• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.1-11
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• 2018

Discrete event system specification (DEVS) has been used in many simulations including hybrid systems featuring both discrete and continuous behavior that require a lot of time to get results. Therefore, in this study, we proposed the acceleration of a DEVS-based hybrid system simulation using multi-cores and GPUs tightly coupled computing. We analyzed the proposed heterogeneous computing of the simulation in terms of the configuration of the target device, changing simulation parameters, and power consumption for efficient simulation. The result revealed that the proposed architecture offers an advantage for high-performance simulation in terms of execution time, although more power consumption is required. With these results, we discovered that our approach is applicable in hybrid system simulation, and we demonstrated the possibility of optimized hardware distribution in terms of power consumption versus execution time via experiments in the proposed architecture.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.124-132
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• 2019

Scheduling problems can be classified into offline and online ones. This paper considers an online scheduling problem to minimize makespan on the identical parallel machines. For dynamically arrived jobs with their ready times, we show that the sequencing order according to the ERD (Earliest Ready Date) rule is optimal to minimize makespan. This paper suggests an algorithm by using the MIP(Mixed Integer Programming) formulation periodically to find a good periodic schedule and evaluates the required computational time and resulted makespan of the algorithm. The comparition with an offline scheduling shows our algorithm makes the schedule very fast and the makespan can be reduced as the period time reduction, so we can conclude that our algorithm is useful for scheduling the jobs under online environment even though the number of jobs and machines is large. We expect that the algorithm is invaluable one to find good schedules for the smart factory and online scheduler using the blockchain mechanism.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1144-1151
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• 2021

The pressing process is a compression process in which a product is made by applying force to a heated or unheated material to transform it into the desired shape. Due to the characteristics of press equipment that produces products through continuous compression for a short time, product defects occur continuously, and systems for solving these problems are being developed using various technologies. This paper proposes a real-time defect detection system based on an artificial intelligence algorithm that detects defects. By attaching various sensors to the press device, the relationship between equipment status and defects is defined and collected based on a big data platform. By developing an artificial intelligence algorithm based on the collected data and implementing the developed algorithm using an embedded board, we will show the practicality of the system by applying it to the actual field.

• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.320-327
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• 2023

In this study, an FPGA-based sealer inspection system was developed to inspect the sealer applied to install vehicle glass on a car body. The sealer is a liquid or paste-like material that promotes adhesion such as sealing and waterproofing for mounting and assembling vehicle parts to a car body. The system installed in the existing vehicle design parts line does not detect the sealer in the glass rotation section and takes a long time to process. This study developed a line laser camera sensor and an FPGA vision signal processing module to solve this problem. The line laser camera sensor was developed such that the resolution and speed of the camera for data acquisition could be modified according to the irradiation angle of the laser. Furthermore, it was developed considering the mountability of the entire system to prevent interference with the sealer ejection machine. In addition, a vision signal processing module was developed using the Zynq-7020 FPGA chip to improve the processing speed of the algorithm that converted the profile to the sealer shape image acquired from a 2D camera and calculated the width and height of the sealer using the converted profile. The performance of the developed sealer application inspection system was verified by establishing an experimental environment identical to that of an actual automobile production line. The experimental results confirmed the performance of the sealer application inspection at a level that satisfied the requirements of automotive field standards.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.1-11
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• 2023

Smart factory environments and digital twin environments are established, and today's factories accumulate vast amounts of production data and are managed in real time as visualized results suitable for user convenience. Production simulation techniques are in the spotlight as a way to prevent delays in delivery and predict factory volatility in situations where production schedule planning becomes difficult due to the diversification of production products. With the development of the digital twin environment, new packages are developed and functions of existing packages are updated, making it difficult for users to make decisions on which packages to use to develop simulations. Therefore, in this study, the concept of Discrete Event Simulation (DES) performed based on discrete events is defined, and the characteristics of various simulation packages were compared and analyzed. To this end, studies that solved real problems using discrete event simulation software for 10 years were analyzed, and three types of software used by the majority were identified. In addition, each package was classified by simulation technique, type of industry, subject of simulation, country of use, etc., and analysis results on the characteristics and usage of DES software were provided. The results of this study provide a basis for selection to companies and users who have difficulty in selecting discrete event simulation package in the future, and it is judged that they will be used as basic data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.230-238
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• 2023

Small and medium-sized food manufacturing enterprises are largely reliant on manual labor, from inputting raw materials to palletizing the final product. Recently, there has been a trend toward smartness and digitization through the implementation of robotics and sensor data technology. In this study, we examined the effectiveness of improvement through 3D simulation on two repetitive work processes within a food manufacturing company. These processes involve workers whose speed cannot match the capacity of the applied equipment. Two manual processes were selected: the weighing and packing process performed by workers after skewer assembly, and the manual batch process of counting randomly delivered frozen foods, packing (both internal and external), and palletizing. The production volume, utilization rate, and number of workers were chosen as verification indicators. As a result of the simulation for improving the 3D process, production increased by 13.5% and 56.8% compared to the existing process, respectively. This was particularly evident in the process of applying palletizing robots. In both processes, as the utilization rate and number of input workers decreased, robots could replace tasks with high worker fatigue, thereby reducing work overload. This study demonstrates the potential to visually compare the process flow improvement using 3D simulations and confirms the possibility of pre-validation for improvement.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.147-155
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• 2016

Industry 4.0's goal is the 'Smart Factory' that integrates and controls production process, procurement, distribution and service based on the fundamental technology such as internet of the things, cyber physical system, sensor, etc. Basic requirement for successful promotion of this Industry 4.0 is the large supply of semiconductor. However, company I who produces dicing blades has difficulty to meet the increasing demand and has hard time to increase revenue because its raw material includes high price diamond, and requires very complex and sensitive process for production. Therefore, this study is focused on understanding the problems and presenting optimal plan to increase productivity of dicing blade manufacturing processes. We carried out a study as follows to accomplish the above purposes. First, previous researches were investigated. Second, the bottlenecks in manufacturing processes were identified using simulation tool (Arena 14.3). Third, we calculate investment amount according to added equipments purchase and perform economic analysis according to cost and sales increase. Finally, we derive optimum plan for productivity improvement and analyze its expected effect. To summarize these results as follows : First, daily average blade production volume can be increased two times from 60 ea. to 120 ea. by performing mixing job in the day before. Second, work flow can be smoother due to reduced waiting time if more machines are added to improve setting process. It was found that average waiting time of 23 minutes can be reduced to around 9 minutes from current process. Third, it was found through simulation that the whole processing line can compose smoother production line by performing mixing process in advance, and add setting and sintering machines. In the course of this study, it was found that adding more machines to reduce waiting time is not the best alternative.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1089-1096
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• 2019

Recently, small-scale IoT services using a small amount of information through low-performance computing have been spread. It requires low cost, low-power, and long-distance communication technologies with wide communication radius, relatively low power consumption. This paper proposes a MAC layer and routing protocol that supports multi-hop transmission in small-scale IoT environment distributed over a large area based on LoRa communication and delivering a small amount of sensing data. The terminal node is mobile and the communication type provides bidirectional transmission between the terminal node and the network application server. By applying the proposed protocol, a production line monitoring system for smart factory was implemented. It was confirmed that the basic monitoring functions are normally performed.