• Journal of Fisheries and Marine Sciences Education
• /
• v.23 no.1
• /
• pp.92-104
• /
• 2011

Korea is a high level of ICT infrastructure Construction in the OECD's PISA 2006. However, the purpose using of ICT was analyzed for the sake of the interest. They have been emphasized a algorithm-based computer education in conference that it was attended by government, industry and university officials. We designed an algorithm curriculum by analyze a case in international ICT training courses and research. And we were proposed various methods, such as web-based contents, play-based programs, outdoor activities and educational programming language learning for algorithm learning at levels of elementary and middle school students.

• International Journal of Advanced Culture Technology
• /
• v.7 no.3
• /
• pp.65-69
• /
• 2019

In this paper, we proposed leadership development instruction program for liberal arts class to develop university students' leadership skill using Flipped Learning methodology, and showed the results which were obtained from classes where we applied the program. Leadership ranks high in the skills of those employed by businesses. But it is not easy to develop and exercise leadership. There are many leadership classes for students in Universities, they focus on theoretic, not practical aspects. Flipped Learning could be an efficient instruction learning tool to develop leadership, and has gained much interest lately due to its process involving prior study(pre-class) followed by the students' classroom involvement(in-class). Most of the students taking a course recognize improvement of their leadership skill and have positive results by the proposed program.

• International Journal of Advanced Culture Technology
• /
• v.8 no.4
• /
• pp.200-206
• /
• 2020

Digital storytelling, a 21st century educational tool that combines digital media with storytelling, is considered as a powerful tool to learn foreign languages in an authentic and meaningful way. This study explored cyber university students' perceptions and experiences with a digital storytelling project implemented to enhance their English writing and speaking skills. The results of 9 week-long research indicated that most of the students' attitudes were positive: the digital storytelling project was effective to increase their English skills, learning motivation and interest in learning English. Most students perceived that the process writing approach and videotaping tasks provided them with good opportunities to develop their English writing and speaking skills. Although many students had difficulties selecting a topic and writing a script in the initial stage, they could handle with these challenges with the help of professors' feedback and successfully completed their projects. After completing the project, most students expressed a strong sense of accomplishment. These results suggest that the digital storytelling project effectively met cyber university students' learning needs and facilitate their English learning.

• Journal of Korean Association for Spatial Structures
• /
• v.20 no.4
• /
• pp.111-121
• /
• 2020

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

• Journal of Semiconductor Engineering
• /
• v.2 no.1
• /
• pp.125-129
• /
• 2021

As the interest of deep learning, techniques to control the color of images in image processing field are evolving together. However, there is no clear standard for color, and it is not easy to find a way to represent only the color itself like the color-palette. In this paper, we propose a novel color palette extraction system by chroma fine-tuning with reinforcement learning. It helps to recognize the color combination to represent an input image. First, we use RGBY images to create feature maps by transferring the backbone network with well-trained model-weight which is verified at super resolution convolutional neural networks. Second, feature maps are trained to 3 fully connected layers for the color-palette generation with a generative adversarial network (GAN). Third, we use the reinforcement learning method which only changes chroma information of the GAN-output by slightly moving each Y component of YCbCr color gamut of pixel values up and down. The proposed method outperforms existing color palette extraction methods as given the accuracy of 0.9140.

• International Journal of Advanced Culture Technology
• /
• v.9 no.1
• /
• pp.190-195
• /
• 2021

Currently, one of the fields leading the 4th industrial revolution is the image recognition field of artificial intelligence, which is showing good results in many fields. In this paper, using is a YOLO V2 model, which is one of the image recognition models, we intend to classify and select into three types according to the characteristics of fruits. To this end, it was designed to proceed the number of iterations of learning 9000 counts based on 640 mandarin image data of 3 classes. For model evaluation, normal, rotten, and unripe mandarin oranges were used based on images. We as a result of the experiment, the accuracy of the learning model was different depending on the number of learning. Normal mandarin oranges showed the highest at 60.5% in 9000 repetition learning, and unripe mandarin oranges also showed the highest at 61.8% in 9000 repetition learning. Lastly, rotten tangerines showed the highest accuracy at 86.0% in 7000 iterations. It will be very helpful if the results of this study are used for fruit farms in rural areas where labor is scarce.

• Journal of IKEEE
• /
• v.25 no.4
• /
• pp.728-734
• /
• 2021

In recent years, there has been a strong interest in the end-to-end autonomous driving based on deep reinforcement learning. In this paper, we present a reward function of latent SAC deep reinforcement learning to improve the longitudinal driving performance of an agent vehicle. While the existing reward function significantly degrades the driving safety and efficiency, the proposed reward function is shown to maintain an appropriate headway distance while avoiding the front vehicle collision.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.3
• /
• pp.110-113
• /
• 2022

Non-playable game character plays an important role in improving the concentration of the game and the interest of the user, and recently implementation of NPC with reinforcement learning has been in the spotlight. In this paper, we estimate an AI target tracking method via reinforcement learning, and implement an AI-based tracking agency of specific target object with avoiding traps through Unity ML-Agents Toolkit. The implementation is built in Unity game engine, and simulations are conducted through a number of experiments. The experimental results show that outstanding performance of the tracking target with avoiding traps is shown with good enough results.

• International Journal of Advanced Culture Technology
• /
• v.12 no.2
• /
• pp.7-15
• /
• 2024

Despite a recent surge in multifaceted research on AI-integrated language learning, empirical studies in this area remain limited. This study adopts a Human-Generative AI parallel processing model to examine students' perceptions, asking 182 college students to independently construct knowledge and then compare their efforts with the results generated through in-classroom conversations with ChatGPT 3.5. In questionnaire responses, most students indicated that they found these activities useful and expressed a keen interest in learning various ways to utilize generative AI for language learning with instructor guidance. The findings confirm that ChatGPT's potential as a virtual conversation partner. Identifying specific reasons for the perceived usefulness of conversation activities and drawbacks of ChatGPT, this study emphasizes the importance of teachers staying informed about both the latest advances in technology and their limitations. We recommend that teachers endeavor to creatively design various classroom activities using AI technology.

• Journal of Platform Technology
• /
• v.11 no.6
• /
• pp.21-27
• /
• 2023

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.