• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.147-148
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• 2023

실내 내비게이션 기술은 시설물 구조에 익숙하지 않은 방문자의 길 안내뿐만 아니라 무인 이동체들의 위치를 파악하는 데 중요한 역할을 한다. 본 논문에서는 UWB 기술을 활용하여 타깃 영역 안에서의 좌표를 추출하고 이를 활용하여 길 안내를 해주는 앱을 개발하였다. 개발된 앱은 보정 기법을 통해 사용자의 실제 위치와 지도 위에 표시되는 위치 사이의 오차를 최소화하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1020-1021
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• 2023

장애인의 사회활동 증대는 개인의 삶의 질 향상 및 사회의 다양성과 포용성 증진에 중요하다. 특히 청각장애인은 환경적, 신체적 장애로 인해 사회적 접근성에 제한을 받는다. 이를 해결하기 위해 AR 글래스 기반의 NEAR를 개발하였다. NEAR는 음성자막 변환 및 위험 알람 기능을 제공하여 청각장애인의 사회활동 제약을 해소한다. STT 기능은 입력 음성을 텍스트로 변환하며, MFCC 기능은 다양한 상황의 소리를 감지하여 위험 알림을 제공한다. 이 연구는 청각장애인의 사회적 참여 향상 및 사회의 포용성 증진에 기여할 것으로 기대된다.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.447-449
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• 2022

This study was based on Android, one of the representative mobile platforms with the characteristics of connecting to the network anytime, anywhere and flexible mobility. In addition, using a demand prediction algorithm that can know the data of defective products based on AI, we will study the real-time monitoring UI design method based on Android studio with demand prediction data and company time series data.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.483-494
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• 2020

With the advent of the 4th Industrial Revolution, interest in AI education is increasing. In order to cultivate talented people with AI competencies who will lead the future, AI education must be conducted in a sound manner at the school site. Although AI education is being conducted at home and abroad, it was determined that the role of the AI education platform is important to implement better AI education, so this study investigated the perception of experts on the AI education platform. A perception survey was conducted based on five criteria: teaching and learning management, educational contents, accessibility, performance of AI education platform, and level suitability of elementary school students. As a results, the number of 103 educational experts selected 'Entry' as the most proper platform among the eight platforms - 'Machine learning for Kids', 'Teachable Machine', 'AI Oceans(code.org)', 'Entry', 'Genie Block', 'Elice', 'mBlock' and etc. Analysis shows that this is because 'Entry' provides quality educational content, has convenient accessibility, is easy to manage teaching and learning, as well as an AI education platform suitable for the level of elementary school. In order to apply various AI education platforms to the school field, it is necessary to train teachers in AI-related training to train them as AI education experts, and to continuously provide opportunities to experience AI education platforms. In this study, there are limitations to what is called 'a population perception survey'. because only 103 people were surveyed, and most of the experts are working in a specific area(Gyeonggi-do). In the future, it is judged that research targeting experts at the national level should be conducted to supplement these limitations.

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.117-132
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• 2020

The purpose of this study is to investigate elementary school teachers' awareness of Artificial Intelligence (AI) and find out how to apply it in elementary science education. The survey was conducted online and involved 95 teachers working in the metropolitan area. The results of this study are as follows. First, teachers need to learn about the general characteristics of AI and how to apply it to education. Second, science classes had the highest preference for AI among elementary school subjects. Third, the preference for AI application by elementary science field was 68.4% for earth and space, 54.7% for exercise and energy, 32.6% for matter, 27.4% for life. Fourth, AI-based Science Education (AISE) teaching- learning strategies were developed based on AI characteristics and the changing perspective of elementary science education, AISE's teaching-learning strategies are five: 'automation', 'individualization', 'diversification', 'cooperation' and 'creativity' and teachers can use them in teaching design, class practice and evaluation stages. Finally, the creative problem-solving Doing Thinking Making Sharing (DTMS) model was devised to implement the creativity strategy in AISE. This model consists of four-steps teaching courses: Doing, Thinking, Making and Sharing based on the empirical learning theory. In the future, follow-up research is needed to verify the effectiveness of this model by applying it to elementary science education.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.89-98
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• 2021

The Ministry of National Defense aims to create an environment in which transparent and reasonable defense policies can be implemented in real time by establishing the vision of smart defense innovation based on the Fourth Industrial Revolution and promoting innovation in technology-based defense operation systems. Artificial intelligence (AI) based defense technology is at the level of basic research worldwide, includes no domestic tasks, and involves classified military operation data and command control/decision information. Further, it is needed to secure independent technologies specialized for our military. In the army, military power continues to decline due to aging and declining population. In addition, it is expected that there will be more than 500,000 units should be managed simultaneously, to recognize the battle situation in real time on the future battlefields. Such a complex battlefield, command decisions will be limited by the experience and expertise of individual commanders. Accordingly, the study of AI core technologies supporting real-time combat command is actively pursued at home and abroad. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable AI digital staff based on data science. This paper describes the recent research trends in AI military staff technology supporting commander decision-making, broken down into five key areas.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.927-934
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• 2021

AI(Artificial intelligence) is gradually taking up a large part of our lives, and the pace of AI development is accelerating. It is called ACT that develop students' computational thinking in the way artificial intelligence learns. Among ACTs, pattern recognition is an essential factor in efficiently solving problems. Pattern analysis is part of the pattern recognition process. In fact, Netflix's personalized movie recommendation service and what it named Covid-19 after repeated symptoms are all the results of pattern analysis. While the importance of ACT, including pattern recognition, is highlighted, software education for kindergarten and elementary school lower grades is much insufficient compared to foreign countries. Therefore, this study aims to design and develop textbooks for the development of artificial intelligence-based computational thinking through pattern analysis for kindergarten students.

• Smart Media Journal
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• v.12 no.4
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• pp.41-46
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• 2023

UWB is a type of technology used for indoor positioning and is characterized by higher accuracy than RSSI-based schemes. Mobile equipment operating based on ROS can monitor the environment around the equipment using lidar and cameras. When applying the loop closing technique to determine the starting position in this monitoring process, the existing method has a problem of low accuracy because the closing operation occurs only when there are feature points on the image. In this paper, to solve this problem, we designed a system that increases the accuracy of loop closing work by providing location information by mounting a UWB tag on a mobile device. In addition, the accuracy of the UWB-based indoor positioning system was evaluated through experiments, and it was verified that it could be used for loop closing techniques.