• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.505-510
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• 2014

Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.13 no.2
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• pp.277-283
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• 1995

In the study, an accuracy of earthwork volume is evaluated according to different methods of the calculation with different criteria. The criteria applied to this study are a interpolation method, a grid intavals and the method of earthwork evaluation. A numerical test has performed on two different terrain models with four different methods of calculation in the earthwork volume and two different grid intervals. The end area method, prismoidal formular, Simpson's formular, and middle area method are applied to the calculation of the earthwork volume. As a result of this study, it is showed that the moving average method with the first order term gives the most accurate result in interpolation, and that also the prismoidal formular and Simpson's formular gives more accurate result than average and area method and middle area method in the calculation of earthwork volume.

• Journal of Digital Convergence
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• v.11 no.8
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• pp.183-190
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• 2013

The Photoplethysmogram is a similar periodic signal that synchrinized to a heartbeat. In this paper, we propose a exponential weight moving average filter that use similarity of Photoplethysmogram. This filtering method has the average value of each samples through separating the cycle of PPG signal. If there are some motion artifacts in continuous PPG signal, disjoin the signal based on cycle. And then, we made these signals to have same cycle by coordinating the number of sample. After arrange these cycles in 2 dimension, we put the average value of each samples from starting till now. So, we can eliminate the motion artifacts without damaged PPG signal.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.75-82
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• 2012

The Photoplethysmogram is a similar periodic signal that synchronized to a heartbeat. In this paper, we propose a periodic moving average filter that use similarity of Photoplethysmogram. This filtering method has the average value of each samples through separating the cycle of PPG signal. If there are some motion artifacts in continuous PPG signal, disjoin the signal based on cycle. And then, we made these signals to have same cycle by coordinating the number of sample. After arrange these cycles in 2 dimension, we put the average value of each samples from starting till now. So, we can eliminate the motion artifacts without damaged PPG signal.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.2
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• pp.73-80
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• 2012

It has been highly required to develope an automatic metal surface inspection system, specifically using image processing techniques, which can replace the visual inspection method in the steel industry. In this paper, we propose a precisional surface measurement system using the moving average image processing technique. When the surface patterns which are generated in the rolling process of metal plates are recognized as defects, the proposed system can measure the actual number of defects. It has been proved that our system shows better results than the conventional FFT method.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.573-589
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• 2023

In a piecewise linear trend model, the change points coincide with the mean change points of the first differenced time series. Therefore, by detecting the mean change points of the first differenced time series, one can estimate the change points of the piecewise linear trend model. In this paper, based on this fact, a method is proposed for detecting change points of the piecewise linear trend model using the simple moving average of the first differenced time series rather than estimates of the slope or residuals. Our Monte Carlo simulation experiments show that the proposed method performs well in estimating the number of change points not only when the error terms in the piecewise linear trend model are independent but also when they are serially correlated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.85-88
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• 2021

There are many irregularly-sampled time series in the manufacturing data which are collected from manufacturing facilities by short intervals. Those time series often have large variance. In this paper, we propose irregularly-sampled time series correction method based on simple moving average. This method corrects time intervals between neighboring values in time series regularly and reduces the variance of the values at the same time. We examine that this method improves performance of anomaly detection in manufacturing facility.

• Steel and Composite Structures
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• v.46 no.2
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• pp.253-261
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• 2023

The goal of this paper is to investigate dynamic responses of simply-supported laminated micro beams under moving load. In the considered micro-scale problem, the modified coupled stress theory which includes the length scale parameter is used. The governing equations of problem are derived by using the Lagrange procedure. In the solution of the problem the Ritz method is used and algebraic polynomials are used with the trivial functions for the Ritz method. In the solution of the moving load problem, the Newmark average acceleration method is used in the time history. In the numerical examples, the effects of stacking sequence of laminas, fibre orientation angles and the length scale parameter on the dynamic responses of laminated micro beams are examined and discussed.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.