• East Asian mathematical journal
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• 제34권5호
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• pp.571-575
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• 2018

Let D be an integral domain, ${\ast}$ a star-operation on D, and S a (not necessarily saturated) multiplicative subset of D. In this article, we prove the Cohen type theorem for $S-{\ast}_{\omega}$-principal ideal domains, which states that D is an $S-{\ast}_{\omega}$-principal ideal domain if and only if every nonzero prime ideal of D (disjoint from S) is $S-{\ast}_{\omega}$-principal.

• 응용통계연구
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• 제8권1호
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• pp.51-60
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• 1995

크기가 $n \times p$인 자료행렬에서 p개의 변수들과 성격이 다소 다른 $p_s$개의 변수를 같이 고려한 크기가 $n \times (p + p_s)$ 자료행렬이 있다 하자. 전통적 주성성분분석은 성격이 다른 변수들로 인하여 효과적인 결과를 제공하지 못한다. 본 논문에서는 이런 점을 개선하기 위해서 성격이 다른 $p_s$개의 변수를 추가변수로 두는 추가적 주성분분석을 소개하려 한다. 이 기법은 전통적 주성분분석의 대수적,기하적인 면을 따른다. 그리고 전통적 주성분분석과 추가적 주성성분분석을 활용한 한국 프로야구의 8개팀과 1982-1992년 동안의 14개의 부문별 기록에 대한 전형적인 자료분석의 한 예를 제시한다. 더불어 두 분석의 결과도 비교하였다.

• 한국환경교육학회지:환경교육
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• 제15권1호
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• pp.31-50
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• 2002

The purpose of this study were the effects of principal related factors on the choice of the subject of environmental education in curriculum of middle and high school. We select 495 middle and high schools from 3 district of Chungbuk, Daejeon and Incheon. And, we send the questionnaires to the principals and withrowed answer questionnaire of 313(63.2%) from them. From the analysis of questionnaire, we obtained the reliability coefficient cronbach alpha value(0.8684). The results of this research as fallows : the principal factors that had chosen the subject of environmental education have significantly higher knowledge, recognition, attitude and practice than that not choiced on the subject of environmental education(p=0.000). The correlation between principal's attitude on the choice of the subject of environmental education and choice state of school was significantly higher than the other factors, and it's correlation coefficient was 0.328(p<0.01). Principal's attitude factor on the choice of the subject of environmental education have a great influence on his practice factor of them, and standard regression coefficient was 0.642(p<0.001). We found that principal's attitude factor on the choice of the subject of environmental education to have a great influence on the choice state of school in view of the results so far achieved.

• Journal of applied mathematics & informatics
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• 제39권5_6호
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• pp.617-622
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• 2021

Let R be a commutative ring with identity, R[X] the polynomial ring over R and S a multiplicative subset of R. Let U = {f ∈ R[X] | f is monic} and let N = {f ∈ R[X] | c(f) = R}. In this paper, we show that if S is an anti-Archimedean subset of R, then R is an S-Noetherian ring if and only if R[X]_U is an S-Noetherian ring, if and only if R[X]_N is an S-Noetherian ring. We also prove that if R is an integral domain and R[X]_U is an S-principal ideal domain, then R is an S-principal ideal domain.

• 대한수학회보
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• 제36권4호
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• pp.655-660
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• 1999

Let R be an integral domain with identity. In this paper we will show that if R is integrally closed or if t-dim $R{\leq}1$, then R[{$X_{\alpha}$}] satisfies the principal ideal theorem for each family {$X_{\alpha}$} of algebraically independent indeterminates if and only if R is an S-domain and it satisfies the principal ideal theorem.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.623-630
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• 2003

Recently, Tanaka(1988) derived two influence functions related to an eigenvalue problem $(A-\lambda_sI)\upsilon_s=0$ of real symmetric matrix A and used them for sensitivity analysis in principal component analysis. In this paper, we deal with the perturbation expansions up to quadratic terms of the same functions and discuss the application to sensitivity analysis in principal component regression analysis(PCRA). Numerical example is given to show how the approximation improves with the quadratic term.

• Korean Journal of Mathematics
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• 제8권1호
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• pp.87-90
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• 2000

Let R be an integral domain with identity. We show that each associated prime ideal of a principal ideal in R[X] has height one if and only if each associated prime ideal of a principal ideal in R has height one and R is an S-domain.

