• Title/Summary/Keyword: subtractive

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Modeling of Left Ventricular Assist Device and Suction Detection Using Fuzzy Subtractive Clustering Method (퍼지 subtractive 클러스터링 기법을 이용한 좌심실보조장치 모델링 및 흡입현상 검출)

  • Park, Seung-Kyu;Choi, Seong-Jin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.4
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    • pp.500-506
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    • 2012
  • A method to model left ventricular assist device (LVAD) and detect suction occurrence for safe LVAD operation is presented. An axial flow blood pump as a LVAD has been used to assist patient with heart problems. While an axial flow blood pump, a kind of a non-pulsatile pump, has relative advantages of small size and efficiency compared to pulsatile devices, it has a difficulty in determining a safe pump operating condition. It can show different pump operating statuses such as a normal status and a suction status whether suction occurs in left ventricle or not. A fuzzy subtractive clustering method is used to determine a model of the axial flow blood pump with this pump operating characteristic and the developed pump model can provide blood flow estimates before and after suction occurrence in left ventricle. Also, a fuzzy subtractive clustering method is utilized to develop a suction detection model which can identify whether suction occurs in left ventricle or not.

Super subtractive process of FPC for small size LCD module

  • See, S.K.
    • 한국정보디스플레이학회:학술대회논문집
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    • 2004.08a
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    • pp.975-977
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    • 2004
  • According to thin and light form-factor and additional function of today's electronic devices, it is required to decrease the pattern pitch of FPC. The high density demand is more and more important trend especially, for small size LCD module. Based on this requirement, the manufacturing process is advancing from subtractive method to super subtractive method.

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On Partitioning and Subtractive Subsemimodules of Semimodules over Semirings

  • Chaudhari, Jaiprakash Ninu;Bond, Dipak Ravindra
    • Kyungpook Mathematical Journal
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    • v.50 no.2
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    • pp.329-336
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    • 2010
  • In this paper, we introduce a partitioning subsemimodule of a semimodule over a semiring which is useful to develop the quotient structure of semimodule. Indeed we prove : 1) The quotient semimodule M=N(Q) is essentially independent of choice of Q. 2) If f : M ${\rightarrow}$ M' is a maximal R-semimodule homomorphism, then $M/kerf_{(Q)}\;\cong\;M'$. 3) Every partitioning subsemimodule is subtractive. 4) Let N be a Q-subsemimodule of an R-semimodule M. Then A is a subtractive subsemimodule of M with $N{\subseteq}A$ if and only if $A/N_{(Q{\cap}A)}\;=\;\{q+N:q{\in}Q{\cap}A\}$ is a subtractive subsemimodule of $M/N_{(Q)}$.

On Partitioning and Subtractive Ideals of Ternary Semirings

  • Chaudhari, Jaiprakash Ninu;Ingale, Kunal Julal
    • Kyungpook Mathematical Journal
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    • v.51 no.1
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    • pp.69-76
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    • 2011
  • In this paper, we introduce a partitioning ideal of a ternary semiring which is useful to develop the quotient structure of ternary semiring. Indeed we prove : 1) The quotient ternary semiring S/$I_{(Q)}$ is essentially independent of choice of Q. 2) If f : S ${\rightarrow}$ S' is a maximal ternary semiring homomorphism, then S/ker $f_{(Q)}$ ${\cong}$ S'. 3) Every partitioning ideal is subtractive. 4) Let I be a Q-ideal of a ternary semiring S. Then A is a subtractive ideal of S with I ${\subseteq}$ A if and only if A/$I_{(Q{\cap}A)}$ = {q + I : q ${\in}$ Q ${\cap}$ A} is a subtractive idea of S/$I_{(Q)}$.

Structural Optimization of Additive/Subtractive Hybrid Machines (3D적층/절삭 하이브리드가공기의 구조최적화에 관한 연구)

  • Park, Joon-Koo;Kim, Eun-Jung;Lee, Choon-Man
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.20 no.2
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    • pp.45-50
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    • 2021
  • In the recent fourth industrial revolution, the demand for additive processes has emerged rapidly in many mechanical industries, including the aircraft and automobile industries. Additive processes, in contrast to subtractive processes, can be used to produce complex-shaped products, such as three-dimensional cooling systems and aircraft parts that are difficult to produce using conventional production technologies. However, the limitations of additive processes include nonuniform surface quality, which necessitates the use of post-processing techniques such as subtractive methods and grinding. This has led to the need for hybrid machines that combine additive and subtractive processes. A hybrid machine uses additional additive and subtractive modules, so product deformation, for instance, deflection, is likely to occur. Therefore, structural analysis and design optimization of hybrid machines are essential because these defects cause multiple problems, such as reduced workpiece precision during processing. In this study, structural analysis was conducted before the development of an additive/subtractive hybrid processing machine. In addition, structural optimization was performed to improve the stability of the hybrid machine.

