• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1364-1367
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• 1996

Recently, direct rolling process, called as strip casting process, has been interested in to save production cost by reducing forming processes. In direct rolling process, since a steel strip of thickness 1-5(mm) can be produced directly from molten metal, it can eliminate secondary hot rolling process. On the other hand, since many process variables are existed in this process and relation of these variables is very complex, it is difficult to realize the process design and the quality control. In this paper, as first step to overcome above difficulties, the quantitative relationship of the process variables affected to quality of the strip has been carried out through the numerical analysis. Also, we determined the process variable to monitor the quality in the direct rolling process. As a result, we show that the solidification final point, called as Nip point, was related directly to quality of the strip.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.278-291
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• 1994

A computer-aided robust design (CARD) technique is developed to search for the design variables, optimal as well as robust in the sense of Taguchi method. The CARD technique can effectively handle inequality problems by employing the variable penalty method, and dynamic problems with many design variables and/or with mixed discrete and continuous variables. It is also capable of providing contributions of each design variables to the object funtion and information for future designs. As the illustrative examples, two dynamic systems, engine mounting system and in-line feeder, are treated.

• Journal of Korean Biological Nursing Science
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• v.21 no.3
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• pp.161-168
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• 2019

Purpose: The purpose of this paper (a literature review study) was to confirm the trend of nursing research for head and neck cancer patients in Korea. Methods: Research databases were reviewed and analyzed from 13 papers (2004 through 2019 using KISS, NDSL, RISS, DBpia, and the National Assembly Library. As a result of this paper, we found that there were many studies that used questionnaires. Results: Measurement variables related to head and neck cancer patients were physical variables related to oral condition, psychological variables related to depression and anxiety, social support, family support related to family, and quality of life as a result variable. Conclusion: Therefore, integrated nursing intervention strategies and clinical nursing research considering the physical, psychological, social, and family aspects of head and neck cancer patients are needed. Based on the results of this study, we propose qualitative research on head and neck cancer patients, development of educational programs, intervention studies to verify effects, and development of clinical practice guidelines.

• Computers and Concrete
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• v.24 no.5
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• IE interfaces
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• v.10 no.1
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• pp.169-187
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• 1997

Recently, in organizations many decisions are being made by groups. And the organization is changing a lot so are groups. To help decision making of changing groups, we need more flexible and more adaptive GDSS. Therefore one of the critical success factors of GDSS is flexibility and incremental improvement. Prior research on specifying design requirements of GDSS suggests generic design requirements. But they are too general to be incorporated directly into system design, because of the disparity between real group and ideal group that the researchers studied. Many design strategies that start from the generic design requirements thus have contingency variables that changes as the characteristics of group change. From the viewpoint of developers, these variables implicate the desirability of flexibility. To achieve flexibility we need new methodology of design and implementation. Nowadays, object-oriented analysis and design methodologies have been progressed to the point that many systems are being developed through these methodologies. In this paper, a design is proposed using Object-Oriented Modeling Techniques(OMT). Exploiting object-oriented paradigm results in a highly flexible and easily upgradable design.

• Journal of Information Technology Applications and Management
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• v.18 no.2
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• pp.61-89
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• 2011

Blog is a new global phenomenon, but many research papers about organizational blog have not been shown. In this study the influencing factors of the organizational blog usage are empirically investigated, and several guidelines are suggested to IT professionals who involves the design and implementation of the organizational blog. The research model consists of seven independent variables, one dependent variable, and two moderating variables. The following variables are established as the independent variables; information, interface, service, communication, enjoy, performance expectation, and social influence. Two dimensions such as need and orientation are suggested for the moderating variables, and the actual usage is adopted as a dependent variable. As a result of multiple regression analysis using a stepwise method, the independent variables except for interface and communication affect the actual usage of organizational blogs. The moderating effects for need and orientation are partially supported. The implications of this study are as the followings; (1) The empirical factors affecting the usage of organizational blogs are empirically investigated, (2) The affecting factors vary according to the type of organizational blogs, and (3) Some guidelines are suggested for organizational blog's design.

• Journal of Korean Academy of Nursing
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• v.44 no.2
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• pp.179-188
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• 2014

Purpose: This study was done to construct and test a structural model to explain health risk behavior of late adolescents. Methods: Data for this study were secondary data from the 2010 Korea Adolescent Health Survey based and 3,675 high school students who participated. Data were analyzed using SPSS 18.0 and AMOS 19.0 programs. Results: After 7 lines were removed, fitness statistics for the hypothetical model were appropriate (${\chi}^2$=559.13, p<.001, GFI=.98, SRMR=.03, RMSEA=.04, NFI=.88, IFI=.90, CFI=.90, TLI=.86, AIC=671.13). The result showed that drinking-smoking is directly affected by 5 variables (32.5%), obesity is directly affected by 2 variables (0.7%), lack of physical activity is directly affected by 5 variables (22.2%), skipping of breakfast is directly affected by 3 variables (11.9%), improper sleep is directly affected by 3 variables (7.5%), and psychological adaptation is directly affected by 4 variables (26.8%). Conclusion: The results of this study, indicate that late adolescents' health risk behavior is affected by many factors with complicate correlations suggesting further study compare youth health risk behaviors in a variety of environments.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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• pp.481-492
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• 1997

The purpose of this study is to construct validity of Tripartite model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses. Exploratory and confirmatory factor analyses are two major approaches to factor analysis. The primary goal of factor analysis is to explain the covariances or correlations between many observed variables by means of relatively few underlying latent variables. In exploratory factor analysis, the number of latent variables is not determined before the analysis, all latent variables typically influence all observed variables, the measurement errors(${\delta}$) are not allowed to correlate, and unidentification of parameters is common. Confirmatory factor analysis requires a detailed and identified initial model. Confirmatory factor analysis techniques allow relations between latent and observed variables that are not possible with traditional, exploratory factor analysis techniques. As a result of exploratory factor analysis, tripartite model of "Attitudes towards Science" being composed of affection, behavioral intention and cognition is empirically identified. But attitude of science career being composed of affection and behavioral intention is identified. In validity test using confirmatory factor analysis, measurement structure of Tripartite model of "Attitudes towards Science" is not correspondent to data set. Because it is concluded that the object of attitudes are not specific.

• Journal of The Korea Institute of Healthcare Architecture
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• v.23 no.1
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• pp.7-14
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• 2017

Purpose: Since the large hospitals are one of the most intensive energy users among building types in Korea, it is important to investigate and apply appropriate energy conservation measures. There are many researches on energy conservation measures for HVAC system in hospitals, but only few useful guidelines for envelope design variables were existed. The building envelope is one of the important factors to building energy consumption and patients' comfort. The purpose of this study is to suggest the most influential envelope design variables for each end-use energy demand. Methods: 100 samples were generated by LHS(Latin Hypercube Sampling) method. After energy performance simulation, global sensitivity analysis was performed by the regression method. DesignBuilder, Simlab 2.2 and JEPlus were used in this process. Results: The most influencing variables are SHGC, SHGC and VT for heating, cooling, and lighting, respectively. However, the most influencing variable for total energy demand is WWR(Window to Wall Ratio). The analysis was conducted based on the coefficient of variance results. Implications: The six envelop design variables were ranked according to the end-use energy demand.