• 제목/요약/키워드: average Hamming distance

검색결과 15건 처리시간 0.02초

유전자 알고리즘에 대한 수렴특성의 개선 (Improvement of Convergence Properties for Genetic Algorithms)

  • 이홍규
    • 한국항행학회논문지
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    • 제12권5호
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    • pp.412-419
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    • 2008
  • 유전자 알고리즘은 효과적으로 최적의 해를 구하는 기법이나 진화연상산자의 선정에 따라 조기에 국부 최적해에 고착되어 전역 최적해로의 탐색을 어렵게 하는 문제점을 가지고 있다. 본 논문에서는 국부 최적해로 수렴하게 되는 원인을 분석하고, 국부 최적해에서 벗어나 전역 최적해로의 천이가 가능하도록 하는 방법을 제안하였다. 본 논문에서 사용한 방법은 평균 해밍거리에 따라 진화연산자를 가변시키는 방법으로서 국부 최적해에 고착되지 않도록 유전자에 다양성을 부여하여 지속적으로 모집단의 진화 특성을 유지하는 방법이다. 제안된 방법은 시뮬레이션을 통하여 효용성을 입증하였다.

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스위칭 동작 최소화를 통한 저 전력 자원할당 알고리즘 (A Low Poorer Resource Allocation Algorithm Based on Minimizing Switching Activity)

  • 신무경;인치호;김희석
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2001년도 하계종합학술대회 논문집(2)
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    • pp.121-124
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    • 2001
  • This paper proposed resource allocation algorithm for the minimum switching activity of functional unit in high level synthesis process as like DSP which is circuit to give many functional unit. The resource allocation method after scheduling use the power function calculating average hamming distance and switching activity of the between two input. First of all, the switching activity is calculated by the input value after calculating the average hamming distance between operation. In this paper, the proposed method though high If level simulation find switching activity in circuit each functional unit exchange for binary sequence length and value bit are logic one value. To use the switching activity find the allocation with minimal power consumption, the proposed method visits all control steps one by one and determines the allocation with minimal power consumption at each control step. As the existing method, the execution time can be fast according to use the number of operator and max control step. And it is the reduction effect from 6% to 8%.

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상위 레벨 합성을 위한 저 전력 스케줄링 및 자원할당 알고리즘 (A Low Power Resource Allocation and Scheduling Algorithm for High Level Synthesis)

  • 신무경;인치호
    • 정보처리학회논문지A
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    • 제8A권3호
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    • pp.279-286
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    • 2001
  • 본 논문은 상위 레벨 합성 과정에서 DSP와 같은 회로를 대상으로 전력소모를 최소로 하는 스케줄링 및 자원할당 알고리즘을 제안한다. 본 논문에서는 스케줄링 시의 저 전력 설계는 리스트 스케줄링 방법을 이용한다. 그리고 자원공유를 통하여 자원할당 시 입력을 재 사용할 수 있는 가능성을 증가시킨다. 스케줄링 후 자원할당 방법은 두 입력 사이의 평균 해밍 거리와 교환동작을 계산한 결과값을 고려하여 전력 함수를 이용한다. 먼저 두 연산자 사이의 평균 해밍 거리를 계산한 후 입력 값에 대한 교환동작을 구하며, 입력 값의 비트 패턴을 이용하여 전력 값을 구한다. 자원 할당 과정은 제어 단계를 한 단계 씩 증가시키면서 각 제어 단계에서 할당 될 수 있는 모든 경우들에 대하여 평균 해밍 거리가 가장 적고 전력 함수에 의한 전력이 가장 적게 소비되는 연산자를 할당한다. 기존 방법과 비교했을 때 그 수행속도는 사용하는 연산자의 개수와 최다 제어 단계에 따라서 빨라진다. 그리고 소모하는 전력이 6%에서 8%까지 감소효과가 있었다.

