• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.59-80
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• 2004

As industrial development standardizes the quality of products, people are increasingly attracted by a certain brand with intangible value. Consumers, surrounded by a variety of brand in a market, want to have relationships with specific brands reflected by their personality. Especially in fashion, brand personality is more important since products are highly involved in terms of personal emotion. The purpose of the study is to identify the dimension of overall fashion brand personality, to specify the 'Dimension of Fashion Brand Personality' according to fashion product categories (i.e., formal wear, casual wear, sports wear), and to investigate the effect of each dimension of fashion brand personality on brand preference and purchase intention. The results were as follows: Firstly, based on Aaker(1997)'s 'Big five factors' and selection fashion brand personality factors from our pretest, the fashion brand personality factors were verified as 'Fashionable/ Innovative', 'Sincere', 'Universal! Stable', 'Professional'. Secondly, according to fashion product categories, brand personality was defined as below. Brand personality for formal wear included 'Innovative/ Active', 'Stable', 'Professional', and 'Universal'. Brand personality for casual wear included 'Fashionable/ Innovative', 'Active', 'Sincere', and 'Stable'. Brand personality for sports wear included 'Innovative', 'Social', 'and 'Sincere'. Finally, overall fashion brand personality factors (' Sincere', 'Universal! Stable', 'Professional') influenced on brand preference and purchase intention. In formal wear, 'Professional' influenced on brand preference and purchase intention. In casual wear, 'Active' influenced on brand preference, but 'Fashionable' and 'Sincere' influenced on purchase intention. In sports wear, 'Sincere' influenced on brand preference and 'Innovative' influenced on purchase intention.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.5
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• pp.737-744
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• 2010

In order to provide fundamental data on development of rice noodles, the quality characteristics of 10 kinds of commercial rice noodles were evaluated. Rice noodles were categorized into 3 groups for examination: 100% rice noodles with different shapes; round shape noodles with different rice contents; and noodles with different rice contents but smilar cooking method. Cooking properties, texture measurement, and sensory characteristics were evaluated. Frozen rice noodles and instant type of noodles made from composite flour of rice and wheat flour exhibited higher scores in overall acceptance. As for the form of noodle, round-shaped noodles seemed to be preferred in comparison to flat-shaped noodles. Generally, noodles with high texture scores of cohesiveness, adhesiveness, chewiness, resilience but not high score of hardness having smooth surface were preferred affecting overall acceptance scores. It appears that cooking methods and cooking time recommended by manufactures, noodle thickness and shape, packaging types such as frozen, refrigerated, and dried noodles were more influential than rice contents in aspects to the quality characteristics of the commercial rice noodle products in this study.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Life Science
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• v.15 no.5 s.72
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• pp.743-748
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• 2005

Histamine can be produced at early spoilage stage through decarboxylation of histidine in red-flesh fish by Proteus morganii, Hafnia alvei or Klebsiella pneumoniae. Allergic food poisoning is resulted from the histamine produced when the freshness of Mackerel degrades. Conversely it has been reported that there are bacteria which decompose histamine at the later stage. We isolated histamine decomposers from salted mackerel and studied the characteristics to help establish hygienic measure to prevent outbreak of salted mackerel food poisoning. All the samples were purchased through local supermarket. Histamine decomposers were isolated using restriction medium using histamine 10 species were selected. Identification of these isolates were carried out by the comparison of 16S rDNA partial sequence; as a result, we identified Pseudomonas putida strain RA2 and Halomonas marina, Uncultured Arctic sea ice bacterium clone ARKXV1/2-136, Halomonas venusta, Psychrobacter sp. HS5323, Pseudomonas putida KT2440, Rhodococcus erythropolis, Klebsiella terrigena (Raoultella terrigena), Alteromonadaceae bacterium T1, Shewanella massilia with homology of $100\%,{\;}100\%,{\;}99\%,{\;}99\%,{\;}99\%,{\;}99\%,{\;}100\%,{\;}95\%,{\;}99\%,{\;}and{\;}100\%$respectively. Turbidometry determination method and enzymic method were employed to determine the ability of histamine decomposition. Among those species Shewanella massilia showed the highest in ability of histamine decomposition. From these results we confirmed various histamine decomposer were present in salted mackerel product in the market.

• Journal of the Korean Home Economics Association
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• v.47 no.3
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• pp.55-65
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• 2009

By definition, smart clothing describes a garment whose functionality is enhanced by technological advancements in order to ease one’'s lifestyle. To know user’'s understanding and needs, the research had consisted of brainstorming sessions, personal interviews, focus group meeting, and a series of content analyses. Ten of the interview subjects and focus group panel were found by contacting women aged 56-64 in the Ithaca community, NY. They were prompted with general questions on style choices for their lifestyles and age group and then presented with two examples of smart clothing to discuss and critique. Meanwhile, the other three interview subjects, located outside of the Ithaca community, were aged either above or below the targeted bracket. These subjects were interviewed as a method of comparison on multiple levels. Findings had shown that there was indeed a need for smart clothing amongst the increasingly populated demographic of older women: devices to help one’'s body temperature regulation and vision problem, and well-designed clothing. However, the functionalities must be carefully constructed and conveyed in order to be taken seriously by the mainstream consumer market. Once successfully designed, the smart clothing will ideally create a greater sense of autonomy for older women.

