• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.170-177
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• 2010

Poker is a game of imperfect information where competing players must deal with multiple risk factors stemming from unknown information while making the best decision to win, and this makes it an interesting test-bed for artificial intelligence research. This paper introduces a new learning AI algorithm with embedded opponent modeling that can be used for these types of situations and we use this AI and apply it to a poker program. The new AI will be based on several graphs with each of its nodes representing inputs, and the algorithm will learn the optimal decision to make by updating the weight of the edges connecting these nodes and returning a probability for each action the graphs represent.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.273-284
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• 2022

The goal of this paper is to propose the use of machine learning platforms in education to train learners to recognize data biases. Learners can cultivate the ability to recognize when learners deal with AI data and systems when they want to prevent damage caused by data bias. Specifically, this paper presents a method of data bias education using MachineLearningforKids, focusing on the case of AI baseball referee. Learners take the steps of selecting a specific topic, reviewing prior research, inputting biased/unbiased data on a machine learning platform, composing test data, comparing the results of machine learning, and present implications. Learners can learn that AI data bias should be minimized and the impact of data collection and selection on society. This learning method has the significance of promoting the ease of problem-based self-directed learning, the possibility of combining with coding education, and the combination of humanities and social topics with artificial intelligence literacy.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.694-702
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• 2022

This paper is the UX-based technology strategy research which is a solution to how conversational AI can be ethically evolved in the Metaverse environment. Since conversational AI influences people's on-offline decision-making factors through interaction with people, the Metaverse AI ethics must be reflected. In the machine learning process of conversational AI, cultural codes along with user's personal experience data must be included and considered to reduce the error value of user experience. Through this, the super-personalized Metaverse service can evolve ethically with social values. With above hypothesis as a result of the study, a conceptual model of a forward-looking perspective was developed and proposed by adding user experience data to the machine learning (ML) process for context-based interactive AI in the Metaverse service environment.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.213-221
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• 2021

With the advent of the 4th industrial revolution, the need for artificial intelligence education has increased. The online learning environment caused by COVID-19 made it possible to use variety of artificial intelligence platforms. In this study, an aritificial intelligence class plan was developed and proposed to achieve the goal of artificial intelligence education using an AI platform. The AI platform used is AI for Oceans, With the theme of creating a program for the environment, designed a 6-hour project class using Novel Engineering-based on STEAM model. Students experience AI for Oceans enough time and learn supervised learning by experience. Based on understanding of supervised learning, students design their own programs for the environment using Entry's AI blocks. In this study, for AI convergence education, this lesson was developed and presented with the goal of acquiring the creative problem solving ability and integrated thinking ability by using the principles of artificial intelligence to solve problems.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.3-12
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• 2020

In this paper, we introduce ETRI AI Strategy #1, "Proactively Securing AI Core Technologies." The first goal of this strategy is to innovate artificial intelligence (AI) service technology to overcome the current limitations of AI technologies. Even though we saw a big jump in AI technology development recently due to the rise of deep learning (DL), DL still has technical limitations and problems. This paper introduces the four major parts of the advanced AI technologies that ETRI will secure to overcome the problems of DL and harmonize AI with the human world: post DL technology, human-AI collaboration technology, intelligence for autonomous things, and big data platform technology.

• The Journal of Economics, Marketing and Management
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• v.11 no.6
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• pp.1-8
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• 2023

Purpose: In the era of the Fourth Industrial Revolution, individuals equipped with fundamental understanding and practical skills in artificial intelligence (AI) are essential. This study aimed to validate the effectiveness of AI education for enhancing AI literacy among business major student. Research design, data and methodology: Data for analyzing the effectiveness of the AI Fundamental Education Program for business major students were collected through surveys conducted at the beginning and end of the semester. Structural equation modeling was employed to perform basic statistical analyses regarding gender, grade, and prior software (SW) education duration. To validate the effectiveness of AI education, seven variables - AI interest, AI perception, data analysis/utilization, AI projects, AI literacy, AI self-efficacy, and AI learning persistence - were defined and derived. Results: All seven operationally defined variables showed statistically significant positive changes. The average differences were observed as follows: 0.47 for AI interest, 0.32 for AI perception, 0.37 for data analysis/utilization, 0.27 for AI projects, 0.25 for AI literacy, 0.39 for AI self-efficacy, and 0.41 for AI learning persistence. Statistically, AI interest exhibited the most substantial average difference. Conclusions: Through this study, the applied AI education was confirmed to enhance learners' overall competencies in AI, proving its utility and effectiveness in AI literacy education for business major students. Future research endeavors should build upon these results, focusing on ongoing studies related to AI education programs tailored to learners from diverse academic backgrounds and conducting continuous efficacy evaluations.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.299-316
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• 2023

With the importance and necessity of using AI in the field of education, this study aims to explore elementary school teachers' perceptions of using Artificial Intelligence (AI) in mathematics education. For this purpose, we conducted a survey using a 5-point Likert scale with 161 elementary school teachers and analyzed their perceptions of mathematics education with AI via four categories (i.e., Attitude of using AI, AI for teaching mathematics, AI for learning mathematics, and AI for assessing mathematics performance). As a result, elementary school teachers displayed positive perceptions of the usefulness of AI applications to teaching, learning, and assessment of mathematics. Specifically, they strongly agreed that AI could assist personalized teaching and learning, supplement prerequisite learning, and analyze the results of assessment. They also agreed that AI in mathematics education would not replace the teacher's role. The results of this study also showed that the teachers exhibited diverse perceptions ranging from negative to neutral to positive. The teachers reported that they were less confident and prepared to teach mathematics using AI, with significant differences in their perceptions depending on whether they enacted mathematics lessons with AI or received professional training courses related to AI. We discuss the implications for the role of teachers and pedagogical supports to effectively utilize AI in mathematics education.

• East Asian mathematical journal
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• v.39 no.2
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• pp.93-117
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• 2023

The aim of this study is to explore the pre-service math teachers' characteristics of education to develop their AI literacy and SW competency, and to derive some implications. We conducted a 14-hours AI and SW education program for pre-service teachers with theory and practice, and an analysis on class observation data, video frames of classes and interview, Python programming assignments and papers. The results of this case study for 3 pre-service teachers are as follows. First, two students understood artificial neural network and deep learning system accurately, furthermore, all students conducted a couple of explorations related with performance improvement of deep learning system with interest. Second, coding and exploration activities using Python improved students' computational thinking as well as SW competency, which help them give convergence education in the future. Third, they responded positively to the necessity of AI literacy and SW competency development, and to applying coding to math class. Lastly, it's necessary to endeavor to give a coding education to the student's eye level according to his or her prerequisite and to ease the burden of student's studying AI technology.