• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.220-222
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• 2003

최근 각 가정에 초고속 인터넷의 급속한 보급에 따라 정보가전간의 제어와 통신을 위한 홈 네트워크가 등장하게 되었다. 이와 같은 홈 네트워크에서는 비디오나 오디오와 같은 멀티미디어 서비스가 주요 트래픽이 될 것으로 예측되고 있다. 그러나 홈 네트워크에서의 각 정보가전은 이질적이고 분산적인 특징을 가지고 있기 때문에 컴퓨팅 능력과 지원 가능한 멀티미디어 타입이 정보가전마다 다르다. 따라서 본 논문에서는 정보가전의 특징에따라 멀티미디어 서비스를 지원할 수 있는 Home Manager(HomeMngr)를 설계하였다. 제안 기법은 이동 에이전트를 이용하여 자원의 동적 재구성과 분산 프로세싱를 수행하여 멀티미디어 서비스를 지원하게 된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.277-280
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• 2009

Mobile Ad-hoc Network(MANET) could provide the reliable monitoring and control of a variety of environments for remote place. Mobility of MANET would require the topology change frequently compared with a static network. To improve the routing protocol in MANET, energy efficient routing protocol would be required as well as considering the mobility would be needed. In this paper, we propose a new method that the CACH(Context-aware Clustering Hierarchy) algorithm, a hybrid and clustering-based protocol that could analyze the link cost from a source node to a destination node. The proposed analysis could help in defining the optimum depth of hierarchy architecture CACH utilize. The proposed CACH could use localized condition to enable adaptation and robustness for dynamic network topology protocol and this provide that our hierarchy to be resilient.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.1024-1026
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• 2013

DVC (Distributed Video Coding) techniques provides a basic theory for the implementation of low-power video encoder. Conventional methods decide the parity bit request at decoder side. These are effective in controlling the bit-rate, but, are not able to control the visual quality. Thus, this paper presents an effective method of estimating visual quality improved by correcting the virtual channel noise for the reconstructed frame. Through several experiments, it is shown that the proposed method are able to estimate effectively the decoded visual quality.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.354-356
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• 1998

축약 분산 기억 장치는 적응적 문제 해결 능력과 하드웨어화의 용이성으로 인해 현실성이 있는 신경망의 한 모델로 주목받고 있다. 그러나 다층 인식자의 개별 뉴론이 선형의 결정 함수로 해 공간을 이분하고 그들이 다양하게 결합하므로써 일반적인 문제 해결 능력을 갖는데 비해, 축약 분산 기억 장치의 뉴론은 해 공간에서 자신을 중심으로 한 일정 반경 영역을 안과 밖으로 이분하고 이들을 단순하게 합하므로 해 공간이 크기 관계를 갖는 경우 비효율적인 모델로 된다. 본 논문에서는 이러한 축약 분산 기억 장치의 특성과 그 원인을 규명하고 해결 방안으로써 개선된 축약 분산 기억 장치를 제안한다. 아울러 새로운 모델의 적용 예를 ATM 호 수락 제어 과정을 통해 보인다.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.92-98
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• 2005

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized teaming control based on adaptive control method. The original motivation of the teaming control field was loaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4A
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• pp.315-321
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• 2010

In Ad-hoc networks, centralized power control is not suitable due to the absence of base stations, which perform the power control operation in the network to optimize the system performance. Therefore, each node should perform power control algorithm distributedly instead of the centralized one. The conventional distributed power control algorithm does not consider the adaptive bit loading operation to change the MCS (modulation and coding scheme) according to the received SINR (signal to interference and noise ratio), which limits the system throughput. In this paper, we propose a novel distributed bit loading and power control algorithm, which considers the adaptive bit loading operation to increase total system throughput and decrease outage probability. Simulation results show that the proposed algorithm performs much better than the conventional algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.688-693
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• 2007

The INS/GPS integration system is designed by employing an adaptive filter that can estimate the measurement noise variance using the residual of the filter. To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulation is performed by assuming that GPS information has large position errors. Simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than those with the Extended Kalman Filter.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.397-400
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• 2003

본 연구에서는 KM-Net(Hidden Markov Network)을 다양한 태스크에의 적용과 화자의 특성을 효과적으로 나타내기 위해 HM-Net 음성인식 시스템에 MLLR(Maximum Likelihood Linear Regression) 적응방법을 도입하였으며, HM-Net 학습 알고리즘을 개량하여 회귀클래스 생성방법을 제안한다. 제안방법은 PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) 알고리즘의 문맥방향 상태분할에 의한 상태레벨 공유를 이용한 방법으로 새로운 화자로부터 문맥정보와 적응화 데이터의 발성 양에 의존하여 결정된 많은 적응 파라미터들을(평균, 분산) 자유롭게 제어할 수 있게 된다. 제안방법의 유효성을 확인하기 위해 국어공학센터(KLE) 452 음성 데이터와 항공편 예약관련 연속음성을 대상으로 인식실험을 수행한 결과, 전체적으로 음소인식의 경우 평균 34-37％, 단어인식의 경우 평균 9％, 연속음성인식의 경우 평균 7-8％의 인식성능 향상을 각각 보였다. 또한 적응화 데이터의 양에 따른 인식성능 비교에서, 제안방법을 적용한 인식 시스템이 적응 데이터의 양이 적은 경우에도 향상된 인식률을 보였으며. 잡음을 부가한 음성에 대한 적응화 실험에서도 향상된 인식성능을 보여 MLLR 적응방법의 특성을 만족하였다. 따라서 MLLR 적응방법을 도입한 HM-Net 음성인식 시스템에 제안한 회귀클래스 생성방법이 유효함을 확인한 수 있었다.