• Journal of The Korean Association of Information Education
• /
• v.22 no.3
• /
• pp.297-305
• /
• 2018

Physical computing is the concept of expanding computing to humans, environments, and objects. It draws attention as a programming learning medium based on physical outputs in integration of hardware and software. This study developed a programming learning model based on interactive prototyping using the characteristics of physical computing with a high degree of technical freedom and analyzed its learning effect in an experiment. To examine the effect of the experimental treatment, this researcher divided fifty nine 5th-grade elementary students into an experimental group and into a control group. the interactive prototyping programming learning model was applied to the experimental group, and a linear sequential programming learning model was applied to the control group. Information Science Creative Personality Test was conducted before and after the experimental treatment. Analysis of Covariance was conducted with the pre-test scores of the two groups. As a result, it was proved that there was the effect of learning at the significance level of .05. It indicates that the physical computing based interactive prototyping programming learning model is applicable to the programming learning for 5th-grade elementary students.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.21-31
• /
• 2016

The changes to Ubiquitous Network Environment leads existing learning environment to Smart Learning Environment. Expecially, Smart Learning Environment is in changing paradigm existing teacher centered environment and learner centered environment, recently the demand of Smart Learning Environment for learners are growing up. This study analyzed Learning Environments for Smart Learning Environment focused on the learners through analyzing Ubiquitous Network Environment that is concentrated on the physical aspects and the non-physical aspects. Also, we suggested learning several ways that can be effectively applied based on the environmental characteristics of Smart Learning.

• Proceedings of the Korea Contents Association Conference
• /
• 2006.11a
• /
• pp.783-787
• /
• 2006

The 'spatial senses' mean environments and the instinctive responds to objects in the environments. The infancy is an important period to develop the basic capacity of the 'spatial senses'. Since young children can develop the 'spatial senses' throughout the actual and active search, it is essential for them to do experience through their physical actions. This paper proposes four motion based-contents for improving the spatial sense of young children: a bubble game, a cyber goalkeeper game, a mud-huddle game, and a shape recognition game. The proposed four games are implemented to the virtual learning environments. Also, the virtual learning environments utilize the realistic interfaces which can recognize motions of young children and then interact with the games as they do the movement at the virtual environments provided. Using the realistic interfaces not only develops young children's spatial sense but also offers them the pleasure and interest of self-study.

• Proceedings of the KSME Conference
• /
• 2003.04a
• /
• pp.816-821
• /
• 2003

Researchers have utilized artificial evolution techniques and learning techniques for studying the interactions between learning and evolution. Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in evolutionary process by comparing the performance of the proposed realtime evolutionary learning method with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome the difficulties in evaluating complicated tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2801-2815
• /
• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• Journal of The Korean Association For Science Education
• /
• v.17 no.2
• /
• pp.139-148
• /
• 1997

This study investigated the influences of the cooperative learning strategies upon students' achievement and their perceptions of learning environments in a middle school physical science course. Prior to instruction, the Group Assessment of Logical Thinking was administered, and its score was used as a blocking variable. Mid-term examination score was used as a covariate. For the treatment group with heterogeneous grouping, cooperative learning instruction (the Learning Together model) was used, which emphasized group reward, individual accountability, and role division. For the control group, traditional instruction was used. After instruction, an achievement test consisting of three subtests (knowledge, understanding, and application), and the perception questionnaire of classroom and laboratory environments, were administered. ANCOVA results revealed that there was a significant interaction between instruction and the level of logical reasoning ability although there were no significant differences in all three subtest scores of the achievement test. For the concrete operational reasoners, the treatment group performed better in the subtests of understanding and application than the control group. For students at the formal and transition levels, however, the treatment group scored lower than the control group. Significant interactions were also found in the perceptions of classroom environment and laboratory environment. For the concrete operational reasoners, the treatment group showed more positive perception than the control group. For the students at the formal and transition levels, the control group had positive perception than the treatment group. Educational implications are discussed.

• International Journal of Contents
• /
• v.17 no.4
• /
• pp.91-100
• /
• 2021

The purpose of this study was to identify factors affecting Visual Display Terminal (VDT) syndrome for elementary school students in the digital learning environment. Multiple regression analyses were performed to identify the factors affecting VDT syndrome in the digital learning environment. This was conducted with 256 elementary school students in grades 5-6 with more than a year of experience in digital learning. The regression model explained 41% of elementary school students' VDT syndrome in the digital learning environment. Variables significantly affecting VDT syndrome include game addiction, sleep time, and air quality with game addiction as the most influential. In the digital learning environment, VDT syndrome is significant because it has physical and psychological impacts on the growth of elementary school students. Therefore, it is necessary to develop guidelines for ideal computer usage habits for this age group.

• The Journal of Korea Robotics Society
• /
• v.19 no.1
• /
• pp.92-97
• /
• 2024

This paper provides a study of distributional perspective on reinforcement learning for application in mobile robot navigation. Mapless navigation algorithms based on deep reinforcement learning are proven to promising performance and high applicability. The trial-and-error simulations in virtual environments are encouraged to implement autonomous navigation due to expensive real-life interactions. Nevertheless, applying the deep reinforcement learning model in real tasks is challenging due to dissimilar data collection between virtual simulation and the physical world, leading to high-risk manners and high collision rate. In this paper, we present distributional reinforcement learning architecture for mapless navigation of mobile robot that adapt the uncertainty of environmental change. The experimental results indicate the superior performance of distributional soft actor critic compared to conventional methods.

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.122-130
• /
• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.2
• /
• pp.267-277
• /
• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.