• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.75-83
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• 2023

Smart manufacturing in Industry 4.0 is developing toward autonomous manufacturing as a last-mile technology. We investigate development trends in manufacturing innovation technologies, review major industrial intelligence projects currently carried out at ETRI, and infer directions of future technology developments.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• The Journal of Bigdata
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• v.6 no.1
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• pp.115-125
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• 2021

Due to various ICT Technology innovations and Digital Transformation, the Internet of Things(IoT) environment is increasingly requiring intelligence, decentralization, and automated service, especially an advanced and stable smart service environment in the Industrial Internet of Things(IIoT) where communication network(5G), data analysis and artificial intelligence(AI), and digital twin technology are combined. In this study, we propose IIoT Edge middleware systems for flexible interface with heterogeneous devices such as facilities and sensors at various industrial sites and for quick and stable data collection and processing.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.117-127
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• 2024

Purpose: The purpose is to conduct basic research to save energy and improve the safety of manufacturing plant infrastructure by comprehensively monitoring energy management, temperature, humidity, dust and gas, air quality, and machine operation status in small and medium-sized manufacturing plants. Method: To this end, energy-related data and environmental information were collected in real time through digital power meters and IoT sensors, and research was conducted to disseminate and respond to situations for energy saving through monitoring and analysis based on the collected information. Result: We presented an application plan that takes into account energy management, cost reduction, and safety improvement, which are key indicators of ESG management activities. Conclusion: This study utilized various sensor devices and related devices in a smart factory as a practical case study in a company. Based on the information collected through research, a basic system for energy saving and safety improvement was presented.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.256-258
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• 2021

최근 전세계 각 연구기관에서 CPS, 클라우드 컴퓨팅, 5G, 빅데이터, IIOT, Milk-run AI 알고리즘 등을 활용한 CIPs(Connected Industrial parks) 아키텍쳐가 다양하게 제안되고 있다. 평균적으로 한국의 중소기업은 기술력과 가격 경쟁력 문제로 많은 어려움을 겪고 있다. 미국, 일본, 유럽 등의 해외 선진국들에 비하여 기술력이 확실한 우위를 보이지 못하고 있으며, 중국, 베트남 등의 국가에 비하여는 제조 가격 경쟁력을 보이지 못하고 있다. 이러한 상황에서 한국의 중소 기업들은 지속 가능한 성장 방안을 찾기 위하여 많은 노력을 하고 있다. 재무적으로 한국의 중소기업들이 수익성을 향상시키기 위해서는 매출을 증대시키는 것 보다 비용을 절감하는 것이 효과적이다. 이러한 문제 의식 속에서 한국 CIPs에 위치한 중소 기업들의 비용 절감을 위한 방안으로서 VJP(Vehicle Junction Problem)를 주목하였다. 중소 기업의 최소 물류 비용 달성을 위한 방법으로 CIPs 결제 한국형 시스템을 연구하였다. 새로운 한국형 CIPs결제 시스템의 세부 항목을 크게 4가지 "데이터(Data)", "업무(Business)", "자금(Finance)", "기술(Technique)"로 구분하여 정리하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.47-52
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• 2021

Recently, demand for artificial intelligence solutions for production process management has been increasing in the manufacturing industry. However, through the application of AI solutions in the manufacturing industry, there are limitations to legacy smart factory solutions such as POP and MES.Therefore, in order to overcome this, this paper aims to improve production management efficiency by applying guidance, an artificial intelligence concept, to image recognition systems. In the system flow, As_is To be separated and actual work flow was applied, and the process was improved for overall productivity efficiency. The pre-processing plan for AI guidance learning was established and the relevant AI model was designed, developed, and simulated, resulting in a 97% recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.65-72
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• 2018

In this paper, we used LSTM as a method to detect abnormal behavior of motors. We fixed the high layout size to 1 and changed the range of the input values and the neural network structure to see what change in power consumption prediction. Now, as the fourth industrial revolution era, smart factories are attracting attention. All the physical actions of smart factories are done using motors. Continuous monitoring of motor malfunctions helps to detect malfunctions and efficient operation. However, it is difficult to acquire the power consumption constantly due to the influence of the noise. We have experimented with a simple experimental environment, a method of predicting similarity to input data by adjusting the range of the input data or by changing the neural network structure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.153-161
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• 2020

Due to the recent development of the 4th industrial revolution, Smart Factories that organically link various technologies such as AI, IoT, Cloud, and Big Data are increasing. Based on this, in the industrial environment where the internal process is controlled automatically, high availability should be secured against the loss caused when the internal process of the Smart Factory is stopped due to the determinism and malicious attack necessary to control the device such as PLC. The research and analysis of industrial network equipment and security equipment used in various industries can improve the efficiency and usability of industrial control systems in national infrastructure and can provide important feedback to build related infrastructure. Therefore, we compared industrial network equipment and security equipment in this paper in a variety of ways and expect to be used as a roadmap for developing technologies for industrial network equipment and industrial security equipment based on the results of this paper.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.7-18
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• 2021

Recently, various connected industrial parks (CIPs) architectures using new technologies such as cloud computing, CPS, big data, fifth-generation mobile communication 5G, IIoT, VR-AR, and ventilation transportation AI algorithms have been proposed in Korea. Korea's small and medium-sized enterprises do not have the upper hand in technological competitiveness than overseas advanced countries such as the United States, Europe and Japan. For this reason, Korea's small and medium-sized enterprises have to invest a lot of money in technology research and development. As a latecomer, Korean SMEs need to improve their profitability in order to find sustainable growth potential. Financially, it is most efficient for small and medium-sized Korean companies to cut costs to increase their profitability. This paper made profitability improvement by reducing costs for small and medium-sized enterprises located in CIPs in Korea a major task. VJP (Vehicle Action Program) was noted as a way to reduce costs for small and medium-sized enterprises located in CIPs in Korea. The method of achieving minimum logistics costs for small businesses through the Korean CIPs payment system was analyzed. The details of the new Korean CIPs payment system were largely divided into four types: "Business", "Data", "Technique", and "Finance". Cost Benefit Analysis (CBA) was used as a performance analysis method for CIPs payment systems.