• 한국HCI학회:학술대회논문집
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• 2007.02b
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• pp.375-380
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• 2007

맥락 정보와 맥락 인식 시스템에 대한 연구는 지난 10 여 년 동안 유비쿼터스 컴퓨팅 분야에서 중요한 이슈로 다루어졌다. 대부분의 맥락 인식 시스템은 위치 정보와 같이 단일 형태의 맥락 정보를 위해서 설계되었거나 연구실 수준의 크기로 제한되었다. 그러나 많은 종류의 센서와 actuator 를 포함하고 다수의 관리 도메인으로 확장 가능한 스케일이 큰 시스템에 대한 개발 및 평가는 여전히 미흡한 수준이다. 특히, 맥락 퓨전과 추론 구조를 가지는 Large-scale 의 맥락 인식 시스템에 대한 평가 방법이 필요한 실정이다. 본 논문에서는 휴리스틱 평가를 이용한 Large-scale 맥락 인식 시스템의 평가 방법에 대하여 제안한다. 그리고 우리는 동적인 맥락 인식 시스템을 지원하고 맥락 퓨전 및 추론을 위한 메커니즘을 포함하는 기본 구조에 대해서 자세히 설명한다. 맥락 인식 시스템 평가를 위해서 제안된 접근법은 사용자 인터페이스 도메인에서 잘 알려진 전문가에 의한 평가 방법으로 Large-scale 맥락 인식 시스템에 적합하도록 특별히 선택된 heuristics 집합을 이용하는 휴리스틱 평가(Heuristic Evaluation)이다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.169-177
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• 2010

A railroad is used to a lot of area and it is an eco-friendly and an effective means of communication which is a transport for passengers and products. In recent years, the accidents caused by subway and railroad are cognate with people. We need to come up with preparing measures to avoid the accidents. In this paper, to prevent an accident developing and implementing a system which is a railroad management safety system is the purpose. The system is able to provide scalability for smart railroad safety service based on context aware information. Besides, it is able to furnish flexibility through the OSGi framework.

• Annual Conference of KIPS
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• 2008.05a
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• pp.40-43
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• 2008

This paper describes a Grid-based context-aware doctor recommender system which recommends appropriate doctors for a patient or user at the right time in the right place. The core of the system is a recommendation mechanism that analyzes a user's demographic profile, user's current context information (i.e., location, time, and weather), and user's position so that doctor information can be ranked according to the match with the preferences of a user. The performance of our architecture is evaluated compare to centralized recommender system.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.109-116
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• 2009

Recently, there are many researches to realize context-awareness service that recognizes surrounding environments as context and provide the citizens with pervasive convenience based on ubiquitous computing technology. In the u-City, various sensors collect information as context, and citizens will receive various context-awareness service, making use of their wireless and mobile devices and the infrastructures of the u-City. We designed ontology that is useful to structure information of sensor or device that is linked to networks and use OWL (Web Ontology Language) that can express information of mutual relation and partial situation. And we propose a context-aware reasoning system for personalized u-City services based on collected context information and user's intention.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.6
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• pp.7-14
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.313-314
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• 2009

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.641-649
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• 2017

As Internet of Things devices become pervasive, services improve to better assess the context and to alert other users to deal with emergencies. Such services use Internet of Things devices to detect the context around the user and promptly notify public institutions, hospitals or other parental users in emergencies. Most of these systems analyze an event when the value of the device is unchanged for a period of time or if it detects an abnormal value. However, just monitoring sensor values makes it difficult to accurately understand the context surrounding a user. Also if the device is inactive, it can not identify the context or provide services again. However, understanding the user requirements, services provided through other devices, information sent to other users lets, appropriate actions be taken. This paper, proposes a device search method and system based on a context-aware model that includes user demands. The proposed system analyzes the user's context and demands by using data collected from the internet of things devices. If user devices are inactive, they can recognize other devices by searching for other devices and providing services to users again. Through the proposed method, the user-centric services are provided. This method also analyzes and responds to requirements in various emergencies.

• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.832-840
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• 2004

In ubiquitous environments, context is any information that can be used to characterize the situation of an entity such as a person or an object. Many sensors and small computers collect contexts and provide applications with them. Thus, ubiquitous applications need to represent contexts and exploit them effectively. In this paper, we design and implement a context-aware messaging system, called UMS (Ubiquitous Messaging System), based on Java and Jini. UMS can represent various contexts using XML scripts, and communicate text messages regardless of user's location using the proxy mechanism of Jini.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.1
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• pp.19-29
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• 2011

The u-Healthcare system, a new paradigm, provides healthcare and medical service anytime, anywhere in daily life using wired and wireless networks. It only doesn't reach u-Hospital at home, to manage efficient personal health in fitness space, it is essential to feedback process through measuring and analyzing a personal vital signs. MBAN(Medical Body Area Network) is a core of this technology. MBAN, a new paradigm of the u-Healthcare system, can provide healthcare and medical service anytime, anywhere on real time in daily life using u-sensor networks. In this paper, an ontology-based context-awareness in MBAN proposed system development methodology. Accordingly, ontology-based context awareness system on MBAN to Elderly/severe patients/aged/, with measured respiratory rate/temperature/pulse and vital signs having small variables through u-sensor network in real-time, discovered abnormal signs and emergency situations which may happen to people at sleep or activity, alarmed and connected with members of a family or medical emergency alarm(Emergency Call) and 119 system to avoid sudden accidents for early detection. Therefore, We have proposed that accuracy of biological signal sensing and the confidence of ontology should be inspected.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.25-32
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• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.