• 제어로봇시스템학회논문지
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• 제13권8호
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• pp.754-759
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• 2007

In this paper, we proposed the Bi-Modal Sensor Fusion Algorithm which is the emotional recognition method that be able to classify 4 emotions (Happy, Sad, Angry, Surprise) by using facial image and speech signal together. We extract the feature vectors from speech signal using acoustic feature without language feature and classify emotional pattern using Neural-Network. We also make the feature selection of mouth, eyes and eyebrows from facial image. and extracted feature vectors that apply to Principal Component Analysis(PCA) remakes low dimension feature vector. So we proposed method to fused into result value of emotion recognition by using facial image and speech.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.703-708
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• 2009

The present paper describes the application of an improved impression transfer vector method (Sakurai et al., 2007) to transform the three basic dimensions (Evaluation, Activity, and Potency) of higher-order impression. First, a set of shapes and surface textures of faces was represented by multi-dimensional vectors. Second, the variation among faces was coded in reduced parameters derived by applying principal component analysis. Third, a facial attribute along a given impression dimension was analyzed to select discriminative parameters from among principal components with higher sensitivity to impressions, and obtain an impression transfer vector. Finally, the parametric coordinates were changed by adding or subtracting the impression transfer vector and the image was manipulated so that its facial appearance clearly exhibits the transformed impression. A psychological rating experiment confirmed that the impression transfer vector modulated three dimensions of higher-order impression. We discussed the versatility of the impression transfer vector method.

• ETRI Journal
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• 제40권5호
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• pp.634-642
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• 2018

Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum-product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.539-551
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• 2004

Multiple responses are often observed in many application fields, such as customer's time-of-day pattern for using internet. Some decision trees for multiple responses have been constructed by many researchers. However, if the response is a high-dimensional vector that can be thought of as a discretized function, then fitting a multivariate decision tree may be unsuccessful. Yu and Lambert (1999) suggested spline tree and principal component tree to analyze high dimensional response vector by using dimension reduction techniques. In this paper, we shall propose factor tree which would be more interpretable and competitive. Furthermore, using Korean internet company data, we will analyze time-of-day patterns for internet user.

• International Journal of Contents
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• 제14권4호
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• 응용통계연구
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• 제28권3호
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• pp.385-392
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• 2015

주성분분석은 차원축소를 위한 대표적 기법이다. 주성분분석에서 변수들이 측정단위가 다르거나 분산의 불균형이 심할 경우 흔히 변수를 표준화한 다음 분석할 것이 권장된다. 표준화 변환은 표준편차를 나누어주는 변환인데, 측정단위에 무관하게 만들기 위해서라면 평균을 나누어주는 변환도 고려해볼 수 있다. 표준화 변환을 한 다음 주성분분석하는 것은 상관행렬로 주성분분석하는 것과 같은데, 평균을 나누어주는 변환을 한 후 주성분분석하는 것은 변동계수와 관련된 행렬로 주성분분석하는 것과 같음을 보이고, 그렇게 변환을 한 다음 주성분분석을 실시하는 것이 왜 필요한가를 설명하였다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.181-184
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• 2000

본 논문에서는 음성 특징추출의 한 방법으로서 기존의 방법들과는 달리 음성의 통계적인 특성들을 고려하여, 입력 공간내에서 변동량이 가장 많은 방향으로 주축을 발견한 다음 그 정보를 이용하여 데이터의 중복성을 제거하는 주성분 해석(PCA:Principal Component Analysis)기법을 사용하여 음성의 특징을 추출하는 방법을 제안한다. 본 논문의 숫자음 인식실험 결과와 비교하기 위하여 기존의 음성특징 파라메타인 Mel-Cepstrum과 비교하였을 때, 0.5%의 인식률 차이가 있었으나, 음성특징 추출시 기존의 파라메타에 비하여 비교적 짧은 시간에 구해지는 점과 데이터의 통계적 특성을 이용한 최적의 기저벡터를 이용한다면 단어나 문장 인식시에 보다 나은 인식률을 얻으리라 사료된다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.372-375
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• 2003

The incidence of blindness resulting from diabetic retinopathy has significantly increased despite the intervention of insulin to control diabetes mellitus. Early signs are microaneurysms, exudates, intraretinal hemorrhages, cotton wool patches, microvascular abnormalities, and venous beading. Advanced stages include neovascularization, fibrous formations, preretinal and vitreous microhemorrhages, and retinal detachment. Microaneurysm count is important because it is an indicator of retinopathy progression. The purpose of this paper is to apply data mining to detect diabetic retinopathy patterns in routine fundus fluorescein angiography. Early symptoms are of principal interest and therefore the emphasis is on detecting microaneurysms rather than vessel tortuosity. The analysis does not involve image-recognition algorithms. Instead, mathematical filtering isolates microaneurysms, microhemorrhages, and exudates as objects of disconnected sets. A neural network is trained on their distribution to return fractal dimension. Hausdorff and box counting dimensions grade progression of the disease. The field is acquired on fluorescein angiography with resolution superior to color ophthalmoscopy, or on patterns produced by physical or mathematical simulations that model viscous fingering of water with additives percolated through porous media. A mathematical filter and neural network perform the screening process thereby eliminating the time consuming operation of determining fractal set dimension in every case.

• The Journal of the Acoustical Society of Korea
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• 제25권3E호
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• pp.89-94
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• 2006

For audio indexing and targeted search of specific audio or corresponding visual contents, the MPEG-7 standard has adopted a sound classification framework, in which dimension-reduced Audio Spectrum Projection (ASP) features are used to train continuous hidden Markov models (HMMs) for classification of various sounds. The MPEG-7 employs Principal Component Analysis (PCA) or Independent Component Analysis (ICA) for the dimensional reduction. Other well-established techniques include Non-negative Matrix Factorization (NMF), Linear Discriminant Analysis (LDA) and Discrete Cosine Transformation (DCT). In this paper we compare the performance of different dimensional reduction methods with Gaussian mixture models (GMMs) and HMMs in the classifying video sound clips.

• 한국수학교육학회지시리즈A:수학교육
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• 제46권3호
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• pp.263-272
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• 2007

Mathematics Teaching Efficacy Beliefs Inventory (MTEBI) was translated into Korean and conducted among Korean pre-service mathematics teachers. The Korean-translated MTEBI consists of two subscales with 16 items. Personal Mathematics Teaching Efficacy (PMTE) subscale has 10 items and Mathematics Teaching Outcome Expectancy (MTOE) subscale has 6 items. The purpose of this study is to investigate the internal reliability and the construct validity of the Korean-translated MTEBI. The Cronbach alpha coefficient of Korean-translated MTEBI and its two subscales are respectively .87, .83, and .74 which imply that the instrument is reliable. The construct validity was achieved by performing factor analysis. Principal component solution with varimax rotation for the Korean-translated MTEBI was used in factor analysis and thus the best fit simple structure was obtained by two factors which correspond to the self-efficacy dimension and the outcome expectancy dimension of Bandura's self-efficacy theory.