• Journal of The Korean Association For Science Education
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• v.23 no.1
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• pp.47-56
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• 2003

The present study was carried out to analyse the frequency(%) for five kinds of inquiry context elements for six kinds of the high school science textbooks in the 7th curriculum. All the elements was classified into three process achievement level such as the basic, supplementary and further level introduced firstly in the 7th curriculum. Five elements of the inquiry context categories appeared as pure scientific context(61.8%), everyday context(22.2%), natural environmental context(7.7%), techno-industrial context(5.9%), and social context(2.4%) in the basic, supplementary and further level. Social context wasn't appeared in the supplementary level. In five elements of inquiry context, total elements appeared 7,139(85.5%) kfrequencies in the basic level and 691(8.1%) frequencies in the further level. However total elements appeared 529(6.4%) frequencies in the supplementary level. The kinds and frequencies of the elements for the inquiry context suggested in the basic level were more than those in the supplementary and in the further level. The social context was not appeared in the supplementary level. However five inquiry context elements were all appeared in the basic and further level.

• Journal of Internet Computing and Services
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• v.7 no.5
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• pp.13-24
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• 2006

Context aware computing is an emerging paradigm to achieve ubiquitous computing environments by enabling computer systems to understand their situational contexts. A context aware system uses context to provide relevant information and services to the user depending on the user's task. In this paper, we propose an ontology based context aware modeling methodology that transmits low level contexts acquired by directly accessing various sensors in the physical environments to high level contexts. With these high level contexts, context aware application can provides proactive and intelligent services using ECA (Event Condition Action) rules. We implemented a presentation service in smart office environment.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.158-161
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• 2004

In this paper, we propose a level of contexts such as low level and high level contexts, and its criteria of categorization are the existence of interaction and composite process done by the inference mechanism. When there is no matching high level service for the associated high level context, this context is described as meaningless. If the services cannot be provided with the entities by the system, this situational information is of little consequence. To provide services with the entities, we propose "community computing" architectural concept which provide the high level service to a group of agents in a community, and can be managed by the service scenario

• Journal of Service Research and Studies
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• v.4 no.1
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• pp.49-59
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• 2014

Context-aware computing is an emerging paradigm to achieve ubiquitous computing environments by enabling computer systems to understand their situational contexts. A context-aware system uses context to provide relevant information and services to the user depending on the user's task. In this paper, we propose an ontology-based context-aware modeling methodology that transmits low-level contexts acquired by directly accessing various sensors in the physical environments to high-level contexts. With these high-level contexts, context-aware application can provides proactive and intelligent services using ECA (Event-Condition-Action) rules. We implemented a home robot service in smart office environment.

• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.322-324
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• 2006

유비쿼터스 컴퓨팅은 사물에 컴퓨터 기능이 내장되어 언제, 어디에서나, 어느 장치로도 편리하게 주변 환경으로부터 서비스를 사용할 수 있게 하는 정보기술의 패러다임이다. 이를 위해선 상황인지가 전제되어야 하는데 여기서 상황인지라 함은 시스템의 다양한 센서 정보를 바탕으로 스스로 상황(Context)을 인지하는 것으로서, 그 정의에 있어서, 아직 논란이 많으나 지능형 서비스를 위한 중요한 개념이다. 본 논문에서는 센서로부터 바로 생성된 Raw Context 정보를 Low-Level Context라고 하고 이를 복합(Fusion)하여 이미 알려진 High-Level Context로 분류하는 논리적 연산을 추론에 기반한 상황인지로서 정의한다. 이때 센서 정보를 통해 특정한 상황정보를 추론하기 위한 모델을 설계하여 상황인지 미들웨어에 적용시켜 보고 그에 따른 효율적인 구조를 기술한다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.781-785
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• 2009

We can constantly gather personal life log from developed mobile device. However, gathered personal life log in mobile environment have a large amount log and uncertainty such as uncertainty of mobile environment, limited capacity and battery of mobile device. Tagging task using a landmark such as a key word should be required to overcome the above problem and to manage personal life log. In this paper, we propose new tagging method and a life log tagging interface using high level context recognizer based on probability. The new tagging method extract high level context such as landmark of life log using recognizer which is modeled from bayesian network and recommend recognized high level context to user using tagging interface. Finally user can directly do tagging task to life log. This task is a special feature in our process. As the result of experiments in task support level which include usability, level of a goal, function and leading, we achieved a feeling of satisfaction of 81%.

• Journal of KIISE
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• v.42 no.3
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• pp.295-306
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• 2015

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

• Journal of Educational Research in Mathematics
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• v.26 no.2
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• pp.189-204
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• 2016

As compared with the gender differences in the achievement of mathematics of the PISA 2009, the results of this study on the PISA 2012 show that the achievement of male students sharply increased, while that of female students maintained the status quo. Based on the premise that this result is derived from the ratio differences between male and female students of high level, this study analyzed the educational context variable effects on the achievements of gender differences observed between male and female students of high level. In particular, this study inquired into the factors which influence the gender difference, by analyzing the identical variables regarding Singapore and Finland of which the achievement of female students registers high among other top high-ranking countries of the PISA 2012. Hence, the binominal logistic multi-level analysis was conducted in order to consider the characteristics of hierarchical structure of PISA, and to compare the features of the educational context variable effects between the high level (above level 5) by country and the highest level (above level 6) by group. The analysis results are as follows: in terms of after-school learning time realized either in private lessons and private institutes, no significant effects were shown in any of the students of these three countries. In terms of after-school homework time, the students of Korea and Singapore gave significant influences on the probability which would be included in the group of high level or the highest level. In particular, regarding the variables which influence the probability of inclusion of Korean female students in the group of high level or the highest level, they correspond to "Homework set by teacher", "Attitude toward school: learning activities", "ESCS of School" and "Teacher-student relations". And "Cultural possessions at home" gave main influences on the probability of inclusion of the female students of Korea, Singapore and Finland in the group of the highest level.

• Journal of Software Engineering Society
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• v.24 no.1
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• pp.19-26
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• 2011

Since smart phones have various embedded sensors and high portability/usability, they have emerged as suitable targets to collect information and to provide intelligent services. That is, with a smart phone, we can collect information about user's circumstances and phone usage from sensors and infer his/her current state which is the significant basis for context-aware services. However, a service system should be founded on a context model to ensure reasonable context-awareness, because context information the system needs depends on its target services. Therefore, in this paper, we propose a hierarchical mobile context model for context inference of smart phone users in their daily life. We classify high-level context which can be draw from sensing data into three levels, Context-Behavior-Situation, and define inference methods for each level. With our mobile context model, we can user's meaningful context in his/her daily life besides simple actions or states.