• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.321-327
• /
• 2007

본 논문에서는 사용자 중심의 커뮤니티를 정의하고, 사용자의 맥락 정보를 이용한 커뮤니티 구성 방법과 맥락정보 변화에 따른 동적 커뮤니티 재구성 방법을 제안한다. 최근 유비쿼터스 스마트 공간에서 협업 서비스를 제공하기 위한 방법으로 커뮤니티 컴퓨팅에 대한 관심이 증대되고 있다. 하지만 기존 커뮤니티 컴퓨팅 관련 연구에서는 정적인 커뮤니티 구성으로 인해 사용자의 행동 변화에 따른 커뮤니티의 동적 구성이 어렵다. 따라서 커뮤니티를 사용자의 행동에 따라 구성하기 위해서는 사용자 맥락 정보를 이용하는 것이 필요하다. 제안된 방법은 사용자 중심의 정형화된 맥락 모델을 이용하여 사용자의 관심사를 추론하고 커뮤니티의 목적을 설정한다. 그리고 주기적인 사용자의 맥락 정보 감시를 통해 사용자의 행동 변화에 따라 동적으로 커뮤니티 재구성이 가능하게 한다. 커뮤니티 관리 모듈은 사용자의 맥락 정보를 이용하여 구성원들 사이의 관계성을 분석하고, 분석된 관계를 바탕으로 커뮤니티의 특성을 결정한다. 제안된 기법의 유용성을 검증하기 위해서, 맥락인식 기반의 증강현실 경험 공유 시스템을 이용하였고, 사용자의 맥락 정보의 변화에 따른 동적 커뮤니티 구성과 커뮤니티 구성원들간의 콘텐츠에 대한 선택적 공유를 실험하였다. 제안된 방법을 활용하면 선택적 공유를 위한 동적 커뮤니티 구성이 가능하게 될 것으로 기대된다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.232-235
• /
• 2004

차세대 정보통신 기술의 가장 중요한 패러다임으로 유비쿼터스 컴퓨팅이 새롭게 주목 받고있다. 그러나 현재 유비쿼터스 환경에서 축적되어 있는 분산데이터베이스의 구체적인 활용 방안에 관한 연구는 아직 불충분하다. 본 논문에서는 분산환경 데이터베이스에 축적되어 있는 데이터를 베이지안 네트워크를 이용하여 인간의 동작이나 행동에 대한 상황 적응형 서비스를 실행하는 방법을 제안한다. 베이지안 네트워크는 변수들 사이의 인과 관계를 표현하기 때문에 사용자의 행동이나 특성들을 기술하는 것이 용이하다 유비쿼터스 환경에서 인간이나 사물의 동작, 행동 등을 축적한 데이터베이스로부터 현재 인간의 상황을 예측하여 인간이 필요로 하는 적절한 서비스를 실행하는 작업이 요구된다. 유비쿼터스 환경 내에서 발생하는 이벤트를 인지하고 인간과 사물간의 대화 생성의 중개역할자로 베이지안 네트워크를 이용하여 적절한 서비스를 추론하고 실행하는 방법을 제시한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.225-228
• /
• 2003

인간은 두뇌의 일부를 이용하여 감각 정보를 수집하고 이에 대한 분석 및 판단을 행한 후에 행동을 취하는 일반적인 과정에 의해, 느끼고 생각하고 말한다. 이런 일련의 과정은 신경생리학적으로 밝혀진 바에 의하면, 대뇌의 시상에 분포한 일차 감각영역에서 감각 정보를 수집한다. 수집된 감각 정보는 과거 기억과의 비교를 통해 인식되고 인식된 정보는 일차 운동영역으로 전달되어 행동으로 나타난다. 수집된 감각 정보를 판단하는 기관은 감각 연합 영역으로 알려져 있으며, 과거 정보를 통해 비교하여 판단하는 방식이다. 본 논문에서는 신경회로망의 적응적 학습 기법을 통해 입력된 감각 정보에 대한 추론 과정에 감정의 변화를 고려하는 학습 모델을 제시하고자 한다. 감정을 고려하지 않은 경우에 비해, 동일한 감각 입력에 대해 감정에 따라 차별화된 행동을 결정할 수 있는 학습 모델을 설정함으로써 단순한 감각 정보 처리의 차원을 극복하고자 한다.

• Journal of the Korean Chemical Society
• /
• v.68 no.1
• /
• pp.40-53
• /
• 2024

The study investigates the number and proportion of questions in each area by examining Chemistry I questions from the College Scholastic Ability Test from 2019 to 2022. The analysis was conducted using a three-dimensional framework that included key concepts in chemistry, behavioral domains in chemistry, and behavioral domains in mathematics. The results indicated that Chemistry I questions on the College Scholastic Ability Test had a relatively even distribution of questions across core individual topics, but highly difficult questions were predominantly biased toward stoichiometry. In terms of the behavioral domains in chemistry, there was a remarkably low proportion of questions related to problem recognition and hypothesis establishment, as well as designing research and implementing research. Conversely, highly difficult questions were more inclined towards drawing conclusions and evaluations. Regarding behavioral domains in mathematics, there was a limited number of questions addressing heuristic reasoning and deductive reasoning. On the other hand, high-difficulty questions favored internal problem-solving ability. Additionally, certain key concepts in chemistry and behavioral domains in chemistry exhibited a strong correlation with specific behavioral domains in mathematics. This characteristic was particularly evident in questions that encompassed higher-dimensional behavioral domains in mathematics, which students tend to find challenging.

