• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1157-1170
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• 2007

LBS must manage various context data and make the best use of this data for application service in ubiquitous environment. Conventional mobile object data management architecture did not consider process of context data. Therefore a new mobile data management framework is needed to process location context data. In this paper, we design a new context management framework for a location based application service. A suggestion framework is consisted of context collector, context manager, rule base, inference engine, and mobile object context database. It describes a form of rule base and a movement process of inference engine that are based on location based application scenario. It also presents an embodiment instance of interface which suggested framework is applied to location context interference of mobile object.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.139-147
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• 2011

There is a growing tendency toward a vehicle demand and a utilization of traffic information systems. Due to various kinds of traffic information systems and increasing of communication data, the traffic information service requires a very high IT infrastructure. A cloud computing environment is an essential approach for reducing a IT infrastructure cost. And the traffic information service needs a provisioning scheduling method for managing a resource. So we propose a provisioning scheduling with conditional probability inference (PSCPI) for the traffic information service on cloud environment. PSCPI uses a naive bayse inference technique based on a status of a virtual machine. And PSCPI allocates a job to the virtual machines on the basis of an availability of each virtual machine. Naive bayse based PSCPI provides a high throughput and an high availability of virtual machines for real-time traffic information services.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.343-349
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• 2007

This paper addressed a recognition method of land registration map based on fuzzy inference scheme, which is able to solve the time complexity problem of typical method [Fig. 2]. Not only line color, thickness but also number, character are used as a fuzzy input parameter. It concentrated on generation of fuzzy association map, and useful informations are extracted result from fuzzy inference. These results are precedent process for estimating the construction space and restoring 3D automatic modeling. It can also utilize to the internet service acceleration propulsion business such as u-Gov based land registration service.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.293-297
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• 2012

In order to provide with personalized recommendation service in context-awareness environment, the collected context data should be analyzed fast and the objective of user should be able to inferred effectively. But, the context collected from the mobile devices is not suitable for applying the existing inference algorithms as they are due to the omission or uncertainty of information and the efficient algorithms are required for mobile environment. In this paper, the behavior pattern was classified using naive bayes classification for minimize the loss caused by the omission or error of information. And pattern matching was used to effectively learn of the users inclination and infer the behavior purpose. The accuracy of the suggested inference model was evaluated by applying to the application recommendation service in the smart phones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.939-948
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• 1997

In this paper, we propose the new rate-based transmission rates control algorithm that allocates the fair band-width for ABR service in ATM network. In the traditional ABR service, bandwidth is allocated with constant rate increment or decrement, but in the proposed algorithm, it is allocated fairly to the connected calls by the fuzzy inference of the available bandwidth. The fuzzy inference uses buffer state and the buffer variant rate as the input variables, and uses the total transmission rate as a output variable. This inference a bandwidth is fairly distributed over all ABR calls in service. By simmulation, we showed that the proposed method improved 0.17% in link effectiveness when RIF, RDF is 1/4, 38.6% when RIF, RDF 1/16, and 82.4% when RIF, RDF 1/32 than that of the traditional EFPCA.

• Journal of KIISE
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• v.41 no.9
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• pp.674-685
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• 2014

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.237-240
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• 2005

As a part of the effort for improving the durability design based on a set of the deem-to-satisfy specifications, it is important and primary to quantitatively identify the environmental impact to a target reinforced concrete structure. In this work, an effort is made to quantitatively calculate the environmental affecting factor with using a fuzzy inference that it indicates the severity of environmental impact to the exposed reinforced concrete structure or member. This system is composed of input region, output region and rule base. For developing the fuzzy inference system surface chloride concentration{chloride), cyclic degree of wet and dry(CWD), relative humidity(RH) and temperature (TEMP) were selected as the input parameter to environmental affecting factor(EAF) of output parameter. The Rules in inference engine are generated from the engineering knowledge and intuition based on some international code of practises as well as various researcher's experimental data. The devised fuzzy inference system was verified comparing the inferred value with the investigation data, and proved to be validated. Thus it is anticipated that this system for quantifying EAF is certain to be considered into the starting point to develop the performance-based durability design considering the service life of structure.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.319-328
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• 2003

With the proliferation of WWW, providing more intelligence to Web sites has become a major concern in e-business industry. In recent days, this trend is more accelerated by prosperity of CRM (Customer Relationship Management) in terms of various aspects such as product recommendation, self after service, etc. To accomplish this goal, many e-companies are eager to embed web enabled rule-based system, that is, expert systems into their Web sites and several well-known commercial tools are already available in the market. Most of those tools are developed based on CGI so far but CGI based systems inherently suffer over-burden problem when there are too many service demands at the same time due to the nature of CGI. To overcome this limitation of the existing CGI based expert systems, we propose a new form of Web-enabled expert system using hyperlink-based inference mechanism. In terms of burden to Web server, our approach is proven to outperform CGI based approach theoretically and also empirically. For practical purpose, our this approach is implemented in a software system, WeBIS and a graphic rule editing methodology, Expert Diagram is incorporated into the system to facilitates rule generation and maintenance. WeBIS is now successfully operated for financial consulting in the web site of a leading financial consulting company in Korea.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.127-134
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• 2009

Recently, much research have been done on ubiquitous computing models in advanced countries as well as in Korea. Ubiquitous computing is defined as a computing environment that isn't bounded by time and space. Different kinds of computers are embedded in artifacts, devices, and environment, thus people can be connected everywhere and every time. To recognize user's emotion, facial expression, temperature, humidity, weather, and lightning factors are used for building ontology. Ontology Web Language (OWL) is adopted to implement ontology and Jena is used as an emotional inference engine. The context-awareness service infrastructure suggested in this research can be divided into several modules by their functions.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.281-286
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• 2007

This paper proposes an intelligent context awareness middleware(ICAM) for Ubiquitous Computing Environment. In this paper we have researched about the context awareness middleware. The ICAM model is based on ontology that efficiently manages analyses and learns about various context information and can provide intelligent services that satisfy the human requirements. Therefore, various intelligent services will improve user's life environment. We also describe the current implementation of the ICAM for service adaptation based on fuzzy inference that help applications to adapt their ubiquitous computing environments according to rapidly changing. For this, after defining the requirements specifications of ICAM, we have researched the inferred processes for the higher level of context awareness. The Fuzzy Theory has been used in process of inferences, and showed constructing the model through the service process. Also, the proposed fuzzy inferences has been applied to smart Jacky, and after inferring the fuzzy values according to the change of temperature, showed the adaptability of Smart Jacky according to the change of surroundings like temperature as showing the optimal value of status.