• Food Science and Industry
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• v.51 no.1
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• pp.16-25
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• 2018

Recently, the importance of analytical techniques related to food safety is emerging in the food industry due to changes in diet patterns, environmental changes, climate change and consumer's interest in food safety. In particular, food safety accidents in the food industry may cause economic losses such as media reports, product recalls, consumer distrust, and so on. Therefore, a systematic, proactive and comprehensive food safety management system is increasingly required to prevent food safety issues. Efforts to ensure the reliability of food safety are essential by introducing various analysis instruments such as LC, GC, ICP, LC/MS/MS, GC/MS/MS, ICP/MS, PCR, and RT-PCR. In addition, recent food safety analytical techniques used in food industry should be shifted paradigm by developing multi-component simultaneous analytical method, low cost with high efficient analytical method, and eco-friendly method.

• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.48-58
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• 2014

Energy efficiency is vital to improve energy security, environmental and social sustainability, and economic performance. Improved energy efficiency also mitigates climate change by lowering greenhouse gas (GHG) emissions. Buildings are the single largest industrial consumer of energy and are therefore key to understanding and analyzing energy consumption patterns and the opportunities for saving energy at the district level in urban environments. This study focused on two representative boroughs in the major metropolitan area of Seoul, South Korea as a case study: Gandong-gu, a typical residential district, and Jung-gu, a typical commercial district. The sources of the energy supplied to the boroughs were determined and consumption patterns in different industry sectors in Seoul used to identify current patterns of energy consumption. The study analyzed the energy consumption patterns for five different building categories and four different sectors in the building using a bottom-up energy modeling approach. Electricity and gas consumption patterns were recorded for different building categories and monthly ambient temperatures in the two boroughs. Finally, a logarithmic equation was developed to describe the correlation between commercial activity and cooling energy intensity in Jung-gu, the commercial district. Based on these results, recommendations are made regarding the current energy consumption patterns at the district level and government energy policies are suggested to reduce energy consumption and, hence, greenhouse gas emissions, in both commercial and residential buildings.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of the Korea Fashion and Costume Design Association
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• v.10 no.3
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• pp.1-13
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• 2008

The purpose of this research is to study the cause of obstruction in various knit pattern structures by understanding the needs of the customers, and to develop knitted fabric goods that satisfy the individual taste of consumers and that reflecting the latest fashions. Actual conditions of the manufacturer's designing processes must be examined. In addition, a comparison analysis of fashion sensibilities and preferences between producers and consumers had been made in order to study developing knit pattern structures. Questionnaires and interviews had been used as research methods. The research involved two groups: one group consisted of 57 producers, which included designers, programmers, and merchandisers working for a knit manufacturer in Seoul, while the other group consisted of 74 consumers, which included students with knitted fabric design as their major and those who have working knowledge of knitted fabrics. For a more accurate evaluation, 28 out of 150 patterns have been selected through a preliminary study conducted by 24 designers. On the 28 knit patterns, a sensibility evaluation had been made through the use of the sense of sight and sense of touch, which was followed by a frequency analysis, cluster analysis, and t-test using SPSS 12.0. The results are as follows: the fashion sensibility evaluation on knit patterns showed that structures, such as racking II or lace II, are typically elegant ('elegance'), while structures, such as links I or racking I, are typically 'active.' Furthermore, a 'country' image was displayed in structures such as cable I, cable II, and miss II. Links I appeared as 'sophisticated'. Miss I and milano were said to have 'modem' images. Lace II was regarded as 'ethnic', while racking II was said to have a 'romantic' image. 2:1 rib and milano were generally thought to be manly ('manish'). The fashion sensibility scale for each knit pattern has been made based on the above results. Based on producer and consumer responses, the sensibility evaluation on knit pattern structures showed that the two groups had similar preferences in knit pattern structures. Therefore, the fashion sensibility scale developed in this study can be used as a basic data for structure development when designing knitted fabric goods.

• International Journal of Costume and Fashion
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• v.10 no.2
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• pp.1-13
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• 2010

The purpose of this study is to investigate the behavioral complexities frequently arising in married couples with respect to clothing choices. Symbolic interaction theory was used to understand how spousal influence affects clothing selection after marriage. A convenient sample of nine married couples was interviewed for the data collection. A content analysis revealed how marriage affected their clothing choices, types of communication conveyed with their spouses about clothes and shopping patterns. The primary result indicated that individuals take their spouses' clothing preferences into consideration. Couples exhibited varying frequencies of communication about clothing choices and developed a variety of shopping patterns. As a result of the findings from this study, future research directions are suggested.

• Korean Institute of Interior Design Journal
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• no.4
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• pp.16-21
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• 1995

Although it has been suggested that lighting influences our moods, little is known about dimensionality of mood patterns. The knowledge of consumer's mood pattern in lighting condition may provide interior designers with a valuable information for the lighting design. This article investigates the underlying structure of moods occuring in lighting conditions. Meanwhile, this examines custom-ers' desired mood type of lighting, and explores the influ-ence of individual characteristics on selecting the preffered lighting moods. The result uncovers five discriminable patterns of mood-dynamic, intimate, delightful, elegant, and serene feeling results from well organized lighting system. Among the five moods, serene atmosphere appears to be the most preffered mood from respondants. It is also found that the perceived preference of lighting atmo-sphere varies across personal characteristics of the cus-tomers, socio-economic differences and the visiting pur-poses to the coffeeshop. These findings present interest-ing and potentially valuable implications for interior de-signers that proper planning of lights which inducemoods can achieve dramatic effects and aid in the cre-ation of the desired atmosphere.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.386-388
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• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.435-437
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• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.1
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• pp.57-64
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• 2011

Recently, all-electrified houses have constructed and some tasks are being carried out to monitor energy consumption patterns. The energy monitoring includes various elements such as temperature, pressure, water flow, $CO_2$, and electricity. In this monitoring, electricity consumption is one of the essential factors because the performance and energy saving levels are described by this physical quantity. This quantity depends on materials, structure, operation of the house, which operation means consumer's life style and usage patterns of home appliances furthermore. Firstly, this paper shows a control simulation method which were developed a few years ago. And some different methods are proposed considering flexible experimental condition.

• Journal of the Korea Fashion and Costume Design Association
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• v.13 no.1
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• pp.135-146
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• 2011

Despite the recession that has affected people's consumption of clothing, the 'Gold Kids' market shows steady growth. This study analyzed the characteristics of components that make up infant and children's wear brands. Firstly, this study found the linguistic features of infant and children's clothing brands. Most brand names have common characteristics such as idiomatic expressions, combination and separation of vocabulary, and more than 6 syllables. Also, when they formative brand quality, they frequently used words like 'girl', 'kid' and 'baby' to clarify the category of the brand product. Also it is meant to present a clear brand concept to the target consumer to offer their brand information and product characteristics. Moreover, English is widely used in the brand names, and they used appropriate symbol graphics that fit the titles and qualities of the brands. Second, to strengthen their brand identity and inherent concepts, infant and children's clothing brands normally use their own particular brand colors wich makes special and strong visual impressions on the target consumer. Also, they mainly use applied sans serif typeface showing the softer brand quality. Brand logos have different visual expressions depending on their brand value and the target consumer. The word type of the logos presents clear brand identity and information to the target consumer. Through the study, we found that infant and children's clothing brands tend to use written brand names for the logo type more than graphic symbol logo patterns. This indicates that infant and children's clothing brands tend to emphasize information transfer rather than visual graphic symbols when they organize brand logos.