• Journal of Internet of Things and Convergence
• /
• v.8 no.4
• /
• pp.49-55
• /
• 2022

This study aimed to examine the meaning of adult learners' experience in which they performed a convergence program for the self-confidence improvement of disabled persons with brain lesion who were daycare center users. For the goal, the study collected data through a 5-session profound interview with those disabled persons and then through this author's observation. This study analyzed all the data and, as a result, categorized three significant themes that best represented the above mentioned meaning, which are 'tension of beginning', 'joy of being in company with others' and 'I as the present being'. With those meaningful themes taken into serious consideration. Finally, this study suggested that field programs for social welfare practice in better connection with adult learners' major should be researched and developed.

• The Journal of Korea Robotics Society
• /
• v.15 no.4
• /
• pp.398-403
• /
• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.9
• /
• pp.1267-1270
• /
• 2021

In this paper, we propose a distributed routing algorithm for mobile ad hoc networks (MANET) where mobile devices can be utilized as relays for communication between remote source-destination nodes. The objective of the proposed algorithm is to minimize the end-to-end communication delay caused by transmission failure with deep channel fading. In each hop, the node needs to select the next relaying node by considering a tradeoff relationship between the link stability and forward link distance. Based on such feature, we formulate the problem with partially observable Markov decision process (MDP) and apply deep reinforcement learning to derive effective routing strategy for the formulated MDP. Simulation results show that the proposed algorithm outperforms other baseline schemes in terms of the average end-to-end delay.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.406-409
• /
• 2023

With the explosion of information, recommendation algorithms are becoming increasingly important in providing people with appropriate content, enhancing their online experience. In this paper, we propose a recommender system using advanced deep reinforcement learning(DRL) techniques. This method is more adaptive and integrative than traditional methods. We selected the MovieLens dataset and employed the precision metric to assess the effectiveness of our algorithm. The result of our implementation outperforms other baseline techniques, delivering better results for Top-N item recommendations.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.633-642
• /
• 2021

This study explores the online learning experiences of Chinese foreign students in Korea by using the CQR process and method. To gather data, researchers conducted online, in-depth interviews with 15 Chinese university students in Korea who were enrolled in the spring and fall semesters in 2020. After compiling the research, the data were segmented into four domains and 13 categories, with 36 subcategories identified from among foreign students' online learning experiences. The results show that Chinese students perceived the convenience of online classes and personalized learning as its strength, but considered lowered motivation and lack of concentration as weaknesses. Also, they experienced an increase in the amount of learning, spending more time studying online, using personal learning strategies, and getting help from friends and the university's online learning system. Moreover, they experienced difficulties related to class notifications, guidance, and interactions with the instructors. Foreign students studying in Korea need their instructor's facilitation in order to understand and participate in online classes, reinforcing a student's self-directed learning ability, and need appropriate guidance and support in terms of the online class environment.

• Electronics and Telecommunications Trends
• /
• v.34 no.4
• /
• pp.55-64
• /
• 2019

In this study, we introduce trends in neural-network-based deep learning research applied to dialogue systems. Recently, end-to-end trainable goal-oriented dialogue systems using long short-term memory, sequence-to-sequence models, among others, have been studied to overcome the difficulties of domain adaptation and error recognition and recovery in traditional pipeline goal-oriented dialogue systems. In addition, some research has been conducted on applying reinforcement learning to end-to-end trainable goal-oriented dialogue systems to learn dialogue strategies that do not appear in training corpora. Recent neural network models for end-to-end trainable chit-chat systems have been improved using dialogue context as well as personal and topic information to produce a more natural human conversation. Unlike previous studies that have applied different approaches to goal-oriented dialogue systems and chit-chat systems respectively, recent studies have attempted to apply end-to-end trainable approaches based on deep neural networks in common to them. Acquiring dialogue corpora for training is now necessary. Therefore, future research will focus on easily and cheaply acquiring dialogue corpora and training with small annotated dialogue corpora and/or large raw dialogues.

• Journal of the Korean Institute of Gas
• /
• v.27 no.2
• /
• pp.49-56
• /
• 2023

As AI (Artificial Intelligence)-related technologies are highly developed due to the 4th industrial revolution, cases of applying AI in various fields are increasing. The main reason is that there are practical limits to direct processing and analysis of exponentially increasing data as information and communication technology develops, and the risk of human error can be reduced by applying new technologies. In this study, after collecting the data received from the 'remote potential measurement terminal (T/B, Test Box)' and the output of the 'remote rectifier' at that time, AI was trained. AI learning data was obtained through data augmentation through regression analysis of the initially collected data, and the learning model applied the value-based Q-Learning model among deep reinforcement learning (DRL) algorithms. did The AI that has completed data learning is put into the actual city gas supply area, and based on the received remote T/B data, it is verified that the AI responds appropriately, and through this, AI can be used as a suitable means for electricity management in the future. want to verify.

• Journal of the Korean association of regional geographers
• /
• v.22 no.2
• /
• pp.397-410
• /
• 2016

Although industrial complexes have played as an engine of the Korean economy for the last 40 years, the majority of industrial complexes shows limitations to the continuous growth such as a lack of innovation capabilities and social capital, conceived as a key to transforming into clusters of innovation. To overcome those problems, the Korean government embarked on the cluster policy from the mid 2000's, focusing on promoting the endogenous development capabilities of individual industrial complexes. Drawing upon the in-depth case study of the Gumi IT cluster, one of the representative large-scale industrial complexes in Korea, the authors conclude that the cluster policy has contributed to making the Gumi IT cluster enhance the capabilities of endogenous development through the facilitation of self-organizing learning communities within the cluster.

• Journal of Digital Convergence
• /
• v.16 no.11
• /
• pp.353-361
• /
• 2018

The purpose of this study was to explore the lived experiences of university students who were placed under academic probation. A qualitative research design was used to understand the experiences of students. The data were collected using individual in-depth interviews with 4 students who had experiences of low academic achievements at least once during 5 semesters. Low self esteem was the main issue in this study and inner struggles were identified within 4 themes: Outstanding students controlled by helpers; A costume play as a outstanding student; Realize that I am a under academic achiever; Realize who I am and what I have to do. This study identified that low academic achievement brings to low self esteem seriously, however, human being has strong resilience in the traumatic circumstances. Thus, it is absolutely necessary devoting efforts to develop suitable student supporting programs in the way of inner and outer network systems.

• Journal of Internet of Things and Convergence
• /
• v.9 no.3
• /
• pp.35-43
• /
• 2023

This study tried to find out the applicability and effectiveness of the AI-based adaptive learning system in university classes by operating an AI-based adaptive learning system on a pilot basis. To this end, an AI-based adaptive learning system was applied to analyze the operation results of 42 learners who participated in basic mathematics classes, and a survey and in-depth interviews were conducted with students and professors. As a result of the study, the use of an AI-based customized learning system improved students' academic achievement. Both instructors and learners seem to contribute to improving learning performance in basic concept learning, and through this, the AI-based adaptive learning system is expected to be an effective way to enhance self-directed learning and strengthen knowledge through concept learning. It is expected to be used as basic data related to the introduction and application of basic science subjects for AI-based adaptive learning systems. In the future, we suggest a strategy study on how to use the analyzed data and to verify the effect of linking the learning process and analyzed data provided to students in AI-based customized learning to face-to-face classes.