• Journal of Environmental Science International
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• v.16 no.11
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.1
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• pp.7-14
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• 2002

In power system study, relibility assessment has been an important topic during past several decards because sudden power interruption can bring about enormous economic loss. although the size of a substation is smaller than that of generation system or transmission system, switching actions after fault(s) make reliability assessment of substation rather complex situations such as switching actions easily and permit various probability distributions in describing substation elements. Despite this ability of Monte Carlo simulation, one-parameter exponential distribution is still popular in this reliability assessment. This paper examines the characteristics of several two-parameter probability distributions, and offers new parameter decision rule based on average and variance of the target to be modelled. In case study, this paper shows the profits by using Weibull distribution which is one of two-parameter probabilistic distributions instead of exponential one.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.730-738
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• 1994

The major advantage of the short-term load forecasting technique using general exponential smoothing is high accuracy and operational simplicity, but it makes large forecasting error when the load changes repidly. The paper has presented new technique to improve those shortcomings, and according to forecasted the technique proved to be valid for two years. The structure of load model is time function which consists of daily-and temperature-deviation component. The average of standard percentage erro in daily forecasting for two years was 2.02%, and this forecasting technique has improved standard erro by 0.46%. As relative coefficient for daily and seasonal forecasting is 0.95 or more, this technique proved to be valid.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.29-32
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• 1993

A technique computing short-term load foadcasting is essential for monitoring and controlling power system operation. This paper shows the use of general exponential smoothing to develop an adaptive forecasting system based on observed value of hourly demand. Forecasts of hourly load with lead times of one to twenty-four hours are computed at hourly intervals throughout the week. Standard error for lead times of one to twenty-four hour range from three to four percent average load. Studies are planned to investigate the use of weather influence to increase forecast accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.105-111
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• 2010

This paper proposes the recursive algorithm for passive proximity detection system based on signal strength. The system is designed to be used in the smart medicine chest in order to provide location-based service for the senior personnel. Due to the system profile, single receiver and uni-direction communication are applied over the signal attenuation model for the determination of user existence within certain proximity. The performance of conventional methods is subjective to the sight between the transmitter and receiver unless the direction of target is known. To appreciate the temporal and spatial locality of human subjects, the authors present exponential moving average (EMA) to compensate the unexpected position error from the direction and/or environment. By using optimal parameter, the experiments with EMA algorithm demonstrates 32.26% (maximum 40.80%) reduction in average of the error probability with 50% of consecutive sight in time.

• Journal of the military operations research society of Korea
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• v.34 no.2
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• pp.79-101
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• 2008

This study is for improvement of repair part demand forecasting method of Republic of Korea Air Force aircraft. Recently, demand prediction methods are Weighted moving average, Linear moving average, Trend analysis, Simple exponential smoothing, Linear exponential smoothing. But these use fixed weight and moving average range. Also, NORS(Not Operationally Ready upply) is increasing. Recommended method of Box-Jenkins' ARIMA can solve problems of these method and improve estimate accuracy. To compare recent prediction method and ARIMA that use mean squared error(MSE) is reacted sensitively in change of error. ARIMA has high accuracy than existing forecasting method. If apply this method of study in other several Items, can prove demand forecast Capability.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1089-1096
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• 2011

We consider an infinite dam with inputs formed by a compound Poisson process and adopt a $P^M_{\lambda}$-policy to control the level of water, where the water is released at rate M when the level of water exceeds threshold ${\lambda}$. We obtain interesting stationary properties of the level of water, when the amount of each input independently follows an exponential distribution. After assigning several managing costs to the dam, we derive the long-run average cost per unit time and show that there exist unique values of releasing rate M and threshold ${\lambda}$ which minimize the long-run average cost per unit time. Numerical results are also illustrated by using MATLAB.

• Journal of Environmental Science International
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• v.28 no.9
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• pp.785-795
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• 2019

In order to analyze the sensitivity of carbon dioxide flux by soil temperature in the grassplot, carbon dioxide flux and soil temperature were observed 24 times from March, 2010 to March, 2011 at nine sites in the grassplot. The average of $CO_2$ in the grassplot is $2.2{\sim}36.7^{\circ}C$, the highest in August, the lowest in January, and the average of carbon dioxide flux is $12{\sim}1479mgCO_2{\cdot}m^{-2}{\cdot}hr^{-1}$, and the carbon dioxide emission from the grassplot to the atmosphere was 10 times higher in summer than in winter. The temperature response coefficient estimated by the exponential function of carbon dioxide flux according to soil temperature was ranged from 0.1065 to 0.1274, and the increase tendency of $CO_2$ flux with soil temperature was linear at $0{\sim}20^{\circ}C$ and exponential at $20{\sim}40^{\circ}C$. The $Q_{10}$ values for each of nine observation sites on the grassplot was in the range of 2.901 ~ 3.575, and the $Q_{10}$ value using the total data observed in the lawn was estimated to be 3.374. In the homogeneous grassplot area, the average of $Q_{10}$ values by observation point and the $Q_{10}$ value by the total data were estimated similarly.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.141-155
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• 2005

It is very important that sellers provide reasonable reserve prices for auction items in internet auction systems. Recently, an agent has been proposed to generate reserve prices automatically based on the case similarity of information retrieval theory and the moving average of time series analysis. However, one problem of the previous approaches is that the recent trend of auction prices is not well reflected on the generated reserve prices, because it simply provides the bid price of the most similar item or an average price of some similar items using the past auction data. In this paper. in order to overcome the problem. we propose a method that generates reserve prices based on the moving average. the exponential smoothing, and the least square of time series analysis. Through performance experiments. we show that the successful bid rate of the new method can be increased by preventing sellers from making unreasonable reserve prices compared with the previous methods.