• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.1-12
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• 2013

Context-awareness is a computing technology that automatically delivers useful services to users based on their situation. Most previous studies on context-awareness adopted the view that the user simply is a consumer of what the developer creates. Few studies addressed catering to the need of personalized services for the user. They are either too complex for the user to grasp or unable to express many useful services due to their weak expressive power. To address these issues, we propose Dobby as a new model and architecture for user-driven context-aware service development. Dobby enables the user to create services that are more suited to his personal preferences. We argue that Dobby offers an enhanced method for defining personalized context-aware services over existing methods.

• Journal of the Ergonomics Society of Korea
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• v.31 no.1
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• pp.49-61
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• 2012

Objective: The aim of this study is to offer a holism based Service Experience Analysis System(HSEAS) for service design. Background: Customer experience has been focused in a lot of service area. But it is difficult to understand the customer's needs and their experiences because it's so complex and uncertain. Thus it needs holistic approach that means it's difficult to explain general character merely through the understanding of small parts that composes an object and it must be analyzed within the overall context. Method: Accordingly, the thesis paper proposes the Service Experience Analysis System that satisfies the four following needs. (1) Need of solid Experience Framework in which the special quality of the service experience is considered, (2) need of support for the semantic cohesion between different kinds of data, (3) need of support for the management and search of vast data, and (4) need of building the knowledge base system for collaborative research. Results: HSEAS combines the short information in the customers' words and behaviors or situations and circumstances and provides a place of analysis where the context of the general experience can be read and allows concrete understanding of the actual state and factor of the problem as a Combined Data Analysis Tool. Conclusion: HSEAS becomes the center of information management, analysis and connection and it provides a free collaboration place where physical condition has no relations to as a knowledge base system based on network. Application: It is expected that length and width will be added to the analysis and assistance for effectively accumulating information will be provided in the area of diverse service.

• Journal of IKEEE
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• v.17 no.3
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• pp.309-316
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• 2013

There are many actively researched works on context-aware workflows for developing intelligent robot services. It is possible to describe not only the definition of robot services but also the contextual information of user's surroundings which can be used as the transition conditions for executing those robot services. Therefore it is required to prepare a context-aware workflow document to support the intelligent robot services. In this paper, we propose a development tool which enables robot service developers to describe context-aware workflow documents and execute them. The tool users can write robot service workflows easily by using intuitive GUI of the tool. In the experiment, we showed processes of writing robot service workflows in scenario format, then automatically documenting and executing them.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.337-340
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• 2005

Context-awareness techniques in ubiquitous computing environment provide various services to users who need to get information via the analysis of collected information from sensors in a spatial area. Context-awareness has been increased in ubiquitous computing and is applied to many different applications such as disaster management system, intelligent robot system, transportation management system, shopping management system, and digital home service. Many researches have recently focused on services that provide the appropriate information, which are collected from Internet by different kinds of sensors, to users according to context of their surrounding environment. In this paper, we propose an abstraction model to manage the large-scale contextual information and their metadata which are collected from different kinds of in-situ sensors in a spatial area and are presented them on the web. This model is composed of the modules expressing functional elements of sensors using sensorML(Sensor Model Language) based on XML language and the modules managing contextual information, which is transmitted from the sensors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.550-557
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• 2007

We predict a context of various structures by using Bayesian Networks Algorithms, Three-Dimensional Structures Algorithms and Genetic Algorithms. However, these algorithms have unavoidable problems when providing a context-aware service in reality due to a lack of practicality and the delay of process time in real-time environment. As far as context-aware system for specific purpose is concerned, it is very hard to be sure about the accuracy and reliability of prediction. This paper focuses on reasoning and prediction technology which provides a stochastic mechanism for context information by incorporating various context information data. The objective of this paper is to provide optimum services to users by suggesting an intellectual reasoning and prediction based on hierarchical context information. Thus, we propose a design of user location prediction algorithm using sequential matching with n-size flexible window scheme by taking user's habit or behavior into consideration. This algorithm improves average 5.10% than traditional algorithms in the accuracy and reliability of prediction using the Flexible Window Scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1159-1166
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• 2010

Today, detecting the location of moving object has been traced as various methods in our world. In this paper, we preset the system to improve the estimation accuracy utilizing detail localization using radar sensor based on WSN and situational awareness for a calibration (context aware) database, Rail concept. A variety of existing location tracking method has a problem with receiving of data and accuracy as tracking methodology, and since these located data are the only data to be collected for location tracing, the context aware or monitering as the surrounding environment is limited. So, in this paper, we enhanced the distance aware accuracy using radar sensor utilizing the Doppler effect among the distance measuring method, estimated the location using the Triangulation algorithm. Also, since we composed the environment data(temperature, illuminancem, humidity, noise) to entry of the database, it can be utilized in location-based service according to the later action information inference and positive context decision. In order to verify the validity of the suggested method, we give a few random situation and built test bed of designed node, and over the various test we proved the utilizing the context information through route tracking of moving and data processing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1048-1052
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• 2004

The ubiquitous environment is to support people in their everyday life in an inconspicuous and unobtrusive way. This requires that information of the person and her preferences, liking, and habits are available in the ubiquitous system. In this paper, we propose the context aware system that can provide the tailored information service for user in ubiquitous computing environment. The system architecture is composed of 4 domain models that can perform some pre-defined tasks independently. And we suggest the hybrid algorithm combined with fuzzy and Bayesian network to reason what information is suitable for user environment. Finally, we apply to agent based RGA(Research Guide Assistant).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.457-464
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• 2005

Recently, Portable Internet being standardized provides fast movement with wider service range than wireless LAN does. If Portable Internet service starts, many people will use Portable Internet and thus wireless traffic is going to increase. In Portable Internet, it is important to reduce handover latency to provide user with satisfactory service when handover occurs. In IEEE 802.16e, MSS sends its own security context information to one Base Station which it will move to reduce handover latency. But this is not suitable in the situation that the BS doesn't know the security context. To reduce handover latency of proactive caching method that is to send security context information to adjacency Base Stations in advance has been proposed by[4]. In this paper we propose effective traffic management algorithm to reduce signaling network traffic caused by proactive caching method.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

• 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.