• The Journal of Industrial Distribution & Business
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• v.12 no.1
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• pp.31-46
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• 2021

Purpose: The purpose of the study is to analyze the characteristics of emotional intelligence and the variables related to emotional intelligence in a comprehensive manner. In addition, the study intends to present research trends and future research directions of emotional intelligence in a Korean context by analyzing the effects of emotional intelligence and its mechanisms. Research Design, Data, and Methodology: 77 KCI listed studies were selected for the analysis, and the research perspective of emotional intelligence, measurement instruments, empirical research and research methods were analyzed. In addition, research directions were suggested based on the analysis results. Results: The results of the analysis were as follows: First, previous researchers used the ability model of emotional intelligence the most. Second, Previous studies tended to focus on behavioral factors as dependent variables affected by emotional intelligence, in addition to attitudes, affection. Third, there were few studies on the antecedents of emotional intelligence, however, most studies dealt with the consequences of emotional intelligence. Fourth, few studies dealt with moderators between emotional intelligence and dependent variables. Fifth, on the research type, most studies were quantitative studies, however, a few of them were qualitative studies (Literature review, in-depth interview). Sixth, with regard to the analysis level, almost all studies were conducted on the individual level of emotional intelligence, and most studies featured a cross-sectional research design (longitudinal research design was rare). Conclusion: First, from the perspective of emotional intelligence, additional research should be focused on not only the ability model of emotional intelligence but also on the trait model or the mixed model in the future. Second, since emotional intelligence is a multidimensional construct, it is necessary to study the profile of emotional intelligence by employing people-centered as well as variable-centered methods. Third, with regard to empirical studies, additional research is needed with respect to not only the emotional intelligence of the subordinate, but also the emotional intelligence of the supervisor (leader) and the emotional intelligence of the group. Fourth, it is necessary to actively utilize not only cross-sectional design but also longitudinal design, and qualitative research and meta-analysis methods should also be adopted.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.57-64
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• 2021

This study presents a method for realtime fuel consumption prediction using real data collected from OBDII. With the advent of the era of self-driving cars, electronic control units(ECU) are getting more complex, and various studies are being attempted to extract and analyze more accurate data from vehicles. But since ECU is getting more complex, it is getting harder to get the data from ECU. To solve this problem, the firmware was developed for acquiring accurate vehicle data in this study, which extracted 53,580 actual driving data sets from vehicles from January to February 2019. Using these data, the ensemble stacking technique was used to increase the accuracy of the realtime fuel consumption prediction model. In this study, Ridge, Lasso, XGBoost, and LightGBM were used as base models, and Ridge was used for meta model, and the predicted performance was MAE 0.011, RMSE 0.017.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.461-468
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• 2022

Development of the big data industry, the cases of providing data utilization services on digital platforms are increasing. In this regard, research in data-related fields is being conducted to apply the FAIR principle that can be applied to the assessment of (meta)data quality, service, and function to data quality evaluation. Especially, the European Open Data Portal applies an assessment model based on FAIR principles. Based on this, a data maturity assessment is conducted and the results are disclosed in reports every year. However, public data portals do not conduct data maturity evaluations based on metadata. In this paper, we propose and evaluate a new model for data maturity evaluation on a big data platform built for multiple domestic public data portals and data transactions, FAIR principles used for data maturity evaluation in Europe's open data portals. The proposed maturity evaluation model is a model that evaluates the quality of public data portal datasets.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.27-40
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• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.389-395
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• 2022

This study aims to develop a core competency model for adult college students. For this purpose, the core competencies of adult college students were derived by analyzing domestic and foreign literature studies. And the Delphi survey was conducted for the validity of core competencies. The SPSS 18.0 program was used for analysis. As results of the analysis, there are 11 core competencies of adult college students derived: communication, problem-solving and meta, interpersonal, personal management and development, digital information literacy, major knowledge, citizenship, convergence, character, professional job, and global. The core competency model of adult college students was developed by assigning numbers from 01 to 11 based on the results of the Delphi survey. Core competencies of adult college students were organically linked, so the cultivation of one core competency affects other core competencies.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.3
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• pp.343-351
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• 2016

