• Korean Journal of Environmental Biology
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• v.28 no.2
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• pp.95-100
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• 2010

Burkholderia cepacia PBSA-7, Bacillus licheniformis PBSA-8 and Burkholderia sp. PBSA-9 previously collected from Korea soil (Joo and Kim, 2009) were analyzed for the presence of genes encoding proteins operative in the degradation of poly(butylene succinate-co-butylene adipate; PBSA). Polymerase chain reaction analyses revealed a 1.5 kb fragment of the lipase gene (lip A) in B. cepacia PBSA-7 and Burkholderia sp. PBSA-9, while B. licheniformis PBSA-8 harbored the same gene fragment at 600 bp. The three strains possessed "Gly-X1-Ser-X2-Gly" and "Ala-X1-Ser-X2-Gly" lipase sequence regions. Burkholderia sp. PBSA-7 lip A displayed 36~40% homology with the family 1-1 lipases and 82~92% homology with the family 1-5. Burkholderia sp. PBSA-8 lip A was 64~65% homologous with the subfamily 1-4 lipases, but displayed no homology with the subfamily 1-5 lipases. Burkholderia sp. PBSA-9 lip A displayed 35~37% homology with the family I1 lipases and 83~94% homology with the family I2 lipases, similar to Burkholderia sp. PBSA-7.

• Korean Journal of Cognitive Science
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• v.12 no.1_2
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• pp.77-89
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• 2001

Applying perceptual hierarchy of facial feature points, a neural network model for recognizing facial expressions was designed. Input data were convolution values of 150 facial expression pictures by Gabor-filters of 5 different sizes and 8 different orientations for each of 39 mesh points defined by MPEG-4 SNHC (Synthetic/Natural Hybrid Coding). A set of multiple regression analyses was performed with the rating value of the affective states for each facial expression and the Gabor-filtered values of 39 feature points. The results show that the pleasure-displeasure dimension of affective states is mainly related to the feature points around the mouth and the eyebrows, while a arousal-sleep dimension is closely related to the feature points around eyes. For the filter sizes. the affective states were found to be mostly related to the low spatial frequency. and for the filter orientations. the oblique orientations. An optimized neural network model was designed on the basis of these results by reducing original 1560(39x5x8) input elements to 400(25x2x8) The optimized model could predict human affective rating values. up to the correlation value of 0.886 for the pleasure-displeasure, and 0.631 for the arousal-sleep. Mapping the results of the optimized model to the six basic emotional categories (happy, sad, fear, angry, surprised, disgusted) fit 74% of human responses. Results of this study imply that, using human principles of recognizing facial expressions, a system for recognizing facial expressions can be optimized even with a a relatively little amount of information.

• Proceedings of the Materials Research Society of Korea Conference
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• 2003.11a
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• pp.109-109
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• 2003

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2006.04a
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• pp.125-126
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• 2006

• Proceedings of the Optical Society of Korea Conference
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• 2004.07a
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• pp.18-19
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• 2004

• 한국초전도학회:학술대회논문집
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• 2007.07a
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• pp.42-42
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• 2007

• Proceedings of the Materials Research Society of Korea Conference
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• 2000.11a
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• pp.66-66
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• 2000

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 1995.11a
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• pp.18-18
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• 1995

• Journal of the Chungcheong Mathematical Society
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• v.8 no.1
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• pp.111-117
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• 1995

A function $f:{\Omega}{\rightarrow}X$ is called intrinsically-separable valued if there exists $E{\in}{\Sigma}$ with ${\mu}(E)=0$ such that $f({\Omega}-E)$ is a separable in X. For a given Dunford integrable function $f:{\Omega}{\rightarrow}X$ and a weakly compact operator T, we show that if f is intrinsically-separable valued, then f is Pettis integrable, and if there exists a sequence ($f_n$) of Dunford integrable and intrinsically-separable valued functions from ${\Omega}$ into X such that for each $x^*{\in}X^*$, $x^*f_n{\rightarrow}x^*f$ a.e., then f is Pettis integrable. We show that a function f is Pettis integrable if and only if for each $E{\in}{\Sigma}$, F(E) is $weak^*$-continuous on $B_{X*}$ if and only if for each $E{\in}{\Sigma}$, $M=\{x^*{\in}X^*:F(E)(x^*)=O\}$ is $weak^*$-closed.

• Communications of the Korean Mathematical Society
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• v.9 no.4
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• pp.951-959
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• 1994

A sequence ${X_j : j \geq 1}$ of random variables is said to be pairwise positive quadrant dependent (pairwise PQD) if for any real $r-i,r_j$ and $i \neq j$ $$ P{X_i > r_i,X_j > r_j} \geq P{X_i > r_i}P{X_j > r_j} $$ (see [8]) and a sequence ${X_j : j \geq 1}$ of random variables is said to be associated if for any finite collection ${X_{i(1)},...,X_{j(n)}}$ and any real coordinatewise nondecreasing functions f,g on $R^n$ $$ Cov(f(X_{i(1)},...,X_{j(n)}),g(X_{j(1)},...,X_{j(n)})) \geq 0, $$ whenever the covariance is defined (see [6]). Instead of association Cox and Grimmett's [4] original central limit theorem requires only that positively linear combination of random variables are PQD (cf. Theorem $A^*$).