• Journal of Digital Convergence
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• 제18권11호
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• pp.59-71
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• 2020

The paper reports on a systematic literature review results concerning the smart factory research in Korea. 144 papers were identified from the articles published in Korean journals listed in the Korean citation index by keyword search related to smart factory. Bibliometric analyses were conducted by way of co-occurrence and network analysis using the VOSViewer. Automation, intelligence, and bigdata were identifed as three critical clusters of research while, operating systems, international policy and cases, concept analysis as other three clusters of research. Internet of Things turned out to be a key technology of smart factory linking all of these areas. Servitization studies were small in numbers but seemed to have a lot of potential. Security researches seemed to be lacking connections with other areas of studies. Results of this study can be used as a milestone for identifying future research issues in smart factories.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제44권3호
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• Journal of the Semiconductor & Display Technology
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• 제22권4호
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• pp.124-130
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• 2023

Recently, fan out wafer level packaging, which enables high integration, miniaturization, and low cost, is being rapidly applied in the semiconductor industry. In particular, FOWLP is attracting attention in the mobile and Internet of Things fields, and is recognized as a core technology that will lead to technological advancements such as 5G, self-driving cars, and artificial intelligence in the future. However, as chip density and package size within the package increase, FOWLP warpage is emerging as a major problem. These problems have a direct impact on the reliability and electrical performance of semiconductor products, and in particular, cause defects such as vacuum leakage in the manufacturing process or lack of focus in the photolithography process, so technical demands for solving them are increasing. In this paper, warpage simulation according to the thickness of FOWLP material was performed using finite element analysis. The thickness range was based on the history of similar packages, and as a factor causing warpage, the curing temperature of the materials undergoing the curing process was applied and the difference in deformation due to the difference in thermal expansion coefficient between materials was used. At this time, the stacking order was reflected to reproduce warpage behavior similar to reality. After performing finite element analysis, the influence of each variable on causing warpage was defined, and based on this, it was confirmed that warpage was controlled as intended through design modifications.

• The Journal of Society for e-Business Studies
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• 제24권3호
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• pp.99-110
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• 2019

As the 4th industrial revolution progressing, manufacturers are trying to apply intelligent information technologies such as IoT(internet of things) and machine learning. In the semiconductor/LCD/tire manufacturing process, schedule plan that minimizes setup change and due date violation is very important in order to ensure efficient production. Therefore, in this paper, we suggest the deep learning based scheduling generation model minimizes setup change and due date violation in parallel machines. The proposed model learns patterns of minimizing setup change and due date violation depending on considered order using the amount of historical data. Therefore, the experiment results using three dataset depending on levels of the order list, the proposed model outperforms compared to priority rules.

• Journal of Platform Technology
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• 제11권3호
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• pp.76-82
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• 2023

Starting from the 4th industrial revolution, core technologies were applied to industries to build various smart environments. Smart factories in the manufacturing industry produce high-quality products by applying IIoT as a core technology that can collect and control a wide range of data for customized production. However, the network environment of the smart factory converted to open through IIoT was exposed to various security risks. In accordance with security breaches, IIoT has shown degradation in the quality of manufactured products and production processes due to network disturbance, use and maintenance of forged IIoT, and can cause reliability problems in business. Accordingly, in this study, a method for safe connection and utilization of IIoT was studied during the initial establishment of a smart factory. Specifically, a study was conducted to check the IIoT connection situation so that the practicality of the IIoT connected to the smart factory could be confirmed and the harmless environment established.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.487-490
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• 2018

As the fourth industrial revolution 4.0 era arrives, the role of smart factory is emerging, which establishes a communication system between production devices and products through the Internet of Things and optimizes the entire production process. Germany wants to use smart factory technologies and data to upgrade and standardize the industry as a whole to create factories around the world, and the United States is aiming to create new business models and revenue streams by analyzing big data and improving productivity based on the technological prowess and innovation across ICT. In addition, Japan and China are also working to change and upgrade their manufacturing industries through smart factories. Accordingly, Korea is attempting to introduce smart factory based on the production industry 3.0. Therefore, this study describes the industrial trends of the fourth industrial revolution and smart factory and compares the major underlying technologies and introduction cases of smart factory.

