• Journal of the Korean Statistical Society
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• v.16 no.1
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• pp.1-9
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• 1987

Recently a new form of the extended Yule-Walker equations for a mixed autoregressive moving-average process of orders p and q has been proposed. It can be used to obtain p+q+1 parameter values from the first p+q+1 autocovariance terms. The autoregressive part of the equations is linear and can be easily solved. In contrast the moving-average part is composed of nonlinear simultaneous equations. Thus some iterative algorithms are necessary to solve them. The iterative algorithm presented by Choi(1986) is very simple but its convergence has not been proved yet. In this paper a Newton-Raphson solution for the moving-average parameters is presented and its convergence is shown. Also numerical example illustrate the performance of the algorithm.

• Atmosphere
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• v.24 no.2
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• pp.197-206
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• 2014

The effects of different averaging operators and atmospheric stability on the turbulent fluxes are investigated using the vertical velocity, air temperature, carbon dioxide concentration, and absolute humidity data measured at 10 Hz by a 3-dimensional sonic anemometer and an open-path $CO_2/H_2O$ infrared gas analyzer installed at a height of 18.5 m on the rooftop of the Jungnang KT building located at a typical residential area in Seoul, Korea. For this purpose, 7 different averaging operators including block average, linear regression, and moving averages during 100 s, 300 s, 600 s, 900 s, and 1800 s are considered and the data quality control procedure such as physical limit check and spike removal is also applied. It is found that as the averaging interval becomes shorter, turbulent fluxes computed by the moving average become smaller and the ratios of turbulent fluxes computed by the 100 s moving average to the fluxes by the 1800 s moving average under unstable stability are smaller than those under neutral stability. The turbulent fluxes computed by the linear regression are 85~92% of those computed by the 1800 s moving average and nearly the same as those computed by 900 s moving average, implying that the adequate selection of an averaging operator and its interval will be very important to estimate more accurate turbulent fluxes at urban area.

• Journal of Sport and Applied Science
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• v.4 no.1
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• pp.17-30
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• 2020

Moving sport tourism refers to travel that combines sport activity and tours to a particular destination. Although moving sport tourism is one of the fastest growing segments of the sport tourism industry, it is still lack of related-knowledge and academic studies. Hence, this study is conducted to explore the characteristics of moving sport tourism and to provide implications for sport tourism industry and future research. For this, ten participants who regularly participated in bicycle tours for the last 10 years were recruited for this study. The sample consisted of 9 males and 1 female. The average age of the participants was 44.6 years of which all but 2 had a full-time job. The average period of participating in bicycle tour was 7.9 years. For analysing data, content analysis approach was used. Interviews were tape recoded and data were multiple reviewed to identify the similar themes and to categorize them into conceptual constructs. Results suggest two requirements of moving sport tourism(moving and concurrent), three characteristics of moving sport tourism(self-directed, exploring, non-linear, purposive-riding, partial competitive, cultural touring) and two categories of moving sport tourism(full moving sport tourism and partial moving sport tourism). Implications for sport tourism industry and future studies were discussed.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.161-167
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• 2015

It is very difficult to predict time-series data. This is because data obtained from the signal having a non-linear characteristic has an uncertainty. In this paper, By differentiating time-series data is the average of the past data under the premise that change depending on what pattern, and find the soft look of time-series change pattern. This paper also apply the probability variables to generalize time-series data having a specific data according to the reflection ratio of the differentiation. The predicted value is estimated by removing cyclic movement and seasonal fluctuation, and reflect the trend by extracting the irregular fluctuation. Predicted value has demonstrated the superiority of the proposed algorithm and compared with the best results by a simple moving average and the moving average.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.35-43
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• 2014

A data allocation technique is essential to improve the performance of data broadcast systems. This paper explores the issues for allocating data items on broadcast channels in the environment where multiple-data queries are submitted, and proposes a new data allocation scheme named DAMA. The proposed scheme employs the strategy that the broadcast frequency of each data is determined by the moving average of its access probability. DAMA could enhance the performance of query response time since it is capable of controlling the influence of access probability properly according to the query size. Simulation is performed to evaluate the performance of the proposed scheme. The simulation results show that the performance of DAMA is superior to other schemes in terms of the average response time.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.818-833
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• 2017

