• Journal of the HCI Society of Korea
• /
• v.13 no.3
• /
• pp.65-73
• /
• 2018

Game balance settings are crucial to game design. Game balancing must take into account a large amount of numerical values, configuration data, and the relationship between elements. Once released and served, a game - even for a balanced game - often requires calibration according to the game player's preference. To achieve sustainability, game balance needs adjustment while allowing for small changes. In fact, from the producers' standpoint, game balance issue is a critical success factor in game production. Therefore, they often invest much time and capital in game design. However, if such a costly game cannot provide players with an appropriate level of difficulty, the game is more likely to fail. On the contrary, if the game successfully identifies the game players' propensity and performs self-balancing to provide appropriate difficulty levels, this will significantly reduce the likelihood of game failure, while at the same time increasing the lifecycle of the game. Accordingly, if a novel technology for game balancing is developed using artificial intelligence (AI) that offers personalized, intelligent, and customized service to individual game players, it would bring significant changes to the game production system.

• The KIPS Transactions:PartA
• /
• v.19A no.3
• /
• pp.147-154
• /
• 2012

The higher thinking skills, such as creativity and problem-solving about a given problem, are difficult to assess and diagnose. For an accurate diagnosis of these higher thinking abilities, we need to fully observe learner's problem-solving process or learner's individual reports. However, in an online learning or virtual class environments, evaluation of learner's problem-solving process becomes more difficult to diagnose. The best way to solve this problem is through reporting by tracking learner's actions when he tries to solve a problem. In this study, we developed a module which can evaluate and diagnose student's problem-solving ability by tracking actions in MS-Office suite, which is used by students to solve a given problem. This module performs based on the learner's job history through user tracking. To evaluate the effectiveness of this diagnostic module, we conducted satisfaction survey from students who were preparing the actual MOS exams. As a result, eighty-one (81) of the participants were positive on the effectiveness of the learning system with the use of this module.

• The Journal of Korean Association of Computer Education
• /
• v.22 no.3
• /
• pp.123-133
• /
• 2019

The purpose of this study is to establish Smart Education Environment for children's personalized learning based on the data are accumulated by Smart Device which is one of element on Smart Education. In this study, we propose the operational improvement plan for the free choice activity in the 5-year-old kindergarten and also implement the Free Choice Activity(FCA) management system for children to select and to evaluate the play plans for themselves. Children participating in this study have fun the whole time for the process of self-planning, the playing activities and the self-assessment of playing. As a result, it is confirmed that children participate actively in decision-making of interesting areas through the smart device than the traditional education environment before. A single teacher using FCA management system with smart device in this study can get useful information without difficulty of individual child's interests, learning and the statistics of children in the classroom.

• The Journal of the Convergence on Culture Technology
• /
• v.4 no.4
• /
• pp.393-399
• /
• 2018

The development of unmanned AI continues, and the development of AI unmanned is aimed at more efficiently, accurately, and speedily the work that has been resolved by manpower such as industry, welfare, and manpower. AI unmanned technology is evolving in various places, and it is time to switch to unmanned systems from many industries and factories. We take this into consideration, and use the Deep Learning technology, which is one of the core technologies of artificial intelligence (AI), not the manpower but the fruits that pour the rails at once in a large orchard. We want to study the unmanned fruit sorting machine that can be operated under manager's supervision without dividing the fruit by type and grade and dividing by country of origin and grade. This unmanned automated classification system aims to reduce the labor cost by minimizing the manpower and to improve the

• Journal of Digital Convergence
• /
• v.17 no.8
• /
• pp.1-13
• /
• 2019

This study examined the effect of digital technologies on adolescent mental health; attention deficit, aggression, depression. Furthermore, we investigated the role of parenting style and peer attachment as they might have effects on digital technologies. Using two-wave longitudinal data, we conducted longitudinal analysis from the Korean Children and Youth Panel Survey done in 2015 and 2016. Results showed that computer and internet usage for learning purpose has a positive effect on mental health, while computer games and social media have negative effects. Also, the positive effects of learning on depression indicated stronger in adolescents who felt less affection from their parents. On the other hand, the negative effects of computer games and social media could be moderated by both parenting style and peer attachment. Implications of these results and directions for future research are discussed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.12
• /
• pp.1602-1608
• /
• 2019

