• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.429-437
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• 2022

Recently, the demand for a QA (Question Answering) system for human-machine communication has increased. Among the QA systems, a closed domain QA system that can handle spatial-related questions is called GeoQA. In this study, a new type of graph database, LPG (Labeled Property Graph) was used to overcome the limitations of the RDF (Resource Description Framework) based database, which was mainly used in the GeoQA field. In addition, GraphQL (Graph Query Language), an API-type query language, is introduced to address the fact that the LPG query language is not standardized and the GeoQA system may depend on specific products. In this study, database was built so that answers could be retrieved when spatial-related questions were entered. Each data was obtained from the national spatial information portal and local data open service. The spatial relationships between each spatial objects were calculated in advance and stored in edge form. The user's questions were first converted to GraphQL through FOL (First Order Logic) format and delivered to the database through the GraphQL server. The LPG used in the experiment is Neo4j, the graph database that currently has the highest market share, and some of the built-in functions and QGIS were used for spatial calculations. As a result of building the system, it was confirmed that the user's question could be transformed, processed through the Apollo GraphQL server, and an appropriate answer could be obtained from the database.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.7-22
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• 2022

Objective : This study aims to provide clinical evidence by systematically reviewing domestic CO-OP intervention studies. Methods : 13 papers suitable for selection criteria were finally selected and conducted. The analysis was divided into general characteristics and qualitative levels. Results : As a result of analyzing the contents of study, research has been conducted continuously since 2015, and the level of evidence for CO-OP intervention study was mainly single subject design. The subjects of the study were applied to both children and adults, and as the target activity areas of CO-OP intervention, children were shown to be play and leisure, and adults were instrumental daily living. COPM/PQRS were frequently used for evaluation to examine effectiveness of interventions. For domain specific strategies, children used body position, attention to doing, task specification, and verbal motor mnemonic. For adults, body position, attention to doing, task specification, and feeling to movement, verbal motor mnemonic, verbal rote script were used. Conclusion : It was found that CO-OP intervention is continuously being applied in occupational therapy, and age and diagnosis group are expanding. It is significant in that it provided evidence for implementing CO-OP interventions in clinical practice through a systematic review of domestic CO-OP intervention studies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.192-207
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• 2023

Commercialization of level-4 (Lv.4) autonomous driving applications requires the definition of a safe road environment under which autonomous vehicles can operate safely. Thus, a risk assessment model is required to determine whether the operation of autonomous vehicles can provide safety to is sufficiently prepared for future real-life traffic problems. Although the risk factors of autonomous vehicles were selected and graded, the decision-making method was applied as qualitative data using a survey of experts in the field of autonomous driving due to the cause of the accident and difficulty in obtaining autonomous driving data. The fuzzy linguistic representation of decision-makers and the fuzzy analytic hierarchy process (AHP), which converts uncertainty into quantitative figures, were implemented to compensate for the AHP shortcomings of the multi-standard decision-making technique. Through the process of deriving the weights of the upper and lower attributes, the road alignment, which is a physical infrastructure, was analyzed as the most important risk factor in the operation risk of autonomous vehicles. In addition, the operation risk of autonomous vehicles was derived through the example of the risk of operating autonomous vehicles for the 5 areas to be evaluated.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.179-188
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• 2022

This study discovers the general characteristics of B-class Emotional Advertisements and analyses their trend. A content analysis was conducted on 498 advertisements on-aired between 2015 and 2020, which were sampled from the advertisements registered in the TVCF(www.tvcf.co.kr), the largest advertisement web portal in the Republic of Korea. The analysis concludes that the B-class Emotional Advertisements, employed in a wide range of genre, is most incorporated in comedy/exaggeration genres and is on a rising trend due to 2020 COVID-19 Pandemic. Furthermore, it is confirmed that the utilization of B-class emotional advertisement has also increased in domain of non-commercial advertisements, such as Public Service Advertisements, Governmental/Organizational Advertisements, and Corporate Public Relations (PR) Advertisements. The study validates the transformation of the B-class emotional advertisements from a demonstration of an eccentric minority subculture to an epitome of a new and adventurous mainstream culture, successfully serving a central role in both the commercial and non-commercial sectors. Depicting the caricatures of the social, cultural and economic phenomenon and the recent surge of individual's depression, fatigue and pessimism, B-class emotional advertisements provide sympathetic and emotional alleviating ground for people that contributed to its rise.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Animal Bioscience
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• v.36 no.10
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• pp.1517-1529
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• 2023

