, sound units (phonemes, words, or phrases, etc.) can be concatenated together to produce required utterance. The quality of the resulting speech is dependent on factors including the phonological/prosodic contour, the quality of basic concatenation units, and how well the units join together. Thus although the quality of each basic sound unit is high, if occur the discontinuity in the concatenation part then the quality of synthesis speech is decrease. To solve this problem, a smoothing operation should be carried out in concatenation parts. But a major problem is that, as yet, no method of parameter smoothing is available for joining the segment together. Thus in this paper, we proposed a new aigorithm that smoothing the unnatural discountinuous parts which can be occured in speech waveform editing. This algorithm used the exponential smoothing method.

• 한국항해항만학회지
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• 제28권6호
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• pp.509-515
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• 2004

여객수와 화물량에 대한 예측은 터미널의 개발 및 계획, 선사의 적정선복량 화보를 위해 중요하다. 본 연구에서는 역전파 학습 알고리즘을 이용한 뉴럴네트웍을 이용하여 목포항 여객수와 화물량을 예측하였다. 그리고 이동평균법, 지수평활법, 뉴럴네트웍의 예측수행을 평균제곱오차, 절대평균오차로 비교하여 뉴럴네트웍의 예측수행능력이 우수함을 검정하였다. 또한 2005년 목포항 여객수와 화물량을 예측하여 여객선 선복량의 적정성을 분석하였다.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2011년도 춘계학술대회
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• pp.177-183
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• 2011

Korea has performed strategies for the third industrial accident prevention in order to minimize industrial accident. However, the occupational fatality rate and industrial accident rate appears to be stagnated for 11 years. Therefore, this paper forecasts the occupational fatality rate and industrial accident rate for 10 years. Also, this paper applies regression method (RA), exponential smoothing method (ESM), double exponential smoothing method (DESM), autoregressive integrated moving average (ARIMA) model and proposed analytical function method (PAFM) for trend of industrial accident. Finally, this paper suggests fundamental strategies for industrial accident prevention by forecasting of industrial accident rate in the long term.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.658-663
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.126-134
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• 2008

As many systems depend on electronics, concern for fault tolerance is growing rapidly. For example, a car with its steering controlled by electronics and no mechanical linkage from steering wheel to front tires (steer-by-wire) should be fault tolerant because a failure can come without any warning and its effect is devastating. In order to make system fault tolerant, there has been a body of research mainly from aerospace field. This paper presents the structure of predictive hybrid redundancy that can remove most erroneous values. In addition, several numerical simulation results are given where the predictive hybrid redundancy outperforms wellknown average and median voters.

• 전기학회논문지
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• 제58권9호
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• pp.1695-1699
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• 2009

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

• 한국조명전기설비학회지:조명전기설비
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• 제6권3호
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• pp.43-49
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• 1992

전력 수요 예측은 전력계통 운용 및 계통 개발의 기본이 되는 것으로 예측의 적부가 전력공급의 신뢰성과 경제성에 미치는 영향이 대단이 크다. 본 논문에서는 예측정도가 높고 운용시 간편성을 지닌 R.G.Brown에 의해 제시된 3중 지수평활법을 이용하여 장기 최대전력수요를 예측하였다. 평활함수는 전체 과거 관측의 선형 결합이고 과거 관측에 주는 가중은 오래된 과거일수록 지수적으로 감소시킨다. 지수평활의 근본 이론은 지수평활의 (n+1)차의 선형결합으로 n차 다항식 모델에서 (n+1)개의 계수추정이 가능함을 보여준다. 이 기법을 이용하여 한국전력 실 계통에 최대전력 수요를 예측한 결과 예측의 정확성과 간편성이 입증되었다.

• 대한토목학회논문집
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• 제33권5호
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• pp.2009-2019
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• 2013

교통량은 교통망 계획, 도로 설계, 도로 관리 등에 직접적으로 활용되는 중요한 기초자료이다. 교통량은 고정식 교통량조사 장비를 설치하여 연속적인 자료를 수집하는 상시조사와 특정일을 조사하는 수시조사로 구분되어 조사되고 있다. 상시조사의 경우 조사 지점에 설치되어 있는 장비의 고장이나 오작동 등으로 인하여 교통량 자료의 결측이 발생하며, 이러한 결측을 보정하기 위하여 다양한 방법이 적용되어 왔다. 본 연구에서는 결측 발생일 전 후의 자료를 활용하는 응용 지수평활화법을 제안하였으며, 평가 결과 교통량 변동계수가 낮은 경우 보정의 정확성이 제고됨을 알 수 있었다. 게다가 지점의 교통량 변동성이 결측 보정의 정확성에 영향을 미치는 중요한 요인으로 작용한다는 것을 확인하였다. 따라서 교통량 결측 보정의 신뢰성을 높이기 위해서는 지점별-시기별 결측 보정 방법이 달리 적용되어야 할 것이다.