• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.831-842
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• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.

• 한국콘텐츠학회논문지
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• 제22권2호
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• pp.326-336
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• 2022

본 연구는 한국 대학생들의 미국 영어 모음 발화를 자질적으로 분석하여 한국인의 영어 모음 발화의 특성을 알아보는 것을 목적으로 한다. 즉 영어 모음의 분절음 정확도 뿐만 아니라 혀의 전후설성, 혀높이, 원순성, 긴장성과 같은 모음의 자질적 특성들을 분석하여 한국인 학습자가 비교적 쉽게 습득할 수 있는 자질들과 어려워하는 영어모음의 자질들을 밝히고자 했다. 영어 비전공자 대학생들이 11개의 영어 모음 /i, ɪ, eɪ, ɛ, æ, ɑ, oʊ, ɔ, ʊ, u, ʌ/가 포함된 1음절 영어 단어를 발화한 음성자료를 통해, 분절음 정확도 뿐만 아니라 이를 4개의 모음 자질로 분석하였다. 자질 분석 결과, 모든 모음을 통해 전후설성이 가장 쉽게 발화한 자질로 확인된 반면 혀높이와 긴장성 자질은 발화에 어려움이 있는 자질로 확인되었다. 전반적으로 후설모음과 중저모음이 전설모음과 고모음 보다 혀높이와 원순성 자질에서 발화의 어려움을 나타냈다. 개별모음을 볼 때 이중모음 /eɪ/가 모든 자질에서 가장 높은 정확도를 보여 쉽게 습득되는 모음으로 확인되었다. 반면 /ɑ, ɔ, ʌ/는 혀높이와 원순성에서 공통적으로 발화의 어려움을 보였고 고모음 /i, ʊ, u/는 긴장성 자질에서 어려움을 보였다. 각 자질들 사이의 상관관계를 분석한 결과에서는 혀높이-원순성, 그리고 혀높이-긴장성 두 자질쌍이 강한 상관성을 나타냈다. 이와 같은 연구 결과를 바탕으로 실제 교실 학습에 적용할 수 있는 교육적인 함축점도 논의되었다.

• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.11-18
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• 2016

In this paper, we propose a gas classification method using combined features for an electronic nose system that performs well even when some loss occurs in measuring data samples. We first divide the entire measurement for a data sample into three local sections, which are the stabilization, exposure, and purge; local features are then extracted from each section. Based on the discrimination analysis, measurements of the discriminative information amounts are taken. Subsequently, the local features that have a large amount of discriminative information are chosen to compose the combined features together with the global features that extracted from the entire measurement section of the data sample. The experimental results show that the combined features by the proposed method gives better classification performance for a variety of volatile organic compound data than the other feature types, especially when there is data loss.

• 한국언어정보학회지:언어와정보
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• 제14권2호
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• pp.33-46
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• 2010

This paper introduces a new linguistic-focused approach for sentiment analysis (SA) of Korean. In order to overcome shortcomings of previous works that focused mainly on statistical methods, we made effective use of various linguistic features reflecting the nature of Korean. These features include contextual shifters, modal affixes, and the morphological dependency of chunk structures. Moreover, in order to eschew possible confusion caused by ambiguous words and to improve the results of SA, we also proposed simple adjustment methods of word senses using KOLON ontology mapping information. Through experiments we contend that effective use of linguistic features and ontological information can improve the results of sentiment analysis of Korean.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• 인터넷정보학회논문지
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• 제17권5호
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• pp.111-119
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• 2016

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.

• 대한공간정보학회지
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• 제17권1호
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• pp.141-147
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• 2009

상점의 정보, 서비스업 등을 영위하기 위한 공간입지에 대한 정보(인구생태학적 변수, 사회생태학적 변수)의 탐색적 자료 분석을 위해 공간 특성분석이 필요하다. 따라서 본 연구에서는 지리적 공간상에서 공간객체간의 상호의존성과 상호작용과 통계적 상관분석을 이용하여 서비스업종간의 상관분석법을 제시하고자 하며, 또한 근린상권의 업종 간 상관관계분석의 도출을 통하여 공간특성에 대한 분석을 하기 위함이다.

