• 산업경영시스템학회지
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• 제22권52호
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• pp.337-345
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• 1999

The long-tailed, positively skewed exponential distribution can be made into an almost symmetric distribution by taking the exponent of the data. In these situations, to use the traditional shewhart control limits on an individuals chart would be impractical and inconvenient. The transformed data, approximately bell-shaped, can be plotted conveniently on the individuals chart and exponentially weighted moving average chart. In this paper, using modifying statistics with transformed exponential of the data, we give a method for constructing control charts. Selecting method of exponent for individual chart is evaluated. And consider that smaller weight being assigned to the older data as time process and properties and taking method of exponent($\theta$), weighting factor($\alpha$) are suggested. Our recommendation, on the basis result of simulation, is practical method for EWMA chart.

• 대한전기학회논문지
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• 제35권5호
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• pp.193-198
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• 1986

In this paper, 1-168 hours ahead load prediction algorithm is developed for power system economic weekly operation. Total load is composed of three components, which are base load, week load and weather-sensitive load. Base load and week load are predicted by moving average and exponential smoothing method, respectively. The days of moving average and smoothing constant are optimally determined. Weather-sensitive load is modeled by linear form. The paramiters of weather load model are estimated by exponentially weighted recursive least square method. The load prediction of special day is very tedious, difficult and remains many problems which should be improved. Test results are given for the day of different types using the actual load data of KEPCO.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.143-154
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• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• 대한전자공학회논문지SP
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• 제47권4호
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• pp.105-111
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• 2010

본 논문에서는 실버케어시스템인 스마트 약상자의 사용자 위치파악을 목적으로 Received Signal Strength Indication (RSSI) 기반 근거리 사용자 탐지 시스템을 제안한다. 상기 시스템은 RSSI값을 사용하여 근거리 내 사용자 유무를 파악하는 단일노드 기반 측위기술을 사용하였다. 단일노드 기반 측위기술의 문제점인 Non Line of Sight (NLoS) 통신환경 내 오차 보정을 목적으로, 시스템에 지수이동평균을 적용하여 RSSI값의 급격한 변화에 강인한 시스템을 구현하였다. 고령자의 행동패턴을 고려한 피실험자 대상 실험을 통하여, NLoS 통신황경 내 RSSI값이 급격히 변화할 경우 지수이동평균을 적용함으로써 오차발생확률이 평균 32.26%, 최대 40.80% 감소함을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.754-759
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• 1993

An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.

• 대한교통학회지
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• 제9권2호
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• pp.101-120
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• 1991

The Subject of this research work is to study the driver's vision and eye-movement ch-aracteristics under the diffrent condiction of road traffic and driving. The analysis of this investigation was conducted spatially or temporaly into three parts'eye-mark distribution, viewing-time percentage and fixation duration. This dissertation focuses on analysis of dr-iver's visual characteristics to improve road circumstamces. In this study driver's ch-aracteristics are measured with eye-mark recorder and analyzed statistically The main features of this study are : 1st Duration distribution of fixation point is significant in 87% at 5% of the significant level in Gamma Distribution. The average of fixation duration by road are 0.33sec on streets 0.45sec on Roads and 0.86sec on highways. The average of fixation duration by visual objects are 0.4sec on road surface 0.26sec on road shoulder 0.49sec on traffic sign 0.37sec on warning sign and 0.67sex on gwide sign. 2st Moving anglrs of a fixation point are fit in the Exponential Distribution. The average moving angle is appeared to be 3.85。 on streets 2.81。 on roads 2.73。 on highway and 5 。 on intersecyion. 3st As a result of examining alignment of guide and warning sign in traffic signs cxisting foundation methods are less affected by lane than by apeed of a vehicle.

• 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.

• 한국국방경영분석학회지
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• 제26권2호
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• pp.39-54
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• 2000

This research is carried out solving problem of reduction in the rate of operation for the k-1 tank in order to increase the availability, caused by the delay in supply of k-1 tank repair parts in field operations. In other words, the study aims to find the most suitable requirement estimate pattern for the main repair parts that are used for k-1 tank. This study intends to present the most suitable requirement estimate pattern for k-1 trank repair pats by comparing the results of repair parts consumption data in relation to their pattern created by the programs of the requirement estimate technique(moving average method) currently used in the Army and adaptive exponential smoothing model. The results of this study numerically proved that the adaptive exponential smoothing model is the most appropriate technique in estimating the requirement for k-1 tank repair parts.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.192-200
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• 2021

In the mid of the December 2019, the virus has been started to spread from China namely Corona virus. It causes fatalities globally and WHO has been declared as pandemic in the whole world. There are different methods which can fit such types of values which obtain peak and get flattened by the time. The main aim of the paper is to find the best or nearly appropriate modeling of such data. The three different models has been deployed for the fitting of the data of Coronavirus confirmed patients in Pakistan till the date of 20^th November 2020. In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Logistic model, Gompertz model and Auto-Regressive Integrated Moving Average Model (ARIMA) model. The fitted models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan.

• 대한전자공학회논문지SP
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• 제46권6호
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• pp.153-162
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• 2009

본 논문에서는 이동 금속 물체 탐지 목적의 무선 센서네트워크 응용 시스템에 이용 가능한 저연산, 저전력 소모를 목적으로 하는 간결한 신호처리 알고리즘을 제안한다. 일반적 센서노드에 주로 사용되는 자기센서의 물리적 특성을 분석하고 Exponential Average method(EA)를 사용하여 시간 영역에서 실시간으로 센서 신호를 처리한다. EA를 사용하여 잡음, 시간, 온도에 따른 자기장 변화, 외부 간섭에 강인하면서 임베디드 프로세서에 적합한 적은 메모리소모와 연산량을 가진다. 또한 통계적 분석을 통해 제안하는 알고리즘의 최적화된 파라미터 값을 도출하고 적용하였다. 보편적으로 사용되는 자기 센서 모델의 시뮬레이션 결과 5%의 오경보 확률에서 90%이상의 이동 물체를 탐지할 수 있었다. 그리고 직접 제작한 센서 노드의 모델링 및 이를 이용한 시뮬레이션과 외부 실험의 결과 60~70% 이상의 탐지 확률을 확인하였다.