• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.271-277
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• 2022

PBFT (Practical Byzantine Fault Tolerant) is a consensus algorithm that can achieve consensus by resolving unintentional and intentional faults in a distributed network environment and can guarantee high performance and absolute finality. However, as the size of the network increases, the network load also increases due to message broadcasting that repeatedly occurs during the consensus process. Due to the characteristics of the PBFT algorithm, it is suitable for small/private blockchain, but there is a limit to its application to large/public blockchain. Because PBFT affects the performance of blockchain networks, the industry should test whether PBFT is suitable for products and services, and academia needs a unified evaluation metric and technology for PBFT performance improvement research. In this paper, quantitative evaluation metrics and evaluation frameworks that can evaluate PBFT family consensus algorithms are studied. In addition, the throughput, latency, and fault tolerance of PBFT are evaluated using the proposed PBFT evaluation framework.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.21-32
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• 2023

The design of complex man-made systems mostly involves a conceptual modeling phase; therefore, it is important to ensure an appropriate analysis method for these models. A key concept for such analysis is the development of a diagramming technique (e.g., UML) because diagrams can describe entities and processes and emphasize important aspects of the systems being described. The analysis also includes an examination of ontological concepts such as states and events, which are used as a basis for the modeling process. Studying fundamental concepts allows us to understand more deeply the relationship between these concepts and modeling frameworks. In this paper, we critically analyze the classic definition of a state utilizing the Thinging machine (TM) model. States in state machine diagrams are considered the appropriate basis for modeling system behavioral aspects. Despite its wide application in hardware design, the integration of a state machine model into a software system's modeling requirements increased the difficulty of graphical representation (e.g., integration between structural and behavioral diagrams). To understand such a problem, in this paper, we project (create an equivalent representation of) states in TM machines. As a case study, we re-modeled a state machine of an assembly line system in a TM. Additionally, we added possible triggers (transitions) of the given states to the TM representation. The outcome is a complicated picture of assembly line behavior. Therefore, as an alternative solution, we re-modeled the assembly line based solely on the TM. This new model presents a clear contrast between state-based modeling of assembly line behavior and the TM approach. The TM modeling seems more systematic than its counterpart, the state machine, and its notions are well defined. In a TM, states are just compound events. A model of a more complex system than the one in the assembly line has strengthened such a conclusion.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.150-190
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• 2024

This article proposes a framework to elucidate the structural dynamics of the generative AI ecosystem. It also outlines the practical application of this proposed framework through illustrative policies, with a specific emphasis on the development of the Korean generative AI ecosystem and its implications of platform strategies at AI platform-squared. We propose a comprehensive classification scheme within generative AI ecosystems, including app builders, technology partners, app stores, foundational AI models operating as operating systems, cloud services, and chip manufacturers. The market competitiveness for both app builders and technology partners will be highly contingent on their ability to effectively navigate the customer decision journey (CDJ) while offering localized services that fill the gaps left by foundational models. The strategically important platform of platforms in the generative AI ecosystem (i.e., AI platform-squared) is constituted by app stores, foundational AIs as operating systems, and cloud services. A few companies, primarily in the U.S. and China, are projected to dominate this AI platform squared, and consequently, they are likely to become the primary targets of non-market strategies by diverse governments and communities. Korea still has chances in AI platform-squared, but the window of opportunities is narrowing. A cautious approach is necessary when considering potential regulations for domestic large AI models and platforms. Hastily importing foreign regulatory frameworks and non-market strategies, such as those from Europe, could overlook the essential hierarchical structure that our framework underscores. Our study suggests a clear strategic pathway for Korea to emerge as a generative AI powerhouse. As one of the few countries boasting significant companies within the foundational AI models (which need to collaborate with each other) and chip manufacturing sectors, it is vital for Korea to leverage its unique position and strategically penetrate the platform-squared segment-app stores, operating systems, and cloud services. Given the potential network effects and winner-takes-all dynamics in AI platform-squared, this endeavor is of immediate urgency. To facilitate this transition, it is recommended that the government implement promotional policies that strategically nurture these AI platform-squared, rather than restrict them through regulations and stakeholder pressures.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.137-152
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• 2024

In order to derive a plan to increase the field effectiveness of the safety and health management(SHM) system, this study suggested plans for practical application of SHM system to the actual sites managed by the branch office of a public enterprise along with practical implications that should be considered. For this, in-depth interviews were conducted with employees in charge of safety and health work at the sites to analyze SHM system of the branch office, and the implementation processes and frameworks for establishing SHM system were suggested by grasping the actual conditions of the construction company performing the construction ordered by the branch office. This study shows that in order for SHM to be internalized in public enterprises, plans and performance indicators that can be applied in the field should be specifically presented in consideration of the hierarchical structure and processes of the organization performing the work, and a work environment should be created to focus on practical works related to safety and health.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.41-49
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• 2024

