• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.233-238
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• 2016

In this paper, we propose the algorithms of processing the uncertainty data using data fusion for the next generation intrusion detection. In the next generation intrusion detection, a lot of data are collected by many of network sensors to discover knowledge from generating information in cyber space. It is necessary the data fusion process to extract knowledge from collected sensors data. In this paper, we have proposed method to represent the uncertainty data, by classifying where is a confidence interval in interval of uncertainty data through feature analysis of different data using inference method with Dempster-Shafer Evidence Theory. In this paper, we have implemented a detection experiment that is classified by the confidence interval using IRIS plant Data Set for anomaly detection of uncertainty data. As a result, we found that it is possible to classify data by confidence interval.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.817-822
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• 2005

Recently tile study that exploits visual information for tile services of robot in indoor environments is active. Conventional image processing approaches are based on the pre-defined geometric models, so their performances are likely to decrease when they are applied to the uncertain and dynamic environments. For this, diverse researches to manage the uncertainty based on the knowledge for improving image recognition performance have been doing. In this paper we propose a Bayesian network modeling method for predicting the existence of target objects when they are occluded by other ones for improving the object detection performance of the service robots. The proposed method makes object relationship, so that it allows to predict the target object through observed ones. For this, we define the design method for small size Bayesian networks (primitive Bayesian netqork), and allow to integrate them following to the situations. The experiments are performed for verifying the performance of constructed model, and they shows $82.8\%$ of accuracy in 5 places.

• Journal of Educational Research in Mathematics
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• v.24 no.1
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• pp.1-27
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• 2014

In the present study, we analyze the four participating 9th grade students' mathematical reasoning processes in their dragging activities while solving open-ended geometry problems in terms of abduction, induction and deduction. The results of the analysis are as follows. First, the students utilized 'abduction' to adopt their hypotheses, 'induction' to generalize them by examining various cases and 'deduction' to provide warrants for the hypotheses. Secondly, in the abduction process, 'wandering dragging' and 'guided dragging' seemed to help the students formulate their hypotheses, and in the induction process, 'dragging test' was mainly used to confirm the hypotheses. Despite of the emerging mathematical reasoning via their dragging activities, several difficulties were identified in their solving processes such as misunderstanding shapes as fixed figures, not easily recognizing the concept of dependency or path, not smoothly proceeding from probabilistic reasoning to deduction, and trapping into circular logic.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.99-112
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• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.175-182
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• 2008

Context-aware service environment should provide many kinds of services according to users' requests. Users want a great variety of services. In response to their demands the service provider should make a new service every time. But making a new service every time may be inefficient even for a small number of users' requests. So, there are studies on how to efficiently support various and complex requests fFom users. In many researches, service compositions have lately attracted considerable attention. However, existing researches have mainly focused on Web services. So they are not proper to rapidly providing services in response to users' requests, especially In context-aware service environment. This paper proposes a rapid service composition using case-based reasoning. For evaluating the proposed algorithm we implement 'purchasing seTvice agent'. With this system, we compare our algorithm and the existing service composition algorithms.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.111-117
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• 2011

This paper suggests the passenger model considered evacuation behavioral characteristics on the passenger ship fire using a rules-based agent technique. The existing evacuation simulation system was modeled only passenger speed. The speed-based model considered passenger's physical characteristics, so it couldn't consider evacuation behavioral characteristics. For solving this problem, we modeled the passenger model using a rule-based agent applied evacuation behavioral characteristics. The rule-based agent consists of knowledge base and inference engine. In knowledge base, we represented evacuation behavioral characteristics, and chose the examples of the evacuation behavioral characteristics to show various patterns of behavior. And we simulated in the IMO MSC/Circ.1238 example 8 and we proved the simulation results could represent variety patterns of human behavior.

• 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.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.47-60
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• 2008

Success of a semantic web application, currently base on web technology, depend on web ontology construction that provided rule and inference function about knowledge. For, this study compared the ontology construction methods that were already proposed, and analyzed, and investigated characteristics of semantic web and web ontology, investigated characteristics of semantic web and web ontology, and defined characteristics of web ontology as-based technology of a semantic web application and knowledge representation steps, and studied a technical element that related currently web technology, and proposed a web ontology construction method for a semantic web application with bases to these. Established web ontologies of various knowledge fields as applied the construction method that proposed. Also evaluate performance of web ontology through inference verification of web ontologies established, web ontologies evaluated performance of web ontology as used inference verification. According to this, we confirmed that proposed construction method that can establish the ontology suitable for semantic web application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1689-1695
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• 2008

The development of low-power wireless communication and low-cost multi-functional smart sensor has enabled the sensor network that can perceive the status information in remote distance. Sensor nodes are sending the collected data to the node in the base station through temporary communication path using the low-cost RF communication module. Sensor nodes get the energy supply from small batteries, however, they are installed in the locations that are not easy to replace batteries, in general, so it is necessary to minimize the average power consumption of the sensor nodes. It is known that the RF modules used for wireless communication are consuming 20-60% of the total power for sensor nodes. This study suggests the probability inference heuristic based non-periodic transmission to send the collected information to the base station node, when the calculated value by probability is bigger than an optional random value, adapting real-time to the variation characteristics of sensing datain order to improve the energy consumption used in the transmission of sensed data. In this transmission method suggested, transmitting is decided after evaluation of the data sensed by the probability inference heuristic algorithm and the directly sensed data, and the coefficient that is needed for its algorithm is decided through the reappearance rate of the algorithm verification data.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.157-168
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• 2021

As a recent global economic trend, the subscription economy is rapidly expanding along with the growth of non-face-to-face online shopping. This study tried to verify the factors influencing online subscription for disposable consumer goods through behavioral reasoning theory. 344 questionnaire responses were collected through a survey in Korea and empirical analysis was performed. As a result of the analysis, value of openness was found to have a positive effect on attitude, reason for adoption and reason against adoption. Reason for adoption showed a positive effect on attitude and adoption intention. Reason against adoption showed a negative effect on attitude but it has no impact on adoption intention. Attitude showed a positive effect on adoption intention. Finally price consciousness and quality barrier was found to the most important factors of reason for adoption and reason against adoption respectively. The results of this study can contribute to subscription companies establishing their marketing strategies. Future research can be conducted with various subscription products and additional variables.