• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.377-389
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• 2021

This paper presents a framework design of a digital twin system for a vertical smart farm. In this paper, a framework of digital twin systems establishes three factors: 1) Client 2) IoT gateway, and 3) Server. Especially, IoT gateway was developed using the Eclipse Ditto, which has been commonly used as the standard open hardware platform for digital twin. In particular, each factor is communicating with the client, IoT gateway, and server by defining the message sequence such as initialization and data transmission. In this paper, we describe the digital twin technology trend and major platform. The proposed design has been tested in a testbed of the lab-scale vertical smart-farm. The sensor data is received from 1 Jan to 31 Dec 2020. In this paper, a prototype digital twin system that collects environment and control data through a raspberry pi in a plant factory and visualizes it in a virtual environment was developed.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.63-68
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• 2022

Due to COVID-19, many paradigm shifts in existing manufacturing facilities and the expansion of non-face-to-face services are accelerating worldwide. A representative technology is digital twin technology. Such digital twin technology, which existed only conceptually in the past, has recently become feasible with the construction of a 5G-based network. Accordingly, this paper designed and implemented a part of the USB process to enable digital twins based on OPC UA communication, which is a standard interlocking structure, between real object objects and virtual reality-based USB process in accordance with this paradigm change. By reflecting the physical characteristics of real objects together, it is possible to simulate real-time synchronization of these with real objects. In the future, this can be applied to various industrial fields, and it is expected that it will be possible to reduce costs for decision-making and prevent dangerous accidents.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.188-195
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• 2012

This paper presents a new mode of expressing a 3D assembly work for creating a 3D CG animation without judgment by human from minimal required information. In the field of manufacturing, there are favorable movements in the utilization of 3D CAD for 3D simulation to shorten lead time for product development and pre-production. But simulating an assembly work has troubles to need huge quantity of manually input data. This paper discusses what minimal necessary information for creating 3D CG animations of assembly works is, focusing on the features of assembly works. Furthermore, a new mode of expressing a 3D assembly work is proposed as "state/change transition diagrams" (SCTD), which express arbitrary scenes in an assembly work as "state" and describe a sequential assembly work with "state" and "change", and the outline of its stepwise generation algorithm is also described. SCTD can be converted to a 3D CG animation of an assembly work without judgment by human. This paper focuses on the creating 3D CG animation of assembly works which workers use only their both hands.

• Smart Media Journal
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• v.5 no.4
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• pp.9-17
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• 2016

Over the next 20 years it will begin the exodus from the Internet and smart phones to the Internet of Things. The heart of IoT gives new utility and value with connectivity among things around people to the human. In future, Industrial environment will be intimately connect all among machines and machines or factories and factories in all processing, and by digitizing of all goods and production life-cycle, which is a combination of virtual world and real world, the digital factory will become reality eventually. The proposed IoT or IIoT based Download OTA (Over-the-Air) provides a flexible mechanism for downloading Media objects of any type and size from a network. Moreover, proposed IoT based DLOTA provides a part of security by lightweight encryption, OTP, and CapBAC technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.75-80
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected. A neural network was introduced to create synthetic data from the collected actual data. Based on this neural network, the RSSI value versus distance was predicted. The real value of RSSI was obtained as a neural network for generating synthetic data, and based on this value, the coordinates of the object were estimated by learning a neural network that tracks the location of a terminal in a virtual environment.