In this study, we developed a "Zetton Children's Traffic Safety Education" application using mixed reality to help children experience a variety of traffic situations indirectly and to help them defend themselves from accidents. We analyze the types of high mortality child traffic accidents to set learning goal. And we developed the experience-oriented contents that players could acquire signal systems and traffic information naturally and funny in the course of playing scenarios according to designed various traffic situations. In order to verify the educational effectiveness of the developed application, children were given traffic safety education through after-school education activities. The result shows that the frequency of right answers to questions related to traffic safety awareness and learning objectives is increased.

• The Journal of the Korea Contents Association
• /
• v.21 no.11
• /
• pp.56-63
• /
• 2021

To solve the digital divide of the elderly, various institutions are educating how to use various smart devices for the elderly. However, it cannot be expected to have a great effect due to the disadvantages of instructor-to-face education and the learning characteristics of the elderly. Accordingly, educational contents using digital devices using robots for the elderly were developed. In this study, Evaluation Indicators were developed to evaluate the usability of digital education using robots. Also, by using usability evaluation based on the developed Evaluation Indicators, we tried to verify the usability of education using robots and to confirm the possibility of expanding the application area. In order to successfully apply the developing robot technology to various fields, it is essential to verify the usability of contents using robots, and this study on Evaluation Indicators and Evaluation methods is expected to serve as a foundation.

• Journal of the Korean earth science society
• /
• v.44 no.4
• /
• pp.331-341
• /
• 2023

The goal of this study was to examine the PCK required for science teachers and PCK required for university teacher educators in terms of school science knowledge, science teaching and learning, and the role of science educators, which are the main axes of science education in future schools, and to explore the relationship between them. This study is a follow-up to a previous stage of research that explored the prospects for changes in schools in the future (2040-2050) in terms of school knowledge, educational methods, and teacher roles. Based on in-depth interviews, qualitative and semantic network analyses were conducted to derive and compare the characteristics of PCK and PCK. As for the main research results, science teacher PCK in future schools should include expertise in organizing science classes centered on convergence topics, expertise in digital platforms and ICT use, and expertise in building a network of learning communities and resources, as part of the expertise of human teachers differentiated from AI. Teacher educators' PCK includes expertise in the research and development of T-L methods using AI, expertise in the knowledge construction process and practice, and expertise in developing preservice teachers' research competencies. Discussed in the conclusion is the change in teacher PCK and teacher educator PCK with changes in science knowledge, such as convergence-type knowledge and cognition-value integrated knowledge; and the need to emphasize values, attitudes, and ethical judgments for the coexistence of humans and non-humans as school science knowledge in the post-humanism future society.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.3
• /
• pp.130-139
• /
• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Korean Journal of Remote Sensing
• /
• v.36 no.2_2
• /
• pp.293-307
• /
• 2020

Every year, the floating macroalgae, green and golden tide, are massively detected at the Yellow Sea and East China Sea. After influx of them to the aquaculture facility or beach, it occurs enormous economic losses to remove them. Currently, remote sensing is used effectively to detect the floating macroalgae flowed into the coast. But it has difficulties to detect the floating macroalgae exactly because of the wavelength overlapped with other targets in the ocean. Also, it is difficult to distinguish between green and golden tide because they have similar spectral characteristics. Therefore, we tried to distinguish between green and golden tide applying the Deep learning method to the satellite images. To determine the network, the optimal training conditions were searched to train the AlexNet. Also, Gaofen-1 WFV images were used as a dataset to train and validate the network. Under these conditions, the network was determined after training, and used to confirm the test data. As a result, the accuracy of test data is 88.89%, and it can be possible to distinguish between green and golden tide with precision of 66.67% and 100%, respectively. It is interpreted that the AlexNet can be pick up on the subtle differences between green and golden tide. Through this study, it is expected that the green and golden tide can be effectively classified from various objects in the ocean and distinguished each other.