• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5392-5418
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• 2017

The primary aim of this paper is to propose a non-linear regression based technique for mapping different network Quality of Service (QoS) factors to an integrated end-user Quality of Experience (QoE) or Mean Opinion Score (MOS) value for an online video streaming service on a mobile phone. We use six network QoS factors for finding out the user QoE. The contribution of this paper is threefold. First, we investigate the impact of the network QoS factors on the perceived video quality. Next, we perform an individual mapping of the significant network QoS parameters obtained in stage 1 to the user QoE based upon a non-linear regression method. The optimal QoS to QoE mapping function is chosen based upon a decision variable. In the final stage, we evaluate the integrated QoE of the system by taking the combined effect of all the QoS factors considered. Extensive subjective tests comprising of over 50 people across a wide variety of video contents encoded with H.265/HEVC and VP9 codec have been conducted in order to gather the actual MOS data for the purpose of QoS to QoE mapping. Our proposed hybrid model has been validated against unseen data and reveals good prediction accuracy.

• Health Policy and Management
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• v.25 no.4
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• pp.333-342
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• 2015

Background: The purposes of this study is to analysis the differences of the job satisfaction between regular and non-regular workers in hospitals. Methods: The samples used for data analysis are 632 workers of 6 hospitals using a standardized questionnaires in B, C, D, and G provinces. In research methodology, all the data were analyzed with descriptive statistics, t-test, Pearson's correlation, and multiple linear regression analysis. Results: In case of regular workers, communication, working conditions and employee benefit, and education were found to have a significant positive (+) effect on job satisfaction. In case of non-regular workers, empowerment, reward systems, communication, working conditions, and employee benefit had a significant positive (+) effect on job satisfaction. Conclusion: These results showed that hospitals needed to reinforce communication, working conditions and employee benefit to regular and non-regular workers in order to improve job satisfaction. Especially, more empowerment, working conditions, and employee benefit should be given to non-regular workers.

• Journal of Industrial Convergence
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• v.16 no.4
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• pp.33-46
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• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• Corrosion Science and Technology
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• v.18 no.2
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• Korean Journal of Ecology and Environment
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• v.34 no.3 s.95
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• pp.215-222
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• 2001

A statistical analysis was performed to evaluate relationships among the pollutant concentrations in non-urban area. The data obtained from two subcatchments in Hwa-Ong watershed during 1999 was used for correlation and regression analyses. Strong correlations were observed among the SS, COD, and TP, while it was not significant with TN. The reason fer weak correlation with TN might be that TN was high in dry-days and runoff in wet-days could not increase enough to change it substantially like in other pollutants. The correlations were stronger for the data in wet-days than in dry-days, and it was influenced by watershed characteristics. While TP-COD showed linear relationship from the regression analysis, SS-TP and SS-COD shelved intrinsically linear relationship between log-transformed TP and COD data and non-transformed SS data. The TP-COD showed strong relationship for all the combinations of monitored data, which implies that these two constituent concentrations varied in a similar pattern. The regression equations reported in the paper might be used to estimate one pollutant concentration from the other in pollutant loading estimates, and its application could be expanded to other non-urban watersheds if their characteristics are not significantly different from the study area. In water quality management projects, rigorous monitoring and its thorough evaluation are recommended to develop more reliable relationships among the pollutant concentrations which could be used in other area.

• Journal of Korean Society of Transportation
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• v.5 no.2
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• pp.55-72
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• 1987

The work analyzes the existing situation and defines special problems concerning traffic accidents in the two countries. The report is divided into three parts: 1) Using the global approach of SMEED, the data were evaluated using multiple regression analysis, and homogeneous groups of countries were defined by cluster analysis. In the global approach, the linear model is better than SMEED's non-linear model in explaining the number of fatalities. Among the different groups of countries, the linear approach was found to be better suited for industrialized countries and the non-linear approach better for the developing countries. T도 comparison of traffic fatality data for the Federal Republic the developing countries. The comparison of traffic fatality data for the Federal Republic of Germany and the Republic of Korea showed different regression equations during the same time period. 2) The BOX/JENKINS time series analysis on a monthly basis points out clearly similar seasonal patterns for the two countries over the years studied. The decrease in traffic accidents following the intensification of the safety belt requirement was proved in the ARIMA model. It amounts to 7 to 8 percent fewer personal injury accidents and fatal accidents. The identified increase in safety in the Federal Republic of Germany since the 1970s is mainly due to the reduction of accident severity in residential areas. 3) Speeds and headways on motorways in th3e two countries were also compared. The measurements point out that German road users drive faster, take more risks, and accept shorter time gaps than Korean road users. However, the accident statistics show accident rates for Korea that are several times higher than those in the Federal Republic of Germany.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.267-275
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• 2013

