• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.127-133
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• 2003

The data models which express all types of video information physically and logically. and the definition of spatiotemporal relationship of video data objects In This paper, we classifies meta-model for efficient management on spatiotemporal relationship between two objects in video image data, suggests meta-models based on Rambaugh's OMT technique, and expanded user model to apply the adaptive model, established from hyper-media or web agent to VOD. The proposed meta-model uses data's special physical feature: the effects of camera's and editing effects of shot, and 17 spatial relations on Allen's 13 temporal relations, topology and direction to include logical presentation of spatiotemporal relation for possible spatiotemporal reference and having unspecified applied mediocrity.

• Korean Journal of Microbiology
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• v.38 no.4
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• pp.245-245
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• 2002

Catabolic pathways for the degradation of various aromatics by Sphingomonas yanoikuyae Bl are intertwined, joining at the level of substituted benzoates, which are further degraded vita ring cleavage reactions. The mutant strain EK497, which was constructed by deleting a large DNA region containing most of the genes for biphenyl, naphthalene, m-xylene, and m-toluate degradation, was unable to grow on all of the aromatics tested except for benzoate as the sole source of carbon and energy.S. yanoikuyae EK497 was found to possess only catechol ortho-ring cleavage activity due to deletion of the genes for the meta-cleavage pathway. Wild-type S. yanoikuyae Bl grown on benzoate has both catechol orthoand meta-cleavage activity. However, m-xylene and m-toluate, which are metabolized through methylbenzoate, and biphenyl, which is metabolized through benzoate, induce only the meta-cleavage pathway, suggesting the presence of a substrate-dependent induction mechanism.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.51-52
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• 2013

In this paper, circularly polarized microstrip for the S-band with a center frequency of 3.5GHz have been studied using the meta-structure of the resonator Designed antenna using CST studio, the meta-structure resonator was etched in the ground plane for miniaturization and circulary polarization was implemented with slot in center of the patch.

• International Journal of Contents
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• v.16 no.3
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• pp.18-32
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• 2020

The aim of this study was to perform a meta-analysis of the learning outcomes of immersive learning technologies in English language teaching (ELT). This study examined 12 articles, yielding a total of 20 effect sizes. The Comprehensive Meta-Analysis (CMA) program was employed for data analysis. The findings revealed that the overall effect size was 0.84, implying a large effect size. Additionally, the mean effect sizes of the dependent variables revealed a large effect size for both the cognitive and affective domains. Furthermore, the study analyzed the impact of moderator variables such as sample scale, technology type, tool type, work type, program type, duration (sessions), the degree of immersion, instructional technique, and augmented reality (AR) type. Among the moderators, the degree of immersion was found to be statistically significant. In conclusion, the study results suggested that immersive learning technologies had a positive impact on learning in ELT.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.153-158
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• 2024

This study aims to summarize the effects of Arduino-based educational programs through a meta-analysis. Eleven eligible primary studies were obtained through a systematic literature review and coded accordingly. The results are as follows: The meta-analysis revealed that the overall effect size for all the studies was 0.518. Analysis of the moderator variables indicated statistically significant differences between them. Regarding the learning domains, the results were ranked in descending order of the cognitive and affective domains. Within the cognitive domain, the effect sizes were ranked in descending order as follows: logical thinking, content knowledge, convergence competency, self-efficacy, computational thinking, and creative problem-solving skills. In terms of subject areas, the descending order of effect sizes was agriculture, STEAM, environmental science, practical arts, artificial intelligence, informatics, and computers. Regarding school level, the results were ranked in the following descending order: college, elementary school, middle school, and high school.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.257-262
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• 2014

Meta-analysis is a statistical integration method that delivers an opportunity to overview the entire result by integrating and analyzing many quantitative research results. This study will find meaningful mediator variables for criterion variables that affect purchase and repurchase intentions in e-commerce, on the basis of the results of a meta-analysis. We reviewed a total of 114 e-commerce studies published in Korean journals between 2000 and 2014, where a cause and effect relationship is established between variables that are specified in the conceptual model of this study. In this meta-analysis, the path between trust and purchase intention showed the biggest effect size. The second biggest effect size was found in the path between commitment and purchase intention, while the smallest one was obtained with perceived. Thus, we present the theoretical and practical implications of these results and discuss the differences among these results through a comparative analysis with previous studies.

• Journal of Pharmaceutical Investigation
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• v.40 no.4
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• pp.225-230
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• 2010

The particulate contamination of intravenously administered fluid has been of major concern. One of the most common complications associated with long term i.v. therapy is post-infusion phlebitis (PIP). We undertook a systematic review and meta-analysis of the effect of inline filters on PIP. An electronic search of Medline, KoreaMed, and KRIST was conducted to identify randomized controlled trials evaluating the effect of inline filters. Meta-analysis was undertaken using STATA 10. A total of 62 literatures were retrieved, of which 7 were included in meta-analysis. Inline filtration for intravenous infusion significantly reduced by 39% of the incidence of phlebitis, with a relative risk of 0.61 (95% CI 0.41-0.90, p=0.012). Therefore, inline filtration is a highly effective means of decreasing the incidence of infusion phlebitis and should be considered as a part of intravenous therapy.

• Quality Improvement in Health Care
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• v.22 no.2
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• pp.27-40
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• 2016

Purpose: Globally, falls are a major public health problem. The study aimed to evaluate the predictive validity of the Timed Up and Go test (TUGT) as a screening tool for fall risk. Methods: An electronic search was performed Medline, EMBASE, CINAHL, Cochran Library, KoreaMed and the National Digital Science Library and other databases, using the following keywords: 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Thirteen studies were analyzed using meta-analysis with MetaDisc 1.4. Results: The selected 13 studies reporting predictive validity of TUGT of fall risks were meta-analyzed with a sample size of 1004 with high methodological quality. Overall predictive validity of TGUT was as follows. The pooled sensitivity 0.72 (95% confidence interval [CI]: 0.67-0.77), pooled specificity 0.58 (95% CI: 0.54-0.63) and sROC AUC was 0.75 respectively. Heterogeneity among studies was a moderate level in sensitivity. Conclusion: The TGUT's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, TGUT is an appropriate tool to apply to all patients at a potential risk of accidental fall in a hospital or long-term care facility.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.30 no.3
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• pp.166-181
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• 2017

Objectives : The study was done to verify the relation between early-onset androgenetic alopecia(AGA) and metabolic syndrome(MetS). Methods : Data were collected through electronic database including KoreaMed, National Assembly Library, KMBASE, NDSL, KCI, KERIS, Google Scholar, Pubmed, Cochrane CENTRAL and EBSCO MEDLINE. A total of 13 case-control studies related to the MetS of early-onset alopecia patients were used for the systematic review and meta-analysis. Risk of bias of included studies were assessed by RoBANS tool. RevMan5.3, CMA3 were used for the meta-analysis. Results : In 13 evaluated articles, most frequent bias was the participant selection bias that was found in 10 articles. Significant association between early-onset AGA and MetS was found in 10(76.9%) out of 13 articles in the systematic review. In meta-analysis, early-onset male AGA was associated with increased risk of metabolic syndrome(OR: 3.73, 95% CI:2.49 -5.61). Conclusions : AGA, particularly early -onset male AGA, is significantly associated with MetS. Therefore all patients with early onset male AGA should be suggested to take preventive treatment to reduce the risk of MetS and various problems associated with it.