• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1687-1693
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• 2007

Advances in technologies of networking, chip integration, and embedded system have enabled sensor networks applicable to a wide range of areas. Sharing some common characteristics, sensor networks are thus diversified in features depending on their applications. An intelligent and digital home can be one area to establish a particular feature of sensor network. This paper proposes a composite sensor and control network, and discusses its applying to the next generation intelligent and digital home. Development results of the network and a test-bed as a virtual test environment are also presented. The proposed network can not only be efficiently applying to achieve new home intelligences but also provide a sound solution to maintenance and operations of home network or devices.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.465-468
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• 2007

An intelligent home network system using low-power and low-cost sensor nodes was designed and implemented. In Intelligent Home Network System, active home appliances control is composed of RSSI (Received Signal Strength Indicator) based user indoor location tracking, dynamic multi-hop routing, and learning integration remote-control. Through the remote-control learning, home appliances can be controlled in wireless network environment. User location information for intelligent service is calculated using RSSI based Triangle measurement method, and then the received location information is passed to Smoothing Algorithm to reduce error rate. In order to service Intelligent Home Network, moreover, the sensor node is designed to be held by user. The gathered user data is transmitted through dynamic multi-hop routing to server, and real-time user location & environment information are displayed on monitoring program.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.87-90
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• 2005

The Intelligent Home provides convenient and comfortable living environment by performing automatic control, heating and air-conditioning, ventilation, home appliances control, home robot control, energy management, visitor management security management, internet, heath state monitoring, etc. through wired/ wireless network and device in the household. Along with the presentation of the features of economical broadband power line communication in the network configuration for new and old houses, its improvement method is proposed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.644-647
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• 2003

This paper describes a neural network based machine vision system designed for inspecting yellow beans in real time. The system consists of a camera. lights, a belt conveyor, air ejectors, and a computer. Beans are conveyed in four lines on a belt and their images are taken by a monochrome line scan camera when they fall down from the belt. Beans are separated easily from their background on images by back-lighting. After analyzing the image, a decision is made by a multilayer artificial neural network (ANN) trained by the error back-propagation (EBP) algorithm. We use the global mean, variance and local change of gray levels of a bean for the input nodes of the network. In an our experiment, the system designed could process about 520kg/hour.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.13-18
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• 2009

The robotics industry, that is the major industry of the future and one of the new growth power, is actively studied around ETRI, that is the leading under state-run research institute of the advanced technique of U.S. and Japanese and knowledge economy part. And positive and negative and academic circles, the research institute, and the industrial circles communally pursue the intelligent service robot enterprise of a network-based called URC. This network-based intelligent robot does the RUPI2.0 platform and URC environment by the base. Therefore, a stability need to be enhanced in the through this near future when the research for the preexistence vulnerability analysis and security request is needed than the commercialized network-based intelligent robot in order to implement the network-based intelligent robot. Thus, in this paper, we propose the efficient authentication Framework which is suitable for the URC environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.325-328
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• 1998

통신처리시스템은 네트워크의 게이트웨이 설비로 56kbps 모뎀을 사용하는 PC통신 가입자를 위해 전화망과 패킷망의 연동뿐만 아니라 인터넷과도 접속 가능한 시스템으로 다양한 이기종 망간 연동 서비스를 제공한다. 그리고 ISDN 가입자 역시 패킷망과 인터넷과의 연동으로 고속 PC통신 서비스를 이용할 수 있다. 본 논문에서는 통신처리시스템을 통한 이기종 네트워킹의 연동 프로토콜을 중심으로 PC통신을 위한 다양한 백본 네트워크의 연동 프로토콜 스택을 제시한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.111-114
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• 2008

보안이 중요시 되는 공간에서 지능형 감시 시스템의 필요성이 점차 중요시 되고 있다. 본 논문에서는 embedded Linux 기반의 Mobile Robot에 Network Camera를 탑재 하여 침입자를 추적할 수 있는 시스템 구현에 목적을 두고 있다. Network Camera부터 Wireless Lan을 이용하여 서버로 영상을 전송하고, 서버에서 블록매칭 알고리즘을 이용하여 침입자의 이동경로를 파악하며 침입자에 대한 방향 정보를 전송하여 침입자를 추적한다. 로봇이 침입자를 추적함에 따라 침입자의 유효 영상을 얻는다. 본 논문에 의해서 구현된 시스템은 다른 감시 시스템과 연동하여 지능형 감시 시스템으로서 신뢰성을 더할 수 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.107-111
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• 1998

This paper presents a now approach to the design of intelligent contorl system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.