• Proceedings of the Korean Society of Computer Information Conference
• /
• 2024.01a
• /
• pp.321-324
• /
• 2024

본 논문에서는 LLM(Large Language Models) 기반의 STT 결합 챗봇 시스템을 제안한다. 제조업 공장에서 안전 교육의 부족과 외국인 근로자의 증가는 안전을 중시하는 작업 환경에서 새로운 도전과제로 부상하고 있다. 이에 본 연구는 언어 모델과 음성 인식(Speech-to-Text, STT) 기술을 활용한 혁신적인 챗봇 시스템을 통해 이러한 문제를 해결하고자 한다. 제안된 시스템은 작업자들이 장비 사용 매뉴얼 및 안전 지침을 쉽게 접근하도록 지원하며, 비상 상황에서 신속하고 정확한 대응을 가능하게 한다. 연구 과정에서 LLM은 작업자의 의도를 파악하고, STT 기술은 음성 명령을 효과적으로 처리한다. 실험 결과, 이 시스템은 작업자의 업무 효율성을 증대시키고 언어 장벽을 해소하는데 효과적임이 확인되었다. 본 연구는 제조업 현장에서 작업자의 안전과 업무 효율성 향상에 기여할 것으로 기대된다.

• Bulletin of the Society of Naval Architects of Korea
• /
• v.57 no.1
• /
• pp.27-30
• /
• 2020

• Journal of Digital Convergence
• /
• v.16 no.1
• /
• pp.103-116
• /
• 2018

In this study, we investigated the influence of smart factory's implementing energy management system on innovation resistance and work performance. We surveyed and analyzed 211 employees of system construction companies in the metropolitan area. In order to increase the introduction performance through diffusion of the energy management system, it is found that it is important to support the organizational-level management strategy to reduce the user's resistance to innovation. In conclusion, this study has implications for positively leading to the efficiency and management performance of the manufacturing process of the manufacturing enterprises, and it is necessary to follow - up studies considering the characteristics of the energy management system and the expansion of the research scope in the future.

• Journal of Practical Engineering Education
• /
• v.9 no.1
• /
• pp.49-56
• /
• 2017

Recently, due to Industry 4.0, there is a great interest in smart factory for productivity improvement and customer satisfaction in manufacturing industry, and construction is also actively pursued by government support. In particular, data integration and fieldbus communication technology to build an efficient production system are essential. Fieldbus is an open control system that is not tied to a specific vendor system and has various advantages such as compatibility with other products, accuracy of data transmission, and remote diagnosis. However, there are no educational equipment for training field buses, training courses and examples for practical training, and there are many limitations in improving the practical skills needed for building smart factories in the industrial field. Therefore, this study develops PLC based fieldbus education equipment and training course based on previous research results that selected PLC and communication technology suitable for domestic industry field for practical fieldbus training and develops the training program of Ethernet IP, Profibus DP, Modbus, CC-Link, and DeviceNet. In addition, it is confirmed that the control and remote diagnosis of distributed field devices are possible by data collection and monitoring.

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

• Korean small business review
• /
• v.42 no.4
• /
• pp.251-283
• /
• 2020

The purpose of this study is to provide assistance to the establishment of related policies to improve the level of acceptance and use of smart factories for SMEs in Korea. To this end, the Unified Technology Acceptance Model (UTAUT) was extended to select additional factors that could affect the intention to accept technology, and to demonstrate this. To achieve the research objective, a questionnaire composed of 7-point Likert scales was prepared, and a survey was conducted for manufacturing-related companies. A total of 136 questionnaires were used for statistical processing. As a result of the hypothesis test, performance expectation and social influence had a positive (+) positive effect on voluntary use, but effort expectation and promotion conditions did not have a significant effect. As an extension factor, the network effect and organizational characteristics had a positive (+) effect, and the innovation resistance had a negative effect (-), but the perceived risk had no significant effect. When the size of the company is large, the perceived risk and innovation resistance are low, and the level of influencing factors for veterinary intentions, veterinary intentions, and veterinary behaviors are excluded. Through this study, factors that could have a positive and negative effect on the adoption (reduction) of smart factory-related technologies were identified and factors to be improved and factors to be reduced were suggested. As a result, this study suggests that smart factory-related technologies should be accepted.

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

• Smart Media Journal
• /
• v.11 no.9
• /
• pp.47-55
• /
• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.2
• /
• pp.41-47
• /
• 2021

Manufacturing is facing radical changes around the world. The manufacturing industry, which has been changing since Germany, is now being introduced, improved, and developed worldwide by manufacturers under the name of smart factory. By utilizing IT technologies such as artificial intelligence and cloud at the production site, the desire to break away from the past manufacturing environment is increasing. How these technologies will be efficient in the future, manufacturing worldwide now faces radical changes. The manufacturing industry, which has been changing since Germany, is now being introduced, improved, and developed worldwide by manufacturers under the name of smart factory. By utilizing IT technologies such as artificial intelligence and cloud at the production site, the desire to break away from the past manufacturing environment is increasing. Discussions continue on how these technologies can be used efficiently and effectively. Increasingly, the expansion of the range from factory areas to regions, countries, and around the world raises the need for international standards for interactions. In this paper, we propose a design and implementation method for managing facilities, sensors, etc. as assets and monitoring facility data collected through OPC UA.

• Proceedings of the Korea Technology Innovation Society Conference
• /
• 2016.10a
• /
• pp.250-254
• /
• 2016