• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.3
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• pp.267-290
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• 2019

The purpose of this study is to investigate the current status and condition of continuing education to improve professionalism of school library and to suggest ways to improve continuing education. The survey was conducted for 69 librarians and school librarians teachers in elementary, middle and high school libraries in Goyang, Gyeonggi-do, and frequency analysis and cross analysis were conducted. The research results are as follows. First, the study found that librarians have a high interest in and participation in continuing education. Second, the type of continuing education with the highest percentage of participation was short-term training and training courses, and the link between educational content and practice was low. Third, managers' awareness needs to be improved, and regional constraints and the lack of suitable continuing education programs are the obstacles to continuing education. Fourth, preference for continuing education during working hours and vacation was emphasized. Through this, it was suggested that continuous and long-term continuing education, policy support for encouraging continuing education, development of continuing education courses for professionalism, manager's awareness call.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.565-573
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• 2019

An optimization model of water intake planning is developed based on a linear programming (LP) for the intelligent water purification plant operation system. The proposed optimization model minimizes the water treatment costs of raw water purification by considering a time-delay of treatment process and hourly electricity tariff, which is subject to various operation constraints, such as water intake limit, storage tank capacity, and water demand forecasts. For demonstration, the developed model is applied to H water purification center. Here, we have tested three optimization strategies and the results are compared and analyzed in economic and safety aspects. The optimization model is expected to be used as a decision support tool for optimal water intake scheduling of domestic water purification centers.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.4
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• pp.725-733
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• 2019

The study aimed to effect of leisure support and leisure constraints on life satisfaction for extreme sports participants. A total of 179 adults from Seoul, In-chon, Gyeonggi, and Chungcheng-do were recruited. I conducted frequency, reliability, confirmatory factor, correction, and structure equaling modeling analyses using PASW Statistics 18.0 and AMOS 18.0. The results were as follows: (1) leisure attitude of leisure activity participants had a positive influence on leisure satisfaction; (2) leisure attitude of leisure activity participants had no influence on psychology happiness; (3) leisure satisfaction of leisure activity participants had no influence on psychology happiness. Leisure satisfaction has no mediating effect between leisure attitude and psychological well-being. However, leisure attitude has a positive influence on leisure satisfaction, so we need to develop infrastructure which it continue participate in leisure activity.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.340-346
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• 2022

Construction industry is considered as one of the oldest industries in the world since human came into being and the need of their own space is realized. All this led to make the world a space of many beautiful constructive ventures. As per the requirements of today's world, every industry is recognizing the need for use and adoption of modern as well as innovative technologies due to their benefits and timely production. Now construction industry has also started adopting the use of modern and innovative technologies during their projects but still the rate of adoption is so slow. From design to completion, construction projects take a lot to manage for which technology based solutions have continuously been proposed. These include Computer Aided Design (CAD), building information modeling (BIM) and cloud computing have been proved to be much successful until now. The construction projects are high budgeted, and direly require timely and successful completion with quality, resource and other constraints. So, the researchers observe the need of more clear and technology based communication between the construction projects and its constructors and other stakeholders is required before and during the construction to take timely precautions for expected issues. This study has analyzed the use of Augmented Reality (AR) technology adopting GammaAR, and ARki applications in construction industry. It has been found that both applications are light-weighted, upgradable, provide offline availability and collaborative environment as well as fulfil most of the requirements of the construction industry except the cost. These applications also support different screen size for better visualization and deep understanding. Both applications are analyzed, based on construction's application requirements, usability of AR and ratings of applications user collected from application's platform. The purpose of this research is to provide a detail insight of construction applications which are using AR to facilitate both the future developers and consumers.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.8-13
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• 2021

The purpose of this study is to propose a throughput costing as a performance measurement tool to measure cost indicators, which are one of the indicators for evaluating organizational performance in a smart factory manufacturing environment. An empirical study by questionnaire was conducted, and 60 experts were surveyed to verify the hypothesis. As a result of the study, it was concluded that the information provided based on throughput costing is helpful in cost measurement and in evaluating organizational performance efficiency and effectiveness, and it was confirmed that this method has usefulness to support the planning and control process. It is proposed that the use of throughput costing by constraint theory, which can maximize throughput and optimize inventory levels in the manufacturing process, can find solutions to bottlenecks affecting the efficiency and effectiveness of organizational performance.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.69-75
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• 2022

We propose energy-efficient scheduling considering real-time constraints and energy efficiency in smart mobile with heterogeneous multi-core structure. Recently, high-performance applications such as VR, AR, and 3D game require real-time and high-level processings. The big.LITTLE architecture is applied to smart mobiles devices for high performance and high energy efficiency. However, there is a problem that the energy saving effect is reduced because LITTLE cores are not properly utilized. This paper proposes a heterogeneous multi-core assignment technique that improves real-time performance and high energy efficiency with big.LITTLE architecture. Our proposed method optimizes the energy consumption and the execution time by predicting the actual task execution time using SVM (Support Vector Machine). Experiments on an off-the-shelf smartphone show that the proposed method reduces energy consumption while ensuring the similar execution time to legacy schemes.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Korea Trade Review
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• v.48 no.4
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• pp.321-342
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• 2023

This study aims to derive policy implications by analyzing the experiences of Korean ICT startups that participated in the government's online export and trade consultation platform, designed to support ICT startups' overseas expansion during the COVID-19 pandemic. The research methodology involved the analysis of semi-structured, in-depth interviews with six startups who participated in an online export and trade consultation platform, using Giorgi's phenomenological methodology. The analysis resulted in the identification of ten subcategories, including two subcategories for each of the five categories. These categories and subcategories offer a comprehensive understanding of the experiences of ICT startups in navigating the online export and trade counseling platforms. The findings suggest that online export and trade consultation platforms for ICT startups offer efficiency benefits by reducing time and space constraints, but they also reveal limitations in terms of practical business continuity. This study holds academic and practical significance by providing insights into how ICT startups navigate online export and trade counseling platforms during the pandemic.

• Smart Media Journal
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• v.12 no.11
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• pp.145-153
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• 2023

In a P2P-based distributed storage system where peers frequently join and leave, it is essential to guarantee not only data availability but also playback quality comparable to that provided by local storage devices when playing back video files with real-time constraints. In addition, cloud storage services based on distributed storage systems provide each user with the functionality to share their files with other users, so when multiple users request playback of the same video file at the same time, all playback should be supported seamlessly in real time. Therefore, in this paper, we propose a scheme that process multiple simultaneous playback requests for each video file in real time as well as data availability in a P2P-based distributed storage system that supports file sharing. This scheme can support real-time simultaneous playback and efficiently use storage space by adjusting the amount of redundant data encoded through erasure coding according to the number of concurrent playback requests for each video file.