• Journal of Korea Society of Industrial Information Systems
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• v.14 no.2
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• pp.1-7
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• 2009

This paper proposes a context gathering system that obtains user's environment information from sensor and generates user's context profile. To design the system, we classify context and design context model based on traditional context-aware computing. The proposed system supports contexts adaptation service by gathering user's environment characteristics and biological characteristics and generating user profile.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.132-133
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• 2004

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.159-166
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• 2019

The collection and manipulation of Internet of Things (IoT) data is increasing at a fast pace and its importance is recognized in every sector of our society. For efficient utilization of IoT data, the vast and varied IoT data needs to be reliable and meaningful. In this paper, we propose an IoT framework to realize this need. The IoT framework is based on a four layer IoT architecture onto which context aware computing technology is applied. If the collected IoT data is unreliable it cannot be used for its intended purpose and the whole service using the data must be abandoned. In this paper, we include techniques to remove uncertainty in the early stage of IoT data capture and collection resulting in reliable data. Since the data coming out of the various IoT devices have different formats, it is important to convert them into a standard format before further processing, We propose the RDF format to be the standard format for all IoT data. In addition, it is not feasible to process all captured Iot data from the sensor devices. In order to decide which data to process and understand, we propose to use contexts and reasoning based on these contexts. For reasoning, we propose to use standard AI and statistical techniques. We also propose an experiment environment that can be used to develop an IoT application to realize the IoT framework.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.47-57
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• 2009

Event detection and recognition is an active and challenging topic recent in Computer Vision. This paper describes a new method for recognizing events caused by human motion from video sequences in an office environment. The proposed approach analyzes human motions using Motion History Image (MHI) sequences, and is invariant to body shapes. types or colors of clothes and positions of target objects. The proposed method has two advantages; one is thant the proposed method is less sensitive to illumination changes comparing with the method using color information of objects of interest, and the other is scale invariance comparing with the method using a prior knowledge like appearances or shapes of objects of interest. Combined with edge detection, geometrical characteristics of the human shape in the MHI sequences are considered as the features. An advantage of the proposed method is that the event detection framework is easy to extend by inserting the descriptions of events. In addition, the proposed method is the core technology for event detection systems based on context-aware computing as well as surveillance systems based on computer vision techniques.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.239-247
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• 2014

Ubiquitous computing environment, as a new paradigm of the digital information era, has been introduced to respond to rapid changes in society and culture. Today, technology research around the world competes for the preoccupancy of the core technology as well as the development of technology for providing context aware system more intelligent, more sophisticated. Research in recent is underway the implementation of intelligent provisioning system using the ontology. Erstwhile provisioning system did not providing a personalized service that provides the service after context aware. Accordingly, there is a need for studies of intelligent context aware provisioning technique considering environments and context necessary to user. In this paper, in order to provide a service that is optimized for the needs of the user, propose an ontology-based intelligent provisioning service method taking into account the usage patterns and the context of the user. The proposed system is recognized the status of the user and demonstrated a process of reasoning techniques for fit service. And it is possible to the expansion of intelligence.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5B
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• pp.542-551
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• 2009

We proposed the context based user profile which is aware of its user's situation and based on user's situation it recommends personalized services. The user profile which consists of (context, service) pair can be acquired by the context and the service usage of a user; it then can be used to recommend personalized services for the user. In this paper, we show how they can be evolved without previously known user information so that not to violate privacy during the learning phase; in the result our user profile can be applied to any new environment without any modification to model only except context profiles. Using context-awareness based user profile, the service usage pattern of a user can be learned by the union of contexts and the preferred services can be recommended by the current environments. Finally, we evaluate the precision of proposed approach using simulation with data sets of UCI depository and Weka tool-kit.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.286-291
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• 2008

Intelligence of collaborative environments starts from a trial to provide optimal services to with users based on real-time information about participants and environments of the collaboration itself. Up to now, we can collect information such as temperature, light, time, each participant's gestures, faces, voices, and locations by adopting ubiquitous computing technologies. However, since social relationship is intrinsic to collaborative activities, the relationships and roles among participants should be fully considered to provide optimal services. To do so, we have to integrate collected data from various sensors and extracted data about relationships and roles among participants as unified one context. Thus, this paper designs collaborative services filtered, by using the integrated data as a context, and introduces an implemented example, context-aware based interactive display service, called as smart meeting system (SMeet system).

• Journal of Digital Convergence
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• v.10 no.1
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• pp.309-315
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• 2012

The dissemination of smartphones is being spread and supplementary services using smartphones are increasing and various as the Mobile network and device are developing rapidly, so smartphones that enables to provide a wide range of services is expected to receive the most attention. It makes users listen to music anytime, anywhere in real-time, use useful applications, and access to Internet to search for information. The service environment is changing on PC into Mobile due to the change of the circumstance mentioned above. these services are done by using just location information rather than other context, and users have to search services and use them. It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also defined the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• 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.