• The Journal of the Convergence on Culture Technology
• /
• v.10 no.1
• /
• pp.517-524
• /
• 2024

Generative AI, exemplified by models like ChatGPT, has revolutionized human-machine interactions in the 21st century. As these advancements permeate various sectors, their intersection with the arts is both promising and challenging. Despite the arts' historical resistance to AI replacement, recent developments have sparked active research in AI's role in artistry. This study delves into the potential of AI in visual arts education, highlighting the necessity of swift adaptation amidst the Fourth Industrial Revolution. This research, conducted at a 4-year global higher education institution located in Gyeongbuk, involved 70 participants who took part in a creative convergence module course project. The study aimed to examine the influence of AI collaboration in visual arts, analyzing distinctions across majors, grades, and genders. The results indicate that creative activities with AI positively influence students' creativity and digital media literacy. Based on these findings, there is a need to further develop effective educational strategies and directions that incorporate AI.

• Journal of dental hygiene science
• /
• v.9 no.4
• /
• pp.443-451
• /
• 2009

The purpose of this study was to examine the views of dental hygiene students about the possible countermeasure of domestic hospitals for the opening of the medical market. The subjects in this study were 269 dental hygiene sophomores, juniors and seniors at two different colleges in the region of Busan. The findings of the study were as follows: The opening of the medical market and views of possible countermove, whether they agreed to that or not made a statistically significant difference to their opinions on the necessity of customer-oriented marketing strategy(p=0.023), analysis of foreign medical markets/attempt to make inroads into the markets(p<0.000) and the improvement of the quality of medical services/the diversification of the services(p=0.025). As to an intention of going to a foreign hospital, they had a statistically significantly different intention about whether to go to a foreign hospital regardless of medical bills(p<0.000), whether to consult a doctor in a foreign hospital after going to a domestic hospital first (p<0.000), whether to consider the distance between their houses and a foreign hospital(p=0.05) and whether to take considerations on the assistance of an interpreter(p=0.023). In regard to preference for foreign hospitals, American hospitals ranked first(41.9), followed by Australian hospitals(19.9) and Canadian ones(14.2).

• Journal of the korean academy of Pediatric Dentistry
• /
• v.31 no.4
• /
• pp.587-597
• /
• 2004

Over the past 20 years, great strides have been made in research regarding the mechanisms involved in the progression of carious lesions, but new equipment and research tools need to be developed to continue these advancements in caries research. Various methods have been applied to reduce the incidence of carious lesions, which have led to a significant decrease in the number of occlusal caries, but a concurrent increase in the proportion of proximal carious lesions. New diagnostic equipment has been developed to detect early stage carious lesions, and these have demonstrated excellent laboratory results and show promise in clinical applications. The research presented here examines the efficacy of the newly developed $DIFOTI^{TM}$ system in detecting proximal carious lesions compared to traditional intraoral exam and bitewing radiography, possible problems or deficiencies of using the system in clinic, possible improvements that can be made to the system, and the efficacy of detecting early, reversible carious lesions that can be remineralized by preventative fluoride applications. The subject pool consisted of 23 grammer school age patients just prior to entering the mixed dentition phase. Each patient was given a thorough oral examination, radiographic examination consisting of bitewing radiographs of the posterior teeth, and $DIFOTI^{TM}$ examination of the anterior and posterior teeth. Each examination was carried out two times by two examiners, and the data were statistically analyzed. The results are as follows: 1. The mean alpha value of reliability test of the visual oral examination was as follows; occlusal surface was 0.8470. mesial surface was 0.6430, distal surface was 0.5727. lingual surface was 0.2807 and distal surface was 0.2339. When the examination was limited to posterior teeth, the mean alpha value was as follows; occlusal surface was 0.8577, distal surface was 0.8211, lingual surface was 0.7728, buccal surface was 0.7152 and mesial surface was 0.6782. 2. The alpha value of reliability test of the radiographic analysis of carious lesions of the occlusal, mesial, and distal surfaces was 0.8500. 3. The alpha value of reliability test of the $DIFOTI^{TM}$ diagnostic analysis of carious lesions of the occlusal, buccal, lingual, mesial, and distal surfaces was determined to be 0.7917. 4. The $DIFOTI^{TM}$ diagnostic system was found to be the most accurate means of detecting occlusal, buccal, and lingual surface carious lesions (p<0.05), while mesial and distal proximal carious lesions were most accurately assessed using bitewing radiography (p<0.05).

• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.3
• /
• pp.70-82
• /
• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Asia Marketing Journal
• /
• v.12 no.4
• /
• pp.165-181
• /
• 2011

In 2005, Yuhan Kimberly found interesting points in the existing customers' U&A survey on the kitchen towel. The usage of Korean consumers is usually restricted to getting rid of the oil from the fans and the fried food. This usage limits could be the barrier to the diffusion of kitchen towel. Although consumers were worried about the hygiene situations about the dishcloth, they also percieved that the existing paper kitchen towels were short of something to soothe their inconveniences. As a result, the company made a decision to seek out the solution for the consumers' worries. The relative shortage of the paper kitchen towel compared to that of the unhygienic and inconvenient dishcloth was its lack of water-endurance. The dishcloth could be reliable in the wet environment which is very common in Korean kitchen, whereas the paper kitchen towel was perceived as very weak and unreliable in removing water form the dishes and the sink. To overcome the common sense of the consumers, it is important to shift the consumers' perception of the kitchen towel category. It is needed to expand the usage time from one time to several times in a day. So it is needed to redesign the customers' kitchen life. The company adopted the brand "Scott^®" to meet the global brand strategy of the parent company, Kimberley Clark. This brand was also adopted and made a succesful launch of the similar product lines in Latin America. Furthermore, to make an emphasis on the differentiation from the existing paper kitchen towels, the company made the slogan, "Scott^® washable kitchen towel." This slogan was designed to expand the familiar product image of convenient paper towels to water-resistance. As a result, consumers show the changes in usage behavior of paper towels and apply them for more various purposes such as cleaning the decks and tables. This change results in the rapid sales increase of "Scott^® washable kitchen towel."

• Asia Marketing Journal
• /
• v.12 no.2
• /
• pp.155-175
• /
• 2010

Most of the service industries including finance and telecommunications have become matured and saturated. The competitions have become severe while the differences among brands become smaller. Therefore maintaining good relationships with customers has been critical for the service providers. In case of credit card and debit card, the similar patterns are shown. It is important for them to maintain good relationships with customers, and therefore, they have used marketing program which provides customized services to customers and utilizes the membership programs. Not only do they build and maintain good relationships, but also highlight their brands from the emotional aspects. For example, KB Card or Hyundai Card uses well-known designers' works for their credit card design. As well, they differentiate the designs of credit cards to stress on their brand personalities. BC Card introduced the credit card with perfume that a customer would like. Even though the credit card is small and not shown to public easily, it becomes more important for those companies to touch the customers' feelings with the brand personalities and their images. This is partly because of changes in consumers' lifestyles. Y-generations becomes highly likely to express themselves in many different ways and more emotional than X-generations. For the Y-generations, therefore, even credit cards in the wallet should be personalized and well-designed. In line with it, credit cards with good design can be seen as an example of brand identity, where different design for each customer can be used to recognize the membership groups that customers want to belong. On the other hand, these credit card companies offer the special treatment benefits for those customers who are heavy users for the cards. For example, those customers who love sports will receive some special discounts when they use their credit cards for sports related products. Therefore this study attempted to explore the relationships between relational benefits, brand identification and loyalty. It has been well known that relational benefits and brand identification lead to loyalty independently from many other studies, but there has been few study to review all the three variables all together in a research model. Furthermore, as reviewed above, in the card industry, many companies attempt to associate the brand image with their products to fit their customers' lifestyles while relational benefits are still playing an important role for their business. Therefore in our research model, relational benefits, brand identification, and loyalty are all included. We focus on the mediating effect of brand identification. From the relational benefits perspective, only special treatment benefit and confidence benefit are included. Social benefit is not applicable for this credit card industry because not many cases of face-to-face interaction can be found. From the brand identification perspective, personal brand identity and social brand identity are reviewed and included in the model. Overall, the research model emphasizes that the relationships between relational benefits and loyalty will be mediated by the effect of brand identification. The effects of relational benefits which are confidence benefit and special treatment benefits on loyalty will be realized when they fit to the personal brand identity and social brand identity. In the research model, therefore, the relationships between confidence benefit and social brand identity, and between confidence benefit and personal identity are hypothesized while the effects of special treatment benefit on social brand identity and personal brand identity are hypothesized. Loyalty, then, is hypothesized to have positive relationships with personal brand identity and social brand identity. In addition, confidence benefit among the relational benefits is expected to have a direct, positive relationship with loyalty because confidence benefit has been recognized as a critical factor for good relationships and satisfaction. Data were collected from college students who have been using either credit cards or debit cards. College students were regarded good subjects because they are in Y-generation cohorts and have tendency to express themselves more. Total sample size was two hundred three at the beginning, but after deleting those data with many missing values, one hundred ninety-seven data points were remained and used for the model testing. Measurement items were brought from the previous literatures and modified for this research. To test the reliability, using SPSS 14, chronbach's α was examined and all the values were from .874 to .928 exceeding over .7. Using AMOS 7.0, confirmatory factor analysis was conducted to investigate the measurement model. The measurement model was found good fit with χ²(67)=188.388 (p= .000), GFI=.886, AGFI=.821, CFI=.941, RMSEA=.096. Using AMOS 7.0, structural equation modeling has been used to analyze the research model. Overall, the research model fit were χ²(68)=188.670 (p= .000), GFI=.886, AGFI=,824 CFI=.942, RMSEA=.095 indicating good fit. In details, all the paths hypothesized in the research model were found significant except for the path from social brand identity to loyalty. Personal brand identity leads to loyalty while both confidence benefit and special treatment benefit have a positive relationships with personal and social identities. As well, confidence benefit has a direct positive effect on loyalty. The results indicates the followings. First, personal brand identity plays an important role for credit/debit card usage. Therefore even for the products which are not shown to public easy, design and emotional aspect can be important to fit the customers' lifestyles. Second, confidence benefit and special treatment benefit have a positive effects on personal brand identity. Therefore it will be needed for marketers to associate the special treatment and trust and confidence benefits with personal image, personality and personal identity. Third, this study found again the importance of confidence and trust. However interestingly enough, social brand identity was not found to be significantly related to loyalty. It can be explained that the main sample of this study consists of college students. Those strategies to facilitate social brand identity are focused on high social status groups while college students have not been established their status yet.

