• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.332-342
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• 2018

This study is aimed at suggesting information about technical choices for designing LED camping lights based on emotional lighting contents of integrated IOT and design areas which take a central role in creation and knowledge based industries and the procedure for materializing them. 'i-Light,' a portable LED camping light, is 'connected lighting' connecting men, space and emotion and a smart camping light based on IOT and emotional lighting contents. 'i-Light' has two functions. One is about lighting for adjusting color and color temperature naturally and the other is about safety for detecting harmful gases. 'i-Light' also has various emotional functions for experiencing interaction and taste of light. For the purpose, portable LED camping lights were designed, first of all, and then a highly color rendering/full-color lighting module, a smart sensor module and an IOT device platform were developed. In addition, efforts were made to establish detailed data about emotional lighting contents and to develop a Web application based on them. Finally, prototypes of portable LED camping lights were made to get a test bench and usability evaluation from related organizations. According to the results, all of 12 developed emotional lighting contents and three IOT safety sensors were suitable and prototypes were satisfactory. This paper will suggest a direction about actual technical choices for development of contents and products integrating artificial intelligence and big data and about the procedure for materializing them.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.277-280
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• 2016

Currently, systems for finding missing children are composed of using communication between a QR code and RFID chip, as the use of a smartphone. However, the current systems for finding missing children have limitations in that children can only be found if there are people in the surrounding area; there is an economic burden on parents required to purchase a smartphone for their children; along with difficulties in finding the missing children without the assistance of those in the surrounding area in critical situations such as a kidnapping, due to the limited duration of the battery life. In order to solve such problems, approaches need to be made from two perspectives: having someone in the surrounding area; and absence of anyone in the surrounding area. This thesis is centered on the development of a IoT (Internet of Things) system for finding missing children that combines two methods, namely, the method of finding missing children without a guardian in the surrounding area -within the limited space in which AP is installed by using a beacon and open source hardware being highlighted as the IoT technology - and the method of finding missing children with the smartphone application in which each individual becomes the Access Point (AP). The Main purpose is to provide accurate information of missing children's location for the 2situations and it is found that the accuracy of smartphones APP is 97.7% and security device AP is 91.1%.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.