• Journal of the Korean Society of Earth Science Education
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• v.14 no.2
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• pp.193-201
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• 2021

This study is a study of factors affecting pre-service teachers' learning immersion in online science classes. The results of a survey on the responses of 88 pre-primary teachers to online science classes were interpreted. In online science class, independent variables were set as ease, usefulness, and social presence, and dependent variables were set as learning immersion. Online science classes were conducted based on the university's LMS system. The results of the study were interpreted by regression analysis for t-test, correlation between factors, and multicollinearity test in pre-post-responses of pre-service teachers for ease, usefulness, and social presence. The results of this study are as follows. First, there was a significant effect in the before-and-after tests of factors in the online science class. Second, the correlation coefficient between factors in online science class is .306 for sense of community and mutual support and concentration at the significance level of .01, and .354 for learning immersion and open communication, indicating that there is a correlation. Third, considering the effective results of the pre-post test of each factor in the online science class, it cannot be interpreted that a particular factor had an effect, but it is interpreted that learning was immersed in ease, usefulness, and a sense of social presence.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.53-66
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• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• 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.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.

• Journal of the military operations research society of Korea
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• v.36 no.3
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• pp.43-56
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• 2010

Recently, the acquisition environment of the Korean defense weapon system is emphasizing more the importance of cost analysis in terms of efficient execution for defense acquisition budget. While cost analysis, however, is emphasized in law and process, its infrastructures are still insufficient We have been using computerized cost models to obtain an estimate at early phase of project. But those models have been developed by foreign companies, and so they have many limitations when using in Korean defense environment. For this reason, it began to sympathize that we need the development of the Korean version cost estimation model suitable for our defense industry environment, and now many studies are proceeding. In this study, we suggest Cost Estimating Relationships(CERs) developing methodologies which is key logics of Korean version cost estimation model. Especially, we proposed a new CER's development process depending upon data characteristics such as, multicolinearity, outlier, small samples and heteroscedasticity. Also, we presented a case study for artillery weapon system using these methods we developed. We find that these CERs could be verified through theoretical methods.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.335-343
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• 2019

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.625-635
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• 2014

The purposes of the study is to examine the relationship between developmental assets, stress and risk behaviors of university students. The subject of the study is 1023 university students. Questionnaire organized by scales of developmental assets, stress and risk behaviors was used. The major findings were as follows; First, internal assets made direct effects on stress and risk behaviors. External assets made direct effects on stress. Second, mediating effects of stress were statistically significant in relations between developmental assets and risk behaviors. External assets were complete mediating effects by making effects indirectly on risk behaviors through the stress. Internal assets showed partial mediating effects.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.67-73
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• 2005

The urbanization of Korea has been rapidly progressed since 1960, but satellite imagery have provided the information only after 1975. Recently released CORONA imagery is one of the few source of satellite image which can provide 1960's topographic information of the Korean Peninsular. It can be applied to change detection in various fields such as urban, forest, and environmental planning. In this research mosaic image of past Korean Peninsular using CORONA imagery in the 1960s were generated. A polynomial equation and a modified collinearity equation were applied for geo-referencing and a comparative analysis was conducted. In this research the 2nd polynomial equations were used for geo-referencing of CORONA imagery. After carrying out geo-referencing, mosaic image was generated using Erdas Imagine. It is assumed that this result image is very useful for various fields such as generation of thematic maps, urban planning, and change detection.

• Journal of the Microelectronics and Packaging Society
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• v.7 no.1
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• pp.13-18
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• 2000

Low temperature co-fired ceramics on metal (LTCC-M) is efficient for embedding passive components with good tolerance in a module due to the dimensional stability in x and y directions by the constraint of metal core during the firing. In addition, the radiation noise can be reduced by metal core. In this paper, embedded passive components were introduced and a power amplifier module (PAM) fabricated by using the passive components was explained. The embedded passive components in test patters showed the tolerance of 10~20% and the good repeatability in tolerance of embedded passives was maintained in module fabrication. The shortened traces in multi chip modules (MCMs) make the signal delay time decreased and the embedded passives simplify the packaging processes owing to the less solder points, which enhance the electrical performance and increase the reliability of the modules. The LTCC-M technology is one of the promising candidates for RF application and is expected to expand its applications to power and high performance devices.