• 제목/요약/키워드: decision algorithm

검색결과 2,348건 처리시간 0.028초

A New Implementation of the LMS Algorithm as a Decision-directed Adaptive Equalizer with Decoding Delay

  • Ahn, Sang-Sik
    • The Journal of the Acoustical Society of Korea
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    • 제15권1E호
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    • pp.89-94
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    • 1996
  • This paper deals with the application of the LMS algorithm as a decision-directed adaptive equalizer in a communication receiver which also employs a sophisticated decoding scheme such as the Viterbi algorithm, in which the desired signal, hence the error, is not available until several symbol intervals later because of decoding delay. In such applications the implemented weight updating algorithm becomes DLMS and major penalty is reduced convergence speed. Therefore, every effort should by made to keep the delay as small as possible if it is not avoidable. In this paper we present a modified implementation in which the effects of the decoding delay can be avioded and perform some computer simulations to check the validity and the performance of the new implementation.

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낮은 SNR과 짧은 프레임에서 터보코드 성능 개선 (Performance Improvement of Turbo Code in low SNR and short frame sizes)

  • 정상연;이용식;심우성;허도근
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 1999년도 하계종합학술대회 논문집
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    • pp.61-64
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    • 1999
  • The turbo code appropriate to IMT-2000 is known to have a good performance whenever the size of frame increases. But it is not appropriate to a sort of video service to need real time because of decoding complexity and long delay time by the size of frame. Therefore this paper proposes decoding decision algorithm of short frame in which soft output is weighted according to iteration number in turbo decoder. Performance of the proposed algorithm is analysed in the AWGN channel when short length of frame is 100, 256, 640. As the result. it is appeared that the proposed decoding decision algorithm has improved in BER other than in the existing MAP decoding algorithm.

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적응적 피부색 구간 설정에 기반한 얼굴 영역 추출 알고리즘 (Face Region Extraction Algorithm based on Adaptive Range Decision for Skin Color)

  • 임주혁;이준우;김기석;안석출;송근원
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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    • pp.2331-2334
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    • 2003
  • Generally, skin color information has been widely used at the face region extraction step of the face region recognition process. But many experimental results show that they are very sensitive to the given threshold range which is used to extract the face regions at the input image. In this paper, we propose a face region extraction algorithm based on an adaptive range decision for skin color. First we extract the pixels which are regarded as the candidate skin color pixels by using the given range for skin color extraction. Then, the ratio between the total pixels and the extracted pixels is calculated. According to the ratio, we adaptively decide the range of the skin color and extract face region. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

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Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

  • Hwang, Don-Ha;Youn, Young-Woo;Sun, Jong-Ho;Kim, Yong-Hwa
    • Journal of Electrical Engineering and Technology
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    • 제9권1호
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    • pp.37-44
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    • 2014
  • This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

A Learning AI Algorithm for Poker with Embedded Opponent Modeling

  • Kim, Seong-Gon;Kim, Yong-Gi
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제10권3호
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    • pp.170-177
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    • 2010
  • Poker is a game of imperfect information where competing players must deal with multiple risk factors stemming from unknown information while making the best decision to win, and this makes it an interesting test-bed for artificial intelligence research. This paper introduces a new learning AI algorithm with embedded opponent modeling that can be used for these types of situations and we use this AI and apply it to a poker program. The new AI will be based on several graphs with each of its nodes representing inputs, and the algorithm will learn the optimal decision to make by updating the weight of the edges connecting these nodes and returning a probability for each action the graphs represent.

미네소타 분류방식에 의한 부정맥 진단 알고리즘에 관한 연구 (A Study on Diagnosis Algorithm of Arrhythmia using Minnesota Code Criteria)

  • 정기삼;신건수;이명호
    • 대한의용생체공학회:의공학회지
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    • 제11권1호
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    • pp.171-178
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    • 1990
  • This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm provides more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it can express clearly the complicated medical problems those are not solved by the mathematical methods. The Decision-Tables have very simple structure. Therefore, it is very easy to correct or expand the system by adding or correcting some rules.