• Journal of Internet Computing and Services
• /
• v.12 no.2
• /
• pp.9-17
• /
• 2011

This paper proposes an algorithm that predicts the state of user's next actions, exploiting the HMM (Hidden Markov Model) on user profile data stored in the ubiquitous home network. The HMM, recognizes patterns of sequential data, adequately represents the temporal property implicated in the data, and is a typical model that can infer information from the sequential data. The proposed algorithm uses the number of the user's action performed, the location and duration of the actions saved by "Activity Recognition System" as training data. An objective formulation for the user's interest in his action is proposed by giving weight on his action, and change on the state of his next action is predicted by obtaining the change on the weight according to the flow of time using the HMM. The proposed algorithm, helps constructing realistic ubiquitous home networks.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.10
• /
• pp.776-780
• /
• 2009

As smartphones appear as suitable devices to implement ubiquitous computing recently, there are many researchers who study about personalized Intelligent services in smartphones. An intelligent synthetic character is one of them. This paper proposes a method generating behaviors of an intelligent synthetic character. In order to generate more natural behaviors for the character, the Bayesian networks are exploited to infer the user's states and OCC model is utilized to create the character's emotion. After inferring the contexts, the behaviors are generated through the behavior selection networks with using the information. A usability test verifies the usefulness of the proposed method.

• Journal of KIISE:Software and Applications
• /
• v.34 no.8
• /
• pp.763-771
• /
• 2007

As software is widely used on various environments today, there is an increasing need for adaptive software. Adaptive software is robust and flexible software that modifies its own behavior in response to the changes in its environment. Due to time constraints, high complexity and so on, it is hard to acquire context information from environment. So, when implementing software, it is common to think easily acquired data to be the environments. This research proposes an adaptive software framework that assumes plane images to be environments. Plane images are easy to acquire and have enough complexity. From this, our framework is able to acquire context information, reasons with action rule, and acts on the result of reasoning. Stand on this framework, implements software that plays a simple game automatically.

• The KIPS Transactions:PartB
• /
• v.13B no.2 s.105
• /
• pp.163-170
• /
• 2006

With rapidly developing Internet applications, an e-mail has been considered as one of the most popular methods for exchanging information. The e-mail, however, has a serious problem that users ran receive a lot of unwanted e-mails, what we called, spam mails, which cause big problems economically as well as socially. In order to block and filter out the spam mails, many researchers and companies have performed many sorts of research on spam filtering. In general, users of e-mail have different criteria on deciding if an e-mail is spam or not. Furthermore, in e-mail client systems, users do different actions according to a spam mail or not. In this paper, we propose a mail filtering system using such user actions. The proposed system consists of two steps: One is an action inference step to draw user actions from an e-mail and the other is a mail classification step to decide if the e-mail is spam or not. All the two steps use incremental learning, of which an algorithm is IB2 of TiMBL. To evaluate the proposed system, we collect 12,000 mails of 12 persons. The accuracy is $81{\sim}93%$ according to each person. The proposed system outperforms, at about 14% on the average, a system that does not use any information about user actions.

• Journal of Families and Better Life
• /
• v.24 no.2 s.80
• /
• pp.15-26
• /
• 2006

The purposes of this study were (1) to investigate children's personalized inferences of characters emotional reactions depending on character's personality trait, emotional situation, children's age and gender, (2) to investigate children's personalized inferences of character's behavioral reactions depending on character's personality trait, emotional situation, children's age and gender, (3) to investigate differences between children's personalized inferences of character's emotional reaction and that of character's behavioral reactions. The subjects were 103 children from three age groups (thirty-four 3-year-olds, thirty-three 5-year-olds and thirty-six 7-year-olds). The statistical methods adopted for the data analysis were frequency, percentile, mean, standard deviation, repeated measure ANOVA and paired t-test. The result showed that there were significant differences in children's personalized inferences of character's emotional reaction depending on character's personality trait, emotional situation and their age. There were significant differences in children's personalized inferences of character's behavioral reaction depending on children's age and gender. There were significant differences between personalized inferences of character's emotional reaction and behavioral reactions.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.2
• /
• pp.181-187
• /
• 2015

In this study, an LED emotional lighting algorithm optimized to the situation was implemented using fuzzy inference for users in exercise and resting situations in indoor environment. The fuzzy theory was used instead of the conventional simple color temperature control in order to control the colors and color temperatures of LED light sources in line with user environment under complex conditions. An LED emotional lighting system based on fuzzy theory was designed through a combination of colors according to the color and temperature of emotional language based on the user behavior. As a result, the color and color temperature can give a good effect on user's emotions for an emotional lighting that is effective for resting and exercise.