• Perspectives in Nursing Science
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• v.9 no.1
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• pp.24-35
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• 2012

Purpose: This research was performed to investigate Korean adolescents' body weight perception, appropriate weight change efforts, and factors that influencing these efforts. Methods: The data were obtained from 68,136 adolescents, aged 12~18 years from the 2007 Third Korean Youth' Risk Behavior Web-based Survey. Descriptive statistical analysis and odds ratio were calculated by logistic regression. Results: The distribution of the body mass index differed in boys and girls. The accuracy of body weight perception was shown in the order of the underweight (91.6%), overweigh t (73.3%), normal weight (55.4%), obesity (41.3%) groups. Adolescents with high perceived economic status tended to have a high prevalence of accuracy of body weight perception. The distribution of appropriate weight change efforts according to the actual body mass index showed that girls were trying to lose weight more than boys. The results of a logistic regression analysis regarding appropriate weight change efforts showed differences according to gender, perceived economic status, mother's educational level, and family affluence scale. Conclusion: Appropriate body weight perception and change management plans are needed for Korean adolescents. In addition, active weight change programs have to be established in the adolescents' living environments, such as schools.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.740-746
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• 2010

Rainwater has been used in many countries as a way of minimizing water availability problems. Rainwater harvesting system (RHS) has been successfully implemented as alternative water supply sources even in Korea. Although RHS is an effective alternative to water supply, its efficiency is often heavily influenced by temporal distribution of rainfall. Since natural precipitation is a random process and has probabilistic characteristics, it will be more appropriate to describe these probabilistic features of rainfall and its relationship with design storage capacity as well as supply deficit of RHS. This study presents the methodology to establish the relationships between storage capacities and deficit rates using probability distributions. In this study, the real three-story building was considered and nine scenaries were developed because the daily water usage pattern of the study one was not identified. GEV, Gumbel and the generalized logistic distribution ware selected according to the results of Kolmogorov-Smirnov test and Chi-Squared test. As a result, a set of curves describing the relationships under different exceedance probabilities were generated as references to RHS storage design. In case of the study building, the deficit rate becomes larger as return period increases and will not increase any more if the storage capacity becomes the appropriate quantity. The uncertainties between design storage and the deficit can be more understood through this study on the probabilistic relationships between storage capacities and deficit rates.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.867-876
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• 2023

In this study, the attributes of the NHPP software development cost model applying the Gamma family distribution (Erlang, Log-Logistic, Rayleigh) were newly analyzed, and after comparing with the Goel-Okumoto basic model to verify the properties of the model, the optimal model was also presented based on this. To analyze software reliability, failure time data that occurred randomly during system operation was used, and the calculation of the parameters was solved using the maximum likelihood estimation. As a result of comprehensive evaluation through various attribute analysis (mean value function, development cost, optimal release time), it was confirmed that the Rayleigh model had the best performance. Through this study, the attributes of the software development cost model applying the Gamma family distribution, which has no previous research case, were newly identified. Also, basic design data could also be presented so that developers can efficiently utilize this research data at an early stage.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.9-20
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• 2013

OSHA(Occupational Safety and Health Act) generally regulates employer's business principles in the workplace to maintain safety environment. This act has the fundamental purpose to protect employee's safety and health in the workplace by reducing industrial accidents. Authors tried to investigate the correlation between 'occupational injuries and illnesses' and level of regulation compliance using Survey on Current Status of Occupational Safety & Health data by the various statistical methods, such as generalized regression analysis, logistic regression analysis and poison regression analysis in order to compare the results of those methods. The results have shown that the significant affecting compliance factors were different among those statistical methods. This means that specific interpretation should be considered based on each statistical method. In the future, relevant statistical technique will be developed considering the distribution type of occupational injuries.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• Journal of Applied Reliability
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• v.16 no.1
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• pp.48-55
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• 2016

Purpose: The purpose of this study is to identify the object-oriented metrics which have strong impact on the reliability and fault-proneness of software products. The reliability and fault-proneness of software product is closely related to the design properties of class diagrams such as coupling between objects and depth of inheritance tree. Methods: This study has empirically validated the object-oriented metrics to determine which metrics are the best to predict fault-proneness. We have tested the metrics using logistic regressions and artificial neural networks. The results are then compared and validated by ROC curves. Results: The artificial neural network models show better results in sensitivity, specificity and correctness than logistic regression models. Among object-oriented metrics, several metrics can estimate the fault-proneness better. The metrics are CBO (coupling between objects), DIT (depth of inheritance), LCOM (lack of cohesive methods), RFC (response for class). In addition to the object-oriented metrics, LOC (lines of code) metric has also proven to be a good factor for determining fault-proneness of software products. Conclusion: In order to develop fault-free and reliable software products on time and within budget, assuring quality of initial phases of software development processes is crucial. Since object-oriented metrics can be measured in the early phases, it is important to make sure the key metrics of software design as good as possible.

• Environmental Engineering Research
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• v.19 no.2
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• pp.117-122
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• 2014

Indexes of safety, restoration, damage impact, and management reliability were developed to assess reliability of drinking water distribution networks (DWDNs) management. The developed indexes were applied to evaluate the reliability of the pipeline management stage during unexpected mechanical and hydraulic accidents of components. The results were used to support the decision-making process in effective management and maintenance by enhancing the administrator's system understanding and by helping to create appropriate maintenance and management policies. The results of this study indicated that application of a management reliability index to assess DWDNs reliability may help create a more effective plan for establishing DWDNs management and maintenance.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.665-676
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• 2022

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved.

• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.211-221
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• 2017

In statistical hydrology, various extreme distributions such as the generalized extreme value (GEV), generalized logistic (GLO) and Gumbel (GUM) models have been widely used to analyze the extreme events. In the case of rainfall events in South Korea, the GEV and Gumbel distributions are known to be appropriate among various extreme distribution models. However, the proper probability distribution model may be different depending on the type of extreme events, rainfall duration, region, and statistical characteristics of extreme events. In this regard, it is necessary to apply a wide range of statistical properties that can be represented by the distribution model because it has two shape parameters. In this study, the statistical applicability of rainfall data is analyzed using the Burr XII distribution and the dimensionless L-moment ratio for 620 stations in South Korea. For this purpose, L-skewness and L-kurtosis of the Burr XII distribution are derived and L-moment ratio diagram is drawn and then the applicability of 620 stations was analyzed. As a result, it is found that the Burr XII distribution for the stations of the Han River basin in which L-skewness is relatively larger than L-kurtosis is appropriate, It is possibility of replacing the distribution of commonly used Gumbel or GEV distributions. Therefore, the Burr XII model can be replaced as an appropriate probability model in this basin.