• 로봇학회논문지
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• 제19권3호
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• pp.254-265
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• 2024

Pattern recognition for surface electromyogram (sEMG) suffers from its nonstationary and stochastic property. Although it can be relieved by acquiring new training data, it is not only time-consuming and burdensome process but also hard to set the standard when the data acquisition should be held. Therefore, we propose an adaptive sEMG pattern recognition algorithm using principal component analysis. The proposed algorithm finds the relationship between sEMG channels and extracts the optimal principal component. Based on the relative distance, the proposed algorithm determines whether to update the existing patterns or to register the new pattern. From the experimental result, it is shown that multiple patterns are generated from the sEMG data stream and they are highly related to the motion. Furthermore, the proposed algorithm has shown higher classification accuracy than k-nearest neighbor (k-NN) and support vector machine (SVM). We expect that the proposed algorithm is utilized for adaptive and long-lasting pattern recognition.

• 대한한의학방제학회지
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• 제11권2호
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• pp.1-18
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• 2003

The Theory for Monarch, Minister, Adjuvant and Dispatcher (or the Theory of Principal, Assistant, Adjuvant and Guiding Korean Oriental Herbal Medicines) has served as a standard principle for newly developed prescription formulas as well as established ones. Despite its significance, however, this theory hasn't been thoroughly studied and covered in the academic journals of Korean Oriental Herbal Medicines (KOHM) yet. This paper inquires into the origin of the theory while presenting the definitions and functions of Principal, Assistant, Adjuvant, and Guiding KOHM. In the end, the recommended doses and number of the KOHM comprising each of Principal, Assistant, Adjuvant, and Guiding KOHM are suggested. The compatibility theory of Principal, Assistant, Adjuvant, and Guiding KOHM can be traced back to the Warring States Period during which it was recorded in the treatise of the various schools of thoughts and their exponents. The theory was firmly established as a full system in ${\ulcorner}Shinnong's\;Pharmacopoeia{\lrcorner}\;and\;{\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$. While ${\ulcorner}Shinnong's\;Pharmacopoeia{\lrcorner}$ focuses on the classification of the properties of KOHM, ${\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$ mainly deals with the principles for writing prescriptions. In this regard, it is ${\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$ that systemized the Theory of Principal, Assistant, Adjuvant, and Guiding KOHM in a real sense. Principal KOHM aims at the causes of diseases and treat main symptoms. The doses are greater than Assistant, Adjuvant and Guiding KOHM. With their comprehensive effects, Principal KOHM is a leading ingredient of any prescription formula. Assistant KOHM are similar to Principal KOHM in its natures and flavors. Although its natures, flavors as well as efficacies may slightly differ from those of Principal KOHM, Assistant KOHM strengthens the therapeutic effects, jointly working with Principal KOHM. They mainly treat accompanying diseases and symptoms. Adjuvant KOHM is divided into two types: facilitator and inhibitor. Facilitators with the similar properties to those of Principal and Assistant KOHM help strengthen the therapeutic effects. Since they usually treat accompanying symptoms or secondary accompanying symptoms (minor accompanying symptoms), there are two kinds of facilitators. (1) The first kind of facilitators assists Principal KOHM, targeting accompanying symptoms. (2) The second ones supporting Assistant KOHM are for accompanying or secondary accompanying symptoms (or minor accompanying symptoms). Inhibitors counteract and thereby complement Principal and Assistant KOHM. Some of them inhibit the side effects or toxicity of Principal KOHM for the sake of the safety of the whole prescription formula while the others generate induced interactions. Guiding KOHM can be used for two purposes: guiding and mediating. The Guiding KOHM for the former purpose leads the other KOHM in a prescription formula to the lesion. But, the Guiding KOHM for mediating coodinate and harmonize all the ingredients in a prescription formula. The number of KOHM for those Principal, Assistant, Adjuvant and Guiding KOHM and their doses are different, depending on the types of prescriptions: classical prescriptions, prescriptions after ${\ulcorner}$Treatise of Cold-Induced Diseases${\lrcorner}$ and prescriptions of Sasang Constitutions Medicines. In the case of the prescriptions after ${\ulcorner}$Treatise of Cold-Induced Diseases${\lrcorner}$, it is highly recommended to follow the view of ${\ulcorner}$Thesaurus of Korean Oriental Medicine Doctors in Chosun Dynasty${\lrcorner}$ for the number of KOHM to be used. For the doses, however, ${\ulcorner}$Elementary Course for Medicine${\lrcorner}$, is found to be more accurate. The most appropriate number of KOHM per prescription is 11-13. To be more specific, for one prescription formula, it is recommended to administer one kind of KOHM for Principal KOHM, 2-3 for Assistant KOHM, 3-4 for Adjuvant KOHM and 5 for Guiding KOHM. As for the proportion of the doses, when 10 units are to be administered for Principal KOHM in a formula, the doses for the other three should be 7-8 units for Assistant KOHM, 5-6 for Adjuvant KOHM and 3-4 for Guiding KOHM. The doses of the KOHM added to or taken out of the prescription correspond to those of Adjuvant and Guiding KOHM.

• Bulletin of the Korean Chemical Society
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• 제29권4호
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• pp.833-841
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• 2008

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.