• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2297-2311
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• 1994

This paper presents the study on the development of a DNC, system IPIS(Interactive Plant Information System)/DNC, which can manage NC machines and robots as a distributed control method in the machine. processing factory. The IPIS/DNC system is composed of a host computer, satellites and NC machines. A set of software modules are developed on the host computer and the satellites separately. By modularizing each functions of the IPIS/DNC system and using multi-taking method, the functions such as NC program management, NC program distribution, and shop monitoring can be performed on the host computer, and the functions such as NC program transfer to the NC machines, and NC program editing can be performed on the satellites. A Relational database which is linked with job scheduling system is used for IPIS/DNC system.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.8
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• pp.640-646
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• 2003

We presents an edge-based point correlation algorithm for fast and stable visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties in applying automated inspection systems to real factory environment. First of all, NGC algorithms involve highly complex computation and thus require high performance hardware for realtime process. In addition, lighting condition in realistic factory environments is not stable and therefore intensity variation from uncontrolled lights gives many troubles for applying NGC directly as pattern matching algorithm. We propose an algorithm to solve these problems, using thinned and binarized edge data, which are obtained from the original image. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the computational complexity. Matching edges instead of using original gray-level image pixels overcomes problems in NGC method and pyramid of edges also provides fast and stable processing. All proposed methods are proved by the experiments using real images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.754-755
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• 2017

CPS refers to a computer-based component and system that closely connects various complicated processes and information of real space with the cyber space that provides data access and processing services through internet. In this paper, CPS was applied to shop-floor as a part of CPS research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.923-924
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• 2014

스마트 기기의 보급은 산업 및 개인생활의 많은 영향을 주었으며, 스마트 기기를 이용한 다양한 서비스 모델에 대한 연구가 진행되고 있다. 특히 오픈 소스 프로그래밍 언어로 부각되고 있는 프로세싱(Prodessing) 개발 환경을 사용하여 안드로이드 운영체제를 사용하는 모바일 장치에 대한 어플리케이션을 간단하고 쉽게 만들 수 있다는 장점이 있다. 이런한 프로세싱 기반의 개발환경을 통하여 개발된 안드로이드 어플리케이션은 식물공장의 전체적인 환경요소를 모니터링 함과 동시에 단순화된 식물공장 내부를 원격에서 관리하고 제어할 수 있는 종합적인 관리시스템을 설계하고자 한다. 본 논문에서는 MIT 미디어 구룹에서 시작된 프로세싱 개발환경을 통하여 안드로이드 기반의 어플리케이션을 개발하여 식물공장에 대한 환경 모니터링과 환경 제어시스템을 제안하고자 한다.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.89-97
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• 2021

Voice interface can not only make daily life more convenient through artificial intelligence speakers but also improve the working environment of the factory. This paper presents a voice-assisted work management system that supports both speech and speaker recognition. This system is able to provide machine control and authorized worker authentication by voice at the same time. We applied two speech recognition methods, Google's Speech application programming interface (API) service, and DeepSpeech speech-to-text engine. For worker identification, the SincNet architecture for speaker recognition was adopted. We implemented a prototype of the work management system that provides voice control with 26 commands and identifies 100 workers by voice. Worker identification using our model was almost perfect, and the command recognition accuracy was 97.0% in Google API after post- processing and 92.0% in our DeepSpeech model.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.240-247
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• 2023

In this paper, effective data management in industrial sites such as intelligent factories using time series data was studied. For effective management of time series data, variables considering the significance of the data were used, and hyper parameters calculated through LSTM AE were applied. We propose an optimized modeling considering the importance of each data section, and through this, outlier data of time series data can be efficiently processed. In the case of applying data significance and applying hyper parameters to which the research in this paper was applied, it was confirmed that the error rate was measured at 5.4%/4.8%/3.3%, and the significance of each data section and the significance of applying hyper parameters to optimize modeling were confirmed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.543-544
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• 2021

본 논문에서는 스마트 팩토리의 자동화 공정을 위하여 제품 자동 판별과 불량 시 작업자에게 자동으로 통보해주는 시스템을 설계한다. 생산라인의 효율을 극대화하기 위해서는 작업자의 개입이 적은 상태로 시스템에 의해서 자동으로 공정이 이루어져야 한다. 따라서 본 시스템을 적용해 작업자는 자동으로 돌아가는 라인에 크게 개입하지 않고 문제가 발생했을 때만 투입되어 조치할 수 있게 된다. 따라서 생산과 효율을 크게 증가시키면서 작업자의 실수를 미연에 방지하고 제품의 신뢰성을 향상시킬 수 있다.

• 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.

• 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 Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.257-270
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• 2022

In this paper, we design a smart factory risk improvement system and risk improvement method using AI edge devices. The smart factory risk improvement system collects, analyzes, prevents, and promptly responds to the worker's work performance process in the smart factory using AI edge devices, and can reduce the risk that may occur during work with improving the defect rate when workers perfom jobs. In particular, based on worker image information, worker biometric information, equipment operation information, and quality information of manufactured products, it is possible to set an abnormal risk condition, and it is possible to improve the risk so that the work is efficient and for the accurate performance. In addition, all data collected from cameras and IoT sensors inside the smart factory are processed by the AI edge device instead of all data being sent to the cloud, and only necessary data can be transmitted to the cloud, so the processing speed is fast and it has the advantage that security problems are low. Additionally, the use of AI edge devices has the advantage of reducing of data communication costs and the costs of data transmission bandwidth acquisition due to decrease of the amount of data transmission to the cloud.