• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.229-237
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• 2007

To solve of complex problems for access and management data stored in the very large spatial database system that need to constructed metadata for the physical and logical elements concerned with spatial and non-spatial data sets. Also, in the underground water management system which managed with spatial and non-spatial elements, need to spatial features metadata system based on feature-based. We, in this paper, proposed metadata model for the feature-based underground water management system using underground water meta-information inputted on the DXF formatted tile-based geological maps. Additional, we modeled metadata level of feature and data set and presented standard specification of underground water metadata.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.529-538
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• 2005

AUML (Agent Unified Modeling Language) is specification anger of agent software system, sight anger, language that do creation by purpose. Do so that may apply Together that is one of automation application program creation system to Agent's BDI in trend sophistication of software, large size Tuesday in this research and investigate this about operation between component system. Standard detailed statement (FIPA:Foundation for Inteligent Physical Agent) that use can consist by data exchange between component and cooperate each other even if type of component is different mutually to base ACL message, and protocole use and study about method and accuracy and consistency that minimize error when embody this using meta model base etc.. through object intention modelling.

• Journal of Veterinary Clinics
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• v.40 no.4
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• pp.243-259
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• 2023

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Advances in nano research
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• v.13 no.5
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• pp.499-512
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• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.205-213
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• 2024

This study uses a large language model (LLM) to identify Aristotle's rhetorical principles (ethos, pathos, and logos) in COVID-19 information on Naver Knowledge-iN, South Korea's leading question-and-answer community. The research analyzed the differences of these rhetorical elements in the most upvoted answers with random answers. A total of 193 answer pairs were randomly selected, with 135 pairs for training and 58 for testing. These answers were then coded in line with the rhetorical principles to refine GPT 3.5-based models. The models achieved F1 scores of .88 (ethos), .81 (pathos), and .69 (logos). Subsequent analysis of 128 new answer pairs revealed that logos, particularly factual information and logical reasoning, was more frequently used in the most upvoted answers than the random answers, whereas there were no differences in ethos and pathos between the answer groups. The results suggest that health information consumers value information including logos while ethos and pathos were not associated with consumers' preference for health information. By utilizing an LLM for the analysis of persuasive content, which has been typically conducted manually with much labor and time, this study not only demonstrates the feasibility of using an LLM for latent content but also contributes to expanding the horizon in the field of AI text extraction.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.904-910
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• 2014

The D&M(DeLone and McLean) IS(Information System) Success model has been widely studied in various criteria such as e-commerce and education, and many empirical research has been performed enough to execute meta-analysis from original model developed in 1992 to updated model in 2003. However, almost all researches have been validated end-user point of view those could lead IS success factors perceived by users but not applicable to developers because developers could not recognize from those result that how much gap exist between one's perception and users. Therefore, in this research, we apply D&M IS success model to A bank developers and users and compare perceptions by multi-group structural equation model.

• Korea Journal of Hospital Management
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• v.21 no.1
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• pp.14-31
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• 2016

Obesity of adolescents causes mental and physical problems as well as social problems, which need prevention and management. Although a number of systematic reviews and meta-analyses on obesity programs for adolescents were conducted, there is no study evaluating the programs based on CCM(Chronic Care Model), an organizing framework for improving chronic illness care. This study was conducted to review the features of studies in obesity management programs and interventions of the selected studies were evaluated in terms of inclusion of components of the Chronic Care Model. 4 databases were searched for relevant studies in obesity management programs, which were published from 1994 to 2014 in Korea. Results were analyzed in a qualitative way. 14 studies were satisfied inclusion criteria. The interventions most frequently utilized the elements of self-management support(66.7%) and only 1 of the studies included more than three components of CCM. This study presents the direction of health policies about managements of metabolic syndrome, which means that we identified effective process of the obesity management programs for adolescents in Korea and also this study will be used as a basic information for the development of obesity management program.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.81-87
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• 2010

In order to calculate the accurate runoff from a basin, nonlinearity in the relationship between rainfall and runoff has to be considered. Many runoff calculation models assume the linearity in the relationship or are too complicated to be analyzed. Therefore, the storage function method has been used in the prediction of flood because of the simplicity of the model. The storage function method has five parameters with related to the basin and rainfall characteristics which can be estimated by the empirical trial and error method. To optimize these parameters, regression method or optimization techniques such as genetic algorithm have been used, however, it is not easy to optimize them because of the complexity of the method. In this study, the metamodel is proposed to estimate those model parameters. The metamodel is the combination of artificial neural network and genetic algorithm. The model is consisted of two stages. In the first stage, an artificial neural network is constructed using the given rainfall-runoff relationship. In the second stage, the parameters of the storage function method are estimated using genetic algorithm and the trained artificial neural network. The proposed metamodel is applied in the Peong Chang River basin and the results are presented.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.653-660
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• 2008

Recently, Service-oriented computing is the emerging computing paradigm. In this paradigm, we require the practical process model to support service oriented software development. The well-known development methods e.g., Unified Software Development Process, UML components have been proposed focused on component. So, these methods cannot support service-oriented computing concepts such as service definition, binding and composition concepts using Business Process Execution Language (BPEL). Also, a few proposed service-oriented approach, for example Service Oriented Unified process (SOUP), and Service Oriented Modeling and Architecture (SOMA) have appeared. However, these approaches do not explicitly represent detailed guideline, artifacts and approach. Therefore, in this paper we propose a practical and simple process model to support service oriented software development. Also, we explicitly represent process model and artifact using Software Process Engineering Metamodel (SPEM) which is proposed by OMG. By using our approach, it can enhance systematization and effectiveness for service-oriented software development.