• Review of Culture and Economy
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• v.17 no.2
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• pp.3-28
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• 2014

The characteristics of performing arts differ from those of utilitarian goods in terms of economics. Factors other than price need to be considered to understand the demand for performing arts. Audience surveys as well as econometric demand studies have confirmed that socio-economic factors such as age, income, employment, and education are major determinants of the demand for performing arts. This study focused on the attributes of concerts rather than consumer characteristics to determine the concerts audiences select in terms of transaction cost. Genre, price, internet search trends, and the purpose of performance as well as price are tested as determinants of demand by using the data set for a major concert hall in Seoul. Genre and the specific purpose of concerts influence the demand for concerts. Internet search trends of the performer are used as indicators of popularity and information exposure, which are positively correlated with demand. This result supports the hypothesis that larger audiences would attend concerts that require lower information search costs. To note, price has a positive effect on demand in the higher price range, which means that concerts at higher prices attract larger audiences, whereas normal goods have a negative slope in the demand curve. This result can be explained by the hypothesis that consumers use price as an indicator of the quality expected of a concert. Transaction cost for selecting classical concerts thus forms an inverse-U shape curve against ticket price. These results provide some explanation of why audiences of classical music choose to attend concerts at high ticket prices while offering evidence in favor of the hypothesis that performing arts are selected in a social context.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.67-75
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• 2022

Roots play important roles in water and nutrient uptake and in response to various environmental stresses. Investigating diversification of cultivars through root phenotyping is important for crop improvement in adzuki beans. Therefore, we analyzed the morphological and architectural root traits of 22 adzuki bean cultivars using 2-dimensional (2D) root imaging. Plants were grown in plastic tubes [6 cm (diameter) × 40 cm (height)] in a greenhouse from July 25^th to August 28^th. When the plants reached the 2^nd or 3^rd trifoliate leaf stage, the roots were removed and washed with tap water to remove soil particles. Clean root samples were scanned, and the scanned images were analyzed using the WinRHIZO Pro software. The cultivars were analyzed based on six root phenotypes [total root length (TRL), surface area (SA), average diameter (AD), and number of tips (NT) were included as root morphological traits (RMT); and link average length (LAL) and link average diameter (LAD) were included as root architectural traits (RAT)]. According to the analysis of variance (ANOVA), a significant difference was observed between the cultivars for all root morphological traits. Distribution analysis demonstrated that all root traits except LAL followed a normally distributed curve. In the correlation test, the most important morphological trait, TRL, showed a strong positive correlation with SA (r = 0.97^***) and NT (r = 0.94^***). In comparison, between RMT and RAT, TRL showed a significantly negative correlation with LAL (r = -0.50^***); however, TRL did not show a correlation with LAD. Based on RMT and RAT, we identified the cultivars that ranked 5% from the top and bottom. In particular, the cultivar "IT 236657" showed the highest TRL, SA, and NT, while the cultivar "IT 236169" showed the lowest values for TRL, SA, and NT. In addition, the coefficient of variance for the six tested root traits ranged from (14.26-40%) which suggested statistical variability in root phenotypes among the 22 adzuki bean varieties. Thus, this study will help to select target root traits for the adzuki bean breeding program in the future, generating climate-resilient adzuki beans, especially for drought stress, and may be useful for developing biotic and abiotic stress-tolerant cultivars based on better root trait attributes.

