• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.849-852
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• 2005

A factorization is an extremely important part of multi-level logic synthesis. The number of literals in a factored from is a good estimate of the complexity of a logic function, and can be translated directly into the number of transistors required for implementation. Factored forms are described as either algebraic or Boolean, according to the trade-off between run-time and optimization. A Boolean factored form contains fewer number of literals than an algebraic factored form. In this paper, we present a new method for a Boolean factorization. The key idea is to identify two-cube Boolean subexpression pairs from given expression. Experimental results on various benchmark circuits show the improvements in literal counts over the algebraic factorization based on Brayton's co-kernel cube matrix.

• 한국산업융합학회 논문집
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• 제3권1호
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• pp.17-27
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• 2000

A factorization is an extremely important part of multi-level logic synthesis. The number of literals in a factored form is a good estimate of the complexity of a logic function. and can be translated directly into the number of transistors required for implementation. Factored forms are described as either algebraic or Boolean, according to the trade-off between run-time and optimization. A Boolean factored form contains fewer number of literals than an algebraic factored form. In this paper, we present a new method for a Boolean factorization. The key idea is to build an extended co-kernel cube matrix using co-kernel/kernel pairs and kernel/kernel pairs together. The extended co-kernel cube matrix makes it possible to yield a Boolean factored form. We also propose a heuristic method for covering of the extended co-kernel cube matrix. Experimental results on various benchmark circuits show the improvements in literal counts over the algebraic factorization based on Brayton's co-kernel cube matrix.

• 한국컴퓨터산업학회논문지
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• 제7권4호
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• pp.293-298
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• 2006

본 논문에서는 부울 분해식을 산출하기 위한 방법을 제시한다. SIS 1.2에서 사용되는 코커널 큐브 행렬은 코커널/커널들로부터 만들어지며, 이 행렬은 단지 대수 분해식만을 산출한다. 제안한 방법은 2개의 항에서 공통인수를 추출하고, 이들로부터 분해식 산출 행렬을 만들고 이로부터 부울 분해식을 산출하는 방법을 제안한다.

• Journal of information and communication convergence engineering
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• 제1권4호
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• pp.209-212
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• 2003

In this paper, we proposed new speech feature parameter through parts-based feature extraction of speech spectrum using Non-Negative Matrix Factorization (NMF). NMF can effectively reduce dimension for multi-dimensional data through matrix factorization under the non-negativity constraints, and dimensionally reduced data should be presented parts-based features of input data. For speech feature extraction, we applied Mel-scaled filter bank outputs to inputs of NMF, than used outputs of NMF for inputs of speech recognizer. From recognition experiment result, we could confirm that proposed feature parameter is superior in recognition performance than mel frequency cepstral coefficient (MFCC) that is used generally.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.49-52
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• 2003

In this paper, we propose new speech feature parameter using NMf(Non-Negative Matrix Factorization). NMF can represent multi-dimensional data based on effective dimensional reduction through matrix factorization under the non-negativity constraint, and reduced data present parts-based features of input data. In this paper, we verify about usefulness of NMF algorithm for speech feature extraction applying feature parameter that is got using NMF in Mel-scaled filter bank output. According to recognition experiment result, we could confirm that proposal feature parameter is superior in recognition performance than MFCC(mel frequency cepstral coefficient) that is used generally.

• ETRI Journal
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• 제39권1호
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• pp.21-29
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• 2017

This paper addresses the problem of unsupervised speech separation based on robust non-negative matrix factorization (RNMF) with ${\beta}$-divergence, when neither speech nor noise training data is available beforehand. We propose a robust version of non-negative matrix factorization, inspired by the recently developed sparse and low-rank decomposition, in which the data matrix is decomposed into the sum of a low-rank matrix and a sparse matrix. Efficient multiplicative update rules to minimize the ${\beta}$-divergence-based cost function are derived. A convolutional extension of the proposed algorithm is also proposed, which considers the time dependency of the non-negative noise bases. Experimental speech separation results show that the proposed convolutional RNMF successfully separates the repeating time-varying spectral structures from the magnitude spectrum of the mixture, and does so without any prior training.

• Journal of applied mathematics & informatics
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• 제15권1_2호
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• pp.299-312
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• 2004

ILUS factorization has many desirable properties such as its amenability to the skyline format, the ease with which stability may be monitored, and the possibility of constructing a preconditioner with symmetric structure. In this paper we introduce a new preconditioning technique for general sparse linear systems based on the ILUS factorization strategy. The resulting preconditioner has the same properties as the ILUS preconditioner. Some theoretical properties of the new preconditioner are discussed and numerical experiments on test matrices from the Harwell-Boeing collection are tested. Our results indicate that the new preconditioner is cheaper to construct than the ILUS preconditioner.

• 컴퓨터교육학회논문지
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• 제15권1호
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• pp.65-72
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• 2012

일반적으로 논리식은 수많은 인수분해식으로 표현이 가능하다. 논리식에 대한 보다 간략화된 인수분해식을 찾는 것이 논리합성의 기본 기능 중의 하나이며 본 논문에서 논리회로 수업의 교육용 도구로 부울 인수분해식을 산출하는 새로운 방법을 제안한다. 제안하는 방법은 서포트와 함께 2개의 항에 대한 나눗셈을 수행하는 것이다. 인수분해식의 리터럴 개수는 논리식의 간략화 정도를 판단하는 기준이 되는데, 제안하는 방법으로 실험한 결과, 기존의 타 방법들 보다 리터럴 개수를 줄이는 효과를 보였다.

• Journal of Computing Science and Engineering
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• 제4권2호
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• pp.97-109
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• 2010

Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data, which is to decompose a data matrix into a product of two factor matrices with all entries restricted to be nonnegative. NMF was shown to be useful in a task of clustering (especially document clustering), but in some cases NMF produces the results inappropriate to the clustering problems. In this paper, we present an algorithm for orthogonal nonnegative matrix factorization, where an orthogonality constraint is imposed on the nonnegative decomposition of a term-document matrix. The result of orthogonal NMF can be clearly interpreted for the clustering problems, and also the performance of clustering is usually better than that of the NMF. We develop multiplicative updates directly from true gradient on Stiefel manifold, whereas existing algorithms consider additive orthogonality constraints. Experiments on several different document data sets show our orthogonal NMF algorithms perform better in a task of clustering, compared to the standard NMF and an existing orthogonal NMF.

• East Asian mathematical journal
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• 제36권2호
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• pp.291-315
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• 2020

In this study, the geometric model of 11 factorization formulas presented in the 2015 revised national curriculum was constructed and the necessary mathematical conditions were derived in the process. As a result of the study, all of the 11 factorization formulas are geometrically modeled and 12 conditions are derived in the process. However, the basic method of directly cutting and attaching a given shape was limited to not being able to make a rectangle or rectangular parallelepiped. Therefore, the problem was solved by changing the perspective and focusing on whether rectangle or rectangular parallelepiped with the same area or volume could be constructed.