• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.245-250
• /
• 2023

Digital twin technology, which copies information from real space into virtual space, is being used in a variety of fields.Interest in digital twins is increasing, especially in advanced manufacturing fields such as Industry 4.0-based smart manufacturing. Operating a digital twin system generates a large amount of data, and the data generated has different characteristics depending on the technology field, so it is necessary to efficiently manage resources and use an optimized digital twin platform technology. Research on digital twin pipelines has continued, mainly in the advanced manufacturing field, but research on high-speed pipelines suitable for data in the plant field is still lacking. Therefore, in this paper, we propose a pipeline design method that is specialized for digital twin data in the plant field that is rapidly poured through Apache Kafka. The proposed model applies plant information on a Revit basis. and collect plant-specific data through Apache Kafka. Equipped with a lightweight CFD engine, it is possible to create a digital twin model that is more suitable for the plant field than existing digital twin technology for the manufacturing field.

• Journal of the Korea Society for Simulation
• /
• v.29 no.2
• /
• pp.11-20
• /
• 2020

As the manufacturing environment becomes more complex, traditional simulation models alone are having a lot of difficulties in reflecting real-time manufacturing situations. Although the Digital Twin concept is actively discussed as an alternative to overcome theses issues, many studies are being carried out only in the product design phase. This research presents a Digital Twin-based manufacturing environment framework for applying the Digital Twin concept to the manufacturing process. Twin model that is operated in virtual space, physical system and databases describing the actual manufacturing environment, are proposed as detailed components that make up the framework. To check the applicability of proposed framework, a simple Digital Twin-based manufacturing system was simulated in a conveyor system using Arena software and Excel VBA. Experiment results have shown that the twin model is transmitted real time data from the physical system via DB and were operating in the same time unit. The Excel VBA fitted parameters defined by cycle time based on historical data that real-time and training data are being accumulated together. This study proposes operating method of digital twin model through the simple experiment examples. The results lead to the applicability of Digital twin model.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.935-940
• /
• 2023

Digital-twin technology is emerging as an innovative solution for all industries, including manufacturing and production lines. Therefore, this paper optimizes all the energy used in a biomass plant based on unused resources. We will then implement a digital-twin prototype for biomass plants and evaluate its performance in order to improve the efficiency of plant operations. The proposed digital-twin prototype applies a standard communication platform between the framework and the gateway and is implemented to enable real-time collaboration. and, define the message sequence between the client server and the gateway. Therefore, an interface is implemented to enable communication with the host server. In order to verify the performance of the proposed prototype, we set up a virtual environment to collect data from the server and perform a data collection evaluation. As a result, it was confirmed that the proposed framework can contribute to energy optimization and improvement of operational efficiency when applied to biomass plants.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.789-795
• /
• 2023

Digital-Twin are recognized as an important core technology for the realization of Smart Factories by simulating and optimizing the monitoring and predictive maintenance of manufacturing equipment and the operation of production lines in a digital space. To implement this system, we adopt the IEC62541-based OPC-UA (Open Platform Communications Unified-Architecture) Protocol, which has strengths in interoperability and connectivity between heterogeneous platforms. Therefore, In this paper, We designed and implemented an IIoT(Industry Internet of Things) system that connects heterogeneous platforms, and developed an OPC-UA simulator based on IEC 62541. We will present whether the data will be applied to the Digital-Twin Platform and whether it will work, and proceed with performance tests and evaluations. We evaluate the operation performance and OPC-UA performance of the Digital-Twin platform lightened by the proposed device, and present the optimal IEC62514-based simulator system. We proceeded with the performance evaluation of sending and receiving data with OPC-UA wrapping with the proposed simulator, and found that a lightweight Digital-Twin platform can be operated. This research can apply the OPC-UA protocol for implementing smart factory and meta-factory in the manufacturing shop floor with limited resources, avoiding the waste of time and space on the shop floor through the OPC-UA simulator. We expect that this will contribute to a significant improvement in efficiency by minimizing.

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.356-367
• /
• 2021

With the recent development of IoT and artificial intelligence technology, research and applications for optimization of real-world problems by collecting and analyzing data in real-time have increased in various fields such as manufacturing and smart city. Representatively, the digital twin platform that supports real-time synchronization in both directions with the virtual world digitized from the real world has been drawing attention. In this paper, we define a digital twin concept and propose a digital twin platform prototype that links real objects and predicted results from the virtual world in real-time by utilizing the oneM2M-based IoT platform. In addition, we implement an application that can predict accidents from object collisions in advance with the prototype. By performing predefined test cases, we present that the proposed digital twin platform could predict the crane's motion in advance, detect the collision risk, perform optimal controls, and that it can be applied in the real environment.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.5
• /
• pp.1001-1010
• /
• 2021

Smart factories are complex systems which combine latest information technology (IT) with operation technology (OT). A smart factory aims to provide manufacturing capacity improvement, customized production, and resource reduction with these complex technologies. Although the smart factory is able to increase the efficiency through the technologies, the security level of the whole factory is low due to the vulnerability transfer from IT. In addition, the response and restoration of the business continuity plan are insufficient in case of damage due to the absence of factory security experts. The cope with the such problems, we propose an information security practice content for analyzing the damage by generating ransomware attacks in a digital twin-based smart factory similar to the real world. In our information security content, we introduce our conversion technique of physical devices into virtual machines or simulation models to build a practical environment for the digital twin. This content generates two types of the ransomware attacks according to a defined scenario in the digital twin. When the two generated attacks are successfully completed, at least 8 and 5 of the 23 virtual elements are take damage, respectively. Thus, our proposed content directly identifies the damage caused by the generation of two types of ransomware in the virtual world' smart factory.

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

• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.55-63
• /
• 2019

Recently, the use of real-world image data has been increasing to express realistic virtual environments in various application fields such as education, manufacturing, and construction. In particular, with increasing interest in digital twins like smart cities, realistic 3D urban models are being built using real-world images, such as aerial images. However, the captured aerial image includes shadows from the sun, and the 3D city model including the shadows has a problem of distorting and expressing information to the user. Many studies have been conducted to remove the shadow, but it is recognized as a challenging problem that is still difficult to solve. In this paper, we construct a virtual environment dataset including the height map of buildings using 3D spatial information provided by VWorld, and We propose a new shadow detection method using height map and deep learning. According to the experimental results, We can observed that the shadow detection error rate is reduced when using the height map.