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테스트시 스위칭 감소를 위해 팬 아웃을 고려한 테스트벡터 재 정렬 (A Test Vector Reordering for Switching Activity Reduction During Test Operation Considering Fanout)

  • 이재훈;백철기;김인수;민형복
    • 전기학회논문지
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    • 제60권5호
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    • pp.1043-1048
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    • 2011
  • Test vector reordering is a very effective way to reduce power consumption during test application. But, it is time-consuming and complicated processes, and it does not consider internal circuit structure, which may limit the effectiveness. In this paper, we order test vectors using fanout count of primary inputs that consider the internal circuit structure, which may reduce the switching activity. Then, we reorder test test vectors again by using Hamming distance between test vectors. We proposed FOVO algorithm to perform these two ideas. FOVO is an effective way to reduce power consumption during test application. The algorithm is applied to benchmark circuits and we get an average of 3.5% or more reduction of the power consumption.

조기수렴 저감을 위한 해밍거리와 적합도의 혼합 유전 연산자 (Hybrid Genetic Operators of Hamming Distance and Fitness for Reducing Premature Convergence)

  • 이홍규
    • 한국항행학회논문지
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    • 제18권2호
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    • pp.170-177
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    • 2014
  • 유전 알고리즘은 강인한 탐색과 최적화 기술이기는 하나 조기 수렴과 국부 최적해에 수렴하는 문제점들을 내포하고 있다. 모집단의 다양성이 작은 값으로 수렴할수록 탐색능력이 감소하고, 국부 최적해에 수렴하지만, 모집단의 다양성이 높은 값으로 수렴할수록 탐색능력이 증가하고 전역 최적해에 수렴할 수 있으나 유전 알고리즘은 발산할 수도 있다. 유전 알고리즘이 전역 최적해에 수렴하는 것을 보장하기 위해서는 유전 연산자가 적절하게 선정되어야 한다. 본 논문에서는 조기 수렴으로부터 벗어나기 위하여 모집단의 다양성을 유지하도록 평균해밍거리와 적합도 값을 혼합한 함수를 이용한 유전 연산자들을 제안하였다. 모의실험을 통하여 다양성의 유지를 위한 돌연변이 연산자와 수렴 특성의 향상을 위한 다른 유전자들의 효과를 확인할 수 있었으며, 본 논문에서 제안한 유전 연산자들이 조기 수렴이나 국부 최적해에 수렴하는 경우를 피하는데 유용한 방법임이 확인되었다.

Upper Bounds for the Performance of Turbo-Like Codes and Low Density Parity Check Codes

  • Chung, Kyu-Hyuk;Heo, Jun
    • Journal of Communications and Networks
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    • 제10권1호
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    • pp.5-9
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    • 2008
  • Researchers have investigated many upper bound techniques applicable to error probabilities on the maximum likelihood (ML) decoding performance of turbo-like codes and low density parity check (LDPC) codes in recent years for a long codeword block size. This is because it is trivial for a short codeword block size. Previous research efforts, such as the simple bound technique [20] recently proposed, developed upper bounds for LDPC codes and turbo-like codes using ensemble codes or the uniformly interleaved assumption. This assumption bounds the performance averaged over all ensemble codes or all interleavers. Another previous research effort [21] obtained the upper bound of turbo-like code with a particular interleaver using a truncated union bound which requires information of the minimum Hamming distance and the number of codewords with the minimum Hamming distance. However, it gives the reliable bound only in the region of the error floor where the minimum Hamming distance is dominant, i.e., in the region of high signal-to-noise ratios. Therefore, currently an upper bound on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix cannot be calculated because of heavy complexity so that only average bounds for ensemble codes can be obtained using a uniform interleaver assumption. In this paper, we propose a new bound technique on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix using ML estimated weight distributions and we also show that the practical iterative decoding performance is approximately suboptimal in ML sense because the simulation performance of iterative decoding is worse than the proposed upper bound and no wonder, even worse than ML decoding performance. In order to show this point, we compare the simulation results with the proposed upper bound and previous bounds. The proposed bound technique is based on the simple bound with an approximate weight distribution including several exact smallest distance terms, not with the ensemble distribution or the uniform interleaver assumption. This technique also shows a tighter upper bound than any other previous bound techniques for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix.

