• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1013-1017
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• 2005

This paper proposes an effective algorithm, P-DCF(Polling DCF) to satisfy real-time requirements in the industrial environments, based on 802.11 DCF protocols. Theoretical analysis and actual experiments are carried out, which shows that the proposed P-DCF brings several benefits: 1) it reduces the time delay for frame transmission 2)it guarantees bounded upper limit of maximum transmission delay 3) it maintains fairness

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12B
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• pp.767-778
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• 2007

In this paper, we propose a Intelligent Service Reasoning (ISR) model using data mining in smart home environments. Our model creates a service tree used for service reasoning on the basis of C4.5 algorithm, one of decision tree algorithms, and reasons service that will be offered to users through quantitative weight estimation algorithm that uses quantitative characteristic rule and quantitative discriminant rule. The effectiveness in the performance of the developed model is validated through a smart home-network simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.50.3-50
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• 2002

Our living spaces are becoming increasingly populated with infinite numbers of intelligent embedded agents we are interacting with. in ubiquitous computing environments the aim is to support the occupants during their everyday lives and enhance their living conditions. In this paper we introduce such an environment, the iDorm, which has arisen from our work in careAgents, a project supported by the UK-Korean S&T collaboration fund and the EU's Disappearing Computer Initiative eGadgets project We discuss the research challenges involved, particularly those relating to intelligently associating and configuring large numbers of embedded agents. The paper presents an intelligent association sys...

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.93-104
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• 2015

In this paper, the design of intelligent_advertisement_operation system(IAdOS) based on user's feedback is proposed for mobile environments. The proposed system stores the advertising contents created by the advertising provider and recommends the personalized advertising contents by analyzing the context information, and then feedback information of the advertisements. Since the proposed system which can recommends provide the smart advertisement contents based on personal preference, it is expected to contribute the new service model development of in the field of advertising market.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2731-2736
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• 2003

In highly informative, perception-rich environments that we call Omniscient Spaces, robots interact with physical objects which in turn afford robots the information showing how the objects should be manipulated. Object manipulation is commonly believed one of the most basic tasks in robot applications. However, no approaches including visual servoing seem satisfactory in unstructured environments such as our everyday life. Thus, in Omniscient Spaces, the features of the environments embed themselves in every entity, allowing robots to easily identify and manipulate unknown objects. To achieve this end, we propose a new paradigm of the interaction through Radio Frequency Identification (RFID). The aim of this paper is to learn about RFID and investigate how it works in object manipulation. Specifically, as an innovative trial for autonomous, real-time manipulation, a likely mobile robot equipped with an RFID system is developed. Details on the experiments are described together with some preliminary results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.66-69
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• 2003

This paper considers a reinforcement learning(RL) which deals with real environments. Most reinforcement learning studies have been made by simulations because real-environment learning requires large computational cost and much time. Furthermore, it is more difficult to acquire many rewards efficiently in real environments than in virtual ones. The most important requirement to make real-environment learning successful is the appropriate construction of the state space. In this paper, to begin with, I show the basic overview of the reinforcement learning under real environments. Next, 1 introduce a state-space construction method under real environmental which is State Partition Method. Finally I apply this method to a robot navigation problem and compare it with conventional methods.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.181-183
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• 2006

This paper proposes an intelligent decision framework for update of the environment model using GSPN(generalized stochastic petri nets). The GSPN has several advantages over direct use of the Markov Process. The modeling, analysis, and performance evaluation are conducted on the mathematical basis. By adopting the probabilistic approach, our decision framework helps the robot to decide the time to update the map. The robot navigates autonomously for a long time in dynamic environments. Experimental results show that the proposed scheme is useful for service robots which work semi-permanently and improves dependability of navigation in dynamic environments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.100-106
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• 2011

Radio Frequency (RF) signal fluctuates dynamically in wireless communication environments, where this fluctuation is severe especially in vehicular environments. Automatic Gain Control (AGC) is critical in wireless communications to establish reliable communication links and compensate the received signal fluctuation. In this paper, we introduce a simple and novel AGC scheme which uses both Received Signal Strength Indicator (RSSI) and analog-to digital converter (ADC) signals. Performance enhancement of the proposed AGC scheme is verified with practical measurements including simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.314-320
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• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• Environmental Engineering Research
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• v.8 no.3
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• pp.130-140
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• 2003