• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.317-318
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• 2022

In accordance with global decarbonization, optimization and productivity improvement using digital twin are being sought, and software development for optimizing ship and marine energy operation is accelerating by selecting digital twin as a future core technology. In order to reduce the operating cost of ships and strengthen the competitiveness of the shipbuilding industry due to the international strengthening of regulations on carbon emissions, it is necessary to predict the carbon emission of ships in advance and provide a carbon reduction operation solution. A plan was carried out for the development of open digital platform technology and the establishment of an environment to support the securing of carbon transparency of the ship and offshore system.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.537-540
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• 2021

One of the most promising technologies that is raised from the fourth industrial revolution is Digital Twin (DT). A DT captures attributes and behaviors of the entity suitable for communication, storage, interpretation or processing within certain context. A digital twin based on microservice framework architecture is proposed in this paper which identifies elements required for the complete orchestration of microservice based Survival Analysis of Lung Cancer Patients. Integration of microservices and Digital Twin Technology is studied.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.135-142
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• 2022

In this paper, we propose a graph-based user route control for rapidly conducting virus inspections in emergency situations (eg, COVID-19) and a framework that can simulate this on a city map. A^* and navigation mesh data structures, which are widely used pathfinding algorithms in virtual environments, are effective when applied to CS(Computer science) problems that control Agents in virtual environments because they guide only a fixed static movement path. However, it is not enough to solve the problem by applying it to the real COVID-19 environment. In particular, there are many situations to consider, such as the actual road traffic situation, the size of the hospital, the number of patients moved, and the patient processing time, rather than using only a short distance to receive a fast virus inspection.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.3
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• pp.143-151
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• 2024

In this study, we present a mapping framework for 3D spatial reconstruction of digital twin model using navigation and perception sensors mounted on an Autonomous Surface Vehicle (ASV). For improving the level of realism of digital twin models, 3D spatial information should be reconstructed as a digitalized spatial model and integrated with the components and system models of the ASV. In particular, for the 3D spatial reconstruction, color and 3D point cloud data which acquired from a camera and a LiDAR sensors corresponding to the navigation information at the specific time are required to map without minimizing the noise. To ensure clear and accurate reconstruction of the acquired data in the proposed mapping framework, a image preprocessing was designed to enhance the brightness of low-light images, and a preprocessing for 3D point cloud data was included to filter out unnecessary data. Subsequently, a point matching process between consecutive 3D point cloud data was conducted using the Generalized Iterative Closest Point (G-ICP) approach, and the color information was mapped with the matched 3D point cloud data. The feasibility of the proposed mapping framework was validated through a field data set acquired from field experiments in a inland water environment, and its results were described.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1122-1135
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• 2022

With the development of digital technology and the influence of the global pandemic, the metaverse, a three-dimensional virtual world, is receiving attention in society, economy and overall industry, and the manufacturing industry is also accepting it as a major strategic agenda for digital transformation. Therefore, in this study, the concept of the industry metaverse from the perspective of the manufacturing industry was defined, and the types of the industry metaverse were classified into four types by reflecting the characteristics of the manufacturing industry based on the general metaverse scenario presented in previous studies. These are Virtual behavior simulation, Augmented operation of business objects and Virtual experience simulation, Augmented decision of business subjects. In addition, through case analysis of solutions used in the manufacturing industry, it was confirmed that the central technology of the Industry Metaverse is the digital twin, and that it is being implemented by convergence with major digital technologies such as virtual reality, augmented reality, digital human, and AI. This study will be able to provide guidelines for future research on the metaverse from the perspective of the manufacturing industry and establishment of a digital transformation strategy for the industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.672-682
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• 2020

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.