저전력 입출력을 위한 반복적인 버스반전 부호화 (Recursive Bus-Invert Coding for Low-Power I/O)

  • 정덕기;손윤식정정화
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 1998년도 추계종합학술대회 논문집
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    • pp.1081-1084
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    • 1998
  • In this paper, we propose the bus coding technique for low power consumption. For CMOS circuit most power is dissipated as dynamic power for charging and discharging node capacitances.Though the I/O and bus are likely to have the very large capacitances associated with them and dissipate much of the power dissipated by an IC, they have little beenthe special target for power reduction. The conventional Bus-Invert coding method can't decrease the peak power dissipation by 50% because the additional invert signal line can invoke a transition at the time when Bus-Invert coding isn't used to code original bus data. The proposed technique always constraints the Hamming distance between data transferred sequentially to be below the half of the bus width, and thus decrease the I/O peak power dissipation and the I/O average power dissipation.

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유전 알고리즘의 조기수렴 저감을 위한 연산자 소인방법 연구 (On Sweeping Operators for Reducing Premature Convergence of Genetic Algorithms)

  • 이홍규
    • 제어로봇시스템학회논문지
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    • 제17권12호
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    • pp.1210-1218
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    • 2011
  • GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

On-Line Estimation of Partial Discharge Location in Power Transformer

  • Yoon, Yong-Han;Kim, Jae-Chul;Chung, Chan-Soo;Kwak, Hee-Ro;Kweon, Dong-Jin
    • Journal of Electrical Engineering and information Science
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    • 제1권2호
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    • pp.45-51
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    • 1996
  • This paper presents a neural network approach for on-line estimation of partial discharge(PD) location using advanced correlation technique in power transformer. Ultrasonic sensors detect ultrasonic signals generated by a PD and the proposed method calculates time difference between the ultrasonic signals at each sensor pair using the cross-correlation technique applied by moving average and the Hamming window. The neural network takes distance difference as inputs converted from time difference, and estimates the PD location. Case studies showed that the proposed method using advanced correlation technique and a neural network estimated the PD location better than conventional methods.

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Optimal Design for Marker-assisted Gene Pyramiding in Cross Population

  • Xu, L.Y.;Zhao, F.P.;Sheng, X.H.;Ren, H.X.;Zhang, L.;Wei, C.H.;Du, L.X.
    • Asian-Australasian Journal of Animal Sciences
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    • 제25권6호
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    • pp.772-784
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    • 2012
  • Marker-assisted gene pyramiding aims to produce individuals with superior economic traits according to the optimal breeding scheme which involves selecting a series of favorite target alleles after cross of base populations and pyramiding them into a single genotype. Inspired by the science of evolutionary computation, we used the metaphor of hill-climbing to model the dynamic behavior of gene pyramiding. In consideration of the traditional cross program of animals along with the features of animal segregating populations, four types of cross programs and two types of selection strategies for gene pyramiding are performed from a practical perspective. Two population cross for pyramiding two genes (denoted II), three population cascading cross for pyramiding three genes(denoted III), four population symmetry (denoted IIII-S) and cascading cross for pyramiding four genes (denoted IIII-C), and various schemes (denoted cross program-A-E) are designed for each cross program given different levels of initial favorite allele frequencies, base population sizes and trait heritabilities. The process of gene pyramiding breeding for various schemes are simulated and compared based on the population hamming distance, average superior genotype frequencies and average phenotypic values. By simulation, the results show that the larger base population size and the higher the initial favorite allele frequency the higher the efficiency of gene pyramiding. Parents cross order is shown to be the most important factor in a cascading cross, but has no significant influence on the symmetric cross. The results also show that genotypic selection strategy is superior to phenotypic selection in accelerating gene pyramiding. Moreover, the method and corresponding software was used to compare different cross schemes and selection strategies.