This paper introduces a new algorithm that renders motion blur using triangular motion paths. A triangle occupies a set of pixels when moving from a position in the start of a frame to another position in the end of a frame. This is a motion path of a moving triangle. For a given pixel, we use a motion path of each moving triangle to find a range of time that this moving triangle is visible to the camera. Then, we sort visible time ranges in the depth-time dimensions and use bitwise operations to solve the occlusion problem. Thereafter, we compute an average color of each moving triangle based on its visible time range. Finally, we accumulate an average color of each moving triangle in the front-to-back order to produce the final pixel color. Thus, our algorithm performs shading after the visibility test and renders motion blur in real time.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.42-55
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• 2006

We propose a single Index approach for subsequence matching that supports moving average transform of arbitrary order in time-series databases. Using the single index approach, we can reduce both storage space overhead and index maintenance overhead. Moving average transform is known to reduce the effect of noise and has been used in many areas such as econometrics since it is useful in finding overall trends. However, the previous research results have a problem of occurring index overhead both in storage space and in update maintenance since tile methods build several indexes to support arbitrary orders. In this paper, we first propose the concept of poly-order moving average transform, which uses a set of order values rather than one order value, by extending the original definition of moving average transform. That is, the poly-order transform makes a set of transformed windows from each original window since it transforms each window not for just one order value but for a set of order values. We then present theorems to formally prove the correctness of the poly-order transform based subsequence matching methods. Moreover, we propose two different subsequence matching methods supporting moving average transform of arbitrary order by applying the poly-order transform to the previous subsequence matching methods. Experimental results show that, for all the cases, the proposed methods improve performance significantly over the sequential scan. For real stock data, the proposed methods improve average performance by 22.4${\~}$33.8 times over the sequential scan. And, when comparing with the cases of building each index for all moving average orders, the proposed methods reduce the storage space required for indexes significantly by sacrificing only a little performance degradation(when we use 7 orders, the methods reduce the space by up to 1/7.0 while the performance degradation is only $9\%{\~}42\%$ on the average). In addition to the superiority in performance, index space, and index maintenance, the proposed methods have an advantage of being generalized to many sorts of other transforms including moving average transform. Therefore, we believe that our work can be widely and practically used in many sort of transform based subsequence matching methods.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.701-706
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• 2007

The measurement accuracy for heart rate or $SpO_2$ using photoplethysmography (PPG) is influenced by how well the noise from motion artifacts and other sources can be removed. Eliminating the motion artifacts is particularly difficult since its frequency band overlaps that of the basic PPG signal. Therefore, we propose the Periodic Moving Average Filter (PMAF) to remove motion artifacts. The PMAF is based on the quasi-periodicity of the PPG signals. After segmenting the PPG signal on periodic boundaries, we average the $m^{th}$ samples of each period. As a result, we remove the motion artifacts well without the deterioration of the characteristic point.

• Journal of Environmental Science International
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• v.27 no.12
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• pp.1227-1239
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• 2018

In this study, daily precipitation data and daily average temperature data of meteorological observatories in Daegu, Busan, Daejeon, Seoul, Mokpo, and Gwangju cities inland and offshore were analyzed by using moving average method. Were compared. Overall, summarizing changes in precipitation and temperature over the 24 seasons, precipitation and temperature in all six stations increased compared to the past 1960s. In the case of precipitation, precipitation increased at the end of July and early August, whereas precipitation in April, September and early October decreased. In the case of temperature, especially in February, the temperature increased, and in Mokpo, the temperature from August to December showed a general decline. Changes in precipitation and temperature due to seasons in the 24 seasons affect agriculture and our everyday life, and further research is needed to determine how these changes will affect agricultural water supply, crop growth and daily life. The results of this study can be useful.