• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.449-449
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• 2000

Many researchers have studied architectures for non-Neumann's computers because of escaping its bottleneck. To avoid the bottleneck, a neuron-based computer has been developed. The computer has only neurons and their connections, which are constructed of the learning. But still it has information processing facilities, and at the same time, it is like as a simplified brain to make inference; it is called "neuron-computer". No instructions are considered in any neural network usually; however, to complete complex processing on restricted computing resources, the processing must be reduced to primitive actions. Therefore, we introduce the instructions to the neuron-computer, in which the most important function is implications. There is an implication represented by binary-operators, but general implications for multi-value or fuzzy logics can't be done. Therefore, we need to use Lukasiewicz' operator at least. We investigated a neuron-computer having instructions for general implications. If we use the computer, the effective inferences base on multi-value logic is executed rapidly in a small logical unit.

• Korean Journal of Mathematics
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• 제11권1호
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• pp.1-7
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• 2003

In this paper, we introduce two types of algebraic operations on fuzzy numbers using piecewise linear functions and then show that the Zadeh implication is smaller than the Diense-Rescher implication, which is smaller than the Lukasiewicz implication. If ($f$, *) is an available pair, then $A*_mB{\leq}A*_pB{\leq}A*_jB$.

• Kyungpook Mathematical Journal
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• 제53권3호
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• pp.435-457
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• 2013

Using the Lukasiewicz 3-valued implication operator, the notion of an (${\alpha},{\beta}$)-intuitionistic fuzzy left (right) $h$-ideal of a hemiring is introduced, where ${\alpha},{\beta}{\in}\{{\in},q,{\in}{\wedge}q,{\in}{\vee}q\}$. We define intuitionistic fuzzy left (right) $h$-ideal with thresholds ($s,t$) of a hemiring R and investigate their various properties. We characterize intuitionistic fuzzy left (right) $h$-ideal with thresholds ($s,t$) and (${\alpha},{\beta}$)-intuitionistic fuzzy left (right) $h$-ideal of a hemiring R by its level sets. We establish that an intuitionistic fuzzy set A of a hemiring R is a (${\in},{\in}$) (or (${\in},{\in}{\vee}q$) or (${\in}{\wedge}q,{\in}$)-intuitionistic fuzzy left (right) $h$-ideal of R if and only if A is an intuitionistic fuzzy left (right) $h$-ideal with thresholds (0, 1) (or (0, 0.5) or (0.5, 1)) of R respectively. It is also shown that A is a (${\in},{\in}$) (or (${\in},{\in}{\vee}q$) or (${\in}{\wedge}q,{\in}$))-intuitionistic fuzzy left (right) $h$-ideal if and only if for any $p{\in}$ (0, 1] (or $p{\in}$ (0, 0.5] or $p{\in}$ (0.5, 1] ), $A_p$ is a fuzzy left (right) $h$-ideal. Finally, we prove that an intuitionistic fuzzy set A of a hemiring R is an intuitionistic fuzzy left (right) $h$-ideal with thresholds ($s,t$) of R if and only if for any $p{\in}(s,t]$, the cut set $A_p$ is a fuzzy left (right) $h$-ideal of R.

• 논리연구
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• 제8권2호
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• pp.85-107
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• 2005

Kleene first investigated a three-valued system which follows the evaluations of the Lukasiewicz infinite-valued logic ${\L}C$ with respect to negation, conjunction, and disjunction, and treats $\rightarrow$ as material-like implication in the sense that A $\rightarrow$ B is defined as ${\sim}A{\vee}B$ in its evaluation. Diense and Rescher extended it to many-valued logic and infinite-valued logic, respectively. This paper investigates a variant of the infinite-valued Kleene-Diense logic KD, which we shall call strong Kleene-Diense logic (sKD): sKD has the same evaluations as KD except that sKD takes a variant of Kleene-Diense implication. Following the idea of Dunn [2], we provide algebraic completeness for sKD together with its deduction theorem.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.509-512
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• 2001

본 논문은 한국어 음성에 대한 한국어 단어의 음소단위 분할을 목적으로 하였다. 대상 단어는 원광대학교 phonetic balanced 452단어 데이터 베이스를 사용하였고 분할 단위는 음성 전문가에 의해 구성된 44개의 음소셋을 사용하였다. 음소를 분할하기 위해 음성을 각각 프레임으로 나눈 후 각 프레임간의 스펙트럼 성분의 유사도를 측정한 후 측정한 유사도를 기준으로 음소의 분할점을 찾았다. 두 프레임 간의 유사도를 결정하기 위해 두 벡터 상호간의 유사성을 결정하는 방법중의 하나인 Lukasiewicz implication을 사용하였다. 본 실험에서는 기존의 프레임간 스펙트럼 성분의 유사도 측정을 이용한 하나의 어절의 유/무성음 분할 방법을 본 실험의 목적인 한국어 단어의 음소 분할 실험에 맞도록 수정하였다. 성능평가를 위해 음성 전문가에 의해 손으로 분할된 데이터와 본 실험을 통해 얻은 데이터와의 비교를 하여 평가를 하였다. 실험결과 전문가가 직접 손으로 분할한 데이터와 비교하여 32ms이내로 분할된 비율이 최고 84.76%를 나타내었다.