• Journal of Distribution Science
• /
• v.9 no.4
• /
• pp.5-13
• /
• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.109-125
• /
• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Journal of Internet Computing and Services
• /
• v.21 no.4
• /
• pp.17-23
• /
• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

• Journal of East-West Nursing Research
• /
• v.8 no.1
• /
• pp.113-125
• /
• 2003

This study is a descriptive investigation into the image of nurses, and attempted to help to advance the profession of nursing and to provide basic data for developing strategies to improve the image of nurses. The Subject of this study was a group of 380 persons from a K Medical Centre in Seoul, including the hospital patients and their guardians, as well as the doctors, assistants and hospital administrative staff. The data have been collected from the 10th to the 30th of May, 2003. We have developed a research tool of 40 questions divided into three categories using a tool developed by Kim, H.J and KIm, H.O.(2001) verifying its construct validity. The reliability of the tool was Cronbache's ${\alpha}=.97$, and by categories, Cronbach's ${\alpha}=.86$ for service image, Cronbach's ${\alpha}=.96$ for professional image and Cronbache's ${\alpha}=.90$ for social image. The collected data have been analysed according to the purpose of this study using SPSS WIN 11.0 for real number, percentage, factors analysis, multiple regression analysis, ANOVA and $x^2$-test, and the results are as follows: 1) There was a significant difference in the image of nurses by job series of the subjects; from patients and guardians for 4.01 to doctors 3.62, assistants 3.54 and staff members 3.41 (F=36.14, p=.000). As well, there was a significant difference in service, professional and social image categories according to the position of the subjects ($F=20.36{\sim}42.35$, p=.000). 2) The main factors that affect on formation the nurse's imaging came by direct experiences with nurses at hospitals for 81.3%, by looking at the every life of the nurses that the subjects personally know for 15.5%, by mass media for 1.6% and by the accounts from the others for 1.6%. 3) 78.4% of the subjects considered that the image of nurses on mass media is described better than for real, 8.2% believed that the image is described worse than for real, and only 13.2% of the subjects perceived that the image of nurses on mass media corresponds the image of nurses in actual life. 4) 74.5% of the subjects said that they got a better image of nurses after their hospitalization while 2% got a worse one and 23.5% said to have had no changes, and the period of hospitalization had no relevance to the image of nurses (X2=5.04, P=.489). However, while 16.8% of the subjects who spent less than one week in hospital said that they got a better image of nurses, 27.5% of those who spent longer than four weeks got a better image of nurses. 5) There was a significant difference in the total image points of nurses by the patients and their guardians according to the period of hospitalization; 4.14 for 1 to 2 weeks, 4.07 for 2 to 4 weeks, 4.02 for 4 weeks and longer and 3.80 for less than a week (F=3.40, P=.019). Upon the results stated above, I should like to propose as below: 1) An investigative enquiry is needed to improve the image of nurses as though being a nurse is very hard and difficult. 2) A continuous monitoring in mass media is needed to create a positive image of nurses.