• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.125-137
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• 2023

This study analyzed the types of knowledge building discourse and knowledge building processes in small group learning using augmented reality. Eight 8th grade students took classes using augmented reality in solubility, boiling and melting points. These classes were carried out twice and all the classes were videotaped and recorded. Every student participated in a semi-structured interview. In the types of knowledge building discourse, the proportion of knowledge sharing and knowledge construction was similar. Beneath the knowledge sharing, the proportion of introductory level discussion was higher than identifying key elements of augmented reality. Recalling existing knowledge rarely appeared. Under the knowledge construction, the proportion of advanced level discussion was the highest and the proportion of sharing and critiquing ideas at a different level and efforts to rise above current levels of explanation was similar. The introductory level discussion and identifying key elements of augmented reality were developed into efforts to rise above current levels of explanation and sharing and critiquing ideas at a different level. Visualized results of knowledge building processes showed all the students' graph drew an upward curve, though cumulative number of impact value was different by each student. As a result of the study, effective ways of improving small group learning using augmented reality are discussed.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.431-436
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• 2023

Recently the incorporation of artificial intelligence approaches in the field of software engineering has been one of the big topics. In the world, there are actively studying in two directions: 1) software engineering for artificial intelligence and 2) artificial intelligence for software engineering. We attempt to apply artificial intelligence to software engineering to identify and refactor bad code module areas. To learn the patterns of bad code elements well, we must have many datasets with bad code elements labeled correctly for artificial intelligence in this task. The current problems have insufficient datasets for learning and can not guarantee the accuracy of the datasets that we collected. To solve this problem, when collecting code data, bad code data is collected only for code module areas with high-complexity, not the entire code. We propose a method for exploring common weakness enumeration by learning the collected dataset based on transfer learning of the CodeBERT model. The CodeBERT model learns the corresponding dataset more about common weakness patterns in code. With this approach, we expect to identify common weakness patterns more accurately better than one in traditional software engineering.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.369-377
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• 2022

Recent deep learning-based face synthesis research shows the result of generating a realistic face including overall style or elements such as hair, glasses, and makeup. However, previous methods cannot create a face at a very detailed level, such as the microstructure of the skin. In this paper, to overcome this limitation, we propose a technique for synthesizing a more realistic facial image from a single face label image by controlling the types and intensity of skin microelements. The proposed technique uses Pix2PixHD, an Image-to-Image Translation method, to convert a label image showing the facial region and skin elements such as wrinkles, pores, and redness to create a facial image with added microelements. Experimental results show that it is possible to create various realistic face images reflecting fine skin elements corresponding to this by generating various label images with adjusted skin element regions.

• Journal of Creative Information Culture
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• v.7 no.3
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• pp.177-185
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• 2021

In future education, the paradigm of education is changing due to changes in learner-led and assessment methods. In addition, AI-based learning infrastructure and software education are increasingly needed. Thus, this study aims to examine the effectiveness of AI-based evaluation in future education by combining it with learner-led assessment. Using AI education and evaluation literature and Step 7 of the Learner-Driven Software Assessment Method, we sought to extract evaluation elements tailored to elementary school level in conjunction with the 2015 revised elementary practical course content elements, software understanding, procedural problem solving, and structural evaluation elements. In the future, we will develop a grading system that applies AI-based learner-led evaluation elements in software education and continuously demonstrate its effectiveness, and help the school site prepare for future education independently through AI-based learner-led assessment in software education.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.2038-2044
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• 2011

Serious games are selected by users according to the original goals such as education, treatment, training and so on. Therefore, those type of games are evaluated inside and outside the game about whether the goals are archived or not. Among quality test elements of serious game, assessment is about whether, in games, ability to verify goal achievement is included or not. In this paper, we examined the achievement-assessing function of serious game through several cases. Furthermore, to utilize for developing serious games for English learning, we designed a level system which achievement-assessing function is applied to. In this level system, we used 'competition and reward' as the core elements of game, and designed the system through simulation of which grades are level-designed along the user's English proficiency level based on notice of MEST(Ministry of Education, Science and Technology). This paper is expected to be useful reference for designing English learning game containing achievement assessing function.

• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Advances in Computational Design
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• v.5 no.2
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• pp.127-146
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• 2020

Building Information Modeling (BIM) is increasingly used throughout the facility's life cycle for various applications, such as design, construction, facility management, and maintenance. For existing buildings, the geometry of as-built BIM is often constructed using dense, three dimensional (3D) point clouds data obtained with laser scanners. Traditionally, as-built BIM systems do not contain the material and textural information of the buildings' elements. This paper presents a semi-automatic method for generation of material and texture rich as-built BIM. The method captures and integrates material and textural information of building elements into as-built BIM using thermal infrared sensing (TIS). The proposed method uses TIS to capture thermal images of the interior walls of an existing building. These images are then processed to extract the interior walls using a segmentation algorithm. The digital numbers in the resulted images are then transformed into radiance values that represent the emitted thermal infrared radiation. Machine learning techniques are then applied to build a correlation between the radiance values and the material type in each image. The radiance values were used to extract textural information from the images. The extracted textural and material information are then robustly integrated into the as-built BIM providing the data needed for the assessment of building conditions in general including energy efficiency, among others.

• The Journal of Pediatrics of Korean Medicine
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• v.13 no.2
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• pp.93-110
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• 1999

The intelligence is the capacity to recognize the things and implies the meaning of abstract thought, learning and adaptability to the circumstance. Recently, as the promotion of learning ablility and memory attracts many people's attention, many studies of this have been accomplished but the pharmacological methods could not promote the intelligence and memory. In oriental medical theory, the human body is composed of four elements - essence, energy, sprit, blood and among these elements, sprit is considered as the concept of vital energy and mind. Especially, from the Jang-Fu physiological point of view, the memory is closly related with the heart and kidney In oriental medicine, some experiments on animal and literature studies on the subject of memory promotion have done. But because of difference in memory mechanism between man and animal, it is not in reason to apply the result of experiment on animal to human. Therefore I have methodological study of memory promotion to set up the concept of oriental medicine and experimental theory about this and can obtain such conclusion. 1. The oriental medical therapy for memory promotion is following. nourishing the heart and blood, regulating the function of spleen, relieving the mental stress, reinforcing the heart and kidney, invigorating and enriching the blood. 2. The insufficient intelligence in a child is considered to not be full and in an old man, it is considered to decline by degrees. 3. It is needed to molecular biological study of neurotransmitter after the using of oriental medical therapy. 4. It is possible to study using the genetic mutation or observing the collateral of brain nerve cell.

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