• 음성과학
• /
• 제14권4호
• /
• pp.81-98
• /
• 2007

This study aims to investigate whether shortened vowel duration conditioned by a following voiceless stop induces phonetic reduction of vowel space in English, and whether the reduction appears more in the height dimension than in the backness dimension (Lindblom, 1963; Flemming, 2005). Fifteen native speakers of American English read minimal pairs containing ten American English vowels in [bVd] and [bVt] syllables in a carrier phrase. All the subjects produced shorter vowels in the voiceless than in the voiced context. However, a reduction in vowel space and a raising of low vowels due to the shortened vowel duration were generally not found. To the contrary, the speakers tended to exhibit even more lowering of low vowels in the voiceless context, and vowel space was more commonly compressed in the backness dimension than in the height dimension. Many speakers, in particular, demonstrated fronting of the high back vowel [u] in the voiceless context. It was interpreted that due to a relatively large number of English vowels in the narrower low vowel space, the raising of low vowels may give rise to confusion in vowel contrasts, and therefore the degree of phonetic vowel reduction is restricted in that region. On the other hand, the high vowel region, being relatively spacious in English, allows a certain degree of phonetic vowel reduction in the F2 dimension. It is possible that heavy requirements for maintaining vowel contrasts may cause speakers to overachieve vowel target values, especially when faced with vowels which are difficult to distinguish due to shortened vowel duration, leading to an over-lowering of the low vowels.

• The Journal of Advanced Prosthodontics
• /
• 제2권3호
• /
• pp.106-110
• /
• 2010

The severe wear of anterior teeth facilitates the loss of anterior guidance, which protects the posterior teeth from wear during excursive movement. The collapse of posterior teeth also results in the loss of normal occlusal plane and the reduction of the vertical dimension. This case report describes 77-year-old female, who had the loss of anterior guidance, the severe wear of dentition, and the reduction of the vertical dimension. Occlusal overlay splint was used after the decision of increasing vertical dimension by anatomical landmark, facial and physiologic measurement. Once the compatibility of the new vertical dimension had been confirmed, interim fixed restoration and the permanent reconstruction was initiated. This case reports that a satisfactory clinical result was achieved by restoring the vertical dimension with an improvement in esthetics and function.

• 응용통계연구
• /
• 제36권4호
• /
• pp.323-335
• /
• 2023

고차원 데이터의 인과 추론에서 고차원 공변량의 차원을 축소하고 적절히 변형하여 처리와 잠재 결과에 영향을 줄 수 있는 교란을 통제하는 것은 중요한 문제이다. 평균 처리 효과(average treatment effect; ATE) 추정에 있어서, 성향점수와 결과 모형 추정을 이용한 확장된 역확률 가중치 방법이 주로 사용된다. 고차원 데이터의 분석시 모든 공변량을 포함한 모수 모형을 이용하여 성향 점수와 결과 모형 추정을 할 경우, ATE 추정량이 일치성을 갖지 않거나 추정량의 분산이 큰 값을 가질 수 있다. 이런 이유로 고차원 데이터에 대한 적절한 차원 축소 방법과 준모수 모형을 이용한 ATE 방법이 주목 받고 있다. 이와 관련된 연구로는 차원 축소부분에 준모수 모형과 희소 충분 차원 축소 방법을 활용한 연구가 있다. 최근에는 성향점수와 결과 모형을 추정하지 않고, 차원 축소 후 매칭을 활용한 ATE 추정 방법도 제시되었다. 고차원 데이터의 ATE 추정 방법연구 중 최근에 제시된 네 가지 연구에 대해 소개하고, 추정치 해석시 유의할 점에 대하여 논하기로 한다.

• 말소리와 음성과학
• /
• 제7권4호
• /
• pp.3-8
• /
• 2015

In this paper, we investigate an input dimension reduction method using continuous word vector in deep neural network language model. In the proposed method, continuous word vectors were generated by using Google's Word2Vec from a large training corpus to satisfy distributional hypothesis. 1-of-${\left|V\right|}$ coding discrete word vectors were replaced with their corresponding continuous word vectors. In our implementation, the input dimension was successfully reduced from 20,000 to 600 when a tri-gram language model is used with a vocabulary of 20,000 words. The total amount of time in training was reduced from 30 days to 14 days for Wall Street Journal training corpus (corpus length: 37M words).

