• Journal of Korean Academy of Nursing Administration
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• v.4 no.1
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• pp.193-214
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• 1998

The purpose of the present study was to examine the causal model of nurses' organizational commitment. Based on literature review and Fishbein's behavioral intentions model ((Fishbein. 1967: Fishbein & Ajzen. 1975). the organizational commitment was conceptualized within a motivational framework that mediate between antecedents variables and outcome variables. Antecedent variables were pay, promotional chances. continuing education opportunity. rigidity of the administration. paticipative decision making, latitude, group support, role conflict, work load, need for achievement. experience and pride for professional nursing. Outcome variable was turnover intention. The subjects were 373 nurses who were working at 2 large general hospitals located in Seoul. It represents a response rate of 94%. Data for this study was collected from August 29 to September 22 in 1997 by Questionnaire. Path analysis with LISREL 7.16 prigram was used to test the fit of the proposed conceptual model to data and to examine the causal relationships among variables. The result showed that both the proposed model and the modified model fit the data excellently. It needs to be notified, however. that path analysis can not count measurment errors: measurement error can attenuate estimates of coefficient and explanatory power. Nontheless the model revealed considerable explanatory power for organizational commitment (58%), pride for professional nursing (50%) and turnover intention(40%). In predicting nurses' organizational commitment, the findings of this study clearly demonstrated 'the pride for professional nursing' might be the most important variables of all the antecedent variables. Group support, role conflict, need for achievement were also found to be important determinants for the organizational commitment and turnover intention, The result showed experience might be a predictor for 'pride for professional nursing' and 'turnover intention' but not 'organizational commitment', 'Rigidity of the administration' and latitude were also found to have important roles in predictingr the organizational commitment, while participative decision making might have an impact on turnover intention. On the other hand promotional chance had an influence on all the outcome variables, while pay only on turnover intention. In predicting turnover intention, the result clearly revealed 'the pride for professional nursing' and 'organizational commitment' might be the most powerful predictors among all the variables. Theses results were discussed, including directions for the future research and practical implications drawn from the research were suggested.

• Journal of Korean Academy of Nursing Administration
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• v.4 no.1
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• pp.183-214
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• 1998

The purpose of the present study was to examine the causal model of nurses' organizational commitment. Based on literature review and Fishbein's behavioral intentions model ((Fishbein, 1967;Fishbein & Ajzen. 1975), the organizational commitment was conceptualized within a motivational framework that mediate between antecedents variables and outcome variables. Antecedent variables were pay, promotional chances, continuing education opportunity, rigidity of the administration, paticipative decision making, latitude, group support, role conflict, work load, need for achievement, experience and pride for professional nursing. Outcome variable was turnover intention. The subjects were 373 nurses who were working at 2 large general hospitals located in Seoul. It represents a response rate of 94%. Data for this study was collected from August 29 to September 22 in 1997 by Questionnaire. Path analysis with LISREL 7.16 prigram was used to test the fit of the proposed conceptual model to data and to examine the causal relationships among variables. The result showed that both the proposed model and the modified model fit the data excellently. It needs to be notified, however, that path analysis can not count measurement errors; measurement error can attenuate estimates of coefficient and explanatory power. Nontheless the model revealed considerable explanatory power for organizational commitment (58%). pride for professional nursing (50%) and turnover intention(40%). In predicting nurses' organizational commitment. the findings of this study clearly demonstrated 'the pride for professional nursing' might be the most important variables of all the antecedent variables. Group support. role conflict, need for achievement were also found to be important determinants for the organizational commitment and turnover intention. The result showed experience might be a predictor for 'pride for professional nursing' and 'turnover intention' but not 'organizational commitment'. 'Rigidity of the administration' and latitude were also found to have important roles in predictor for the organizational commitment, while participative decision making might have an impact on turnover intention. On the other hand promotional chance had an influence on all the outcome variables, while pay only on turnover intention. In predicting turnover intention, the result clearly revealed 'the pride for professional nursing' and 'organizational commitment' might be the most powerful predictors among all the variables. Theses results were discussed, including directions for the future research and practical implications drawn from the research were suggested.

• Journal of the Korean School Mathematics Society
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• v.17 no.3
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• pp.337-358
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• 2014

The purpose of this study is to investigate how storytelling is embodied in the Mathematics I textbooks for first grade high school students in the 2009 revised curriculum and the perception of secondary math teachers and students of those books. Furthermore, in order to have some implications on newly ongoing textbook development, this thesis sets up the following goals for inquiry into the effect on storytelling. First, are there any noticeable differences among the 10 types of mathematics I textbooks for high school first graders in the 2009 revised curriculum? Second, what do teachers and students think of textbooks which apply storytelling techniques? The results are as follows. The frequency of storytelling types that appeared in the textbooks is as follows: real-life connection type and inter-scholarship type take up 47.55% and 24.51% respectively, followed by decision-making type with 10.52%, math history type with 10.17% and tool-using type with 7.05%. Within the contents, math history type showed up on reading material from every textbook. And it is worth considering that real-life-connection type has the most various topics and is mainly for arousing interest and checking up on some concepts. However, inter-scholarship type is usually related to science, and decision-making type is included for error analysis and tool-using type for reading materials about math programs. The results of this study suggest that many of the teachers who participated showed some kind of understanding of storytelling but there were not many who are actually incorporating that into their own classes. It is also essential that we develop textbooks that are effective for storytelling classes, hold regular symposiums as well as teacher training, and create tools for proper assessment. Furthermore, students think that textbooks based on storytelling would have positive effects as long as they are supported by enough time, a sufficient number of classes and tests with validity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1650-1662
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• 2014

