• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.87-98
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• 2019

As artificial intelligence is actively studied, it is being applied to various fields such as image, video and natural language processing. The natural language processing, in particular, is being studied to enable computers to understand the languages spoken and spoken by people and is considered one of the most important areas in artificial intelligence technology. In natural language processing, it is a complex, but important to make computers learn to understand a person's common sense and generate results based on the person's common sense. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn common sense easily from computers. However, the existing knowledge graphs are organized only by focusing on specific languages and fields and have limitations that cannot respond to neologisms. In this paper, we propose an intelligent chatbotsystem that collects and analyzed data in real time to build an automatically scalable knowledge graph and utilizes it as the base data. In particular, the fine-tuned BERT-based for relation extraction is to be applied to auto-growing graph to improve performance. And, we have developed a chatbot that can learn human common sense using auto-growing knowledge graph, it verifies the availability and performance of the knowledge graph.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.533-538
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• 2021

The purpose of this study is to consider the perception, which education-related workers and parents have, associated with guaranteeing the right to learn for youth migrants in Korea. The study was especially intended to analyze the opportunities and adaptions of youth migrants and make policy suggestions accordingly. To this purpose, this study implemented one-on-one in-depth interviews with research participants to collect and analyze data. This research yielded four categories: initial settlement, social security support as a fundamental right, learning rights guarantees, and psychological support. Also, seven subcategories were elicited. The suggestions based on results are followings: first, the legal basis for learning support for middle-aged adolescents; second, curriculum composition for school maladjusted middle-aged adolescents; third, individualized support system; fourth, the active promotion of support systems such as information provision; fifth, the diversification of policy for psychological stability.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.487-495
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• 2021

The purpose of this study is to make it easier, more fun and more convenient to learn a foreign language through the development of an efficient audio contents platform that utilizes each person's native language ability. In order to achieve the goal to produce audio contents centering on the native language used in real life. Contents that are created without much effort in daily life could be used as precious contents for foreign language learners to learn the natural use of the language. Currently, most of the online foreign language learning platforms have problems with the contents depletion and the low practicality of contents. Accordingly, I am expecting this platform improves the existing shortcomings, giving foreign language learners the opportunity to learn a foreign language more realistically and at the same time giving native speakers an opportunity to generate additional revenue by utilizing their spare time.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.427-436
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• 2021

The purpose of this study is to implement a machine learning language model that learns literary texts. Literary texts have an important characteristic that pairs of question-and-answer are not frequently clearly distinguished. Also, literary texts consist of pronouns, figurative expressions, soliloquies, etc. They hinder the necessity of machine learning using literary texts by making it difficult to learn algorithms. Algorithms that learn literary texts can show more human-friendly interactions than algorithms that learn general sentences. For this goal, this paper proposes three text correction tasks that must be preceded in researches using literary texts for machine learning language model: pronoun processing, dialogue pair expansion, and data amplification. Learning data for artificial intelligence should have clear meanings to facilitate machine learning and to ensure high effectiveness. The introduction of special genres of texts such as literature into natural language processing research is expected not only to expand the learning area of machine learning, but to show a new language learning method.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.231-240
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• 2023

It remains a challenge for robots to learn avoiding obstacles automatically in path planning using deep reinforcement learning (DRL). More and more researchers use DRL to train a robot in a simulated environment and verify the possibility of DRL to achieve automatic obstacle avoidance. Due to the influence factors of different environments robots and sensors, it is rare to realize automatic obstacle avoidance of robots in real scenarios. In order to learn automatic path planning by avoiding obstacles in the actual scene we designed a simple Testbed with the wall and the obstacle and had a camera on the robot. The robot's goal is to get from the start point to the end point without hitting the wall as soon as possible. For the robot to learn to avoid the wall and obstacle we propose to use the double deep Q networks (DDQN) to verify the possibility of DRL in automatic obstacle avoidance. In the experiment the robot used is Jetbot, and it can be applied to some robot task scenarios that require obstacle avoidance in automated path planning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.193-198
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• 2023

We study a learner-customized lecture recommendation project using deep learning. Recommendation systems can be easily found on the web and apps, and examples using this feature include recommending feature videos by clicking users and advertising items in areas of interest to users on SNS. In this study, the sentence similarity Word2Vec was mainly used to filter twice, and the course was recommended through the Surprise library. With this system, it provides users with the desired classification of course data conveniently and conveniently. Surprise Library is a Python scikit-learn-based library that is conveniently used in recommendation systems. By analyzing the data, the system is implemented at a high speed, and deeper learning is used to implement more precise results through course steps. When a user enters a keyword of interest, similarity between the keyword and the course title is executed, and similarity with the extracted video data and voice text is executed, and the highest ranking video data is recommended through the Surprise Library.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.

• The Journal of Bigdata
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• v.8 no.2
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• pp.15-25
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• 2023

Extreme learning machines (ELMs) are a major analytical method in various prediction fields. ELMs can accurately predict even if the data contains noise or is nonlinear by learning the complex patterns of time series data through optimal learning. This study presents the recent trends of machine learning models that are mainly studied as tools for analyzing online time series data, along with the application characteristics using existing algorithms. In order to efficiently learn large-scale online data that is continuously and explosively generated, it is necessary to have a learning technology that can perform well even in properties that can evolve in various ways. Therefore, this study examines a comprehensive overview of the latest machine learning models applied to big data in the field of time series prediction, discusses the general characteristics of the latest models that learn online data, which is one of the major challenges of machine learning for big data, and how efficiently they can learn and use online time series data for prediction, and proposes alternatives.

• Journal of Educational Research in Mathematics
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• v.18 no.3
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• pp.407-434
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• 2008

This research is to analyze the descriptions in the statistic chapter of the grade 7's current textbooks. The analysis is based on the distribution concepts suggested by Nam(2007). Thus we assumed that the goal of this statistic chapter is to establish concepts on the distributions and to learn ways of communication and comparison through distributional presentations. What we learned and wanted to suggest through the study is the followings. 1) Students are to learn what the distribution is and what are not. 2) Every kinds of presentational form of distributions is to given its own right to learn so that students are more encouraged to learn them and use them more adequately. 3) Density histogram is to be introduced to extend student's experiences viewing an area as 3 relative frequency, which is later to be progressed into a probability density. 4) Comparison of two distributions, especially through frequency polygons, is to be an hot issue among educational stakeholder whether to include or not. It is very important when stochastic correlations be learned, because it is nothing but a comparison between conditional distributions. 5) Statistical literacy is also an important issue for student's daily life. Especially the process ahead of the data collection must be introduced so that students acknowledge the importance of accurate and object-oriented data.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.643-651
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• 2016

This study considers the value of English-language films as material for learning a foreign tongue using posh discourse. In daily life, when we decline an invitation or convey unpleasant information to a listener, we use polite expressions; we are careful with our words. English language learners need to learn polite expressions in order to interact peacefully with others; doing so can minimize conflict, which is inherent in social relationships. This study uses the British drama Downton Abbey, which is about aristocracy. This study analyzes the posh discourse used in Downton Abbey and insists that students need to learn it explicitly. It is important to learn the polite expressions of this authentic drama in a real classroom. This study suggests that students work in groups to create a short video, and to try to understand the characters' personalities. Movies, TV dramas, and sitcoms provide great content that shows the various functions of the language that students want to learn. As a source of learning material, film can help improve students' motivation and interest in learning a foreign language.