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움직임 벡터 추정을 위한 탐색 영역 결정 방식 (A Search Range Decision Algorithm For Motion Vector Estimation)

  • 이민구;홍민철
    • 한국통신학회논문지
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    • 제28권2C호
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    • pp.141-146
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    • 2003
  • 본 논문은 동영상 압축 방식에서 사용되는 움직임 벡터 추정의 탐색 영역을 적응적으로 결정하는 방식에 대해 제안한다. 일반적인 동영상 압축 방식에서 사용되는 움직임 벡터 예측 방식의 성능은 압축 효율을 결정하는 움직임 벡터 예측을 위한 전처리 과정의 역할을 하는 제안된 동적 탐색 영역 방식은 인접 블록의 움직임 벡터의 통계적 특성에 따라 효율적으로 탐색 영역을 결정하여 영상 화질의 저하 없이 평균 60(%) 이상의 계산량을 절감하게 된다. 제안된 방식의 성능은 실험을 통해서 확인할 수 있었다.

A Semi-Markov Decision Process (SMDP) for Active State Control of A Heterogeneous Network

  • Yang, Janghoon
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권7호
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    • pp.3171-3191
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    • 2016
  • Due to growing demand on wireless data traffic, a large number of different types of base stations (BSs) have been installed. However, space-time dependent wireless data traffic densities can result in a significant number of idle BSs, which implies the waste of power resources. To deal with this problem, we propose an active state control algorithm based on semi-Markov decision process (SMDP) for a heterogeneous network. A MDP in discrete time domain is formulated from continuous domain with some approximation. Suboptimal on-line learning algorithm with a random policy is proposed to solve the problem. We explicitly include coverage constraint so that active cells can provide the same signal to noise ratio (SNR) coverage with a targeted outage rate. Simulation results verify that the proposed algorithm properly controls the active state depending on traffic densities without increasing the number of handovers excessively while providing average user perceived rate (UPR) in a more power efficient way than a conventional algorithm.

Rule Extraction from Neural Networks : Enhancing the Explanation Capability

  • Park, Sang-Chan;Lam, Monica-S.;Gupta, Amit
    • 지능정보연구
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    • 제1권2호
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    • pp.57-71
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    • 1995
  • This paper presents a rule extraction algorithm RE to acquire explicit rules from trained neural networks. The validity of extracted rules has been confirmed using 6 different data sets. Based on experimental results, we conclude that extracted rules from RE predict more accurately and robustly than neural networks themselves and rules obtained from an inductive learning algorithm do. Rule extraction algorithm for neural networks are important for incorporating knowledge obtained from trained networks into knowledge based systems. In lieu of this, the proposed RE algorithm contributes to the trend toward developing hybrid and versatile knowledge-based system including expert systems and knowledge-based decision su, pp.rt systems.

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OPKFDD 최소화를 위한 노드의 확장형 결정 (Decision of the Node Decomposition Type for the Minimization of OPKFDDs)

  • 정미경;황민;이귀상;김영철
    • 정보처리학회논문지A
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    • 제9A권3호
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    • pp.363-370
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    • 2002
  • OPKFDD(Ordered Pseudo-Kronecker Functional Decision Diagram)는 각 노드에서 다양한 확장방법(decomposition)을 취할 수 있는 Ordered-DD(Decision Diagram)의 한 종류로서 각 노드마다 Shannon, positive Davio, 그리고 negative Davio 확장중의 하나를 사용하도록 하며 다른 종류의 DD와 비교해서 작은 수의 노드로 함수를 표현할 수 있다. 그러나 각 노드마다 각기 다른 확장 방법을 선택할 수 있는 특징 때문에 입력 노드에 대한 확장 방법의 결정에 의해서 OPKFDD의 크기가 좌우되며 최소의 노드 수를 갖는 OPKFDD의 구성은 매우 어려운 문제로 알려져 있다. 본 논문에서는 DD 크기의 기준을 노드 수로 하여 기존의 OBDD(Ordered Binary Decision Diagram) 자료구조에서 각 노드의 확장방법을 결정하는 직관적(heuristic)인 방법을 제시하고, 주어진 입력변수 순서에 대해서 각 노드의 확장 방법을 결정하는 알고리즘을 제안하고 실험 결과를 제시한다.