• Journal of the Ergonomics Society of Korea
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• v.33 no.1
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• pp.69-76
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• 2014

Objective: This study aims to present literature providing researchers with insights on specific fields of research and highlighting the major issues in the research topics. A systematic review is suggested using content analysis on literatures regarding "visual search", "eye movement", and "eye track". Background: Literature review can be classified as "narrative" or "systematic" depending on its approach in structuring the content of the research. Narrative review is a traditional approach that describes the current state of a study field and discusses relevant topics. However, since literatures on specific area cover a broad range, reviewers inherently give subjective weight on specific issues. On the contrary, systematic review applies explicit structured methodology to observe the study trends quantitatively. Method: We collected meta-data of journal papers using three search keywords: visual search, eye movement, and eye track. The collected information contains an unstructured data set including many natural languages which compose titles and abstracts, while the keyword of the journal paper is the only structured one. Based on the collected terms, seven categories were evaluated by inductive categorization and quantitative analysis from the chronological trend of the research area. Results: Unstructured information contains heavier content on "stimuli" and "condition" categories as compared with structured information. Studies on visual search cover a wide range of cognitive area whereas studies on eye movement and eye track are closely related to the physiological aspect. In addition, experimental studies show an increasing trend as opposed to the theoretical studies. Conclusion: By systematic review, we could quantitatively identify the characteristic of the research keyword which presented specific research topics. We also found out that the structured information was more suitable to observe the aim of the research. Chronological analysis on the structured keyword data showed that studies on "physical eye movement" and "cognitive process" were jointly studied in increasing fashion. Application: While conventional narrative literature reviews were largely dependent on authors' instinct, quantitative approach enabled more objective and macroscopic views. Moreover, the characteristics of information type were specified by comparing unstructured and structured information. Systematic literature review also could be used to support the authors' instinct in narrative literature reviews.

• Journal of Korean Home Economics Education Association
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• v.21 no.2
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• pp.23-43
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• 2009

This study examined the relativity between school uniform behavior(especially attitude toward school uniform, school uniform satisfaction, school uniform alteration) and attitude toward appearance in the adolescence. From November to December in 2007, 898 middle school and high school students from four schools in Jeollabuk-do Province were analyzed and the influence of relevant variables was understood to gather basic information about the fashion life in the adolescence. This study used SPSS 11.5 for Windows Program to conduct frequency analysis, factor analysis, reliability analysis, ${\chi}^2$-test, t-test, One-way ANOVA, Duncan's multiple comparison test, and Pearson's correlation. The following summarizes the findings of this study: The results of analysing the factors to the response attitude toward uniforms and attitude toward appearance and clothing attitude emerged four dimensions(fashion, symbolism, cleanliness, alternation), three dimensions(needs value conformity toward appearance). In attitude toward uniform, female students were more actively altering their uniforms than male students and middle school students were more sensitive to trends than high school students. However, high school students were more aware of the cleanliness and alteration of uniforms. Overall, most students were not very satisfied with their uniforms (design, color, texture). When students' attitude toward appearance and demographical characteristics were examined, it was found that female students were more aware of desire, value, and conformity in appearances than male students. By age, it was found that high school students were more aware than middle school students. Also, students receiving KRW 30,000 or more for monthly allowance were more aware than others who receive a lower amount between KRW 10,000 and KRW 20,000. Therefore, most students(62.1%) have experiences in altering uniforms. Especially, more female students and more high school students had such experiences. Uniform alteration is more related to attitude to appearance. In other words, students who choose to alter their uniforms are highly aware of desire, value, and conformity toward appearance. Students who are satisfied with all factors regarding attitude toward uniform, except for alteration, did not alter their uniforms that much. In addition, when the relativity between students' attitude toward appearance with uniform was considered, students that are not very satisfied with their uniforms were more aware of desire, value, and conformity toward appearance. In conclusion, it was found that students' attitude toward appearance and school uniform behavior are closely related and their interests in appearance lead to alteration of uniforms, the clothing in which they spend most time of the day. From now on, students' opinions shall be considered when selecting or designing school uniforms.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.640-649
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• 2018

