• The Korean Journal of Health Service Management
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• v.14 no.2
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• pp.1-14
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• 2020

Objectives : The purpose of this study was to test fitness of the structured model of SNS activities for health information. Methods : A structured questionnaire were administered to 500 subjects. A structural equation model was applied to collected data. Results : The response rate was 73.9%. The respondents mostly used Facebook and KakaoStory. They spent 70 minutes per day and 21~30% of this usage was taken by health information. In the variances, those who has religion more actively exchanged information about diseases and medical institutions. The goodness-of-fit of the model was .81(GFI) and .90(CFI). The main path was bridging capital -> bonding capital -> credibility -> SNS activities for health information. The path from quality of sharing information to SNS activities was not significant. It could be explained by the restriction of digital literacy. Conclusions : SNS activities for health information were determined by credibility, currency and bonding social capital. Bridging social capital, indirectly, influenced SNS activities through bonding social capital. Thus building bonding social capital would be a critical success factor for SNS.

• Genomics & Informatics
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• v.20 no.3
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1097-1107
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• 2009

In this paper, three species stage-structured predator-prey model with time delayed and periodic constant impulsive perturbations of predator at fixed times is proposed and investigated. We show that the conditions for the global attractivity of prey(pest)-extinction periodic solution and permanence of the system. Our model exhibits a new modelling method which is applied to investigate impulsive delay differential equations. Our results give some reasonable suggestions for pest management.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.51-54
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• 1990

A parametrization for a linear system is presented to design a direct model reference adaptive pole placement controler. This parametrized model is one of the structured nonminimal models. The exponentially weighted least-squres algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference models.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.14-20
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• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1095-1106
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• 2014

Symbolic data analysis extends the data mining and exploratory data analysis to the knowledge mining, we can suggest the SDA tree model on clinical and genomic data with new knowledge mining SDA approach. Using SDA application for huge genomic SNP data, we can get the correlation the availability of understanding of hidden structure of HCC data could be proved. We can confirm validity of application of SDA to the tree structured progression model and to quantify the clinical lab data and SNP data for early diagnosis of HCC. Our proposed model constructs the representative model for HCC survival time and causal association with their SNP gene data. To fit the simple and easy interpretation tree structured survival model which could reduced from huge clinical and genomic data under the new statistical theory of knowledge mining with SDA.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.573-584
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• 2003

Recent massive data generation by genomics and proteomics requires bioinformatic tools to extract the biological meaning from the massive results. Here we introduce ROSPath, a database system to deal with information on reactive oxygen species (ROS)-mediated cell signaling pathways. It provides a structured repository for handling pathway related data and tools for querying, displaying, and analyzing pathways. ROSPath data model provides the extensibility for representing incomplete knowledge and the accessibility for linking the existing biochemical databases via the Internet. For flexibility and efficient retrieval, hierarchically structured data model is defined by using the object-oriented model. There are two major data types in ROSPath data model: ‘bio entity’ and ‘interaction’. Bio entity represents a single biochemical entity: a protein or protein state involved in ROS cell-signaling pathways. Interaction, characterized by a list of inputs and outputs, describes various types of relationship among bio entities. Typical interactions are protein state transitions, chemical reactions, and protein-protein interactions. A complex network can be constructed from ROSPath data model and thus provides a foundation for describing and analyzing various biochemical processes.

• Journal of Engineering Education Research
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• v.1 no.1
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• pp.5-20
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• 1998

In this paper, the traditional curriculum model for engineering education, called simple hierarchical model, is critically reviewed and the problems of this curriculum model are discussed. Then a new curriculum model for engineering education is proposed to overcome the problems of simple hierarchical model and to provide an innovative education for engineering students. This new curriculum model for engineering education, called $\Pi$-structure, consists of the General Attainments Program, the Fundamental Major Program, and the Advanced Major Program, with the three components stacked in $\Pi$ shape where the first two programs are put in parallel and the third program are put on their top. The necessity, feasibility and various benefits of the $\Pi$-structured education system are widely discussed in the paper.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.232-243
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• 2007

Metadata IR model has high precision and low recall because the query in Metadata IR model is strict that is, the query can express user information need exactly, while Full-text IR model has low precision and high recall because the query in Full-text IR model is a kind of simple keyword query which expresses user information need roughly. If user can translate one's information need into structured query well, the retrieval result will be improved. However, it is little possible to make relevant query without understanding characteristics of metadata. Unfortunately, most users do not interested in metadata, then they cannot construct well-made structured query. Amount of information contained in metadata is less than text information. In this paper, we suggest hybrid IR model using metadata and text which can provide users with lots of relevant documents by retrieving from metadata field and text field complementarily.

• Journal of Industrial Convergence
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• v.22 no.3
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• pp.121-135
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• 2024

This study is a structural equation modeling study that describes patient safety incident management activities for nursing students with clinical practice experience and uses Ajzen's theory of planned behavior and safety culture climate-safety behavior model as conceptual bases, proposes a hypothetical model of nursing students' patient safety incident management activities based on the literature review, and verifies the appropriateness of the model and hypotheses through the collected data. Data were collected from 251 nursing students with clinical practice experience using a structured questionnaire. The results of this study confirmed that the model is appropriate and that patient safety management attitude, patient safety culture, and safety motivation are predictors of nursing students' patient safety management activities. Therefore, in order to improve patient safety management activities, it is necessary to provide effective patient safety incident management education programs for nursing students so that nursing students can perform correct patient safety management behaviors from the clinical practice site to the clinical practice site after graduation, and it is necessary to explore how to continuously lead such education programs to the practice site.