• Journal of Forest and Environmental Science
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• v.16 no.1
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• pp.1-16
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• 2000

This study was carried out to examine the process of plant succession through vegetation invasion and to select appropriate endemic plant species for rapid stabilization and good visual effect on cut-slopes of forest roads. Establishing total of sixty $1m{\times}1m$ sample plots. fifteen for each forest road constructed in the year of '93 (six-year-old), '95(four-year-old), '97(two-year-old), and '98(one-year-old), the ecological attributes of invading vegetation on cut-slopes were analyzed. The results are summarized as follows: 1. The rate of vegetation coverage was highly associated with soil hardness and aspect of cut-slope. Higher rate of vegetation coverage was caused by larger number of invading plant species. 2. The dominant woody species were Rubus crataegifolius, Rhus chinensis, Lespedeza bicolor, Salix hulteni, Alnus hirsuta, and Pinus densiflora. The visual attractive for the fruit of Rubus crataegifolius and the autumn coloration of Rhus chinensis was noteworthy. The dominant herbaceous species were Youngia sonchifolia, Spodiopogon sibiricus, and Lysimachia clethroides in all study forest roads. Spring flower of Potentilla freyniana and Viola rossii: summer flower of Lysimachia clethroides, Commelina communis, Glycine soja. Persicaria sieboldi, and Oenothera odorata: and autumn flower of Artemisia stolonifera and Impatiens textori were abundant and remarkable. 3. The diversity index of woody species tended to be increased as years passed after construction, and that of herbaceous species were decreased. 4. The dominance of Th of dormancy form was early high in the first year of construction, getting decreased thereafter. And that of MM + M + N was increased as years passed after construction. but that of Ch+H+G+Th+HH was decreased. 5. The degrees of succession were estimated by 359, 111, 97, and 87 for the construction year of '93, '95, '97, and '98. respectively, increased as years passed after construction.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.8
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• pp.1164-1173
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• 2014

The present study demonstrates the impact of specific breed on the characteristics of dry-cured ham. Eighty thighs from Korean native pig (KNP), crossbreed (Landrace${\times}$YorkshireLandrace${\times}$Yorkshire)♀${\times}$Duroc♂ (LYD), Berkshire (Ber), and Duroc (Du) pig breeds (n = 10 for each breed) were used for processing of dry-cured ham. The thighs were salted with 6% NaCl (w/w) and 100 ppm $NaNO_2$, and total processing time was 413 days. The effects of breed on the physicochemical composition, texture, color and sensory characteristics were assessed on the biceps femoris muscle of the hams. The results revealed that the highest weight loss was found in the dry-cured ham of LYD breed and the lowest weight loss was found in Ber dry-cured ham. The KNP dry-cured ham contain higher intramuscular fat level than other breed hams (p<0.05). It was observed that the dry-cured ham made from KNP breed had the lowest water activity value and highest salt content, while the LYD dry-cure ham had higher total volatile basic nitrogen content than the Ber and Du hams (p<0.05). Zinc, iron and total monounsaturated fatty acids levels were higher in KNP ham while polyunsaturated fatty acids levels were higher in Du ham when compared to other breed hams (p<0.05). Additionally, the KNP dry-cured ham possessed higher Commission International de l'Eclairage (CIE) $a^*$ value, while the Du dry-cured ham had higher $L^*$, CIE $b^*$ and hue angle values (p<0.05). Furthermore, breed significantly affected the sensory attributes of dry-cured hams with higher scores for color, aroma and taste found in KNP dry-cured ham as compared to other breed hams (p<0.05). The overall outcome of the study is that the breed has a potential effect on the specific chemical composition, texture, color and sensorial properties of dry-cured hams. These data could be useful for meat processors to select the suitable breeds for economical manufacturing of high quality dry-cured hams.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.193-215
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• 2016