In this paper, we validate simulation results for the design optimization of a Superconducting Fault Current Limiter (SFCL) intended for use in Medium Voltage Direct Current systems (MVDC). With the increasing integration of renewable energy and grid connections, researchers are focusing on medium-voltage systems for balancing energy in new and renewable energy networks, rather than traditional transmission or distribution networks. Specifically, for DC distribution networks dealing with fault currents that must be rapidly blocked, current-limiting systems like superconducting current limiters offer distinct advantages over the operation of DC circuit breakers. The development of such superconducting current limiters requires finite element analysis (FEM) and an extensive design process before prototype production and evaluation. To expedite this design process, the design outcomes are assimilated using a Reduced Order Model (ROM). This approach enables the verification of results akin to finite element analysis, facilitating the optimization of design simulations for production and mass production within existing engineering frameworks.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.259-270
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• 2024

This paper introduces a fundamental investigation into the establishment of a framework for integrating fieldwork into special education teacher preparation programs. This paper introduces a comprehensive framework for the implementation of semester-based practicum systems in special education teacher training programs. The paper delineates essential stages including preparation, adaptation, responsibility readiness, mutual responsibility, and evaluation, emphasizing the importance of collaboration, hands-on training, and feedback interactions. Moreover, the study delves into practical learning frameworks such as the Experiential Learning Model, Sociocultural Theory, and the Apprenticeship of Observation Model. It highlights their significance in connecting theoretical concepts with practical applications and in cultivating reflective practices among pre-service special education teachers. Moreover, the study explores two significant practicum models, namely the Class-Academy Model and the Professional Development Schools (PDS) Model, examining their elements, advantages, and impacts on teacher education programs. The paper provides valuable insights into improving the preparation of special education teachers by incorporating structured practicum experiences and innovative models that blend theoretical knowledge with practical application.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.811-831
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• 2024

This paper has two main objectives. The primary objective is to conduct a comprehensive review of existing survey papers on the advantages, disadvantages, taxonomy, and technical vulnerabilities of cryptocurrency custody services. Additionally, we examined recent regulatory developments, the application of existing frameworks, conditions for performing custody services, and service providers' obligations. The secondary objective is to identify DeFi protocols in the regulatory 'grey area' and analyze their technical aspects and governance mechanisms. By synthesizing these findings, we propose guidelines for assessing DeFi decentralization and their potential for integration within the regulatory framework, providing insights for industry experts, regulators, and policymakers to balance industry needs with societal benefits.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.487-509
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• 2024

This study aimed to compare and analyze the content structures of the information literacy curriculum 'Library and Information Life' and domestic and international digital literacy curricula to discuss the scope of digital literacy integration based on information literacy. Common areas and learning elements were extracted from domestic and international digital literacy curricula and mapped against the 'Library and Information Life' curriculum. Results showed that while the middle school curriculum aligned in information literacy, creation and sharing, and digital ethics, 'Library and Information Life' did not mention ICT utilization, communication, and collaboration competencies. The high school 'Media and Information Life' curriculum also addressed ICT utilization, communication, and collaboration competencies in a limited manner. This study is significant in proposing a concrete discussion on the scope of digital literacy integration based on information literacy. The findings can serve as foundational data for determining the scope of digital literacy application when revising the 'Library and Information Life' curriculum in the future.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.129-135
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• 2024

In this paper, we design and implement a worker musculoskeletal assessment platform. The three core components of this platform are the Mobile App, the Modeling Server, and the Web Platform. The Mobile App is an Android application developed in Kotlin, targeting Android platform 12 (S) and Android API Level 31 devices. The app utilizes the camera to capture various worker motion data and transmits it to the Modeling Server. The Modeling Server is implemented using Node.js. This server converts the worker's motion data-such as points, skeleton, and x, y, z coordinate data, measured by the mobile app-into multidimensional arrays. It then applies machine learning frameworks like TensorFlow and Keras to predict the worker's posture. The worker posture learning model is built using Teachable Machine. The Web Platform is developed using React and visualizes the worker's movements as 3D animations along a timeline. The machine learning-based worker musculoskeletal assessment platform developed in this paper aims to contribute to minimizing musculoskeletal disorders in workers at industrial sites.