• Korean Journal of Child Studies
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• v.33 no.1
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• pp.237-259
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• 2012

The purpose of this study is to examine how children perceived the use of intelligent service robots in early childhood education and identifying the characteristics of the interaction between the children concerned and the robots. The subjects of this study were 49 kindergarten students from Girin Kindergarten in Gyeonggi-do. The results of this study suggested that the children personalized the robot and recognized it as their friend, regardless of their ages. In the interactions between the children and the robot, the children engaged in physical contact with the robot and occasionally tried to control its functions. In the child-robot interaction, the children searched their favorite functions and used them repeatedly, but also lost interest in those repeated functions. Regardless of their interest levels, however, the attendance or portfolio organization functions. With regard to the interaction between peers, there were frequent quarrels regarding the use of the robot at first, but these conflicts were resolved by the intervention of peers or teachers, and the children who were familiar with the use of the robot helped their friends; this was viewed as constituting cooperative behavior. Children usually used the robot with their friends. The robot was a medium for children to find new friends. Peer group activities were explored and new friendships were created as a result of the use of the robot.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.463-471
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• 2023

This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.360-366
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• 2007

유비쿼터스 컴퓨팅 환경의 사용자는 환경에서 제공되는 수 많은 서비스 (U-서비스)중에서, 사용자의 특성, 필요, 선호도에 따라 적합한 서비스를 쉽게 선택하여 사용할 수 있어야 한다. 본 논문에서는 사용자의 맥락에 따라 U-서비스를 선택하고 상호작용을 할 수 있는 사용자 인터페이스로 personal companion 을 제안한다. Personal companion 은 서비스 발견 기법과 카메라 기반의 상호작용 방법을 통해 서비스를 선택하고, 선택한 서비스의 인터페이스를 개인화 함으로써 다수의 서비스와 직관적인 상호작용을 가능케 한다. 이를 위해 기존 마커의 가시성을 줄이는 새로운 형태의 마커를 제안하고 카메라 기반의 상호작용 방법에 응용한다. Personal companion 의 유용성 검증을 위해 PDA 와 UMPC 플랫폼에 구현한 후, 스마트 홈 테스트 베드의 여러 응용 서비스를 선택하고 상호작용을 하는데 적용하였다. 제안한 personal companion 은 유비쿼터스 컴퓨팅 환경에서 사용자와 U-서비스를 사용자 중심적으로 연결시켜 주는 중요한 매개체의 역할을 할 수 있을 것으로 기대된다.

• Educational Technology International
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• v.18 no.2
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• pp.73-99
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• 2017

The learning analytics dashboard (LAD) is a supporting tool for teaching and learning in its personalized, automatic, and visual aspects. While several studies have focused on the effect of using dashboard on learning achievement, there is a research gap concerning the impacts of learners' characteristics on it. Accordingly, this study attempted to verify the differences in learning achievement depending on learning motivation level (high vs. low) and dashboard intervention (use vs. non-use). The final participants were 231 university students enrolled in a basic statistics course. As a research design, a 2 × 2 factorial design was employed. The results showed that learning achievement varied with dashboard intervention and the interaction effect was significant between learning motivation and dashboard intervention. The results imply that the impact of LAD may vary depending on learner characteristics. Consequently, this study suggests that the dashboard interventions should be offered after careful consideration of individual students' differences, particularly their learning motivation.

• Archives of design research
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• v.18 no.2 s.60
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• pp.273-282
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• 2005

In this study, we proposed Context Cube as a conceptual model of user context, and a Multi-modal Interaction Design framework to develop ubiquitous service through understanding user context and analyzing correlation between context awareness and multi-modality, which are to help infer the meaning of context and offer services to meet user needs. And we developed a case study to verify Context Cube's validity and proposed interaction design framework to derive personalized ubiquitous service. We could understand context awareness as information properties which consists of basic activity, location of a user and devices(environment), time, and daily schedule of a user. And it enables us to construct three-dimensional conceptual model, Context Cube. Also, we developed ubiquitous interaction design process which encloses multi-modal interaction design by studying the features of user interaction presented on Context Cube.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.183-192
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• 2014

This study aims to empirically investigate how personalized and socialized mobile media impacts on users' acceptance of mobile advertising, given the consideration of users' mobile possession attachment. For this, the current study used focus group interview and surveyed mobile users. Interestingly, the findings reveal that personalized and socialized mobile characteristicssignificantly affect users' acceptance of mobile ads. That is to say, users' personalized mobile characteristics play an important role in either awareness of mobile ads or exposure to mobile ads.

• Genomics & Informatics
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• v.15 no.1
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• pp.19-27
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• 2017

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.873-878
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• 2020

Due to the recent development of 3D printers, interest in 3D printing is growing. 3D printing should reflect individual needs and various requirements by designing products to suit the user's requirements. Research on how to use 3D printed products for the purpose of purchasing products according to user's demand characteristics is insufficient. Therefore, this study conducted a survey and statistical analysis to find out the factors influencing the intention of purchasing custom controller products in 3D printing environments. Research has confirmed that user innovation and convenience safety are important factors for the satisfaction and purchase intent of personal-tailored controller products. Considering user innovation, convenience, and safety when producing controller products using 3D printing, it is expected that value of custom controller manufacturing can be increased. Research is needed on the personalized product development framework that successfully introduces and systematically supports the production methods of personalized products in the early stages.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.421-428
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• 2006

Intelligent Tutoring System(ITS), which offers individualized learning environment that consider many learners' variable, is realized by the effective alternative to take the place of domain expert. Accordingly, research on Learning Companion System(LC) is currently noticing. However, to develop LCS which applies effective interaction, it is necessary to combine several LCs, and personalized knowledge base have to be made first. Therefore, in this paper, we propose the 'Knowledge Base Object Medel', which is based on connectionist' in cognition structure, represents learner's knowledge to self-learnig object, and grows adaptive object by proprietor, verify the validity. This model lays the groundwork for design of personalized knowledge base, offers clue to development of adaptive ITS using knowledge base object.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.