• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.306-306
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• 2019

전지구적 기온상승으로 인해 미래기후의 관한 연구가 중요시 되고 있다. 위와 같은 현상으로 인하여 다양한 기후변화 연구가 진행되고 있다. 미래기후 연구에는 GCM (General Circulation Model) 모의 결과가 이용된다. 격자 자료로 구성된 GCM은 연구 지점으로 지역적 상세화와 연구지역의 관측자료 사이의 편이 보정(bias correction)이 필수적이다. 위와 같은 근거로 편이 보정 방법의 선택은 매우 중요하며 편의 보정의 방법에 따라서 결과가 다르게 도출될 수 있다. 또한 국내외 연구에서는 다양한 상세화 기법과 편이 보정 기법을 분석 및 평가하는 연구가 진행되고 있으며, 편의 기법 중 대표적인 기법인 Quantile mapping과 Random Forest 기법이 있다. Quantile mapping 기법은 GCM의 과거 모의 데이터와의 편이 보정에 있어서 우수하게 나타났으나, GCM 데이터의 미래 예측 기간(2010년~2018년)까지의 데이터에서는 극한 강수를 정량적으로 분석 가능한 Random Forest 기법이 편이 보정 과정에서 성능이 우수할 것으로 판단된다. 본 연구에서는 우리나라 21개 관측소를 기준으로 총 4개의 GCM(GISS, CSIRO, CCSM4,MIROC5)의 과거 기간 자료(1970년~2005년)를 실제 관측소에서 관측된 강수량을 편의 보정하는 방법에 있어서 편의 보정 기법의 성능을 비교한 결과와 GCM 미래 예측 기간 자료(2010년~2018년)에서의 편의 보정 기법의 성능 결과를 비교하였다. 이를 토대로 편이 보정 기법의 결과를 6개의 평가지수를 이용하여 정량적으로 분석하였으며, 다기준의사결정기법인 TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution)를 이용하여 편이 보정기법들의 성능에 있어서 우선순위를 선정하였다. 본 연구에서 편이 보정 방법으로 Quantile mapping 방법을 사용했으며, Quantile mapping의 기법으로는 비모수 변환법(non-parametric transformation)과 분포기반 변환법(distribution derived transformation)이 사용되었다. 또한 머신러닝 방법 중 하나인 Random Forest 방법을 동시에 사용하여 결과를 비교하였다. 또한 GCM 자료가 격자식으로 제공하고 있기 때문에 관측소 강수량도 공간적으로 환산하여야 하는데, 본 연구에서는 역거리 가중치법(inverse distance weighting, IDW) 방법을 이용하였다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1223-1232
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• 2004

This study is directed toward determining the number and characteristics of psychologically meaningful perceptual dimensions required for assessing the sound quality with respect to vehicle noises, and toward identifying the acoustical and/or psychoacoustical bases underlying the preference and similarity judgments. For the purpose of analyzing the paired comparison data produced by subjective ratings we used nonmetric multidimensional scaling(MDS). The perceptual dimensions based upon preference ratings could explain 76.3 % of the variance by maximum dB(A) and sharpness acum. The correlation between objective and subjective positions of the stimuli is $R^2$=0.97(F(1,13)=195.45, p < .01), corrected $R^2$=0.93. The less the intensity of the stimulus the more becomes the subjective Position would be over-estimated relative to the objective one. The same is valid for the opposite case. The perceptual dimensions based upon similarity judgments could be accounted for 47.8 % and 23.5% of the variance, each of which might be a match for the maximum dB(A) and the sharpness acum, respectively. The correlation between objective and subjective positions of the stimuli is $R^2$=0.94(F(1,13)=92.38, p < .01), corrected $R^2$=0.87. The more the intensity of the stimulus the more becomes the subjective position would be over-estimated relative to the objective one. The same is valid for the opposite case. In other words, it is likely that the larger the amount of two stimuli which to compare would be judged similar. So far it should be further clarified that whether the relationship between preference ratings and psychological distances nay be optimized through which psycho-physical models.