Objective: The objective of this study was to investigate the phylogenetic and expression analysis of the angiopoietin-like (ANGPTL) gene family and their role in lipid metabolism in pigs. Methods: In this study, the amino acid sequence analysis, phylogenetic analysis, and chromosome adjacent gene analysis were performed to identify the ANGPTL gene family in pigs. According to the body weight data from 60 Jinhua pigs, different tissues of 6 pigs with average body weight were used to determine the expression profile of ANGPTL1-8. The ileum, subcutaneous fat, and liver of 8 pigs with distinct fatness were selected to analyze the gene expression of ANGPTL3, ANGPTL4, and ANGPTL8. Results: The sequence length of ANGPTLs in pigs was between 1,186 and 1,991 bp, and the pig ANGPTL family members shared common features with human homologous genes, including the high similarity of the amino acid sequence and chromosome flanking genes. Amino acid sequence analysis showed that ANGPTL1-7 had a highly conserved domain except for ANGPTL8. Phylogenetic analysis showed that each ANGPTL homologous gene shared a common origin. Quantitative reverse-transcription polymerase chain reaction analysis showed that ANGPTL family members had different expression patterns in different tissues. ANGPTL3 and ANGPTL8 were mainly expressed in the liver, while ANGPTL4 was expressed in many other tissues, such as the intestine and subcutaneous fat. The expression levels of ANGPTL3 in the liver and ANGPTL4 in the liver, intestine and subcutaneous fat of Jinhua pigs with low propensity for adipogenesis were significantly higher than those of high propensity for adipogenesis. Conclusion: These results increase our knowledge about the biological role of the ANGPTL family in this important economic species, it will also help to better understand the role of ANGPTL3, ANGPTL4, and ANGPTL8 in lipid metabolism of pigs, and provide innovative ideas for developing strategies to improve meat quality of pigs.

• Asia Marketing Journal
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• v.10 no.2
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• pp.71-98
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• 2008

Considerably many numbers of studies on country-of-origin(hereafter COO) effects have been presented in international business and marketing areas. Recent studies have been included the effects of COO of manufacture, parts, and design, as well as the effects of brand origin, reflected by the accelerating convergent manufacture circumstances and increasingly competitive environments. Moderating constructs such as knowledge of product category and involvement as individual variables, have been also introduced and researched in various angles. In addition, how the effects of COO occur as processes is also argued in previous studies. This research has attempted to explain business corporation's strategic decisions on choosing a domain of its product manufacturing for several critical reasons, for cost reduction or better image. We displayed two constructs of brand and manufacture in a positive and negative country image group to reconfirm the existence of the effects of COO. Additionally, the effects of respondents' regulatory fit between their motivational focus and the contents of product messages, have been declared. Furthermore the respondents' motivational focus moderates the main effect of COO on product evaluations in a positive 'made-in' combination, while, surprisingly, it does not statistically moderate in a negative, except attitude. Based on the results, implications and suggestions on how to plan and execute more effective marketing strategies regarding COO dimensions, especially COO of manufacture, are separately presented for each situations when it has already been determined and when it is to be.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.505-512
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• 2022

The purpose of this study is to synthesize effects of the living lab-based education through meta-analysis. Seven primary studies reporting the effect of living lab-based education were carefully selected for data analysis. Research questions are as follows. First, what is the overall effect size of the living lab-based education? The overall effect size refers to the effect on the cognitive and affective domains. Second, what is the effect size of the living lab-based education according to categorical variables? Categorical variables are outcome characteristics, study characteristics, and design characteristics. Results are summarized as follows. First, the overall effect size of living lab-based education was 0.347. Second, the effect size according to the cognitive domain was 1.244 for information process, 0.593 for communication, 0.261 for problem solving, and 0.26 for creativity. Third, the effect size according to subject area was shown in the order of electrical and electronic engineering 1.146, technology and home economics 0.489, artificial intelligence 0.379, and practical arts 0.168. Fourth, the effect size according to school level was 1.058 for high school, 0.312 for middle school, and 0.217 for elementary school. Fifth, the effect size by grade level was 0.295 when two or more grades were integrated and 0.294 for a single grade.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.182-199
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• 2023

Since 2021, government departments have been promoting Automated Driving Technology Development and Innovation Project as national research and development(R&D) project. The automated vehicles and service technologies developed as part of these projects are planned to be subsequently provided to the public at the selected Living Lab City. Therefore, it is important to determine a spatial area and operation section that enables safe and stable automated driving, depending on the purpose and characteristics of the target service. In this study, the static Operational Design Domain(ODD) elements for Level 4 automated driving services were reclassified by reviewing previously published papers and related literature surveys and investigating field data. Spatial analysis techniques were used to consider the reclassified ODD elements for level 4 in the real area of level 3 automated driving services because it is important to reflect the spatial factors affecting safety related to real automated driving technologies and services. Consequently, a total of six driving mode changes(disengagement) were derived through spatial information analysis techniques, and the factors affecting the safety of automated driving were crosswalk, traffic light, intersection, bicycle road, pocket lane, caution sign, and median strip. This spatial factor analysis method is expected to be useful for determining special areas for the automated driving service.