• Archives of design research
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• v.18 no.2 s.60
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• pp.273-282
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• 2005

In this study, we proposed Context Cube as a conceptual model of user context, and a Multi-modal Interaction Design framework to develop ubiquitous service through understanding user context and analyzing correlation between context awareness and multi-modality, which are to help infer the meaning of context and offer services to meet user needs. And we developed a case study to verify Context Cube's validity and proposed interaction design framework to derive personalized ubiquitous service. We could understand context awareness as information properties which consists of basic activity, location of a user and devices(environment), time, and daily schedule of a user. And it enables us to construct three-dimensional conceptual model, Context Cube. Also, we developed ubiquitous interaction design process which encloses multi-modal interaction design by studying the features of user interaction presented on Context Cube.

• Korean Management Science Review
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• v.21 no.3
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• pp.23-38
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• 2004

Recently the usage of mobile devices which enable the accessibility to Internet has been dramatically increased. Most of the mobile services, however, so far tend to be simple such as infotainment service. In order to fully taking advantage of wireless network and corresponding technology, personalized web service based on user's context could be needed. Meanwhile, optimization techniques have been vitally incorporated for optimizing the development and administration of electronic commerce. However, applying context-aware optimization mechanism to personalized mobile services is still very few. Hence, the purpose of this paper is to propose a methodology to incorporate optimization techniques into personalization services. Multi agent-based web service approach is considered to realize the methodology. To show the feasibility of the methodology proposed in this paper, a prototype system, CAMA-myOPt(Context-Aware Multi-Agent system for my Optimization), was implemented and adopted in mobile comparative shopping.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.780-786
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• 2012

Surveillance has become one of promising application areas of wireless sensor networks which allow for pervasive monitoring of concerned environmental phenomena by facilitating context awareness through sensor fusion. Existing systems that depend on a postmortem context analysis of sensor data on a centralized server expose several shortcomings, including a single point of failure, wasteful energy consumption due to unnecessary data transfer as well as deficiency of scalability. As an opposite direction, this paper proposes an energy-efficient distributed context-aware surveillance in which sensor nodes in the wireless sensor network collaborate with neighbors in a distributed manner to analyze and aware surrounding context. We design and implement multi-modal sensor stations for use as sensor nodes in our wireless sensor network implementing our distributed context awareness. This paper presents an initial experimental performance result of our proposed system. Results show that multi-modal sensor performance of our sensor station, a key enabling factor for distributed context awareness, is comparable to each independent sensor setting. They also show that its initial performance of context-awareness is satisfactory for a set of introductory surveillance scenarios in the current interim stage of our ongoing research.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.172-178
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• 2015

Constructing a bilingual multi-word lexicon is confronted with many difficulties such as an absence of a commonly accepted gold-standard dataset. Besides, in fact, there is no everybody's definition of what a multi-word unit is. In considering these problems, this paper evaluates and analyzes the context vector approach which is one of a novel alignment method of constructing bilingual lexicons from parallel corpora, by comparing with one of general methods. The approach builds context vectors for both source and target single-word units from two parallel corpora. To adapt the approach to multi-word units, we identify all multi-word candidates (namely noun phrases in this work) first, and then concatenate them into single-word units. As a result, therefore, we can use the context vector approach to satisfy our need for multi-word units. In our experimental results, the context vector approach has shown stronger performance over the other approach. The contribution of the paper is analyzing the various types of errors for the experimental results. For the future works, we will study the similarity measure that not only covers a multi-word unit itself but also covers its constituents.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.397-402
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• 2013

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

• The Journal of the Korea Contents Association
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• v.6 no.7
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• pp.13-21
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• 2006

Context-based information services are required in ubiquitous application. In the ubiquitous application, ubiquitous commerce addresses information service that recommends items to consumers with the use of the situated contexts. This paper proposes a multi-context based information service for ubiquitous commerce. This enables a consumer to get multi-context based information efficiently by using consumer's attention for each context. The method is described and an ubiquitous commerce application prototype based on multi-context is presented. Several experiments are performed and the results verify that the proposed method's performance is better than other existing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.143-148
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• 2005

In this paper, we introduce smart home service based on ubiquitous environment and context awareness. We define the multi- context environment and the context collision caused by many user that the existing study of smart home is unconcerned with. Heal home is the space where various contexts are created and disappeared in. Smart home appliances are restricted within their service. We divide the home space by main uses of rooms and group smart service by sensory organ. And we introduce the multi-context manager consist with context interpreter, context collision manager and smart service manager.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.1-17
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• 2007

A virtual enterprise (VE) can be mapped into a multi-agent system (MAS) that consists of various agents with specific role(s), communicating with each other to accomplish common goal(s). However, a MAS for enacting VE requires more advanced mechanism such as context that can guarantee autonomy and dynamism of VE members considering heterogeneity and complex structure of them. This paper is to suggest a context-based MAS as a platform for constructing and managing virtual enterprises. In the Context-based MAS a VE is a collection of Actor, Interaction (among Actors), Actor Context, and Interaction Context. It can raise the speed and correctness of decision-making and operation of VE enactment using context, i.e., information about the situation (e.g., goal, role, task, time, location, media) of Actors and Interactions, as well as simple data of their properties. The Context-based MAS for VE we proposed('VECoM') may consists of Context Ontology, Context Model, Context Analyzer, and Context Reasoner. The suggested approach and system is validated through an example where a VE tries to find a partner that could join co-development of new technology.

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