• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.403-405
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• 2022

본 논문에서는 4차 산업에서 필요로 하는 스마트 기기의 소형 및 저전력 부품에 사용되는 센싱 플랫폼을 제안하였다. 특히 스마트팩토리의 공장 자동화와 정밀 측정의 핵심 부품인 리니어 모션 가이드(LM Guide)를 고정밀, 고정도로 제어할 수 있는 센싱 시스템을 개발하였다. 이를 위하여 기존의 변위 센서 기법의 한계를 극복할 수 있도록 와전류(Eddy Current) 기법을 이용함으로써 LC 공진기와 전도체를 LM 가이드에 장착할 수 있도록 구현하였다. 또한 미세 인덕턴스 값을 측정할 수 있도록 디지털 신호처리 기술과 컴퓨터/산술 기술을 FPGA를 이용한 HW 시스템을 제작하여 구현함으로써 실험을 진행했다. 본 논문에서 구현한 HW 센싱 시스템을 이용하여 LM 가이드를 동작시킴으로 실시간으로 변위 값을 디스플레이 부로 출력되어 측정이 가능하고, 변위 값의 분해능과 응답속도 면에서 우수함을 확인할 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.227-234
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• 2020

This paper introduces the state definition and diagnostic algorithm for autonomic control of manufacturing facilities. Smart factory systems through cyber-physical systems and digital twin technology are increasing the productivity and stability of existing manufacturing plants, which has become an issue recently. A Smart factory system is one of the key technologies that make up a smart factory system, to improve productivity, enable workers to make better decisions, and to control abnormal process flows. However, performing an autonomic control process based on large number of integrated plat data requires significant advance work. Therefore, in this paper, we define an abstracted facility state for manufacturing facility autonomic control and propose an algorithm to diagnose the current state. This makes the autonomic control process simpler by autonomic control based on the facility status rather then integrated facility data.

• The Korean Fashion and Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.744-747
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• 1997

High performance digital computer technology enables the digital computer-based controllers to replace traditional analog controllers used for factory automations. This replacement, however, brings up the side effects caused by discrete quantization and non-real-time execution of control softwares. This paper describes the structure of real-time simulator and controller that can be used for design and verification of real-time digital controllers. The virtual machine concept adopted by real-time simulator make the proposed simulator be independent from the specific hardware platforms. The proposed system can also be used in the loosely coupled distributed environments connected through local area network using real-time message passing algorithm and virtual data table based on the shared memory mechanism.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.559-560
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• 2021

본 논문에서는 인공지능, 사물인터넷 등 4차 산업혁명 기술을 기반으로 생산 업체와 공급 업체 간의 공급망의 가시성, 안전성, 효율성 향상을 위한 물류 표준을 준수하며 고도화 및 지능화된 스마트 SCM 솔루션을 제시하고자 한다. 이를 위해 사물 인터넷, 인공지능 기술을 기반으로 공급망의 가시성, 안전성, 효율성 향상을 위한 물류 표준을 준수하며 고도화되고 지능화된 효율적인 자동 발주 솔루션을 제시한다. 자동 발수 솔루션은 협력업체와의 생산계획정보, 발주정보, 납품정보, 품질판정정보, 재고현황 등의 제품 데이터를 실시간 공유하는 웹 기반 솔루션이다.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.73-82
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• 2019

In order to build a smart factory, building a CPPS (Cyber Physical Product System) is an important system that must be accompanied. Through the CPPS, it is the reality of smart factories to move physical factories to a digital-based cyber world and to intelligently and autonomously monitor and control them. But The existing CPPS architectures present only an abstract modeling architecture, and the research that applied the OPC UA Framework (Open Platform Communication Unified Architecture), an international standard for data exchange in the smart factory, as the basic system of CPPS It was insufficient. Therefore, it is possible to implement CPPS that can include both cloud and IoT by collecting field data distributed by CPPS architecture applicable to actual factories and concentrating data processing in a centralized In this study, we implemented CPPS architecture through central OPC UA Server based on OPC UA conforming to central processing OPC UA Framework, and how CPPS logical process and data processing process are automatically generated through OPC UA modeling processing We have proposed the CPPS architecture including the model factory and implemented the model factory to study its performance and usability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.205-212
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• 2024

This study developed a deep learning model that distinguishes the front (with garnish) and the back (without garnish) surface of the dried semi-finished product (dried bukak) for screening operation before transfter the dried bukak to oil heater using robot's vacuum gripper. For deep learning model training and verification, RGB images for the front and back surfaces of 400 dry bukak that treated by data preproccessing were obtained. YOLO-v5 was used as a base structure of deep learning model. The area, surface information labeling, and data augmentation techniques were applied from the acquired image. Parameters including mAP, mIoU, accumulation, recall, decision, and F1-score were selected to evaluate the performance of the developed YOLO-v5 deep learning model-based surface detection model. The mAP and mIoU on the front surface were 0.98 and 0.96, respectively, and on the back surface, they were 1.00 and 0.95, respectively. The results of binary classification for the two front and back classes were average 98.5%, recall 98.3%, decision 98.6%, and F1-score 98.4%. As a result, the developed model can classify the surface information of the dried bukak using RGB images, and it can be used to develop a robot-automated system for the surface detection process of the dried bukak before deep frying.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.1005-1008
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• 2008

Electronic Paper Displays (EPDs) incorporating electrophoretic foils have made digital reading as pleasant as reading normal print. We will report on progress to replace glass-based displays with light and robust plastic EPDs using only a few extra process steps in a standard TFT-LCD factory.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.337-341
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• 1991

Fieldbus is a low level serial digital network which will be used for factory automation. This paper describes lower layer implementation of a Fieldbus network interface. Physical layer provides hardware interface between IBM-PC and Fieldbus. Also, physical layer uses manchester coding, shielded twisted pair lines and RS-485 electrical standard. Data link layer includes Intel's iDCX96 real time executive for 8797 one chip microcontroller and Fieldbus data link protocol software.