The purpose of this study was to propose some plan which could satisfy consumer's expectation emotional needs by comparing emotional scale between fashion brand image and brand website coloration image. For this study, 12 brand websites within four fashion zone, men's clothing, women's clothing, casual wear, and sports wear were chosen. The questionnaires were comprised of 27 emotional adjectives which were selected from previous studies. The questionnaires were distributed to university students and office workers for 3 to 17 on September. Among them, 118 questionnaires were analyzed by SPSS tool. The qualitative analysis for emotional adjective sorting, content analysis for website color chip sorting, and quantitative analysis for consumers were used in this study. Some differences exist between brand image and website coloration band image as the result. As the numbers of internet user became larger, the costumer's emotional image which gives maximum satisfaction is getting more important in fashion brand website. Therefore, fashion website managers should satisfy consumers with functional and emotional needs.

혼합 싹채소의 MAP에 적합한 포장재를 선발하기 위해 알팔파, 브로콜리, 양배추, 무, 그리고 적무의 혼합 싹채소를 50μm 두께의 low-density polyethylene 필름(PE 50), 50μm 두께의 polypropylene 필름(PP 50), 50μm 두께의 ceramic 필름(CE 50), 25μm 두께 ceramic 필름(CE 25), 10~13μm 두께의 polyethylene film(wrap), 그리고 통기구가 있는 polyethylene terephthalate 박스(box)로 포장하여 8℃에서 저장 비교하였다. 저장 중 혼합 싹채소의 생체중은 7%의 감소를 보인 box처리구를 제외한 모든 처리구에서 2% 미만의 감소만을 보였다. 포장재 내부 대기는 필름 종류에 따라 차이를 보였다. CE 25는 산소와 이산화탄소 모두 5% 수준이었으나, PE 50과 CE 50은 이보다 높은 이산화탄소와 낮은 산소 농도를 보였는데, 이러한 대기 조성 변화가 이들 처리구에서 가장 이취가 가장 심했던 원인이라 생각된다. 저장 10일째 포장재 내 에틸렌 농도는 box가 가장 낮았고, 다음으로 PP 50, wrap, CE 25의 순서로 높았으며 외관상 품질이 저하가 가장 심하였던 PE 50과 CE 50에서 가장 높았다. 이상의 결과를 종합할 때, 1% 미만의 생체중 감소와 5% 수준의 이산화탄소와 산소 농도, 그리고 4ppm 이하의 에틸렌 농도를 보인 CE 25가 혼합 싹채소에 가장 적합한 포장재인 것으로 나타났다.

By a SLAM (simultaneous localization and mapping) method, we get a map of an environment for autonomous navigation of a robot. In this case, we want to know how accurate the map is. Or we want to know which map is more accurate when different maps can be obtained by different SLAM methods. So, several methods for map comparison have been studied, but they have their own drawbacks. In this paper, we propose a new method which compares the accuracy or error of maps relatively and quantitatively. This method sets many corresponding points on both reference map and SLAM map, and computes the translational and rotational values of all corresponding points using least-squares solution. Analyzing the standard deviations of all translational and rotational values, we can know the error of two maps. This method can consider both local and global errors while other methods can deal with one of them, and this is verified by a series of simulations and real world experiments.