• Journal of Korea Water Resources Association
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• v.45 no.4
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• pp.393-407
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• 2012

This study suggests the results of temporal and spatial variations for rainfall data in the Korean Peninsula. We got the index of the rainfall amount, frequency and extreme indices from 65 weather stations. The results could be easily understood by drawing the graph, and the Mann-Kendall trend analysis was also used to determine the tendency (up & downward/no trend) of rainfall and temperature where the trend could not be clear. Moreover, by using the FARD, frequency probability rainfalls could be calculated for 100 and 200 years and then compared each other value through the moment method, maximum likelihood method and probability weighted moments. The Average Rainfall Index (ARI) which is meant comprehensive rainfalls risk for the flood could be obtained from calculating an arithmetic mean of the RI for Amount (RIA), RI for Extreme (RIE), and RI for Frequency (RIF) and as well as the characteristics of rainfalls have been mainly classified into Amount, Extremes, and Frequency. As a result, these each Average Rainfall Indices could be increased respectively into 22.3%, 26.2%, and 5.1% for a recent decade. Since this study showed the recent climate change trend in detail, it will be useful data for the research of climate change adaptation.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.39-52
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• 2002

Traffic data by vehicle classification is difficult for mutual exchange of data due to the different vehicle classification from each other by the data sources; as a result, application of the data is very limited. In Particular. in case of TCS vehicle classification in national highways, passenger car, van and truck are mixed in one category and the practical usage is very low. The research standardize the vehicle classification to convert other data and develop the model which can estimate national highway traffic data by the standardized vehicle classification from the raw traffic data obtained at the highway tollgates. The tollgates are categorized into several groups by their features and the model estimates traffic data by the standardized vehicle classification by using the point estimation and bootstrap algorithm. The result indicates that both of the two methods above have the significant level. When considering the bias of the extreme value by the sample size, the bootstrap algorithm is more sophisticated. Using result of this study, we is expect the usage improvement of TCS data and more specific comparison between the freeway traffic investigation and link volume on freeway using the TCS data.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.6
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• pp.588-595
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• 2006

Indoor radon($^{222}Rn$) concentrations of subway stations in Seoul area were measured to survey the environmental indoor radon levels and to identify sources of radon. The radon concentration of indoor air by method of long-term measuring with a-track detector were surveyed at 232 subway stations from 1998 to 2004. And the radon concentration in ground-water was measured with a method of alpha particle counting. To trace main source of radon, 8 out of 232 stations were selected and their radon concentrations in tunnel and on platform were analyzed. Total geometric mean and arithmetic mean of radon concentrations in all stations from 1998 to 2004 were $1.40{\pm}1.94pCi/L,\;1.65{\pm}1.07$ respectively. Geometric means of radon concentrations on platform and concourse were $1.54{\pm}1.96pCi/L,\;1.23{\pm}1.88pCi/L$ respectively, with higher concentration at the platform than at the concourse. The geological structure was significantly correlated to the indoor radon concentration in subway stations region. Radon concentrations of adjacent tunnel and ground-water of subway station was significantly correlated to the indoor radon concentration in subway stations. And There was a significant difference in radon concentration, depending on the depth levels in platform of subway stations(p<0.05).

• Information Systems Review
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• v.24 no.1
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• pp.39-57
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• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.185-191
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• 2004

The mean and Clustering are important methods of data mining, which is now widely applied to various multi-attributes problem However, feature weighting and feature selection are important in those methods bemuse features may differ in importance and such differences need to be considered in data mining with various multiful-attributes problem. In addition, in the event of arithmetic mean, which is inadequate to figure out the most fitted result for structure of evaluation with attributes that there are weighted and ranked. Moreover, it is hard to catch hold of a specific character for assume the form of user's group. In this paper. we propose a dispersion mean algorithm for evaluation of similarity measure based on the geometrical figure. In addition, it is applied to mean classified by user's group. One of the key issues to be considered in evaluation of the similarity measure is how to achieve objectiveness that it is not change over an item ranking in evaluation process.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.324-324
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• 2019

