• Journal of the Korean Society for information Management
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• v.28 no.3
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• pp.123-139
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• 2011

Context-awareness as a key technology of ubiquitous computing needs a context model that understands and processes situational information coming from diverse sensors and devices, and can be applied diversely in various domains. Semantic web based ontologies use structured standard format and express meaning of information, so it is possible to recognize effectively context-awareness situations, allowing the system to share information and understand situation by inference. In this paper, we propose a layered ontology model to support generality and scaleability of the context-awareness system, and applied the model to u-Convention domain. In addition, we propose a effective reasoning method to handle compound situation by combining OWL-DL and SWRL rules.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.37-52
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• 2005

Traditional access control models like role-based access control model are insufficient in security needs in ubiquitous computing environment because they take no thought of access control based on user's context or environment condition. In these days, although researches on context-aware access control using user's context or environment conditions based on role-based access control are emerged, they are on the primary stage. We present context definitions md an access control model to provide more flexible and dynamic context-aware access control based on role-based access control. Specially, we describe the conflict problems occurred in the middle of making an access decision. After classifying the conflict problems, we show some resolutions to solve them. In conclusion, we will lay the foundations of the development of security policy and model assuring right user of right object(or resource) and application service through pre-defined context and context classification in ubiquitous computing environments. Beyond the simplicity of access to objects by authorized users, we assure that user can access to the object, resource, or service anywhere and anytime according to right context.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1282-1290
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• 2017

Since the mitigation of fear of crime significantly enhances the consumptions in a city, studies focusing on urban safety analysis have received much attention as means of revitalizing the local economy. In addition, with the development of computer vision and machine learning technologies, efficient and automated analysis methods have been developed. Previous studies have used global features to predict the safety of cities, yet this method has limited ability in accurately predicting abstract information such as safety assessments. Therefore we used a Convolutional Context Neural Network (CCNN) that considered "context" as a decision criterion to accurately predict safety of cities. CCNN model is constructed by combining a stacked auto encoder with a fully connected network to find the context and use it in the CNN model to predict the score. We analyzed the RMSE and correlation of SVR, Alexnet, and Sharing models to compare with the performance of CCNN model. Our results indicate that our model has much better RMSE and Pearson/Spearman correlation coefficient.

• Journal of The Korean Association For Science Education
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• v.21 no.5
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• pp.822-840
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• 2001

This article presents a theoretical model for conceptual change that relates cognitive conflict and the role of context. The model assumes that students derive alternative conceptions from everyday contexts while scientific concepts presume an idealized context, and hence, that the source of cognitive conflict results from the difference between the two contexts. Test results and analysis of the model are presented by applying it in a class studying the inertial motion of bodies. The subjects are 37 seventh grade boys.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.6
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• pp.121-130
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• 2020

In order to provide customized services according to a specific user or environment, various service scenarios should be developed based on context-aware workflow model. As the context-aware workflow model is constructed using limited context information and service information in the service domain, overlapping elements can occur in many service scenarios. The repetitive work process that results from these overlapping elements delays the development process of the service scenario. Therefore, the elements of the context-aware workflow model must be reused to solve the unnecessary work processes of service scenario development. In this paper, we propose a reuse method XML Object in context-aware workflow model to improve the process of service scenario development. The proposed method documents and manages the independent XML Object of the context-aware workflow model and reuses it by invoking the unit document in the service scenario development process. It can also be applied to new service scenarios by changing the attribute values of reusable elements. Experiments show example that the development process of the service scenario is simplified by reusing the elements of the context-aware workflow model.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.185-194
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• 2006

The ubiquitous computing environment focuses on recognizing the context and Physical entities, whereas, previous computing environments mainly focused on the conversational interactions between the computer and the user. For this reason, there has been an increase in the research of context aware computing environments. In previous researches , context services are designed using context ontology used in context aware middleware. So, context service cannot change the context ontology in execution time. We propose a hierarchical context ontology management for context aware service to change their ontology in execution time. And we also a resolution model for context conflict which is occurred in inference of context. We have designed a middleware based on this model and implemented the middleware. As the middleware is implemented on the OSGi framework, it can cause interoperability among devices such as computers, PDAs, home appliances and sensors. It can also support the development and operation of context aware services, which are required in the ubiquitous computing environment.

• MALSORI
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• v.68
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• pp.33-47
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• 2008

In this paper, we propose a hybrid acoustic and pronunciation model adaptation method based on context dependency for Korean-English speech recognition. The proposed method is performed as follows. First, in order to derive pronunciation variant rules, an n-best phoneme sequence is obtained by phone recognition. Second, we decompose each rule into a context independent (CI) or a context dependent (CD) one. To this end, it is assumed that a different phoneme structure between Korean and English makes CI pronunciation variabilities while coarticulation effects are related to CD pronunciation variabilities. Finally, we perform an acoustic model adaptation and a pronunciation model adaptation for CI and CD pronunciation variabilities, respectively. It is shown from the Korean-English speech recognition experiments that the average word error rate (WER) is decreased by 36.0% when compared to the baseline that does not include any adaptation. In addition, the proposed method has a lower average WER than either the acoustic model adaptation or the pronunciation model adaptation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.