In recent years, the proliferation of diverse social networking services has led users to use many mediums simultaneously depending on their individual purpose and taste. Besides, while collecting information about particular themes, they usually employ various mediums such as social networking services, Internet news, and blogs. However, in terms of management, each document circulated through diverse mediums is placed in different categories on the basis of each source's policy and standards, hindering any attempt to conduct research on a specific category across different kinds of sources. For example, documents containing content on "Application for a foreign travel" can be classified into "Information Technology," "Travel," or "Life and Culture" according to the peculiar standard of each source. Likewise, with different viewpoints of definition and levels of specification for each source, similar categories can be named and structured differently in accordance with each source. To overcome these limitations, this study proposes a plan for conducting category mapping between different sources with various mediums while maintaining the existing category system of the medium as it is. Specifically, by re-classifying individual documents from the viewpoint of diverse sources and storing the result of such a classification as extra attributes, this study proposes a logical layer by which users can search for a specific document from multiple heterogeneous sources with different category names as if they belong to the same source. Besides, by collecting 6,000 articles of news from two Internet news portals, experiments were conducted to compare accuracy among sources, supervised learning and semi-supervised learning, and homogeneous and heterogeneous learning data. It is particularly interesting that in some categories, classifying accuracy of semi-supervised learning using heterogeneous learning data proved to be higher than that of supervised learning and semi-supervised learning, which used homogeneous learning data. This study has the following significances. First, it proposes a logical plan for establishing a system to integrate and manage all the heterogeneous mediums in different classifying systems while maintaining the existing physical classifying system as it is. This study's results particularly exhibit very different classifying accuracies in accordance with the heterogeneity of learning data; this is expected to spur further studies for enhancing the performance of the proposed methodology through the analysis of characteristics by category. In addition, with an increasing demand for search, collection, and analysis of documents from diverse mediums, the scope of the Internet search is not restricted to one medium. However, since each medium has a different categorical structure and name, it is actually very difficult to search for a specific category insofar as encompassing heterogeneous mediums. The proposed methodology is also significant for presenting a plan that enquires into all the documents regarding the standards of the relevant sites' categorical classification when the users select the desired site, while maintaining the existing site's characteristics and structure as it is. This study's proposed methodology needs to be further complemented in the following aspects. First, though only an indirect comparison and evaluation was made on the performance of this proposed methodology, future studies would need to conduct more direct tests on its accuracy. That is, after re-classifying documents of the object source on the basis of the categorical system of the existing source, the extent to which the classification was accurate needs to be verified through evaluation by actual users. In addition, the accuracy in classification needs to be increased by making the methodology more sophisticated. Furthermore, an understanding is required that the characteristics of some categories that showed a rather higher classifying accuracy of heterogeneous semi-supervised learning than that of supervised learning might assist in obtaining heterogeneous documents from diverse mediums and seeking plans that enhance the accuracy of document classification through its usage.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.297-306
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• 2012

In this study we investigated emotional characteristics of various whites which have slightly different nuances to suggest guidelines that help designers to select appropriate colors when designing white based products. The study involved three different procedures. In experiment 1, we selected 20 emotional words through a survey (N=30) among 60 words, which we picked from literature review that was thought to be appropriate to evaluate product colors. In experiment 2, we evaluated the emotional characteristics of 13 basic colors from the I.R.I Hue & Tone 120 system (N=30) using previously selected emotional words, to find relative emotional positions of white in comparison to other colors. Based on the ratings, factor analysis was conducted and consequently four factors were extracted: flamboyant, elegant, clear, and soft. Accordingly, the emotional characteristics of the 13 colors were profiled and compared with those of white. Finally, in experiment 3, we conducted an evaluation of emotional characteristics on 25 whites with different nuances facilitating the four factors obtained in experiment 2. The color stimuli used in experiments were measured in terms of CIE 1976 $L^*a^*b^*$, and regression analysis was performed in order to predict the emotional characteristics through the L, a, and b values of a color, as long as that is perceived as a white. Throughout three empirical studies, we observed three overruling tendencies : First, there are four important factors when evaluating product color - flamboyant, elegance, clearness and softness; second, white is dominantly the most elegant in comparison to other colors; third, the emotional factors of the study were affected by some combinations of attributes of colors rather than by all three-hue, saturation and brightness. In addition, the equations derived from the regression analysis in experiment 3, it is expected that designers may predict the emotional distinction between nuances of white.

• Journal of The Korean Association For Science Education
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• v.29 no.8
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• pp.990-1010
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• 2009

To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.