• Archives of design research
• /
• no.16
• /
• pp.137-146
• /
• 1996

This study is to examine different preferences of color according to personalities in terrns of color attributes hue, value, and chroma. The female college students who are majoring in Textile and Clothing Design are employed as the participants so that they are expexted to gave enough senes of color. For the data collection, the questionnaire is uesd. The resuls of this action research are summarized as the following: Conceming seasonal hue preferences according to personalities, it is proved that the introvert persons preferred winter and Fall color, while they didn't prefer Summer and Spring color the best and then Fall and summer color, whilc they didnt't prefer spring color. The conservative persons showed their hue preferences as the following order; Winter Fall. Spring, and Summer color. Conceming value prefences, the introvert persons showed high preferences of low valuc, while they showed the lowest prefessional and aggressive perons preferred low value and then they didn't show their preferences of medium value, medium value, while they didn't prefer high balue. The conservative ones showed the highest preferesces of low value and then high value, while they dedn't show their prederences of medium value. Concerning chrima preferences, the introvert persons showed high perferences of low chroma, while did lowest preferences of high chroma and medium chroma. The professional, aggressive and conservative perons preferred low chroma the best and high chroma nexts, while they didn't preferred medium chroma.

• Science of Emotion and Sensibility
• /
• v.24 no.4
• /
• pp.149-156
• /
• 2021

The mechanical properties of a woven fabric made of SiC (silicon carbide) fibers were determined in this study using the KES-FB system. The woven fabric is used in high heat settings above 1500℃. Composite spun yarns were used to create SiC fibers. By analyzing the wearing properties, we studied the prospect of using the textiles as fire-retardant work clothes. Mechanical properties determine the wearing attributes. Therefore, the tensile linearity (LT), tensile resilience (RT), and shear stiffness (G) values of the fabric varied according to the yarn type (filament or spun yarn). The thickness, weight per square meter, and density of the fabric were found to have an effect on the shear hysteresis (2HG) and compression resilience (RC) values. In terms of wearable clothing qualities, the fabric qualities of the SiC composite yarn demonstrated the highest ratio of compressive energy to thickness (WC/T), which indicates bulkiness. The fabric manufactured from SiC composite yarns passed the KFI criteria for carbonation length and cumulative flame time in the flame-retardant test. Therefore, we discovered that the material can be used as a fire-resistant work cloth.

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