• 말소리와 음성과학
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• 제1권4호
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• pp.127-132
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• 2009

The feature vectors which are used in conventional speaker recognition (SR) systems may have many correlations between their neighbors. To improve the performance of the SR, many researchers adopted linear transformation method like principal component analysis (PCA). In general, the linear transformation of the feature vectors is based on concatenated form of the static features and their dynamic features. However, the linear transformation which based on both the static features and their dynamic features is more complex than that based on the static features alone due to the high order of the features. To overcome these problems, we propose an efficient method that applies linear transformation and temporal information of the features to reduce complexity and improve the performance in speaker verification (SV). The proposed method first performs a linear transformation by PCA coefficients. The delta parameters for temporal information are then obtained from the transformed features. The proposed method only requires 1/4 in the size of the covariance matrix compared with adding the static and their dynamic features for PCA coefficients. Also, the delta parameters are extracted from the linearly transformed features after the reduction of dimension in the static features. Compared with the PCA and conventional methods in terms of equal error rate (EER) in SV, the proposed method shows better performance while requiring less storage space and complexity.

• 한국지형학회지
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• 제17권1호
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• pp.73-84
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• 2010

본 논문은 가시권 분석에서의 지형 요소의 효율성과 활용성에 대한 내용으로써 지형 요소별로 가시권 분석 결과를 비교하여 각 지형 요소와 가시권 분석간의 관련성을 분석한 연구이다. 이를 위해 본 연구에서는 peak, pass, pit, ridge, valley를 지형 요소로 선정하고 조망 지점에서 최대 가시 면적을 확보하는 문제를 가시권 문제로 정의하였다. 또한 다양한 지형적 요인을 고려하기 위해 산악 지역에서부터 평야 지역까지를 대상으로 하였다. 분석 결과는 다음과 같다. 첫째, 해발고도와 가시 면적과의 상관 관계는 낮게 나타났다. 이는 넓은 가시권 확보를 위해 고도가 높은 지점들을 우선적으로 선정하는 방법은 효과적이지 않다는 의미이다. 둘째, 넓은 가시 범위를 보이는 상위 지점들은 해발 고도의 편차가 적다는 점이다. 이는 넓은 가시 범위를 확보하기 위해서는 어느 정도의 해발 고도 지점을 대상으로 해야 한다는 의미이다. 셋째, 가시권 상위 지점들과의 가시권 비교 결과, 모든 연구 지역에 걸쳐 다섯 유형의 지형 요소가 최대 가시권 확보 면적과 유사한 결과를 나타내었다. 이 결과는 가시권 분석에서 지형적 요인의 중요성과 지형 요소의 기여방안에 대한 논의 토대가 될 수 있음을 의미한다. 또한 본 연구의 결과는 향후 최대 가시권 분석에 있어 가시권에 영향을 주는 요인 및 변수 선정에 기여할 것으로 기대할 수 있다.

• 한국의류학회지
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• 제29권7호
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• pp.926-937
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• 2005

The analysis category of Art to Wear was text analyzed from the research material of 100 projects put together by fashion specialist. The conclusion of Art to Wear was comprehended the general features of it were compared and analyzed from a semiotics context. According to this analysis, the formative features of modern Art to Wear is categorized into three different dimensions from a semiotics light. The formative features of modem Art to Wear in the light of syntactic dimension was divided as an open constructed shape of Space Extension, non-typical Deformation, Geometrical Plasticity. The formative features of modem Art to Wear in the light of semantic dimension express symbolic meaning through metaphorical sign. These sign reflect the body image of the life and death and its objective of Abjection, Hybrid of discultural appearance and the image of Hyper-reality, which are features used to comprehend the inner meaning. The formative features of modem Art to Wear in the light of pragmatic dimension divided the artist emotion and meaning system delivered by Emotive Image, the Phatic Image that arouse inner signification and the Poetic Image which contain artistic and aesthetic meaning within it.