• Electronics and Telecommunications Trends
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• 제34권5호
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• pp.14-25
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• 2019

As the Internet of Things, artificial intelligence and big data have received a lot of attention as key growth engines in the era of the fourth industrial revolution, data acquisition and utilization in mobile, automotive, robotics, manufacturing, agriculture, health care and national defense are becoming more important. Due to numerous data-based industrial changes, demand for sensor technologies is exploding, especially for intelligent sensor technologies that combine control, judgement, storage and communication functions with the sensors's own functions. Intelligent sensor technology can be defined as a convergence component technology that combines intelligent sensor units, intelligent algorithms, modules with signal processing circuits, and integrated plaform technologies. Intelligent sensor technology, which can be applied to variety of smart IT convergence services such as smart devices, smart homes, smart cars, smart factory, smart cities, and others, is evolving towards intelligent and convergence technologies that produce new high-value information through recognition, reasoning, and judgement based on artificial intelligence. As a result, development of intelligent sensor units is accelerating with strategies for miniaturization, low-power consumption and convergence, new form factor such as flexible and stretchable form, and integration of high-resolution sensor arrays. In the future, these intelligent sensor technologies will lead explosive sensor industries in the era of data-based artificial intelligence and will greatly contribute to enhancing nation's competitiveness in the global sensor market. In this report, we analyze and summarize the recent trends in intelligent sensor technologies, especially those for four core technologies.

• Journal of Broadcast Engineering
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• 제26권4호
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• pp.356-367
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• 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 Academia-Industrial cooperation Society
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• 제22권3호
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• pp.268-278
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• 2021

Technologies that are leading the fourth industrial revolution, such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cyber-physical systems (CPS) are developing and generalizing. The demand to improve productivity, economy, safety, etc., is spreading in various industrial fields by applying these technologies. Digital twins are attracting attention as an important technology trend to meet demands and is one of the top 10 tasks of the Korean version of the New Deal. In this study, papers, magazines, reports, and other literature were searched using Google. In order to investigate the contribution or role of geospatial information in the digital twin application, the definition of a digital twin, we investigated technology trends of domestic and foreign companies; the components of digital twins required in manufacturing, plants, and smart cities; and the core techniques for driving a digital twin. In addition, the contributing contents of geospatial information were summarized by searching for a sentence or word linked between geospatial-related keywords (i.e., Geospatial Information, Geospatial data, Location, Map, and Geodata and Digital Twin). As a result of the survey, Geospatial information is not only providing a role as a medium connecting objects, things, people, processes, data, and products, but also providing reliable decision-making support, linkage fusion, location information provision, and frameworks. It was found that it can contribute to maximizing the value of utilization of digital twins.

• Journal of Biomedical Engineering Research
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• 제44권6호
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• pp.363-376
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• 2023

According to the COVID-19, development of various medical software based on IoT(Internet of Things) was accelerated. Especially, interest in a central software system that can remotely monitor and control ventilators is increasing to solve problems related to the continuous increase in severe COVID-19 patients. Since medical device software is closely related to human life, this study aims to develop central monitoring system that can remotely monitor and control multiple ventilators in compliance with medical device software development standards and to verify performance of system. In addition, to ensure the safety and reliability of this central monitoring system, this study also specifies risk management requirements that can identify hazardous situations and evaluate potential hazards and confirms the implementation of cybersecurity to protect against potential cyber threats, which can have serious consequences for patient safety. As a result, we obtained medical device software manufacturing certificates from MFDS(Ministry of Food and Drug Safety) through technical documents about performance verification, risk management and cybersecurity application.The purpose of this study is to conduct a usability assessment to ensure that ergonomic design has been applied so that the ventilator central monitoring system can improve user satisfaction, efficiency, and safety. The rapid spread of COVID-19, which began in 2019, caused significant damage global medical system. In this situation, the need for a system to monitor multiple patients with ventilators was highlighted as a solution for various problems. Since medical device software is closely related to human life, ensuring their safety and satisfaction is important before their actual deployment in the field. In this study, a total of 21 participants consisting of respiratory staffs conducted usability test according to the use scenarios in the simulated use environment. Nine use scenarios were conducted to derive an average task success rate and opinions on user interface were collected through five-point Likert scale satisfaction evaluation and questionnaire. Participants conducted a total of nine use scenario tasks with an average success rate of 93% and five-point Likert scale satisfaction survey showed a high satisfaction result of 4.7 points on average. Users evaluated that the device would be useful for effectively managing multiple patients with ventilators. However, improvements are required for interfaces associated with task that do not exceed the threshold for task success rate. In addition, even medical devices with sufficient safety and efficiency cannot guarantee absolute safety, so it is suggested to continuously evaluate user feedback even after introducing them to the actual site.