• Communications for Statistical Applications and Methods
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• v.6 no.3
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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.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.326-336
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• 2022

This study aims to examine Korean learners' production of American English vowel focused on feature analysis. Specifically, the present study adopts feature analysis so that vowel production is analyzed in terms of vowel features as well as overall segmental accuracy. To this end, 22 Korean college students participated in a production test which contained 11 English vowels /i, ɪ, eɪ, ɛ, æ, ɑ, oʊ, ɔ, ʊ, u, ʌ/. The results revealed that the degree of difficulty varied depending on features; the Korean participants showed higher accuracy for front/back features than for tongue height features and tense/lax features. In particular, the participants had more difficulty producing back vowels and non-high vowels than front vowels and high vowels with respect to tongue height features and lip rounding features. Among the individual vowels, /eɪ/ showed the highest accuracy in feature analysis. On the other hand, /ɑ, ɔ, ʌ/ showed low accuracy with respect to height features and lip rounding features, and high vowels /i, ʊ, u/ showed low accuracy with respect to tense/lax features. As for the correlation between the vowel features, tongue height features and lip rounding features are significantly correlated. Also, tongue height features and tense/lax features showed a strong correlation. Finally, pedagogical implications for teaching English vowels were further discussed based on the findings of the current study.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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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.

• Language and Information
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• v.14 no.2
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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.

• Journal of Korea Multimedia Society
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• v.20 no.8
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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.

• Journal of Internet Computing and Services
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• v.17 no.5
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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.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.141-147
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• 2009

A analysis on the spatial features is required for exploratory spatial data analysis of information about space location(population ecological factor, social ecological factor) to manage the store factors, the service industry, etc. Therefore, the purpose of this study is to provide correlation analysis method between the types of service trade using dependence between spatial objects on the geographical space and statistical correlation and to analyze the spatial features through the deduction of correlation analysis between the types of the neighborhood trade area.

• Phonetics and Speech Sciences
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• v.1 no.4
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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.

• Journal of The Geomorphological Association of Korea
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• v.17 no.1
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• pp.73-84
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• 2010

This paper is to analyze effectiveness and efficiency of topographic features in visibility analysis. For this research aim, this paper compares the analysis results of topographic features and relationships between topographic features and their visibility analysis on surfaces. This paper employs peak, pass, pit, ridge and valley features from the topographic features for which five areas including mountain and plain areas in Britain are selected and their DEM data are generated. The summaries of the analysis results are as follows: Firstly, it is clear that relationship between high elevation points and their visibility is not highly correlated. This means that highly elevated points are not necessarily better visible areas and they are not suitable for searching for large visible areas. Secondly, the positions that can see large visible areas are highly correlated with their elevation and are distributed within a certain range which has small deviation of their correlation between visibility and elevation. This means that to search for large visible areas, it is necessary to employ the positions located at relatively high elevation area. Thirdly, for all of the five areas, the visibility results of the topographic features are compared with maximal visibility resulted from a while surface areas, and it is identified that topographic features show similar visibility performances of that maximal visibility. From the results stated above, it can be inferred that topographic features and its topographic characteristics are enable to be a research motivation to the visibility analysis topics. Furthermore, the results of this paper can be contributed to explore suitable variables and factors for solving multiple viewshed problems.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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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.