• 한국멀티미디어학회논문지
• /
• 제20권9호
• /
• pp.1567-1573
• /
• 2017

The k-nearest neighbor (k-NN) algorithm is one of the most widely used benchmark algorithm in classification. Nowadays it has been further applied to predict time series. However, one of the main concerns of the algorithm applied on short-term electricity load forecasting is high computational burden. In the paper, we propose an approach of dimension reduction that follows the principles of highlighting the temperature effect on electricity load data series. The results show the proposed approach is able to reduce the dimension of the data around 30%. Moreover, with temperature effect highlighting, the approach will contribute to finding similar days accurately, and then raise forecasting accuracy slightly.

• Communications for Statistical Applications and Methods
• /
• 제29권5호
• /
• pp.603-614
• /
• 2022

The naive Bayes classifier is one of the most straightforward classification tools and directly estimates the class probability. However, because it relies on the independent assumption of the predictor, which is rarely satisfied in real-world problems, its application is limited in practice. In this article, we propose employing sufficient dimension reduction (SDR) to substantially improve the performance of the naive Bayes classifier, which is often deteriorated when the number of predictors is not restrictively small. This is not surprising as SDR reduces the predictor dimension without sacrificing classification information, and predictors in the reduced space are constructed to be uncorrelated. Therefore, SDR leads the naive Bayes to no longer be naive. We applied the proposed naive Bayes classifier after SDR to build a recommendation system for the eyewear-frames based on customers' face shape, demonstrating its utility in the top-k classification problem.

• 정보처리학회논문지B
• /
• 제15B권5호
• /
• pp.435-448
• /
• 2008

사용자 포즈의 3차원 데이터 생성을 통한 3차원 포즈 인식은 2차원 포즈 인식의 문제점을 해결하기 위해서 많이 연구되고 있지만, 3차원 표면 데이터의 방대한 양으로 포즈 인식에서 중요한 특징 추출(feature extraction)이 어렵고 수행 시간이 많이 걸리는 문제점을 가지고 있다. 본 논문에서는 3차원 포즈 인식의 두 가지 문제점인 특징 추출의 어려움과 느린 처리속도를 개선하기 위해서 3차원 형상복원 기술로 모델의 3차원 표면 점들로 구성된 데이터를 2차원 데이터로 변환하는 차원 축소(dimension reduction) 방법을 제안한다. 실린더형 외곽점을 이용한 메쉬없는 매개변수화(meshless parameterization) 방법은 방대한 데이터인 3차원 포즈 데이터를 2차원 데이터로 변환하여 특징 추출과 매칭과정의 연산 속도를 향상 시키며, 특징 추출의 효율성 검증을 위해 간단한 환경에서 실험이 가능한 손 포즈 인식 및 인간 포즈 인식에 적용하였다.

• ETRI Journal
• /
• 제35권5호
• /
• pp.911-914
• /
• 2013

This letter presents Progression-Preserving Projection, a dimension reduction technique that finds a linear projection that maps a high-dimensional sensor dataset into a two- or three-dimensional subspace with a particularly useful property for visual exploration. As a demonstration of its effectiveness as a visual exploration and diagnostic means, we empirically evaluate the proposed technique over a dataset acquired from our own virtual-reality-enhanced ball-intercepting training system designed to promote the upper extremity movement skills of individuals recovering from stroke-related hemiparesis.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2008년도 정기 학술대회
• /
• pp.422-427
• /
• 2008

This study is to illustrate the usefulness of Kriging Dimension Reduction Method(KDRM), which is to construct probability distribution of response function in the presence of the physical uncertainty of input variables. DRM has recently received increased attention due to its sensitivity-free nature and efficiency that considerable accuracy is obtained with only a few number of analyses. However, the DRM has a number of drawbacks such as instability and inaccuracy for functions with increased nonlinearity. As a remedy, Kriging interpolation technique is incorporated which is known as more accurate for nonlinear functions. The KDRM is applied and compared with MCS methods in a compression coil spring design problem. The effectiveness and accuracy of this method is verified.

• Communications for Statistical Applications and Methods
• /
• 제22권3호
• /
• pp.305-312
• /
• 2015

We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.