• 인지과학
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• 제22권2호
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• pp.103-121
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• 2011

본 연구는 초기 화면의 차원 정보와 초기-검사 화면의 차원 일치 여부가 목표 대상이 $0^{\circ}$, $90^{\circ}$, $180^{\circ}$, $270^{\circ}$로 회전하는 상황에서 참가자들의 위치 탐색 수행에 어떠한 영향을 미치는지를 보고자 했다. 실험 결과, 초기 화면이 2차원으로 제시되었을 때 참가자들의 수행 정확도가 높았고, 특히 3차원 초기 화면의 경우 검사 화면과 차원이 일치할 때 위치 정보의 심적 회전이 요구되는 상황에서 위치 탐색에 어려움을 겪는 것을 발견하였다. 이러한 결과는 운전 상황과 같이 보조 자료로부터 위치 정보를 얻는 것과 동시에 운전자의 위치와 방향이 변하는 경우, 실제와 시각적으로 유사하게 설계된 3차원 자료보다는 정확한 위치 정보를 제공하는 2차원의 자료가 유리할 수 있음을 시사한다.

• 대한수학회보
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• 제57권3호
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• pp.763-780
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• 2020

Let R be a commutative ring. An R-module M is said to be w-flat if Tor ^R₁ (M, N) is GV -torsion for any R-module N. It is known that every flat module is w-flat, but the converse is not true in general. The w-flat dimension of a module is defined in terms of w-flat resolutions. In this paper, we study the w-flat dimension of an injective w-module. To do so, we introduce and study the so-called w-copure (resp., strongly w-copure) flat modules and the w-copure flat dimensions for modules and rings. The relations between the introduced dimensions and other (classical) homological dimensions are discussed. We also study change of rings theorems for the w-copure flat dimension in various contexts. Finally some illustrative examples regarding the introduced concepts are given.

• 한국수자원학회논문집
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• 제33권S1호
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• pp.37-47
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• 2000

The method of delays is widely used for reconstruction chaotic attractors from experimental observations. Many studies have used a fixed delay time ${\tau}_d$ as the embedding dimension m is increased, but this is not necessarily the best choice for obtaining good convergence of the correlation dimension. Recently, some researchers have suggested that it is better to fix the delay time window ${\tau}_w$ instead. Unfortunately, ${\tau}_w$ cannot be estimated using either the autocorrelation function or the mutual information, and no standard procedure for estimating ${\tau}_w$ has yet emerged. However, a new technique, called the C-C method, can be used to estimate either ${\tau}_d\;or\;{\tau}_w$. Using this method, we show that, for small data sets, fixing ${\tau}_w$, rather than ${\tau}_d$, does indeed lead to a more rapid convergence of the correlation dimension as the embedding dimension m in increased.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2000년도 학술발표회 논문집
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• pp.37-47
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• 2000

The method of delays is widely used fur reconstructing chaotic attractors from experimental observations. Many studies have used a fixed delay time ${\tau}_d$ as the embedding dimension m is increased, but this is not necessarily the best choice for obtaining good convergence of the correlation dimension. Recently, some researchers have suggested that it is better to fix the delay time window ${\tau}_w$ instead. Unfortunately, ${\tau}_w$ cannot be estimated using either the autocorrelation function or the mutual information, and no standard procedure for estimating ${\tau}_w$has yet emerged. However, a new technique, called the C-C method, can be used to estimate either ${\tau}_d{\;}or{\;}{\tau}_w$. Using this method, we show that, for small data sets, fixing ${\tau}_w$, rather than ${\tau}_d$, does indeed lead to a more rapid convergence of the correlation dimension as the embedding dimension m is increased.

• 패션비즈니스
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• 제10권6호
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• pp.91-109
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• 2006

This study aims at strengthening the national competitiveness of Korea's clothing industry as it provides substantial information on the human body proportion and proportional dimension standards for Chinese women and improves the fitness of clothing, considering human proportion in the production of clothing products for export to China. This study selected by simple random sampling Chinese women in Beijing and Shanghai, China, whose age is between 19-50 from June 23 to August 7, 2004. The stature of the average Chinese woman belonging to the High Frequency group is 7.09 times as long as the length of the head. We developed the body proportional dimension standards with the same proportion with High-Frequence group. The basic size of proportional dimension standards for Chinese women has the stature of 159cm, (chest circumference)/2 of 42.8cm.

• Journal of applied mathematics & informatics
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• 제26권3_4호
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• pp.733-738
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• 2008

Using an information of dimensions of divergence points, we give full information of dimensions of the completely decomposed class of the lower(upper) distribution sets of a self-similar Cantor set. Further using a relationship between the distribution sets and the subsets generated by the lower(upper) local dimensions of a self-similar measure, we give full information of dimensions of the subsets by the local dimensions.

• Journal of the Korean Data and Information Science Society
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• 제9권1호
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• pp.63-70
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• 1998

The singular value decomposition is a tool which is used to find a linear structure of reduced dimension and to give interpretation of the lower dimensional structure about multivariate data. In this paper the singular value decomposition is reviewed from both algebraic and geometric point of view and, is illustrated the way which the tool is used in the multivariate techniques finding a simpler geometric structure for the data.

• 정보관리학회지
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• 제19권3호
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• pp.237-261
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• 2002

본 연구는 기대와 성과간의 비교를 통하여 이용자 관점에서 도서관 온라인 서비스에 대한 품질을 평가하였다. 이를 위해 22개 SERVQUAL 문항에 정보품질에 관련된 7개 항목을 추가하여 총29개의 측정항목을 개발하였고, 대덕연구단지내의 한 연구 도서관을 대상으로 온라인 설문을 실시하여 데이터를 수집하였다. 요인분석 결과 온라인 서비스의 품질을 결정하는 요소인 서비스차원은 5개로 묶여졌고, 정보품질 차원은 별개로 분리되어 나타났다. IPA(Importance Performance Analysis) 분석결과 정보품질, 신뢰성, 그리고 보증성이 중요한 서비스 요소임이 밝혀졌고, 다중회귀분석 결과 이용자의 만족에 가장 커다란 영향을 미치는 서비스 요소로는 유형성과 정보품질인 것으로 규명되었다.

• Korean Journal of Mathematics
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• 제16권2호
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• pp.157-163
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• 2008

Using informations of subsets of divergence points and the relation between members of spectral classes, we give another complete decomposition of spectral classes generated by lower(upper) local dimensions of a self-similar measure on a self-similar Cantor set with full information of their dimensions. We note that it is a complete refinement of the earlier complete decomposition of the spectral classes. Further we study the packing dimension of some uncountable union of distribution sets.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.275-285
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• 2004

We explore classification analysis using graphical methods such as sliced inverse regression and sliced average variance estimation based on dimension reduction. Some useful information about classification analysis are obtained by sliced inverse regression and sliced average variance estimation through dimension reduction. Two examples are illustrated, and classification rates by sliced inverse regression and sliced average variance estimation are compared with those by discriminant analysis and logistic regression.