• Bulletin of Korea Environmental Preservation Association
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• v.26 no.9_10
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• pp.18-21
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• 2004

환경산업은 그간 환경오염의 사전예방이나 사후정화와 관련된 재화 · 서비스를 제공하는 '공해방지사업'이라는 협의의 범주로 이해되었으나, 1990년대 이후, 미국, 유럽 등 선진국을 위주로 청정생산, 자원절약 · 재생, 환경친화제품 등으로 환경산업의 범주가 확대되는 추세이다.(중략)

• Spatial Information Research
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• v.19 no.4
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• pp.81-89
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• 2011

This paper presents a multivariate kriging algorithm that integrates categorical data as secondary data for spatial interpolation of sparsely sampled ground survey data. Instead of using constant mean values in each attribute of categorical data, disaggregated local mean values at target grid points are first estimated by area-to-point kriging and then are used as local mean values in simple kriging with local means. This algorithm is illustrated through a case study of spatial interpolation of a geochemical copper element with geological map data. Cross validation results indicates that the presented algorithm leads to significant respective improvement of 15% and 25% in prediction capability, compared with univariate ordinary kriging and conventional simple kriging with constant mean values. It is expected that the multivariate kriging algorithm applied in this study would be effectively applied for spatial interpolation with categorical data.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• Journal of the Korean Society for information Management
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• v.33 no.2
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• pp.33-59
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• 2016

This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (${\beta}$, ${\lambda}$) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.477-484
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• 2011

When an event of target variable is rare, a widespread strategy is to build a model on the sample that disproportionally over-represents the events, that is over-sampled. Using the data over-sampled from the original data set, the predicted values would be biased; however, it can be easily corrected to represent the population. In this study, we investigate into the relationship between the proportion of rare event on a data-mart and the model performance using real world data of a Korean credit card company. Also, we use the methods for adjusting of posterior probability for over-sampled data of the offset method and the weighted method. Finally, we compare the performance of the methods using real data sets.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.127-135
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• 2018

This study is to investigate the influence of brand benefit on brand extension, especially focusing on the mediating effect of trademark belief and the moderating effect of categorical similarity. This study restates that brand benefit affect consumers' brand extension attitude and confirms that it is completely mediated by trademark belief. In addition, this study finds that categorical similarity moderates the effects of brand benefit on brand extension attitude. The results of this study suggest a theoretical implication that trademark belief can be used as one of the brand extension strategies and a practical implication that the brand communication strategy based on brand benefits should be changed with the categorical similarity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.13-20
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• 2011

The spatio-temporal pattern in precipitation is a significant element in defining characteristics of precipitation. In this study, a new scheme on regionalization utilizing temporal information was introduced on the basis of existing approaches that is mainly based on simple moments of data and geographical information. Given the identified spatio-temporal pattern, this study was extended to characterize regional pattern of annual maximum rainfall over Korea. We have used circular statistics to characterize the temporal distribution on the precipitation, and the circular statistics allow us to effectively assess changes in timing of the extreme rainfall in detail. In this study, a modified K-means method was incorporated with derived temporal characteristics of extreme rainfall in order to better characterize hydrologic pattern for regional frequency analysis. The extreme rainfall was reasonably separated into five categories that considered most attributes in both quantitative and temporal changes in extremes. The results showed that the proposed approach is a promising approach for regionalization in term of physical understanding of extreme rainfall.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.117-124
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• 2013

This study examined that how precisely MFCC, LPC, energy, and pitch related parameters of the speech data, which have been used mainly for voice recognition system could predict the vocal emotion categories as well as dimensions of vocal emotion. 110 college students participated in this experiment. For more realistic emotional response, we used well defined emotion-inducing stimuli. This study analyzed the relationship between the parameters of MFCC, LPC, energy, and pitch of the speech data and four emotional dimensions (valence, arousal, intensity, and potency). Because dimensional approach is more useful for realistic emotion classification. It results in the best vocal cue parameters for predicting each of dimensions by stepwise multiple regression analysis. Emotion categorizing accuracy analyzed by LDA is 62.7%, and four dimension regression models are statistically significant, p<.001. Consequently, this result showed the possibility that the parameters could also be applied to spontaneous vocal emotion recognition.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.505-534
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• 2010

The purpose of this study was aimed to compare the lexical access and representation of semantic system in the bilinguals. The participants(late Korean-English bilinguals) performed the word-picture matching task. The task was to decide whether the pictures presented after the words(basic-level categories) represent the Korean(L1) or English(L2) words' meaning or not. The stimuli were consisted of common object belonged to four different categories(animal, part of body, clothes, tool). To control the translation strategies, the SOA(stimulus onset asynchrony) were manipulated as 650ms(Exp. 1) and 200ms(Exp. 2). In both experiment, the RTs were faster in L1 condition. The decision time of the part of body categories were shorter than the animal in L1 condition. In L2 condition, clothes were responded faster than the tools. The differences of the lexical access time implied that the bilingual semantic system seemed to be structured by more sub-level categories than the super-level, living or non-living things, and the ways to access the bilingual lexicon might be differentiated according to the languages.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.279-285
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• 2012

This study classified the categorization process of convergence products as a rule-based and a similarity-based categorization process. And we examined that how the categorization process was determined according to information types(visual vs. visual + verbal) about the components of two prototypes before convergence and thinking styles(holistic vs. analytic). The result of this study showed: (1) The rule-based categorization process appeared more in case of visual information with verbal information than only visual information. (2) Analytic thinkers chose a rule-based categorization process more than holistic thinkers. These findings provide the theoretical and practical implications to comprehend the categorization process of convergence products and the judgement for consideration set from various convergence products.