• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.597-604
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• 2011

This research analyzed the runoff patterns and estimated unit loads of selected pollutatnts using monitored data conducted for three years in a bridge area. Three estimating methods; the arithmetic average method, the regression method and the rainfall class method were used to estimate the unit load. Results of three estimating methods were compared with the unit pollutant loads from landuses in Korea and the unit pollutant loads from urban watersheds in Milwaukee, USA. Unit load using the arithmetic mean method were found to be overestimated. In terms of TSS, unit loads of two estimate were half lower than that of USA. Estimated TN and TP unit loads of three estimate were lower than that of Ministry of Environment in Korea.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.63-70
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• 2003

In this study, we employed an arithmetic process (AP) approach to resolve gearbox maintenance problems. The approach is realistic and direct in modelling the characteristics of a deteriorating system such as a gearbox since a decreasing AP can model a gearbox's successive operating times and an increasing AP can model the corresponding consecutive repair times. First of all, two test statistics were used to check whether the process is arithmetic or not. Next, model parameters of the AP were estimated using the simple linear regression method. Finally, the optimal replacement policy based on minimising the long-run average cost per day was determined for each type of gearbox.

• Journal of Multimedia Information System
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• v.5 no.4
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• pp.273-276
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• 2018

In video compression standards, the entropy coding is essential to the high performance compression because redundancy of data symbols is removed. Binary arithmetic coding is one of high performance entropy coding methods. However, the dependency between consecutive binary symbols prevents improving the throughput. For the throughput enhancement, a new probability model is proposed for encoding multi-symbols at one time. In the proposed method, multi-symbol encoder is implemented with only adders and shifters, and the multiplication table for interval subdivision of binary arithmetic coding is removed. Compared to the compression ratio of CABAC of H.264/AVC, the performance degradation on average is only 1.4% which is negligible.

• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.757-768
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• 2015

Recently, climate change has affected short time concentrated local rainfall and unexpected heavy rain which is increasingly causing life and property damage. In this research, arithmetic average analysis, weighted average analysis, and principal component analysis are used for predicting flood risk. This research is foundation for application of predicting flood risk based on annals of disaster and status of urban planning. Results obtained by arithmetic average analysis, weighted average analysis, and principal component analysis using many factors affect on flood are compared. In case of arithmetic average analysis, each factor has same weights though it is simple method. In case of weighted average analysis, correlation factors are complex by many variables and multicollinearty problem happen though it has different weights. For solving these problems, principal component analysis (PCA) is used because each factor has different weights and the number of variables is smaller than other methods by combining variables. Finally, flood risk assessment considering flood control ability and urban environment risk in former research is predicted.

• Nuclear Engineering and Technology
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• v.29 no.6
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• pp.488-499
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• 1997

The measurements of the moderator temperature coefficient (MTC) are performed to demonstrate that the calculational model produces results that are consistent with the measurements. Since negative MTC is also a technical specification value that may limit the cycle length, it is important to measure it as accurately as possible. In this report, preferred choice of test method depending on the time in cycle, best power indication and temperature definition in MTC calculation were determined based on the MTC test results taken during initial startup testing and at 2/3 cycle burnup in the Yonggwang nuclear power plant. The results show that the ratio and rodded methods provided good agreement with the predictions during initial startup testing. However, near end-of-cycle the depletion method gives better results, and so is suggested to be used in the MTC measurements at 2/3 cycle burnup. The use of primary Delta T power as a power indicator in the MTC calculations is highly advisable since it responds with good consistent results very quickly to changes unlike secondary calorimetric power. For the appropriate temperature definitions used in the MTC calculations, it is considered that the arithmetic average temperature measured simply by inlet and outlet thermocouples is preferred. Although volumetric average temperature provides better results, the improvement is not sufficient to compensate for the simplicity of calculations by arithmetic average temperature.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.63-73
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• 2003

COSMOS(Cycle, Offset, Split Model for Seoul), a real-time traffic adaptive signal system. estimates queue lengths on each approach on the basis of arithmetic average spot speeds calculated on loop detectors installed at each of two adjacent lanes. In this paper, A new method, a traffic volume-weighted average method, was studied and compared with the existing arithmetic average method. It was found that the relationship between the ratio of volumes of two lanes and the difference of average speed of each lane has a linear form. With field data, The two methods were applied and the proposed method shows more stable and reasonable queue estimation results.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.20 no.2
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• pp.123-135
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• 2016

This article deals with the pricing of Asian options under a constant elasticity of variance (CEV) model as well as a stochastic elasticity of variance (SEV) model. The CEV and SEV models are underlying asset price models proposed to overcome shortcomings of the constant volatility model. In particular, the SEV model is attractive because it can characterize the feature of volatility in risky situation such as the global financial crisis both quantitatively and qualitatively. We use an asymptotic expansion method to approximate the no-arbitrage price of an arithmetic average Asian option under both CEV and SEV models. Subsequently, the zero and non-zero constant leverage effects as well as stochastic leverage effects are compared with each other. Lastly, we investigate the SEV correction effects to the CEV model for the price of Asian options.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.4
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• pp.471-475
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• 2017

This study aims to suggest the methodology to improve to estimate back-calculated fish growth parameters using weighted average. It is to contribute to correct errors in the calculation of back-calculated growth equation with unequal numbers of sample by age. If the numbers of sample were evenly collected by age, each back-calculated length at age was equal between arithmetic and weighted averages. However, most samples cannot be evenly collected by age in reality because of different catchability by fishing gear and limitation of environment condition. Therefore, the estimation of back-calculated length by weighted average method is essential to calculate growth parameters. There were some published growth equations from back-calculated length using a simple arithmetic average with different numbers of samples by age when searching for back-calculated growth equations from 91 relevant papers. In this study, the process of deriving growth equation was investigated and two different average calculations were applied to a fish growth equation, for example of Acheilognathus signifer. Growth parameters, such as $L_{\infty}$, k and $t_0$, were estimated from two different back-calculated averages and the growth equations were compared with growth performance index. Based on the correction of back-calculated length using weighted average by age, the changes by female and male were -14.19% and -5.23% for $L_{\infty}$, and 59.28% and 18.91% for k, respectively. The corrected growth performance index by weighted average improved at 7.05% and 2.46% by female and male, respectively, compared to the arithmetic averages.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.