• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.2
• /
• pp.174-190
• /
• 2010

In ubiquitous computing, invisible devices and software are connected to one another to provide convenient services to users [1][2]. Users hope to obtain a personalized service which is composed of customized devices among sharable devices in a ubiquitous smart space (which is called USS in this paper). However, the situations of each user are different and user preferences also are various. Although users request the same service in the same USS, the most suitable devices for composing the service are different for each user. For these user requirements, this paper proposes a device recommender system which infers and recommends customized devices for composing a user required service. The objective of this paper is the development of the systems for recommending devices through context-aware inference in peer-to-peer environments. For this goal, this paper considers the context and user preference. Also I implement a prototype system and test performance on the real ubiquitous mobile object (UMO).

• KSCI Review
• /
• v.14 no.1
• /
• pp.175-184
• /
• 2006

In this paper, we study to two research direction about information security in ubiquitous computing environment. First, researches on context-aware access control using user's context or environment conditions based on role-based control. Second, researches on model for access control about context information in ubiquitous computing utilizing role base access control model. Two research directions are the one of the most important point technology in that embody ubiquitous environment in the actual world.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.3
• /
• pp.1263-1279
• /
• 2020

Activating appropriate roles for a session in the role-based access control (RBAC) model has become challenging because of the so-called role explosion. In this paper, factors and issues related to user-driven role management are analysed, and a session role activation (SRA) problem based on reasonable assumptions is proposed to describe the problem of such role management. To solve the SRA problem, we propose an extended RBAC model with context-based role filtering. When a session is created, context conditions are used to filter roles that do not need to be activated for the session. This significantly reduces the candidate roles that need to be reviewed by the user, and aids the user in rapidly activating the appropriate roles. Simulations are carried out, and the results show that the extended RBAC model is effective in filtering the roles that are unnecessary for a session by using predefined context conditions. The extended RBAC model is also implemented in the Apache Shiro framework, and the modifications to Shiro are described in detail.

• The Journal of the Korea Contents Association
• /
• v.14 no.7
• /
• pp.29-44
• /
• 2014

To provide context aware service, certain phenomena and situation information of the user should be detecting and monitoring using sensors. But, the sensor data has heterogeneity about system, syntax, structure, and semantics. So, it is difficult to apply to various services. To solve this issue, we should remove these heterogeneities and convert to context information that is easy to provide context aware services. Also, the existing context aware service mash-up environment require relevant sensor type, criteria of context, programing ability to define the service situation. So, it is difficult to create service for a non-technical user. Therefore, we propose ontology based user-centric service environment to provide context aware IoT services. It provide context awareness and user-desired services using data that acquired via IoT paradigm. The proposed environment consist of the ontology based semantic sensor data processing and the user-centric service environment. It provide convert the object data to the context information and service mash-up process to create user service in easy way.

• Proceedings of the KIEE Conference
• /
• 2008.10b
• /
• pp.241-242
• /
• 2008

본 논문에서는 fuzzy granular computing 방법 중의 하나인 context-based FCM을 이용하여 granular-based radial basis function neural network에 대한 기본적인 개면과 그들의 포괄적인 설계 구조에 대해서 자세히 기술한다. 제안된 모델에 기본이 되는 설계 도구는 context-based fuzzy c-means (C-FCM)로 알려진 fuzzy clustering에 초점이 맞춰져 있으며, 이는 주어진 데이터의 특징에 맞게 공간을 분할함으로써 효율적으로 모델을 구축할 수가 있다. 제안된 모델의 설계 공정은 1) Context fuzzy set에 대한 정의와 설계, 2) Context-based fuzzy clustering에 대한 모델의 적용과 이에 따른 모델 구축의 효율성, 3) 입력과 출력공간에서의 연결된 information granule에 대한 parameter(다항식의 계수들)에 대한 최적화와 같은 단계로 구성되어 있다. Information granule에 대한 parameter들은 성능지수를 최소화하기 위해 Least square method에 의해서 보정된다. 본 논문에서는 모델을 설계함에 있어서 체계적인 설계 알고리즘을 포괄적으로 설명하고 있으며 더 나아가 제안된 모델의 성능을 다른 표준적인 모델들과 대조함으로써 제안된 모델의 우수성을 나타내고자 한다.

• Journal of Korea Multimedia Society
• /
• v.6 no.4
• /
• pp.565-575
• /
• 2003

This paper proposed context based annotation model and annotation ambiguity correction methods. The proposed model provides various annotation types, semantic models, and pen-based free drawing interface. Annotation correction method that is specifically based on the context which includes various textual and structure information between free-form marking and annotation. Also, interface for XML environment using the proposed model and correction methods is proposed and possibilities of application is looked at. The results from the implementation of the proposed method show that the annotated areas included in the free-form marking information are more accurate, achieving more accurate exchange results amongst multiple users in a heterogeneous document environment

• Korean Management Science Review
• /
• v.21 no.3
• /
• pp.23-38
• /
• 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 the Korea Institute of Information and Communication Engineering
• /
• v.19 no.4
• /
• pp.1009-1016
• /
• 2015

In this paper, we propose EPCM(Efficient Prediction-based Context-awareness Matrix) algorithm analyzing connectivity by predicting cluster's context data such as velocity and direction. In the existing DTN, unrestricted relay node selection causes an increase of delay and packet loss. The overhead is occurred by limited storage and capability. Therefore, we propose the EPCM algorithm analyzing predicted context data using context matrix and adaptive revision weight, and selecting relay node by considering connectivity between cluster and base station. The proposed algorithm saves context data to the context matrix and analyzes context according to variation and predicts context data after revision from adaptive revision weight. From the simulation results, the EPCM algorithm provides the high packet delivery ratio by selecting relay node according to predicted context data matrix.

• Journal of Information Processing Systems
• /
• v.16 no.3
• /
• pp.612-628
• /
• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.960-965
• /
• 2006

Privacy and security are latent problems in pervasive healthcare system. For the sake of protecting health monitoring information, it is necessary to classify and categorize the various contexts in terms of obfuscation. In this paper, we propose the physiological context categorization and specification methodology by exploiting data fusion network for automatic context alignment. In addition, we introduce the methodologies for making various level of physiological context on the context aware application model, which is wear-UCAM. This physiological context has several layers of context according to the level of abstraction such as user-friendly level or parametric level. This mechanism facilitates a user to restrict access to his/her monitoring results based on the level of details in context.