Among data mining techniques, the association rule is the most recently developed technique, and it finds the relevance between two items in a large database. And it is directly applied in the field because it clearly quantifies the relationship between two or more items. When we determine whether an association rule is meaningful, we utilize interestingness measures such as support, confidence, and lift. Interestingness measures are meaningful in that it shows the causes for pruning uninteresting rules statistically or logically. But the criteria of these measures are chosen by experiences, and the number of useful rules is hard to estimate. If too many rules are generated, we cannot effectively extract the useful rules.In this paper, we designed a variety of non-linear regression equations considering all association thresholds between the number of rules and three interestingness measures. And then we diagnosed multi-collinearity and autocorrelation problems, and used analysis of variance results and adjusted coefficients of determination for the best model through numerical experiments.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.5
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• pp.505-513
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• 1995

The major factors affecting productivity on daily farms in Queensland, Australia, were determined using the stepwise linear regression approach. The data were obtained from a survey conducted on the total population of daily farms in Queensland in 1987. These data were divided into six major dailying regions. The technique was applied using 12 independent variables believed by a panel of experienced research and extension personnel to exert the most influence on milk production. The regression equations were all significant (p < 0.001) with the percentage coefficients of determination ranging from 62 to 76% for equations developed using' total farm milk: production as the dependent variable. Three of the variables affecting total farm milk: production were found to be common to all six regions. These were; the amount of supplementary energy fed, the area set aside to irrigate winter feed and the size of the area used for dailying. Higher production farms appeared to be more efficient in that they consistently produced milk production levels higher than those estimated from the regression equation for their region. Other methods of analysis including robust regression and non linear regression techniques were unsuccessful in overcoming this problem and allowing development of a model appropriate for farms at all levels of production.

• Journal of Life Science
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• v.13 no.5
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• pp.751-755
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• 2003

Cyclic AMP receptor proteins(CRP) activate many genes in Escherichia coli by binding of cAMP with not fully known mechanism. CRP existed as apo-CRP in the absence of cAMP, $CRP;(cAMP)_2$$_2$ at low(micromolar) cAMP concentration, or $CRP;(cAMP)_4$ at high(millimolar) concentration of cAMP. This study is designed to measure the thermal stability of S83G CRP, which substituted glycine for serine at amino acid 83 position, with CD spectrapolarimeter at 222nm by the constant elevation of temperature from $20^{\circ]C\; to\; 90^{\circ}C\; at\; 1^{\circ}C/min$. The non-linear regression analysis showed that melting temperatures were 68.4, 72.0, and $82.3^{\circ}C$ for no cAMP, 0.1mM cAMP, and 5mM cAMP, respectively. Result showed the strong thermal stability of CRP by binding of additional cAMP molecules to region between the hinge region and helix-turn-helix(HTH) motif at 5mM cAMP concentration.

• Horticultural Science & Technology
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• v.32 no.2
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• pp.241-251
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• 2014

This study was carried out to develop a simple, rapid and reliable assessment model to predict cold tolerance in Pittosporum tobira, a broad-leaved evergreen commonly used in the southern region of South Korea, which can minimize the possible experimental errors appeared in a electrolyte leakage test for cold tolerance assessment. The modeling procedure comprised of regrowth test and a electrolyte leakage test on the plants exposed to low temperature treatments. The lethal temperatures estimated from the methodological combinations of a electrolyte leakage test including tissue sampling, temperature treatment for potential electrical conductivity, and statistical analysis were compared to the results of the regrowth test. The highest temperature showing the survival rate lower than 50% obtained from the regrowth test was $-10^{\circ}C$ and the lethal was $-10^{\circ}C{\sim}-5^{\circ}C$. Based on the results of the regrowth test, several methodological combinations of electrolyte leakage tests were evaluated and the electrolyte leakage lethal temperatures estimated using leaf sample tissue and freeze-killing method were closest to the regrowth lethal temperature. Evaluating statistical analysis models, linear interpolation had a higher tendency to overestimate the cold tolerance than non-linear regression. Consequently, the optimal model for cold tolerance assessment of P. tobira is composed of evaluating electrolyte leakage from leaf sample tissue applying freeze-killing method for potential electrical conductivity and predicting lethal temperature through non-linear regression analysis.