This study aimed to develop smartphone application contents for the medication reconciliation of high-alert medications and to evaluate the satisfaction for this application. The xcode 4.5 and ios 6.1 SDK(software development kit) were used for constructing screen of the system. After implementation during 4 weeks, thirty five ICU(Intensive Care Unit) nurses were asked function related, contents related, and usage related satisfaction using 12 items. And differences of satisfaction according to the number of daily use and the frequency of use were evaluated. Data were analyzed using descriptive analysis, ANOVA with the SPSS 18.0. We developed the formula for drug dosage calculation, the alarming procedure, and the information of the high alert medication. In the satisfaction items, the mean score of 'This application is helpful to perform drug dosage calculation' was 3.14. However, 'I satisfy this application' was relatively low as 2.94. There were no differences in satisfaction according to the daily use and frequency of use. Based on the results of this study, more advanced smartphone application for medication reconciliation of high-alert medications will provide an important platform for patient safety.

• Korean Journal of Construction Engineering and Management
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• v.8 no.4
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• pp.167-175
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• 2007

A renovation project of apartment housing is generally divided into four stages: Project inquiry stage, planning and feasibility stage, design stage and construction stage. Currently, procedure and managerial technique for a remodeling project has not been systematically established so that many companies have difficulty in doing their business. In particular, the design stage in participant which a project managers, designers, residents and engineers is more complicate than the other stages in terms of roles and working procedure. The design documents can be found trial and error if it is not well managed. The insufficient design caused by lack of ability to efficiently utilize information obtained during the stage and inappropriate decision-making not only results in diverse problems in the construction stage but also makes people suspect satisfaction and reliability on the remodeling improvements. Therefore the design stage is very significant issue on the renovation project. As the consequence, the design stage of a remodeling project is so important that it should be carefully managed, and composite understanding of the process to be executed. To do this, we provide roles and responsibility of each participant in the design stage and analyze the process where information is utilized in design stage. The information is analyzed in terms of input, tool and output.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.246-251
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• 2017

Crop models have been used to predict a heading date for efficient management of fertilizer application. Recently, the ORYZA (v3) model was developed to improve the ORYZA2000 model, which has been used for simulation of rice growth in Korea. Still, little effort has been made to assess applicability of the ORYZA (v3) model to rice farms in Korea. The objective of this study was to evaluate reliability of heading dates predicted using the the ORYZA (v3) model, which would indicate applicability of the model to a decision support system for fertilizer application. Field experiments were conducted from 2015-2016 at the Rural Development Administration (RDA) to obtain rice phenology data. Shindongjin cultivar which is mid-late maturity type was grown under a conventional fertilizer management, e.g., application of fertilizer at the rate of 11 Kg N/10a. Another set of heading dates was obtained from annual reports at experiment farms operated by the National Institute of Crop Science and Agricultural Technology Centers in each province. The input files for the ORYZA (v3) model were prepared using weather and soil data collected from the Korean Meteorology Administration (KMA) and the Korean Soil Information System, respectively. Input parameters for crop management, e.g., transplanting date and planting density, were set to represent management used for the field experiment. The ORYZA (v3) model predicted heading date within 1 day for two seasons. The crop model also had a relatively small error in prediction of heading date for three ecotypes of rice cultivars at experiment farms where weather input data were obtained from a near-by weather station. Those results suggested that the ORYZA (v3) model would be useful for development of a decision support system for fertilizer application when reliable input data for weather variables become available.

• Korean Journal of Social Welfare Studies
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• v.49 no.4
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• pp.65-100
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• 2018

Korean Youth are facing diverse problems. For-instance Korean youth are even called '7 given-up generation' which indicates that they gave up marriage, giving birth, social relationship, housing, dream and the hope. From this point, the study concludes that the influential factors of the alcohol problems of youth should be studied based on the eco social perspectives. And it adopted data-mining methods, using SAS-Enterprise Miner for the analysis, targeting 2538 youths. Specifically, the study analyzed and chose the most predictable model using decision tree analysis, artificial neural network and logistic analysis. As the result, the study found that gender, age, smoking, spouse, family-number, jobsearching and economic participation are statistically significant determinants of alcohol problems of youth. Precisely, those who are male, younger, have the spouse, have less family number, searching jobs, have more income and have the job were more prone to have the alcohol problems. Based on the result, this study proposed the addiction problems targeting youth and etc. and expect to have the contribution on implementing procedures for the alcohol problems.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.90-103
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• 2018

This paper presents the development of two ANN models which can predict an optimum production rate by controlling choke size in oil well, and gas injection rate in gas-lift well. The input data was solution gas-oil ratio, water cut, reservoir pressure, and choke size or gas injection rate. The output data was wellhead pressure and production rate. Firstly, a range of each parameters was decided by conducting sensitive analysis of input data for onshore oil well. In addition, 1,715 sets training data for choke size decision model and 1,225 sets for gas injection rate decision model were generated by nodal analysis. From the results of comparing between the nodal analysis and the ANN on the same reservoir system showed that the correlation factors were very high(>0.99). Mean absolute error of wellhead pressure and oil production rate was 0.55%, 1.05% with the choke size model, respectively. And the gas injection rate model showed the errors of 1.23%, 2.67%. It was found that the developed models had been highly accurate.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.