• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.133-142
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• 2010

As the ubiquitous technology has penetrated into almost every aspect of modern life, the research of the security technology to solve the weakness of security in the ubiquitous environment is received much attention. Because, however, today's security systems are usually based on the fixed rules, many security systems can not handle diverse situations in the ubiquitous environment appropriately. Although many existing researches on context aware security service are based on ACL (Access Control List) or RBAC (Role Based Access Control), they have an overhead in the management of security policy and can not manipulate unexpected situations. Therefore, in this paper, we propose a context-aware security service providing multiple authentications and authorization from a security level which is decided dynamically in a context-aware environment using FCM (Fuzzy C-Means) clustering algorithm and Fuzzy Decision Tree. We show proposed model can solve typical conflict problems of RBAC system due to the fixed rules and improve overhead problem in the security policy management. We expect to apply the proposed model to the various applications using contextual information of the user such as healthcare system, rescue systems, and so on.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.712-717
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• 2011

Recently, diverse schedule management agents are being researched for the efficient schedule management of smart devices users, but they remain at a confirmatory level. In order to efficiently manage user's schedules, execution of planned schedules should be monitored to help users properly execute their schedules, or feedback must be given so that when setting up new schedules, users can plan their schedule according to their schedule establishment patterns. This research proposes a schedule management agent that infers the user's behaviors by using acquired user context, and provides schedule related feedback depending on the user's behavior patterns, when users are executing their schedules or planning new schedules. For this, collected user context information is preprocessed and user's behavior is inferred by Bayesian network. Also, in order to provide feedbacks necessary for confirming the user's schedule execution and new schedule establishment, a context tree pattern matching method for the user's schedule, location and time contexts was applied, then verified with 6 weeks of user simulation in a mobile environment.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.287-292
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• 2012

In this paper, user personalized services through the emotion perception required to support location-based sensing data preprocessing techniques and emotion data preprocessing techniques is studied for user's emotion data building and preprocessing in V-A emotion model. For this purpose the granular context tree and string matching based emotion pattern matching techniques are used. In addition, context-aware and personalized recommendation services technique using probabilistic reasoning is studied for personalized services based on context awareness.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.496-500
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• 2009

In EBCOT, the context modeling process takes excessive calculation time and this paper proposed a method to reduce this calculation time. That is, if the finest resolution coefficient is less than a pre-defined transfer factor the coefficient and its descendents skip the context modeling process. There is a trade-off relationship between the calculation time and the image quality or the amount of output data such that as this threshold value increases, the calculation time and the amount of output data decreases, but the image degradation increases. The experimental results showed that in this range the resulting reduction rate in calculation time was from 3% to 64% in average, the reduction rate in output data was from 32% to 73% in average.

• Bulletin of the Korean Chemical Society
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• v.30 no.11
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• pp.2717-2722
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• 2009

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.175-177
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• 2018

This paper proposes an algorithm to speed up block structure partition of quad tree plus binary tree (QTBT) in Joint Exploration Test Model (JEM) encoder. The proposed fast encoding of QTBT block partition employs three spatially neighbor coded blocks, such as left, top-left, and top of current block, to early terminate QTBT block structure pruning. The propose algorithm is organized based on statistical similarity of those spatially neighboring blocks, such as block depths and coded block types, which are coded with overlapped block motion compensation (OBMC) and adaptive multi transform (AMT). The experimental results demonstrate about 30% encoding time reduction with 1.3% BD-rate loss on average compared to the anchor JEM-7.1 software under random access configuration.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.4
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• pp.254-263
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• 2006

Ontology-based context models are widely used in ubiquitous computing environment because they have advantages in the acquisition of conceptual context through inferencing, context sharing, and context reusing. Among the benefits, inferencing enables context-aware applications to use conceptual contexts which cannot be acquired by sensors. However, inferencing causes processing delay and thus becomes the major obstacle to the implementation of context-aware applications. The delay becomes longer as the amount of contexts increases. In this paper, we propose a context pre-fetching method to reduce the size of contexts to be processed in a working memory in attempt to speed up inferencing. For this, we extend the query-tree method to identify contexts relevant to the queries of a context-aware application. Maintaining the pre-fetched contexts optimal in a working memory, the processing delay of inference reduces without the loss of the benefits of ontology-based context model. We apply the proposed scheme to our ubiquitous computing middleware, Active Surroundings, and demonstrate the performance enhancement by experiments.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.437-444
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• 2012

In this paper, we propose a method for predicting a user's location based on their past movement patterns. There is no restriction on the length of past movement patterns when using this method to predict the current location. For this purpose, a modified search tree has been devised. The search tree is constructed in an effective manner while it additionally learns the movement patterns of a user one by one. In fact, the time complexity of the learning process for a movement pattern is linear. In this process, the search tree expands to take into consideration more details about the movement patterns when a pattern that conflicts with an existing trained pattern is found. In this manner, the search tree is trained to make an exact matching, as needed, for location prediction. In the experiments, the results showed that this method is highly accurate in comparison with more complex and sophisticated methods. Also, the accuracy deviation of users of this method is significantly lower than for any other methods. This means that this method is highly stable for the variations of behavioral patterns as compared to any other method. Finally, 1.47 locations were considered on average for making a prediction with this method. This shows that the prediction process is very efficient.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.285-292
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• 2002

One of the problems that should be solved in Text-To-Speech (TTS) is discontinuities at unit-joining points. To cope with this problem, a smoothing method using a low-pass filter is employed in this paper, In the proposed soothing method, a filter coefficient that controls the amount of smoothing is determined according to contort information to be synthesized. This method efficiently reduces both discontinuities at unit-joining points and artifacts caused by undesired smoothing. The amount of smoothing is determined with discontinuities around unit-joins points in the current synthesized speech and discontinuities predicted from context. The discontinuity predictor is implemented by CART that has context feature variables. To evaluate the performance of the proposed method, a corpus-based concatenative TTS was used as a baseline system. More than 6075 of listeners realized that the quality of the synthesized speech through the proposed smoothing is superior to that of non-smoothing synthesized speech in both naturalness and intelligibility.

• Journal of Environmental Science International
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• v.7 no.3
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• pp.251-262
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• 1998

A number of illustrated floras are generally used when we want to idenufy a name of a certain tree. This method, however, has not been efficient because it has taken a great deal of time and effort for persons who do not have much knowledge on the group of the plant. In contrast to this method, the use of taxonomical keys is very efficient for finding out the group of a certain plant. But this method Is very dirtficult for non-specialists to understand the taxonomical ternunology and thus has not been of general use. In thins context, this study was conducted to present the efficient way of the group of a certain tree through the computer database by using the brief forms of plant organs. The database consists of effect fields such as form of trunk and evergreen or deciduous, types of leaf and leaf arrangement, existence of price and hair on twig, form of leaf margin, leaf form and venation, existence of hair on leaf surface and petiole, length of leaf and petiole. The 222 species of tree used for landscape architecture were sorted by items of the eight fields. As a result, the 222 species were divided into 185 groups and the one group contains 7species, 2 groups contain 3 species, 27 groups contain 2 species and 155 groups contain only one species. Therefore it is suggested that the use of computer database and illustrated floras is very easy and efficient in identifying a tree.