최근 급격한 기후변화 및 도시화로 인해 강우시 첨두유량이 증가하고, 도심 및 농촌지역에 비점오염원과 관련된 문제가 빈번하게 발생하고 있는 실정이다. 비점오염원에 대한 관리는 발생원 및 특성 파악이 어려운 특성으로 인해 관리가 미흡하며, 이에 따라 비점오염원 관리의 중요성이 커지고 있다. 각 토지이용별로 발생하는 비점오염원을 적절하게 관리하기 위해서는 유역별 비점오염원 발생특성에 대한 파악이 우선적으로 이루어져야 하며 다양한 관리기법에 대한 분석이 필요하다. 이에 본 연구에서는 SWAT모형을 이용하여 비점저감시설을 적용하였을 때의 비점오염부하량의 변화에 대한 분석을 진행하였다. 본 연구에서는 도시유역, 농업유역, 복합유역을 대상으로 비점저감시설을 적용하였을 때 비점오염부하량의 변화를 분석하였다. 우선 유역별 유출량 및 비점오염부하량을 모의하기 위하여 SWAT모델을 사용하였다. 모의된 유출량 및 비점오염부하량은 수질 측정 성과를 이용하여 보정을 수행하였다. 이후 비점오염 취약 소유역 선정을 위해 수질항목별 연간 비점오염부하량 크기에 따라 순위를 산정하고, 순위를 산술 평균하여 소유역별 전체 오염원에 대한 비점오염부하량 순위를 산정하였다. 비점오염 취약 소유역에 BMPs 및 LID를 적용한 결과, 도촌천의 비점오염 취약 소유역에서 SS, $NO_3-N$, TP의 평균 저감효율은 각각 11.17%, 3.47%, 18.85%로 나타났다. 공지천 도시지역에 LID 적용시 비점오염 취약 소유역에서 SS, $NO_3-N$, TP의 평균 저감효율은 각각 0.67%, 5.77%, 1.86%로 나타났다. 또한 공지천 농촌지역에 BMPs 적용시 SS, TN, TP의 평균 저감효율은 각각 14.22%, 1.67%, 4.43%로 나타났다. 또한 설성천의 비점오염 취약 소유역에서의 SS, TN, TP의 평균 저감효율은 각각 57.29%, 7.48%, 14.84%로 나타났다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.4D
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• pp.595-601
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• 2011

There is currently applied to the unit cost per a distance (KRW/km) for estimating the conceptual cost of civil work on basic planning stage of high speed railroad. This unit cost is an arithmetic average value based on historical data, which could be in big error. It also is difficult to explain the deficiency comparing the estimated cost derived from next basic design stage. This study provides the conceptual estimation model using by the parametric method and regression analysis. Independent variables are the distance and the geological materials (earth, weathered rock, soft-rock, hard-rock), extracting from the actual data to 36 contracts. The deviation between the unit costs estimated using the developed model and the actual cost data is presented in the range from -0.4% to +31%. This range is acceptable compared the typical range "-30% to + 50%". This model will improve the accuracy of existing method and be expected to contribute to effective total cost management and the economic aspects, reduce the financial expenditure.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.201-211
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• 2013

This study compares Customer Satisfaction Index(CSI) and the weight for each dimension by applying various methods of weight calculation and attempts to suggest some implications. For the purpose, the study classified the methods of weight calculation into the subjective method and the statistical method. Constant sum scale was used for the subjective method, and the statistical method was again segmented into correlation analysis, principal component analysis, factor analysis, structural equation model. The findings showed that there is difference between the weights from the subjective method and the statistical method. The order of the weights by the analysis methods were classified with similar patterns. Besides, the weight for each dimension by different methods of weight calculation showed considerable deviation and revealed the difference of discrimination and stability among the dimensions. Lastly, the CSI calculated by various methods of weight calculation showed to be the highest in structural equation model, followed by in the order of regression analysis, correlation analysis, arithmetic mean, principal component analysis, constant sum scale and factor analysis. The CSI calculated by each method showed to have statistically significant difference.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5033-5043
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• 2012

The purpose of this study is to complement the problem of lecture evaluation items and to improve the lecture evaluation items to fit to original purpose of lecture evaluation to enhance the lecture's quality. For this, meanings of good teaching, lecture evaluation domains and elements of preceding study, the contents and problems of lecture evaluation tools in A college were searched, and in this foundation, an improved lecture evaluation tool was suggested. As the result of this study, important features of the improved tool are followed: First, the compositions of evaluation domains, evaluation elements, and evaluation items were reconstituted. Second, to acquire the important information for the better lecture, the items were devised according to the features of good teaching in colleges. Third, the items concerned of evaluation elements which is commonly suggested by the lecture evaluation tools of preceding study were developed. Forth, if there is the information which is required for the enhancement of the lecture quality, the items were developed though the result could not be presented in the arithmetical means. Fifth, evaluation items to improve the problems of lecture evaluation tools which had been carried out in A college were developed.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.35-41
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• 2020

This paper proposes an algorithm to improve the compression performance of the adaptive multi-rate (AMR) speech coding using the context tree weighting (CTW) method. AMR is the voice encoding standard adopted by IMT-2000, and supports 8 transmission rates from 4.75 kbit/s to 12.2 kbit/s to cope with changes in the channel condition. CTW as a kind of the arithmetic coding, uses a variable-order Markov model. Considering that CTW operates bit by bit, we propose an algorithm that re-orders AMR data and compresses them with CTW. To verify the validity of the proposed algorithm, an experiment is conducted to compare the proposed algorithm with existing compression methods including ZIP in terms of compression ratio. Experimental results indicate that the average additional compression rate in AMR data is about 3.21% with ZIP and about 9.10% with the proposed algorithm. Thus our algorithm improves the compression performance of AMR data by about 5.89%.