• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.73-85
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• 2020

While Korea became one of manufacturing powers in the world through a fast-follower strategy as well as implementing the approach of advancing manufacturing business focused on quantitative input, The advent of the fourth industrial revolution and demand becoming more complicated than ever both require a system that quickly detects the change of markets in advance and reflects it in the manufacturing strategy. Accordingly, the introduction of a smart factory is not optional but mandatory in order to strengthen the competitiveness of manufacturing business using ICT. This paper aims to investigate key factors having influence on the intention to continuously use a smart factory, the innovative IT device, on the basis of the technology acceptance model. This paper analyzed the influence of the leadership of CEO, organizational learning and perceived switching costs on the intention to continuously use a smart factory by the parameters of perceived ease of use and usefulness, the major belief valuables of the IT acceptance model.

• Journal of IKEEE
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• v.23 no.3
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• pp.1092-1095
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• 2019

In this paper, we propose the development of an embedded board for construction of smart factory. The proposed embedded board for construction of smart factory consists of main module, ADC module, I/O module. Main module is a main calculating device which includes communication pard that allows interface with external device with using industrial protocol and is ported operating system makes board operating into. ADC module takes part in transferring digital signal has converted from electrical signal to the main module from the external sensor which is installed on the field. I/O module is an input and output module which transfers to the main module about a status, alarm, command signal of field device and it has a function that blocks external noises from field device with isolation circuit into it. In order to evaluate the performance of the proposed embedded board for construction of smart factory, it has been tested by an authorized testing institute. As a result, quantity of interacting protocol was 5, speed of hardware clock synchronization was under 10us and operating time of battery without source power was over 8 hours. It produced the same result as the world's highest level.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.1-18
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• 2021

The purpose of this study is to study the influencing factors that can affect the acceptance intention and acceptance behavior of the 4th Industrial Revolution and smart factory-related technologies by using the expanded UTAUT. Through this, by grasping which influencing factors affect the introduction and acceptance of related technologies, it is to derive strategies for responding to the fourth industrial revolution by manufacturing companies and accepting smart factory related technologies. A survey was conducted on various manufacturing companies, and 167 copies were used for research. As a result of the testing of research hypotheses, performance expectation, social impact, promotion conditions, network effect, and innovation have a positive (+) significant effect on acceptance intention. However, expectation of effort had a positive (+) effect on acceptance intention, but was not significant. Acceptance intention was tested to have a positive (+) significant effect on acceptance behavior. Therefore, factors that should be improved by individual manufacturing companies in the process of responding to the 4th industrial revolution and the introduction and acceptance of smart factory-related technologies are clearly presented.

• 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.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.179-189
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• 2018

Currently, research on smart factories is steadily being carried out in terms of implementation strategies and considerations in construction. Various studies have not been conducted on companies that introduced smart factories. This study conducted a questionnaire survey for SMEs applying the basic stage of smart factory. And the cluster analysis was conducted to examine the characteristics of the company. In addition, we conducted Decision Tree and Naive Bay to examine how the characteristics of a company are derived and compare the results. As a result of the cluster analysis, it was confirmed that the group was divided into the high satisfaction group and the low satisfaction group. The decision tree and the Naive Bay analysis showed that the higher satisfaction group has high productivity.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.5
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• pp.479-485
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• 2013

In this paper, we propose a smart farming system using the visible light communication based on the software defined radio (SDR) technology and the conventional RF radio. The proposed system can continuously monitor growth environments of the LED plant factory and automatically control the LED plant factory to keep optimal growth environments. Furthermore, by creating a database from various growth factors, the LED plant factory can be efficiently managed.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.196-205
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• 2021

As industrial safety increases, various industrial accident prevention technologies using smart factory technology are being studied. However, small and medium enterprises (SMEs), which account for the majority of industrial accidents, are having difficulties in preventing industrial accidents by applying these smart factory technologies due to practical problems. In this study, customized monitoring and warning systems for each type of industrial accident were developed and applied to the actual field. Through this, we demonstrated industrial accident prevention technology through appropriate smart factory technology used by SMEs. A customized monitoring system using vision, current, temperature, and gas sensors was established for the four major disaster types: worker body access, short circuit and overcurrent, fire and burns due to high temperature, and emission of hazardous gas. In addition, a notification method suitable for each work environment was applied so that the monitored risk factors could be recognized quickly, and real-time data transmission and display enabled workers and managers to understand the disaster risk effectively. Through the application and demonstration of these appropriate smart factory technologies, the spread of these industrial safety technologies is to be discussed.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.77-87
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• 2021

Smart Factory refers to a factory that can be controlled by itself with an intelligent factory that improves productivity, quality and customer satisfaction by combining the entire process of manufacturing and production with digital automation solutions. The manufacturing industry around the world is rapidly changing, with Germany, Europe, and the United States at the center. In order to cope with such changes, the Korean government is also implementing a policy to spread the supply of smart factories for small and medium-sized companies, and related ministries and agencies such as the Ministry of Commerce, Industry and Energy, the Ministry of SMEs and Venture Business, the Korea Institute of Technology and Information Promotion, and local technoparks, as well as large companies such as Samsung, SK and LG are actively investing in smart manufacturing projects to support smart factories[1]. Factory Automation (FA) construction has many issues regarding the connection of heterogeneous equipment. The most difficult aspect of configuring various communications from various equipment is the reason. Although it may not be known if there are standards or products made up of the same company, it is not easy to build equipment that is old, up-to-date, and different use environments through a series of communications. To solve this problem, we would like to propose a method of communication using Modbus, one of FieldBus, which is one of the many industrial devices of PLC, a representative facility control system, and is used as a communication standard.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.219-227
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• 2019

Manufacturing methods have been changed from labor-intensive methods to technological intensive methods centered on manufacturing facilities. As manufacturing facilities replace human labour, the importance of monitoring and managing manufacturing facilities is emphasized. In addition, Big Data technology has recently emerged as an important technology to discover new value from limited data. Therefore, changes in manufacturing industries have increased the need for smart factory that combines IoT, information and communication technologies, sensor data, and big data. In this paper, we present strategies for existing domestic manufacturing factory to becom big data based smart-factory through technologies for distributed storage and processing of manufacturing facility data in MongoDB in real time and visualization using R programming.