This study analyzed the consumer awareness of cycling wear using web mining, one of the big data analysis methods. For this, the texts of postings and comments related to cycling wear from 2006 to 2017 at Naver cafe, 'people who commute by bicycle' were collected and analyzed using R packages. A total of 15,321 documents were used for data analysis. The keywords of cycling wear were extracted using a Korean morphological analyzer (KoNLP) and converted to TDM (Term Document Matrix) and co-occurrence matrix to calculate the frequency of the keywords. The most frequent keyword in cycling wear was 'tights', including the opinion that they feel embarrassed because they are too tight. When they purchase cycling wear, they appeared to consider 'price', 'size', and 'brand'. Recently 'low price' and 'cost effectiveness' have become more frequent since 2016 than before, which indicates that consumers tend to prefer practical products. Moreover, the findings showed that it is necessary to improve not only the design and wearability, but also the material functionality, such as sweat-absorbance and quick drying, and the function of pad. These showed similar results to previous studies using a questionnaire. Therefore, it is expected to be used as an objective indicator that can be reflected in product development by real-time analysis of the opinions and requirements of consumers using web mining.

• Information Systems Review
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• v.16 no.3
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• pp.113-134
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• 2014

Since a movie is an experience goods, purchase can be decided upon preliminary information and evaluation. There are ongoing researches on what impact online reviews might have on movie revenues. Whereas research in the past was focused on the effect of online reviews. The influence of online reviews appears to be significant in products like a movie because it is difficult to evaluate the feature prior to "consuming" the product. Since an online review is regarded to be objective, consumers find it more trustworthy. Contrary to prior research focused on movie review ratings and volume, we focus moves on movie features related specific reviews. This research proposes a predictive model for movie revenue generation. We decided 15 criteria to classify movie features collected from online reviews through the online review mining and made up feature keyword list each criterion. In addition, we performed data preprocessing and dimensional reduction for data mining through factor analysis. We suggest the movie revenue predictive model is tested using discriminant analysis. Following the discriminant analysis, we found that online review factors can be used to predict movie popularity and revenue stream. We also expect using this predictive model, marketers and strategic decision makers can allocate their resources in more parsimonious fashion.

• Research in Community and Public Health Nursing
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• v.12 no.1
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• pp.278-287
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• 2001

The purpose of this study is to provide with basic information on application of hand acupuncture as a complementary and alternative therapy by giving some recognition of efficiency and effectiveness of hand acupuncture. And so, answers for questionnaires of 290 respondents were used for this research and collected from June 5 through 13, 1999 from adults twenty and over who were participating in the hand acupuncture training program in Seoul and had some direct experiences with hand acupuncture therapy, whatever they had been treated and/or had treated. To secure reliability of measurement tool. Cronbach'a has been calculated and Factor Analysis was done as Validity Analysis of question classification. Demograprucal characteristics of hand acupuncture experienced people and factors related to hand acupuncture experiences are calculated based on the real number and percentage. The degree of recognition of efficiency and effectiveness of hand acupuncture is made as average and standard deviation, while the degree of recognition of efficiency and effectiveness based on general characteristics come from one-way ANOVA. 1. According to socio-demographical analysis. the questioned could be classified firstly as age (40-49 : 32.5%. 30-39 : 24.9%. 50-59 : 21.9%. 60-69 : 14.7%. 20-29 : 6.0%). secondly gender (male 36.6%. female 63.4%). thirdly occupation (housewife: 43.8%. self-employed: 15.5%. company-employee: 14.8%). fourthly education (high school graduate: 41.9%, college graduate: 37.9%), and lastly monthly-income (1 to 2 million: 51.4%. 2 to 3 million: 20,3%) 2, As for the general aspects related to hand acupuncture. 80,0% of the respondents answered almost zero for the monthly average number of visit to hospital and 15.5% responded 1 to 2 visits, 6,2% of the respondents is complaining of a disorder of digestive system. 19,0% circulatory disease, 10.7% bad nervous system. By utilizing hand acupuncture, 84% of the questioned have following experiences in curing diseases: digestive system 47.3%, circulatory system 9.3%, nervous system 8.3%, 54,1% are curing 1 to 2 and 10.3% 3 to 4 patients on a daily basis with hand acupuncture. Research on the demerits of giving medical treatment with hand acupuncture shows 23,8% are feeling economic burden. 16.6% difficulty of learning and 16.2% weak theoretical backgrounds. 3. Among the efficiency recognition, possibility of general application is average 4,29 and simple treatment is 4,19. economic merits 4.36. possibility of establishment with supplementary and alternative medicine 4.17, medical effectiveness 4.09. 4, As a result of demographical analysis on the efficiency and effectiveness of hand acupuncture therapy, it appears that the recognition of efficiency based on occupation and the recognition of effectiveness based on monthly income are most significant to be noticed. In an orderly fashion. government-employee, self-employed, company-employee. and then housewife have perceived hand acupuncture very efficiently, And those who recognize hand acupuncture to be most effective are people earn 1 million to 2 million won a month, 5. The efficiency(p = .003) and effectiveness (p= .049) of hand acupuncture therapy by number of visit to hospital were statiscally significant, and effectiveness of hand acupuncture therapy by disease exist was statiscally significant (p= .033).