A Simple and Efficient Subtractive Cloning Method

  • Min, Hyun-Jin;Park, Sang-Soo;Cho, Tae-Ju
    • BMB Reports
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    • v.34 no.1
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    • pp.59-65
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    • 2001
  • In subtractive hybridization, target sequences in the tester are enriched by hybridizing with an excess amount of driver, followed by removing the tester hybridized with the driver. All of existing subtractive cloning methods are designed to remove the tester/driver hybrid. The removal of hybrid, however, is often unsatisfactory For various reasons. In this study we developed a subtractive enrichment protocol in which the tester/driver can be completely removed by selecting only the tester/tester after hybridization. In this protocol both the tester and driver DNAs are ligated with same linker DNAs and amplified by polymerase chain reaction (PCR). The tester DNA is then digested with two different enzymes and used in subsequent hybridization with an excess driver. After hybridization, the DNA is ligated with the adaptor that is only compatible with the tester/tester. Since only the tester/tester can have the new adaptor, no tester/driver can be amplified by PCR in this protocol. Unlike other methods, a 100% subtraction efficiency can be achieved even though the enzymatic treatments used in the enrichment procedure are incomplete. Furthermore, only the hybridized tester DNA can have the new adaptor and be amplified by PCR, resulting in 100% denaturation in effect. The efficacy of this novel method was verified with the model system in which a known amount of the target sequence is included.

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On Partitioning Ideals of Semirings

  • Gupta, Vishnu;Chaudhari, Jayprakash Ninu
    • Kyungpook Mathematical Journal
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    • v.46 no.2
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    • pp.181-184
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    • 2006
  • We prove the following results: (1) Let R be a strongly euclidean semiring. Then an ideal A of $R_{n{\times}n}$ is a partitioning ideal if and only if it is a subtractive ideal. (2) A monic ideal M of R[$x$], where R is a strongly euclidean semiring, is a partitioning ideal if and only if it is a subtractive ideal.

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Genetically Optimization of Fuzzy C-Means Clustering based Fuzzy Neural Networks (Subtractive Clustering 알고리즘을 이용한 퍼지 RBF 뉴럴네트워크의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • 2008.10b
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    • pp.239-240
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    • 2008
  • 본 논문에서는 Subtractive clustering 알고리즘을 이용한 Fuzzy Radial Basis Function Neural Network (FRBFNN)의 규칙 수를 자동적으로 생성하는 방법을 제시한다. FRBFNN은 멤버쉽 함수로써 기존 RBFNN에서 가우시안이나 타원형 형태의 특정 RBF를 사용하는 구조와 달리 Fuzzy C-Means clustering 알고리즘에서 사용하는 거리에 기한 멤버쉽 함수를 사용하여 전반부의 공간 분할 및 활성화 레벨을 결정하는 구조이다. 본 논문에서는 데이터의 밀집도에 기반을 두어 클러스터링을 하는 Subtractive clustering 알고리즘을 사용하여 퍼지 규칙의 수와 같은 의미를 갖는 분할할 입력공간의 수와 분할된 입력공간의 중심값을 동정하며, Least Square Estimator (LSE) 알고리즘을 사용하여 후반부 다항식의 계수를 추정 한다.

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Unsupervised Cluster Estimation using Subtractive HyperBox Algorithm (차감 HyperBox 알고리듬을 이용한 Unsupervised 클러스터 추정)

  • Moon, Seong-Hwan;Choi, Byeong-Geol;Kang, Hun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.87-90
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    • 1997
  • Mountain Method의 다른 형태인 Subtractive 클러스터링 알고리듬은 계산이 간단하고 기존의 클러스터링 방법들과는 달리 초기 클러스터 중심의 개수 선정이 필요 없기 때문에 클러스터를 추정하는데 효과적인 알고리듬이다. 또한 클러스터의 간격을 결정하는 파라미터의 값에 따라 클러스터의 개수를 다르게 할 수 있다. 그러나 이 파라미터에 의해 동일한 그룹(Class)내에서 여러 개의 클러스터 중심이 발생될 수도 있다. 본 논문에서는 Subtractive HyperBox 알고리듬을 사용하여 이 파라미터의 영향을 줄이고 발생한 클러스터 중심이 속한 그룹의 경계를 판정함으로서 같은 그룹내에서 하나의 클러스터만 발생하도록 하고, 순차적으로 클러스터링 한 후 결과를 Subtractive 클러스터링 알고리듬과 비교하여 보았다.

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