• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.3
• /
• pp.291-296
• /
• 2006

In context-aware computing environment, some context is characterized by a single event, but many other contexts are determined by a sequence of events which happen with some timing constraints. Therefore context inference could be conducted by monitoring the sequence of event occurrence along with checking their conformance with timing constraints. Some context could be described with fuzzy concepts instead of concrete concepts. Multiple entities may interact with a service system in the context-aware environments, and thus the context inference mechanism should be equipped to handle multiple entities in the same situation. This paper proposes a context inference model which is based on the so-called fuzzy colored timed Petri net. The model represents and handles the sequential occurrence of some events along with involving timing constraints, deals with the multiple entities using the colored Petri net model, and employs the concept of fuzzy tokens to manage the fuzzy concepts.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.1
• /
• pp.21-26
• /
• 2008

It was already realized at a current technological level of home network systems that the systems recognizes a user's simple order and carry out the order in the ubiquitous computing environment. However home is not a simple environment consisting into a large number of family members, so various order and situation would be needed accordingly. From now on we need to reach the technological level to infer that how is the user's behavior patterns and what kinds of service is the fittest to user who belong to the ubiquitous computing environment by using the result of the context interpreter. In this regards, active inferred-model needs to be suggested upgrading user's command into one step more higher level than the simple one adapting diversified feature. This study would like to suggest this active model recognizing context, which is user's environmental information applying basic network and inferring Context-based Service that user wants through the recognized result This study proposes a new method that can infer the user's desire in ubiquitous computing environment. First of all, we define a context as user's information of ubiquitous computing environment situation that user belongs to and we classify the context into 4W1H(Where, Who, When, What) formats. We construct Bayesian network and put the factor of context use as Bayesian network nodes. As a result, we can infer the user's behavior pattern and most proper service for user in the intelligent space from the probabilistic result of Bayesian network.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.6
• /
• pp.659-664
• /
• 2006

The Context-Aware system is the core technology in the Ubiquitous Computing Environment. Recently, the practical use of a sensor is magnified and the application fields of it are gradually extended in order to collect necessary context information. Context-Aware service integrates the context information which is collected by sensors, and then provides, a suitable service to a user through the process of analysis and reasoning. This service is studied in a variety of fields such as marketing, medical treatment, education and so on. In this paper, we analyze the method of recognizing surrounding context and the result of the awareness by using differential and structural property of multiple valued logic function; propose the model that provides appropriate service depending on the change of surrounding contort; confirm the applicability of the Context-aware system by showing the example of application.

• ETRI Journal
• /
• v.41 no.6
• /
• pp.771-781
• /
• 2019

We propose the VS³-NET model to solve the task of question answering questions with machine-reading comprehension that searches for an appropriate answer in a given context. VS³-NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit-based sentences and self-matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question-type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine-reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS³-NET model has an exact-match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.

• The Journal of Information Systems
• /
• v.21 no.1
• /
• pp.93-116
• /
• 2012

Internet users' desire to share shopping experiences has resulted in the appearance of the paradigm shift called social commerce, which combines social media with commerce. However, while foreign market realizes social commerce in a various way, current domestic social commerce market still seems to be highly filled with social shopping sites offering 50 percent discount, thus causing concerns about market distortion from a cutthroat competition. In order to overcome the limitations of social commerce remaining in the early stage, and to enhance the communication among consumers, this study conducted an empirical study on the social-commerce business models design based on context. The results of the study brought the imaginary, optimal combination model by conjoint analysis, using the segmented group through cluster analysis. To overcome the uniformity of social commerce and to enhance communication among consumers, the context-based optimal combination model should be proposed as the alternative social commerce business model in the domestic market which remains as a simple group-purchase function. Furthermore, this study will be used as an informative reference designing customer oriented service model for social commerce business.

• Journal of KIISE:Software and Applications
• /
• v.33 no.1
• /
• pp.105-113
• /
• 2006

In this paper we propose wear-UCAM, which is a toolkit for context-aware application model in wearable computing. As the rapid developments of mobile technologies and relevant technologies, the interests in wearable computer also become indispensible in both academic and industrial fields. However, there are few research activities on the application framework or toolkit for wearable computing. Hence, we suggest wear-UCAM as the development toolkit for wearable computing applications, where we focus on how to collect relevant user context, manage it, and provide services based on the recognized context. The proposed wear-UCAM includes the abstraction of context processing as well as independence among sensors and services with other components. For the sake of rapid prototyping of the proposed toolkit, we utilize PDA with wireless LAN as a wearable computer. The detail explanation of the implementation and its discussion are presented in this paper.

• Journal of KIISE:Software and Applications
• /
• v.30 no.5_6
• /
• pp.550-558
• /
• 2003

Context-aware application plays an important role in ubiquitous computing (ubiComp) environment by providing user with anytime, anywhere services even without any explicitly triggered command. In this paper we propose a unified context-aware application model which is an essential part to develop various applications in ubiquitous computing environment. The proposed model affirms the independence between sensor and application by using unified context in the form of 5W1H. It also ensures that application exploits relatively accurate context to trigger personalized services. To show usefulness of the proposed model, we apply it to the sensors and applications in the ubiHome, a test bed for ubiComp-enabled home applications. According to the experimental results, without loss of generality, we believe it can be extended to various context-aware applications in daily life.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.6
• /
• pp.378-387
• /
• 2007

To enhancing the exercise effect, exercise management systems are introduced and generally used. They create the proper exercise program through exercise prescription after determining the personal body status. When the exercise programs are created, they will consider $2weeks{\sim}3months$ period. And, existing exercise programs cannot respect with personal exercise habits or exercise period which are changing variedly. If exercise period is long, it can be caused inappropriate exercise about user current status. To solve these problems in legacy systems, this paper proposes a Context Aware Exercise Model (CAEM) to provide the exercise program considering the user context. Also, we implemented that as Intelligent Fitness Guide (IFG) System. The IFG system is selectively received necessary measurement values as input values according to user's context. If exercise kinds, frequency and strength of user are changing, that system creates the exercise program through exercise optimization algorithm and exercise knowledge base. As IFG is providing the exercise program in a real time, it can be managed the effective exercise according to user context.

• Journal of Information Technology Services
• /
• v.10 no.4
• /
• pp.269-280
• /
• 2011

Mobile Augmented Reality (mobile AR), as one of the most prospective mobile applications, intends to provide richer experiences by annotating tags or virtual objects over the scene observed through camera embedded in a handheld device like smartphone or pad. In this paper, we analyzed the current status of the art of mobile AR and proposed a novel Points of Interest (POIs) data model based on ontology to provide context-aware information retrievals on lots of POIs data. Proposed ontology was expanded from the standard POIs data model of W3C POIs Working Group and established using OWL (Web Ontology Language) and Protege. We also proposed a context-aware mobile AR platform which can resolve three distinguished issues in current platforms : interoperability problem of POI tags, POIs data retrieval issue, and context-aware service issue.

• Journal of Korea Multimedia Society
• /
• v.23 no.3
• /
• pp.447-459
• /
• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.