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.129-142
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• 2021

The purpose of this study was to provide educational data on teaching clothing culture by examining the effects of clothing lifestyle on attitudes toward teachers' clothing for 270 secondary school pre-service teachers. For data analysis, factor analysis, Cronbach's α reliability coefficients, t-tests, one-way analysis of variance, Duncan's multiple comparison verification, and multiple regression analysis were performed using SPSS 24.0 program. As a result of the study, first, the clothing lifestyle of secondary school pre-service teachers was classified as fashion trend orientation, clothing importance orientation, attractive appearance orientation, and economic orientation factors. In addition, the attitude toward teacher's clothes was classified into activity, fashionability, and modesty factors. Second, there was a statistically significant difference in the attitudes toward clothing lifestyle and teacher clothing of secondary school pre-service teachers according to gender and year in college, which are demographic variables. Third, it was found that the clothing lifestyle of secondary school pre-service teachers, who are Generation Z, had an effect on the attitude toward teacher clothes. In conclusion, this study proposes that school administrators and teachers should depart from the former stereotypes about teacher clothes and to encourage a culture in which teachers can dress and perform their role of teaching according to individual's changing lifestyles.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.124-129
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• 2002

This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ - λ$\^$obj/)$^2$, where λ$\^$obj/ is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and causes unstable control of the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation. without respect to the time evolutions, the running times of this approximation grow only about as N$^2$ compared to as N$\^$5/T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.

• Journal of Life Science
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• v.19 no.5
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• pp.587-592
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• 2009

A dramatic morphological change of embryos occurs at peri-implantation. Maternal and embryonic cross-talk during this period, initiated by signals from embryo(s), provides signals for maternal recognition of pregnancy and establishing and maintaining the pregnancy. However, the cellular, biochemical and genetic processes that direct embryo remodeling in mammalian species are not well studied or understood. In order to identify potential genes responsible for morphological change and cross-talk between embryo and uterus, an initial EST analysis was performed. A catalog of expressed genes (Transcriptome) from the d12 peri-implanting porcine embryos was constructed. Six clones were chosen from the initial ESTs for elucidation of their expression patterns during embryogenesis in early pregnancy. A number of these genes demonstrated unique expression profiles in a tissue, cell-type, and temporal fashion, indicating dynamic regulation of embryonic and endometrial gene expressions at different stages of pregnancy. Cross-talk between the embryo and endometrium of the pregnant uterus has provided a suitable micro-environment for the embryo's rapid and dramatic morphological changing process at the peri-implantation stage.

• Korean Journal of Human Ecology
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• v.19 no.2
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• pp.375-387
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• 2010

The Technology Acceptance Model (TAM) presented by Davis (1989) has been regarded as highly explanatory as well as the clearest model in explaining consumers' adoption of innovative technology or products. Existing studies have expanded the model by adding related external variables to improve the explanation depending on the type of innovative technology. This study expanded TAM by adding two more variables, namely consumers' technology innovation and clothing involvement considering the feature of smart clothing. The objectives of this study are as follows: 1. to suggest the extended TAM in explaining the adoption process of smart clothing, 2. to verify the differences in the path hypotheses according to the type of smart clothing. A total of 815 effective samples were collected from adults over 20 years old, and AMOS 5.0 package was employed for data analysis. As a result, it was proved that the extended TAM was appropriate for explaining the process of adopting smart clothing according to the path hypotheses of smart clothing types. Technology innovation and clothing involvement were confirmed as antecedent variables in affecting TAM. The perceived usefulness appeared to be a more crucial variable than the perceived ease of use and attitude was found to be an important parameter in adopting smart clothing. Considering the path hypotheses of MP3 playing clothes, perceived usefulness had a direct influence on acceptance intention unlike other types of smart clothing. As for photonic clothes, the influence of perceived ease of use on attitude was supported while it was rejected in the case of MP3 playing clothes and sensing sportswear.