• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5A
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• pp.849-859
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• 2006

This paper presents an extensive robust analysis of a ${\mu}$-controller in the hybrid system for various uncertainties using the benchmark cable-stayed bridge. The overall system robustness may be deteriorated by introducing active devices and the active controller may cause instability due to small margins. Therefore, a ${\mu}$-synthesis method that simultaneously guarantees the performance and stability of the closed-loop system (robust performance) with uncertainties is used for active devices to enhance the robustness in company with the inherent reliability of passive devices. The robustness of the ${\mu}$-synthesis method is investigated with respect to the additional mass on the deck, structural stiffness matrix perturbation, time delay of actuator, and combinations thereof. Numerical simulation results show that the proposed control system has the good robustness without loss of control performances with respect to various uncertainties under earthquakes considered in this study. Furthermore, the control system robustness is more affected by the perturbation of structural stiffness matrix than others considered in this study. Therefore, the hybrid system controlled by a ${\mu}$-synthesis method could be proposed as an improved control strategy for a seismically excited cable-stayed bridge containing many uncertainties.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.185-193
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• 2019

Young children facilitate their social and emotional development by doing a lot of play activities with their peers. As an empirical survey study, this study is aimed at analyzing how the peer play interaction influences peer competency and self-regulation. To achieve that, a questionnaire survey was conducted with 271 young children aged 5 years who were going to the kindergarten in the G district of Seoul. With the data collected in the survey, frequency analysis, descriptive statistics, reliability test, correlation analysis and regression analysis were conducted by SPSS program. The analysis results are presented as follows: firstly, peer play interaction positively influenced young children's sociability, pro-sociality, and leadership as their peer competency factors; secondly, peer play interaction positively influenced their patience, endurance of waiting, and adaptation as their self-regulation factors. This study drew the conclusion that peer play interaction is a critical variable to predict young children's peer competency and self-regulation. Therefore, it will be necessary to continuously develop a variety of play activity programs which young children can join in the inside and outside of kindergarten in order to helps young children improve their peer competency and self-regulation, and to actively connect the programs with Nuri curriculum.

• Asia Marketing Journal
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• v.11 no.3
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• pp.91-124
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• 2009

With the intensity of competition and the standardization of technical attributes in telecommunications service market increasing, differentiated activity and customer experience in service encounter is regarded as an important means for creating customer value, however, there is a dearth of good literature examining what MOT activity is composed of according to consumption chain, and how service quality of MOT has influenced customer performance. Especially there exist various services across different phase of Product life cycle(PLC) in household telecommunication service market, customer requirement for MOT might depend on whether its phase is introduction-growth stage or maturity-decline stage, the empirical study is completely lacking. This study classified household telecommunication services into two types by PLC, VOIP and IPTV as Introduction-growth stage services, Internet and PSTN as maturity-decline stage service, and investigated whether there exists a gap between service types in how consumer have experienced MOT, what they consider as important and the relative importance of quality dimension how service quality of MOT has influence on consumer performance. The empirical result from 858 participants shows that there is a difference in consumer experience and requirements across different phases of the PLC, tangibles and assurance are regarded as the most important service quality factors which have a positive influence on customer performance (consumer satisfaction, repurchase intention and word of mouth) at the introduction-growth stage, whereas, reliability, empathy and interactivity are at the maturity-decline stage. Finally, managerial implication is made, limitation is clarified and a direction for further studies is suggested.

• Economic and Environmental Geology
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• v.56 no.6
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• pp.887-897
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• 2023

Leading waste disposal countries, such as Sweden, Switzerland, and the United Kingdom, conduct safety assessments across all stages of High-Level Radioactive Waste Deep Geological Disposal Facilities-from planning and site selection to construction, operation, closure, and post-closure management. As safety assessments are repeatedly performed at each stage, generating vast amounts of diverse data over extended periods, it is essential to construct a database for safety assessment and establish a data management system. In this study, the safety assessment data management systems of leading countries, were analyzed, categorizing them into 1) input and reference data for safety assessments, 2) guidelines for data management, 3) organizational structures for data management, and 4) computer systems for data management. While each country exhibited differences in specific aspects, commonalities included the classification of safety assessment input data based on disposal system components, the establishment of organizations to supply, use, and manage this data, and the implementation of quality management systems guided by instructions and manuals. These cases highlight the importance of data management systems and document management systems for securing the safety and enhancing the reliability of High-Level Radioactive Waste Disposal Facilities. To achieve this, the classification of input data that can be flexibly and effectively utilized, ensuring the consistency and traceability of input data, and establishing a quality management system for input data and document management are necessary.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.83-92
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• 2023

The purpose of this study is to provide a rational management strategy to improve the management performance of companies through intellectual property activities. Through this study, we aim to explore countermeasures to strengthen competitiveness in a changing global environment. A survey of 200 companies was conducted from September 1 to October 30, 2023. Statistical analysis was conducted using frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, multiple regression analysis, and difference analysis. The conclusions are as follows. First, the impact of intellectual property activities on management performance was found to be creation and utilization. Second, the impact of management strategies on management performance was found to be differentiation strategy, cost advantage strategy, and concentration strategy. Third, cost advantage strategy has a partial mediation effect on the relationship between creation activities and managerial performance of intellectual property activities. Fourth, the differentiation strategy has a partial mediating effect on the relationship between the creation of intellectual property activities and managerial performance. In addition, differentiation strategy has a full mediating effect on the relationship between the utilization of intellectual property activities and performance. Fifth, concentration strategy has a partial mediating effect on the relationship between intellectual property activity utilization and management performance. Sixth, there is a difference between creation activities, protection activities, utilization activities, cost advantage strategy, differentiation strategy, financial performance, and non-financial performance based on venture certification status. As the importance of intellectual property is increasing in the era of technological hegemony, IoT companies will need to improve their management performance through venture certification and strategies utilizing intellectual property in order to secure future competitiveness. Based on this study, we hope that IoT companies will maximize their performance by implementing efficient strategies that consider IP activities.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.53-63
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• 2024

Biodiversity holds significant importance within the framework of environmental impact assessment, being utilized in site selection for development, understanding the surrounding environment, and assessing the impact on species due to disturbances. The field of environmental impact assessment has seen substantial research exploring new technologies and models to evaluate and predict biodiversity more accurately. While current assessments rely on data from fieldwork and literature surveys to gauge species richness indices, limitations in spatial and temporal coverage underscore the need for high-resolution biodiversity assessments through species richness mapping. In this study, leveraging data from the 4th National Ecosystem Survey and environmental variables, we developed a species distribution model using Random Forest. This model yielded mapping results of 24 mammalian species' distribution, utilizing the species richness index to generate a 100-meter resolution map of species richness. The research findings exhibited a notably high predictive accuracy, with the species distribution model demonstrating an average AUC value of 0.82. In addition, the comparison with National Ecosystem Survey data reveals that the species richness distribution in the high-resolution species richness mapping results conforms to a normal distribution. Hence, it stands as highly reliable foundational data for environmental impact assessment. Such research and analytical outcomes could serve as pivotal new reference materials for future urban development projects, offering insights for biodiversity assessment and habitat preservation endeavors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.