Although the chemical, physical, and nutritional properties of bovine milk have been extensively studied, only a few studies have attempted to characterize milk-synthesizing genes using RNA-seq data. RNA-seq data was collected from 21 Holstein samples, along with group information about milk production ability; milk yield; and protein, fat, and solid contents. Meta-analysis was employed in order to generally characterize genes related to milk production. In addition, we attempted to investigate the relationship between milk related traits, parity, and lactation period. We observed that milk fat is highly correlated with lactation period; this result indicates that this effect should be considered in the model in order to accurately detect milk production related genes. By employing our developed model, 271 genes were significantly (false discovery rate [FDR] adjusted p-value<0.1) detected as milk production related differentially expressed genes. Of these genes, five (albumin, nitric oxide synthase 3, RNA-binding region (RNP1, RRM) containing 3, secreted and transmembrane 1, and serine palmitoyltransferase, small subunit B) were technically validated using quantitative real-time polymerase chain reaction (qRT-PCR) in order to check the accuracy of RNA-seq analysis. Finally, 83 gene ontology biological processes including several blood vessel and mammary gland development related terms, were significantly detected using DAVID gene-set enrichment analysis. From these results, we observed that detected milk production related genes are highly enriched in the circulation system process and mammary gland related biological functions. In addition, we observed that detected genes including caveolin 1, mammary serum amyloid A3.2, lingual antimicrobial peptide, cathelicidin 4 (CATHL4), cathelicidin 6 (CATHL6) have been reported in other species as milk production related gene. For this reason, we concluded that our detected 271 genes would be strong candidates for determining milk production.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.837-850
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• 2002

In this paper, we propose an EEG-based response time prediction method during a yes/no cognitive decision task. In the experimental task, a subject goes through responding of visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining CT (cut time), ST (selection time), and RP (repeated period). Based on the assumption between ST and RT in the mental model, we predict subjects' response time by detection of selection time. To recognize the subjects' selection time ST, we extract 3 types of feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, ${\gamma}$ waves in 4 electrode pairs combined by spatial relationships. From the extracted features, we construct specific rules for each subject and meta rules including common factors in all subjects. Applying the ST detection rules to 8 subjects gives 83% success rates and also shows that the subjects will hit a key in 0.73 seconds after ST detected. To validate the detection rules and parameters, we test the rules for 2 subjects among 8 and discuss about the experimental results. We expect that the proposed detection method can be a basic technology for brain-computer-interface by combining with left/right hand movement or yes/no discrimination methods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.3
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• pp.207-221
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• 2020

This study conducted a meta-analysis on the effects of innovation, risk-taking, and enterprising on entrepreneurship. From 2013 to 2020, 392 papers, which were judged as quantitative research from doctoral and master thesis, and academic journals published in Korea were selected as research subjects. 28 duplicates of thesis and thesis are excluded. A total of 52 papers were finally selected, excluding 312 papers that were insufficient to be used as research data because there were no statistical values such as correlation coefficients. For the 52 selected papers, the homogeneity of the variables was first verified. As a result of the homogeneity test, the innovativeness, risk-taking, initiative, and entrepreneurship education all showed great effects on heterogeneity, and the average effect size was analyzed by random effect model. The average effect size analyzed was 0.38 ~ 0.49, and all four variables showed moderate average effect size. As a result of analyzing the average effect size by forest plot, all showed proper results. From the results of funnel plot analysis of entrepreneurship education, published errors were confirmed asymmetric. Research data on entrepreneurship education shows that it cannot represent the whole. It is a structural equation model with entrepreneurship and entrepreneurial intention as a parameter. Iinnovation and risk-taking have an impact on entrepreneurship by taking entrepreneurship education as a parameter. Initiative had an effect on the entrepreneurial intention a business, regardless of entrepreneurship education. In a number of studies, university entrepreneurship education has had an impact on the entrepreneurial intention. It should be changed to entrepreneurship education that combines theory and practice. Entrepreneurship education should be transformed into continuous and field-oriented education.

• The Korean Journal of Pesticide Science
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• v.9 no.3
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• pp.199-204
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• 2005

The herbicidal activities against barnyardgrass (Echinochloa crus-galli) by R-groups on the hexahydroisoindol-1-one ring of new 2-(4-chloro-5-(2-chloroallyloxy)-2-fluorophenyl) -3-thioalkoxy-2,3,4,5,6,7-hexahydroisoindol-1-one derivatives were studied using molecular holographic quantitative structure-activity relationships (HQSAR) methodology. Based on the results, the statistical results of the optimised HQSAR model (I-2) exhibited the best predictability and fitness for the herbicidal activities based on the cross-validated value ($r^2_{cv.}$ or $q^2=0.714$) and non-cross-validated value ($r^2_{ncv.}=0.922$), respectively. From the based graphical analyses of atomic contribution maps, herbicidal activities against barnyardgrass were confirmed depends upon the C4-C6 atoms of hexahydroisoindoline-l-one ring, carbon atom of ortho-position and meta-methyl group of 3-tolylthio substituent (8).