• Journal of Environmental Science International
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• v.17 no.4
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• pp.423-437
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• 2008

Until comparatively lately, the annual time series of the $SO_2$ concentration had been shown in a decreasing trend in Ulsan as well as other Korean cities. However, the high concentration of $SO_2$ was frequently found in the specific countermeasure region including the national industrial complex such as Mipo and Onsan in the Ulsan city for the period of $2001{\sim}2004$. There are many conditions that can influence the high concentration of $SO_2$ at monitoring sites in Ulsan, such as: First, annual usage of the fuel including sulfur increased in comparison with the year before in spite of the fuel conversion policy which wants to use low sulfur oil less than 3% and LNG. Second, point source, such as the power plants and the petroleum and chemistry stacks, was the biggest contributor in $SO_2$ emission, as a analyzed result of both the air quality modeling and the stack tole-monitoring system (TMS) data. And third, the air pollutants that occurred in processes of homing and manufacturing of the fuel including sulfur were transported slow into a special monitoring site by accumulating along the frontal area of see-breeze. It was concluded that Ulsan's current environmental policy together with control methods should be changed into the regulation on total amount of emission, including a market-based emission trading with calculating of atmospheric environmental critical loads, for the $SO_2$ reduction like the specific countermeasure for the $O_3$ and PM10 reduction in the Seoul metropolitan area. And this change should be started in the big point sources of $1{\sim}3$ species because they are big contributors of Ulsan's $SO_2$ pollution. Especially it is necessary to revitalize of the self-regulation environmental management. Other control methods for sustaining the $SO_2$ reduction are as follows: maintenance of the fuel conversion policy, reinforcement of the regional stationary source emission standard, and enlargement of the stack TMS.

• Culinary science and hospitality research
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• v.15 no.2
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• pp.1-16
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• 2009

The purpose of the study of the book is aimed for basic document security to show necessity for the outside restaurant accommodation to the university meal service and I grasp the university meal service use action of the university student and a desire with this purpose and show it and am going to fine an article and the basics document which are necessary for the market for rationalization of the management of the university meal service. As for the reason why students like fast food to be able to choose outside dining room introduction at the part of an innovative image strategy of the university meal service. Food is serve of early and low price. The big problems of the university meal service is bad taste, high price, low service. Price satisfaction is low in the quality of the university meal service in comparison with the outside dining room. The university students who liked Korea food and Hamburger at time of the outside dining room introduction evaluated the quality of the university meal service. Therefore the outside dining room accommodation is necessary for the university meal service development also the image improvement of the university meal service and improvement of the quality and suitable price rage.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1038-1044
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• 2009

Interests for User Interface directly influencing on usability are also growing as the competition between DCATV and IPTV come to the surface, according to getting activated Interactive TV market. Therefore, this study has the purpose to understand mental models of users for Interactive TV service & Interface. The methods for this study are as follows. First, as the step considering literature study, we compared differences between DCATV and IPTV and caught up the concepts of DCATV and IPTV among some types of Interactive TV. Second, we examinated and analysed Hello TV, C&M, Broad&TV, MegaTV, myLGtv as the step of analysis for examples for Interactive TV. Third, as the step of researches observing users we analysed usability problems caused by differences of mental models between DCATV and IPTV. As a result, we could find there are some differences for mental model of users in Interface & services between DCATV and IPTV Especially, it was found that users feel confused very much regarding perception of real time broadcasting and VOD. We regards this findings can be used as basic for concept makings of Interactive TV, when TV interface is developed in the future.

• Korean Institute of Interior Design Journal
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• no.12
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• pp.57-66
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• 1997

This study was carried out to find out some similarities and differences of the ornamental technique and motifs in folk furniture between Denmark and Korea which had quite different background of development of folk furniture as one of a cross-cultural study. Furniture made and used in the 18th and 19th century was investigated in order to eliminate the influence of internationalism in the design area in the 20th century. This study was fulfilled by many study trips all over the districts in Denmark to identify Danish folk furniture as well as literature study. Study trips to folk museums. Insa-dong, Janghanpyung furniture market were done for Korean folk furniture. Characteristics of ornament in Danish folk furniture were as followings; Main materials of the Danish folk furniture were conifers, especially pine tree, as pine was very common and easy to get from their surroundings. The most popular and unique types of decoration in Danish folk furniture was painting. Colors used in painted furniture were very bright and vivid. This might be a reaction to the long and dark winter of Scandinavian countries. Motifs used in Danish folk furniture had been chosen to reflect their surroundings. Flowers, six-angular stars, animals, human figures and Biblical motifs were popular sources of decoration for Danish folk furniture. Characteristics of Korean folk furniture were as followings; Main materials of Korean folk furniture were broad-leaved trees as well as conifers, because of their beautiful wood grain. The Korean ways of decoration were different from Danish ones. The method of painting with bright from Danish ones. The method of painting with bright colors was hardly ever used in Korea, except only in lacquering. The most popular decoration method for Korean folk furniture was revealing the natural wood grain with transparent vegetable oil finish, instead of painting. Metal ornament was unique to Korean folk furniture. therefore a lot of metal ornaments were attached on the furniture. Motifs used in Korean folk furniture were more like symbolic than Danish ones. Korean people tried to express their longings and norms through the motifs, such as longevity, prosperity, good luck, and many sons, etc. Therefore, it was natural for Korean motifs to have special symbolic meanings.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.