• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1042-1042
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• 비교문화연구
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• 제38권
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• pp.433-459
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• 2015

This paper aims at a better understanding of the concept of korean separable syntactic atom, of which inner parts is separable in syntax, and at examining whether or not this concept can apply to derivatives, functional complex constructions, idiomatic expressions in korean. I defined a syntactic atom as a minimum unit which is drawn directly from lexicon and then is applied to syntactic rules. And I insist that so-called 'lexical island constraint' has some problems and that the syntactic rules can be applied to inner parts of syntactic atom, if the syntactic rules is irrelevant to new syntactic atom formation. The greater part of derivatives is non-separable syntactic atoms. But the likes of '반짝거리다', '죄송스럽다', '칭얼대다' are the separable syntactic atoms. The degree of separability of them is different in the insertion of korean particles or negative adverbs and the omission of root of sytactic atom. The derivatives of 'X-적', of which roots is regular nominal roots, permit the syntactic link between roots and the syntactic combination of the root and its argument. These kinds of derivatives is separable syntactic atoms. Also the derivatives of 'bracket paradox' and 'X-답-' derivatives is separable syntactic atoms. All functional complex constructions are not separable syntactic atoms. According to the degree of grammaticalization, inner parts of some are separable, some is non-separable. Separable functional complex constructions only permit the switching of endings or Josas but not application of other syntactic rules. All idiomatic expressions which are composed of two or more syntactic atoms are separable syntactic atoms. Some of them have so strong separability to allow the insertion of syntactic atom, adverb or adnominal modification and the noun in idiomatic expression to become the head of the relative clause. And some idiomatic expressions which have weak separability only permit interrogative's substitution or form change in fraction of idiomatic expressions.

• 한국안광학회지
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• 제21권1호
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• pp.1-10
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• 2016

목적: 본 연구는 60대 이상의 노년층을 대상으로 착색안경렌즈의 처방이 시력교정의 질에 미치는 영향을 평가하고 가장 효과적인 착색렌즈의 색상을 제시하고자 하였다. 방법: 60대 이상(평균 $71.0{\pm}6.3$세)의 50명(남17, 여33)을 대상으로 원거리 시력이 0.5 이상이 되도록 시험테를 이용하여 교정한 후 무착색, 브라운착색 및 그레이착색렌즈를 덧댐하였다. 각각 착색안경렌즈 덧댐 시의 시력은 원거리 최소가독시력 및 최소분리시력을 측정한 후 LogMAR 시력으로 환산하여 비교하였으며, 시기능은 근거리 입체시와 대비감도를 측정하여 비교하였다. 또한 대상자들의 착색안경렌즈 선호도와 시지각 및 움직임에 대한 자각증상을 설문조사하였다. 결과: 원거리 최소가독시력과 최소분리시력은 무착색렌즈의 사용 시 가장 좋았으며, 브라운착색 및 그레이착색렌즈 순으로 나타났다. 근거리 입체시, 대비감도 및 시지각은 브라운착색렌즈의 사용 시 가장 좋은 것으로 나타났다. 자각적 불편감은 그레이착색렌즈 착용 시 가장 크게 나타났으며, 대상자가 선호하는 안경렌즈는 브라운착색렌즈로 조사되었다. 결론: 이상의 결과로 착색안경렌즈의 사용으로 노년층의 시력과 시기능이 개선될 수 있으나, 시력 및 시기능의 변화가 자각적 만족도와는 반드시 일치하는 것은 아님을 알 수 있었다. 본 연구결과, 1,000 lux정도의 조도에서는 원거리 시생활이 보편화된 노년층에게는 무착색 및 브라운착색렌즈의 사용을, 근거리 작업이 많은 경우에는 브라운 및 그레이착색렌즈의 사용을 제안할 수 있겠다.

• 전자공학회논문지
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• 제50권12호
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• pp.191-196
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• 2013

본 논문에서는 실제 환경에서 인접 배치된 무지향성 스테레오 마이크로폰을 활용하여 녹음받은 스테레오 오디오 신호를 양이간 강도차에 기반하여 원하는 방위각에 존재하는 음원을 추출하는 음원 분리 기법을 제안한다. 먼저, 최소 분산 무손실 응답빔형성기를 활용하여 스테레오 오디오 신호의 양이간 강도차를 극대화하고, 강도차 기반의 음원 분리 기법을 적용한다. 제안된 기법의 성능을 검증하기 위하여 stereo audio source separation evaluation campaign (SASSEC)에서 제공하는 객관적 성능평가 지표인 source-to-distortion ratio (SDR), source-to-interference ratio (SIR), sources-to-artifacts ratio (SAR)을 측정하였다. 측정한 결과, 음원 분리 기법에 빔형성기까지 적용한 결과가 높은 성능을 보인 것으로 평가되었다.