• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.131-142
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• 2018

In Korea, for the past 50 years, industrial parks have played an important role in economic growth as a cluster of national key industries. However, due to various problems of these old industrial parks, they are weakening competitiveness. It is necessary to be converted into a model for the management and fostering of high-tech industrial complex park by classifying them into development plans, management plans, and support plans according to types and characteristics of industrial parks. For this purpose, we propose CIPs (Connected-Industrial parks) using new technologies such as Cloud Computing, RFID, WSN, CPS, and Big Data analysis based on IoT. It is a hub that supports various services in transportation, warehousing and manufacturing fields while possessing and operating physical assets as concept. each CIP (Connected-Industrial park) is connected and expanded Through such CIPs, network-type collaborative manufacturing and intelligent logistics innovation enables cost reduction, delivery shortening, quality improvement.

• Information Systems Review
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• v.19 no.2
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• pp.95-113
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• 2017

The advent of information communication technology or the Fourth Industrial Revolution facilitated the fusion of equipment and management systems, such as Manufacturing Execution System, Enterprise Resource Planning, and Product Lifecycle Management, in the successful implementation of smart factories. The government supports the early adoption of these systems in small and medium companies to enhance their global competitiveness in producing products that can be recognized in a dramatically changing manufacturing environment. This study introduces smart factories to improve company competitiveness and address influences from the government assistance, CEO leadership, external consultancy, and organizational participation. We analyzed 101 results received from the questionnaires circulated to small- and medium-sized manufacturing companies. Given a successful smart factory implementation, company competitiveness is the factor that mostly influences organizational participation, government assistance, external consultancy, and CEO leadership. This study suggests several perspectives to implement a smart factory, which is the most important aspect of company competitiveness.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

• The Journal of Bigdata
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• v.4 no.1
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• pp.1-10
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• 2019

As FTA is spreading, it is becoming more and more necessary to improve the accuracy and reliability of the country of origin information at the domestic trading stage in preparation for the actual surveys expected to surge in the near future. However, there are many problems in collecting and managing information related to origin. It is pointed out that the shortage of export-oriented profits and the incentive for issuance of FTA-related profits, as well as the lack of information on the distribution and management of origin information of domestic manufacturing companies are also pointed out as important causes. In this paper, we propose a method to improve the efficiency of management and circulation of smooth FTA origin (comprehensive) certificate of Korean companies and to improve reliability through manipulation prevention by building prototype of origin information management system based on private block chain Hyperledger. The block chain, called Distributed Ledger or Trusted Internet, provides a technical infrastructure that enables various related companies to distribute origin information with high reliability and immediate distribution in the supply chain, but research on its application is still in the beginning stage.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Korean small business review
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• v.42 no.1
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• pp.43-56
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• 2020

The technology conflict between the U.S. and China is deepening recently. The U.S.-China battle began as a national security issue but is comprehending as a U.S.'s check for China's rapid technological advancement. China is rapidly growing in several indexes including R&D expenditure, patent application, and publications, and is challenging the U.S. in 5G and Artificial Intelligence. In 2018, Huawei became the largest 5G network/equipment provider and second largest smart phone manufacturer in the world. Now, Huawei is outperforming at AI chipset manufacturing, Bigdata analysis and cloud, positioning to become a critical player in the 4th industrial revolution. The purpose of this research is to analyze the effect of recent Huawei issues to Korean SMEs focusing on the relation between Huawei and Korean companies; the cooperation status from the Global Value Chain (GVC) perpsective, and Korean government's policies related to Huawei's information security issues will be the three main frames for the analysis. Then, this research proposes policy implications such as increasing Korea's competitiveness in manufacturing and information security.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.789-799
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• 2018

This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.

• Information Systems Review
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• v.25 no.3
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• pp.199-220
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• 2023

Projects to deploy and diffuse smart factories in South Korea are aimed at enhancing national manufacturing competitiveness. However, a significant portion of deployed companies remain at the basic stage and struggle to utilize smart factories regularly. Existing studies have primarily focused on the technical aspects of smart factories, using data analytics and case studies, leading to a gap in empirical research on continuous use and upgrade intentions. This study identifies key factors influencing smart factory usage and user satisfaction, drawing on the Information Systems Success Model (ISSM) and previous research. It empirically examines the impact of these factors on continuous use intention, management performance, and advancement acceptance intention through smart factory usage and user satisfaction. A structural equation model is employed to validate the research hypotheses, using survey data from 287 small and medium-sized manufacturing enterprises (SMEs) that have adopted smart factories. Results demonstrate that system quality, information quality, service quality, and government support significantly affect smart factory usage, while service quality and government support influence user satisfaction. Furthermore, smart factory usage and user satisfaction have positive effects on management performance, continuous use intention, and subsequently advancement acceptance intention. This study provides novel insights by demonstrating the specific impact mechanisms of smart factory user satisfaction on the business and the intentions of manufacturing SMEs regarding continuous use and advancement acceptance, leveraging the ISSM.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.203-212
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• 2019

Along with the recent fourth industrial revolution, the world's manufacturing powerhouses are pushing for national strategies to revive the sluggish manufacturing industry. Moon Jae-in, the government is in accordance with the trend, called 'advancement of science and technology is leading the fourth round of the Industrial Revolution' strategy. Intelligent information technology such as IoT, Cloud, Big Data, Mobile, and AI, which are key technologies that lead the fourth industrial revolution, is promoting the emergence of new industries such as robots and 3D printing and the smarting of existing major manufacturing industries. Advances in technologies such as smart factories have enabled IoT-based sensing technology to measure various data that could not be collected before, and data generated by each process has also exploded. Thus, this paper uses data generators to generate virtual data that can occur in smart factories, and uses them to analyze the cause of the defect in real time and to predict the replacement cycle of the facility.