• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -2
• /
• pp.1161-1164
• /
• 2002

We propose a simple method that can recognize human emotion from monocular dance image sequences. The method only exploits the information within image sequences and does not require cumbersome attachments like sensors. This makes the method a simple, human-friendly one. Moreover, the method is more robust and efficient by taking into account the statistical property of image sequences based on PCA (Principal Component Analysis). The correct recognition rate in real-time is about 75% in a variety of experiments.

• International Journal of Contents
• /
• 제7권3호
• /
• pp.1-5
• /
• 2011

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

• 대한기계학회논문집A
• /
• 제27권12호
• /
• pp.2004-2010
• /
• 2003

Fracture life and crack retardation behavior were examined experimentally using CT specimens of aluminum alloy 5083. Crack retardation life and fracture life were a wide difference. between 0.8 and 0.6 in proportion to ratio of load reduction. The wheeler model retardation parameter was used successfully to predict crack growth behavior. By using a crack propagation rule, prediction of fracture life can be evaluated quantitatively. A statistical approach based on Weibull distribution was applied to the test data to evaluate the dispersion in the retardation life and fracture life by the change of load reduction.

• Asian-Australasian Journal of Animal Sciences
• /
• 제13권3호
• /
• pp.413-422
• /
• 2000

In the class of models which include random effects, the variance component estimates are important to obtain accurate predictors and estimators. Variance component estimation is straightforward for balanced data but not for unbalanced data. Since orthogonality among factors is absent in unbalanced data, various methods for variance component estimation are available. REML estimation is the most widely used method in animal breeding because of its attractive statistical properties. Recently, Bayesian approach became feasible through Markov Chain Monte Carlo methods with increasingly powerful computers. Furthermore, advances in variance component estimation with complicated models such as generalized linear mixed models enabled animal breeders to analyze non-normal data.

• International Journal of Reliability and Applications
• /
• 제3권2호
• /
• pp.61-80
• /
• 2002

Tree-harvesting worker data of 508 separate worker accidents are analyzed and an exploratory approach taken. The worker accident data cover a sample of five years. The scope of the study was the southeastern United States of America. As might be hypothesized, the chainsaw was the most hazardous type of tree-harvesting equipment. It accounted for 55％ of the tree-harvesting accidents. Most chainsaw accidents resulted in injuries to the lower extremities and were more frequent among younger employees. The probability of one or more chainsaw accidents occurring in any 30-day period was approximately 0.856. Chainsaw accidents were more likely to occur in late morning and early afternoon. We used statistical tools such as Pareto charts, c-charts and Ishikawa diagrams. Such tools are useful in diagnosing the root-cause of tree-harvesting worker accidents and help in developing preventive safety programs. Recommendations to help improve the quality of information of accident data collected by insurance companies and others are briefly given. The strategy and culture of continuous process improvements are stressed.

• 산업경영시스템학회지
• /
• 제35권2호
• /
• pp.64-70
• /
• 2012

This paper presents a statistical analysis method for the selection of electroencephalogram (EEG) electrode positions and spectral features to recognize emotion, where emotional valence and arousal are classified into three and two levels, respectively. Ten experiments for a subject were performed under three categorized IAPS (International Affective Picture System) pictures, i.e., high valence and high arousal, medium valence and low arousal, and low valence and high arousal. The electroencephalogram was recorded from 12 sites according to the international 10~20 system referenced to Cz. The statistical analysis approach using ANOVA with Tukey's HSD is employed to identify statistically significant EEG electrode positions and spectral features in the emotion recognition.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
• /
• pp.111-114
• /
• 2008

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised ${\kappa}$-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures.

• Communications for Statistical Applications and Methods
• /
• 제13권2호
• /
• pp.233-241
• /
• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2003년도 춘계 학술발표회 논문집
• /
• pp.167-170
• /
• 2003

We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: an approximate crossvalidation scheme to estimate the smoothing parameter inherent in regression splines, as well as Monte Carlo Expectation Maximization (MCEM) and Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM and Bayesian approaches using simulation, showing that they appear approximately equally efficient, with the approximate cross-validation method being computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2003년도 춘계 학술발표회 논문집
• /
• pp.13-17
• /
• 2003

본 논문에서는 낮은 대역폭, 높은 지연, 그리고 잦은 네트워크 단절로 인한 모바일 컴퓨팅 환경의 문제점들을 해결하기 위한 효과적인 캐시 적재 기법으로서 협업 추천 기반의 데이터 마이닝 전략을 제안하였다. 캐시 적재가 모바일 클라이언트의 이러한 문제점들을 해결하기 위한 효율적인 방법이 된다는 기존의 연구는 많이 진행되어 왔다. 하지만 모바일 컴퓨터의 요구에 대한 이력 정보만을 이용한 기존의 연구는 모바일 클라이언트가 필요로 하는 모든 정보 요구를 만족하지 못하였다. 특히 저장 공간의 제약을 갖는 모바일 컴퓨터의 한계 때문에 더욱 큰 어려움을 갖게 되었다. 본 연구에서는 모바일 클라이언트의 이력 정보에 대하여 데이터 마이닝 기법을 적용한 캐시 적재 기법을 제안하여 적은 캐시 용량만으로도 모바일 클라이언트의 요구를 만족할 수 있는 아이템들을 효과적으로 서비스할 수 있도록 하였다. CSIM Simulator를 이용하여 모의 데이터를 생성하여, 제안 모형의 성능 평가를 위한 실험을 수행하였다. Cache hit ratio를 이용한 객관적인 성능 평가를 통하여 제안된 모형이 모바일 클라이언트의 캐시 적재 기법으로서 우수한 성능을 보임이 확인되었다.