• Review of Korea Contents Association
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• v.15 no.3
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• pp.21-26
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• 2017

• International Journal of Highway Engineering
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• v.17 no.6
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• pp.11-18
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• 2015

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.

• Clinical and Experimental Pediatrics
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• v.59 no.2
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• pp.80-90
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• 2016

Purpose: Studies have been conducted to identify predictive factors of resistance to intravenous immunoglobulin (IVIG) for Kawasaki disease (KD). However, the results are conflicting. This study aimed to identify laboratory factors predictive of resistance to high-dose IVIG for KD by performing meta-analysis of available studies using statistical techniques. Methods: All relevant scientific publications from 2006 to 2014 were identified through PubMed searches. For studies in English on KD and IVIG resistance, predictive factors were included. A meta-analysis was performed that calculated the effect size of various laboratory parameters as predictive factors for IVIG-resistant KD. Results: Twelve studies comprising 2,745 patients were included. Meta-analysis demonstrated significant effect sizes for several laboratory parameters: polymorphonuclear leukocytes (PMNs) 0.698 (95% confidence interval [CI], 0.469-0.926), C-reactive protein (CRP) 0.375 (95% CI, 0.086-0.663), pro-brain natriuretic peptide (pro-BNP) 0.561 (95% CI, 0.261-0.861), total bilirubin 0.859 (95% CI, 0.582-1.136), alanine aminotransferase (AST) 0.503 (95% CI, 0.313-0.693), aspartate aminotransferase (ALT) 0.436 (95% CI, 0.275-0.597), albumin 0.427 (95% CI, -0.657 to -0.198), and sodium 0.604 (95% CI, -0.839 to -0.370). Particularly, total bilirubin, PMN, sodium, pro-BNP, and AST, in descending numerical order, demonstrated more than a medium effect size. Conclusion: Based on the results of this study, laboratory predictive factors for IVIG-resistant KD included higher total bilirubin, PMN, pro-BNP, AST, ALT, and CRP, and lower sodium and albumin. The presence of several of these predictive factors should alert clinicians to the increased likelihood that the patient may not respond adequately to initial IVIG therapy.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.43-58
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• 2008

In this paper, we have presented a dynamic, predictive location update scheme that takes into account each user's mobility patterns. A user's past movement history is used to create two-dimensional transition probability matrix which makes use of two directional consecutive location areas. A mobile terminal utilizes the transition probability to develop a predictive path which consists of several predictive nodes and then the location update is saved as long as a mobile user follows the predictive path. Using continuous-time Markov chain, cost functions of location update and paging are derived and it is shown that the number of predictive nodes can be determined optimally. To evaluate the proposed scheme, simulations are designed and the numerical analysis is carried out. The numerical analysis features user's mobility patterns and regularity, call arrival rates, and cost ratio of location update to paging. Results show that the proposed scheme gives lower total location management cost, compared to the other location update schemes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.239-240
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• 2010

• Journal of the Korean Geophysical Society
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• v.9 no.3
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• pp.171-179
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• 2006

Predictive equations of ground motions are one of the most important factors in the seismic hazard analysis. Unfortunately, studies on predictive equations of ground motions in Korea had been hampered due to the lack of seismic data. To overcome the lack of data, seismologists adopted the stochastic method based on the seismological model. Korean predictive equations developed by the stochastic method show large differences in their predictions. It was turned out through the analysis of the existing studies that the main sources of the differences are the uncertainties in the (Brune) stress drop and spectral decay rate . Therefore, it is necessary to focus the future research on the reduction of the uncertainties in the two parameters.

• Journal of Korean Academy of Nursing
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• v.43 no.3
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• pp.442-451
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• 2013

Purpose: This descriptive study was done to develop a predictive model of depression in rural elders that will guide prevention and reduction of depression in elders. Methods: A cross-sectional descriptive survey was done using face-to-face private interviews. Participants included in the final analysis were 461 elders (aged${\geq}$ 65 years). The questions were on depression, personal and environmental factors, body functions and structures, activity and participation. Decision tree analysis using the SPSS Modeler 14.1 program was applied to build an optimum and significant predictive model to predict depression in rural elders. Results: From the data analysis, the predictive model for factors related to depression in rural elders presented with 4 pathways. Predictive factors included exercise capacity, self-esteem, farming, social activity, cognitive function, and gender. The accuracy of the model was 83.7%, error rate 16.3%, sensitivity 63.3%, and specificity 93.6%. Conclusion: The results of this study can be used as a theoretical basis for developing a systematic knowledge system for nursing and for developing a protocol that prevents depression in elders living in rural areas, thereby contributing to advanced depression prevention for elders.

• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.1-13
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• 1992

This article deals with variable selection problem under a newly formed predictive heteroscedastic discriminant rule that accounts for mulitple homogeneous covariance matrices across the K multivariate normal populations. A general version of predictive discriminant rule, a variable selection criterion, and a criterion for stopping with further selection are suggested. In a simulation study the practical utilities of those considered are demonstrated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1399-1410
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• 2015

Gartner is requiring companies to considerably change their survival paradigms insisting that companies need to understand and provide again the upcoming era of data competition. With the revealing of successful business cases through statistic algorithm-based predictive analytics, also, the conversion into preemptive countermeasure through predictive analysis from follow-up action through data analysis in the past is becoming a necessity of leading enterprises. This trend is influencing security analysis and log analysis and in reality, the cases regarding the application of the big data analysis framework to large-scale log analysis and intelligent and long-term security analysis are being reported file by file. But all the functions and techniques required for a big data log analysis system cannot be accommodated in a Hadoop-based big data platform, so independent platform-based big data log analysis products are still being provided to the market. This paper aims to suggest a framework, which is equipped with a real-time and non-real-time predictive analysis engine for these independent big data log analysis systems and can cope with cyber attack preemptively.

• The Research Journal of the Costume Culture
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• v.1 no.2
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• pp.113-130
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• 1993

The purpose of this study was to investigate the role of perceived attributes of innovation in predicting the fashion innovativeness of female college students and to compare results with the predictive efficacy of selected psychographic variables. The data were analyzed by factor analysis and stepwise multiple regression. Frequency, percentage and man values were used to evaluate the descriptive data. The major findings derived from analysis are as follows: 1. Of the psychographic variables used to predict fashion innovativeness fashion interest was the most predictive of fashion innovativeness followed by venturesomeness. 2. So only perceived attributes variables found to be predictive of fashion innovativeness was perceived risk.