• Journal of the Korean Society of Fashion and Beauty
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• v.5 no.2 s.13
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• pp.68-74
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• 2007

The purpose of this study was to find out the difference between high self-esteem and low self-esteem about the product. The study used the questionnaire method to find out the control effect of self-esteem on apparel brand types. The survey data was analyzed by SPSS Hangul 10.0 Statistic Package. 16 apparel brands that had been selected by a preliminary study were surveyed by using. Brand Mapping was performed in each group for brand classification, and ANOVA was conducted in order to compare the variables depending on brand types. The self-esteem was surveyed by using the self-esteem scale by Rosenberg was used. Multivariate analysis was conducted to identify brand preference, product involvement according to purchase intention and the interactive effects of the brand types that are divided into familiarity and control recognition. For the purpose of the study was to compare searched the results of the high self-esteem comparison with the low self-esteem. The results of the study found the differences on perception about the brand between the high self-esteem and the low self-esteem on the preference. People with high self-esteem liked the brand that was perceived obedience. But the low self-esteem liked the brand that was perceived control.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.101-101
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• 2019

전 지구적 기온상승으로 인한 기후변화는 사회적, 수문학적, 다양한 분야에 영향을 미친다. 또한 IPCC(Intergovernmental Panel on Climate Change)의 보고서에 따르면 미래에도 지속적으로 기온상승이 예상되며, 이러한 현상은 인류의 삶에 큰 영향을 미칠것으로 예상된다. 또한 수자원 및 관련 분야에서도 기온 상승에 따른 강수량, 강수의 주기 변동, 극한 기후사상의 심도(severity)와 빈도 변화에 따른 다양한 연구가 진행되고 있으며, 미래의 강우량과 온도를 예측하는 기후변화연구에서는 다양한 기후모형을 고려하여 분석한다. 하지만 모든 기후모형이 우리나라에 적합한 것은 아니므로 과거 기후를 모의한 결과를 토대로 성능이 뛰어난 모형의 결과에 더 높은 가중치를 주고 미래를 예측하는 연구가 활발히 진행되고 있다. 일반적으로 기후모형으로 GCM (General Circulation Model) 모의 결과가 이용되는데 우리나라에 대한 GCM 결과의 정확성을 분석하는 연구는 부족한 실정이다. 따라서 본 연구에서는 21개의 GCM을 대상으로 과거 모의 자료(1970년~2005년)를 실제 관측소에서 관측된 강수량과 비교하여 각 GCM들의 성능을 평가하고 이를 토대로, GCM들의 우선순위를 선정하였다. 또한 격자 기반 GCM 결과를 IDW (Inverse Distance Weighted) 방법을 사용하여 기상관측소로 지역적 상세화를 수행하였으며, GCM과 관측자료 사이의 편이를 보정하기 위해 6가지의 Quantile Mapping 방법과 Random Forest 기법을 사용하였다. 또한 편이 보정 기법 중 성능이 좋은 기법을 선택하여 관측소에 적용하였다. 편이 보정된 GCM 모의결과에 대한 성능을 토대로 우수한 GCM 순위를 도출하기 위해 다기준의사결정기법 중 하나인 TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)를 이용하였다. 그리고 GCM의 전망기간인 2010년부터 2018년까지의 Machine learning 방법과 Quantile mapping의 기법을 비교 및 성능이 우수한 편이 보정 방법을 선택한 후 전망기간 동안의 GCM 성능의 우선순위를 선정하였다.

• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.5
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• pp.699-712
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• 1995

The purpose of this study was 1) to identify the constructing factors of jeans brand image 2) to analyze the domestic jeans market using perceptual maps of three benefit segments based on stdy(I). The questionnaire consisted of brand preference, attribute of brand image and wearer image was selected from the previous studies or developed for this study. The subjects were 350 male and female university students who have purchased at least one of the nine jeans wear brand selected for the study. For statistical analysis, reliability test, factor analysis, MANOVA, and multiple regression were used. The results of this study were as follows: 1. Symbolism, quality, and economy were found out as constricting factors of brand image in the attribute dimensions, while innovative and active image were found out in the wearer image dimensions. 2. 9 Perceptual maps of attribute dimensions and 3 perceptual maps of wearer image dimensions were constructed and each ideal vector was drawn.

• Korean Journal of Food Science and Technology
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• v.40 no.2
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• pp.160-165
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• 2008

This study was conducted to analyze sensory profiles of commercial sojues using a standardized sensory evaluation procedure, and to investigate the effects of sensory characteristics and brands on consumer liking for soju. Descriptive analysis and consumer taste testing were conducted for seven commercial sojues. For the descriptive analysis, eight panelists generated and evaluated 12 flavor and one pain-sensation attributes for the soju, and there were significant differences among the soju samples for all the 13 sensory attributes. For the descriptive data, principal component analysis was performed to summarize the sensory characteristics of the sojues. For the consumer testing, 224 soju drinkers (20-29 year-olds) were recruited and randomly divided into two groups; a blind group and a group with the knowledge of brand. While the hedonic ratings obtained from the blind group didn't indicate significant differences among the sojues, the ratings obtained from the brand-informed group showed significant differences. Finally, the individual preferences of the 112 consumers in each group were investigated by preference mapping techniques.

• Culinary science and hospitality research
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• v.16 no.3
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• pp.66-75
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• 2010

The purpose of this study is to examine the competitive positions of five coffee shop brands(i.e., Starbucks, Coffee Bean & Tea Leaf, Hollys, Angelinus, and Tom N Toms) in Korea. For this study, data were gathered from the residents of Seoul, Busan and Daegu from September 22 to October 11, 2009. In order to accomplish the purpose of the study, MDS was utilized to investigate differences in customer's perception of the position of five coffee shop brands. The results of positioning analysis showed that there was competitive relationship between Hollys and Angelinus. Also, the positioning of Tom N Toms was close to Hollys and Angelinus. However, Starbucks, the market leader, and Coffee Bean & Tea Leaf, the market follower, were their own identity in their brands. According to the result of the study, it will be helpful for the marketers who need to establish a marketing strategy. Future studies could include other various variables and more thorough investigation into them.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.10-15
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• 2012

In a color image, people and especially facial patterns are important and interesting visual objects. Thus, effective skin color reproduction is essential, as skin color is a key memory color in color application systems. Previous studies suggested skin color reproduction by mapping only to the center value of preferred skin region. However, it is not suitable to determine one preference color because preference color from the observer's preference test is not dominant. In this paper, skin color reproduction using multiple preferred skin colors for each race is proposed. The proposed method first defines multiple preferred skin colors for each race according to their luminance level. After that, skin region is detected in an image. The race is then selected by calculating distance between average chromaticity of detected region and that of each racial skin from a database to assign preferred skin color for each race. Next, each corresponding preferred skin color is determined for each selected race. Finally, input skin color is proportionally mapped toward preferred skin color according to the difference between the input skin color and the preferred skin color for a smoothly reproduced skin color. In the experimental results, the proposed method gives better color correction on the objective and subjective evaluation than the previous methods.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1455-1464
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• 2006

In this paper, we propose an adaptive image enhancement algorithm. The proposed algorithm is classified with the MIE technique for intensity enhancement of input image and MSE techniques for saturation enhancement. The MIE technique is proposed to control the gamut mapping problem and a sudden change in image-brightness while Luminance signal is processing, The MSE techniques are proposed to control de-saturation or over-saturation while chrominance signal is processing. The proposed algorithm is focused on processing preference color for human vision in order to generate better image quality than the algorithms focused on processing uniformly to whole images, This algorithm can be applied to a monitor, TV and other display devices for high quality image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.522-541
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• 2016

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.