• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.2
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• pp.3-12
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• 2011

Purpose: With increasing medical use of radiation and radioactive isotopes, there is a need to better manage the risk of radiation exposure. This study aims to grasp and analyze the individual radiation exposure situations of radiation-related workers in a medical facility by specific job, in order to instill awareness of radiation danger and to assist in safety and radiation exposure management for such workers. Materials and Methods: From January 1, 2010 December 31, 2010, medical practitioners working in the radiation is classified as a regular personal radiation dosimetry, and subsequently one year 540 people managed investigation department to target workers, dose sectional area, working period, identify the job function-related tasks for a deep dose, respectively, the annual average radiation dose were analyzed. Frequency analysis methods include ANOVA was performed. Results: Medical radiation workers in the department an annual radiation dose of Nuclear and 4.57 mSv a was highest, dose zone-specific distribution of nuclear medicine and in the 5.01~19.05 mSv in the high dose area distribution showed departmental radiation four of the annual radiation dose of Nuclear and 7.14 mSv showed the highest radiation dose. More work an average annual radiation dose according to the job function related to the synthesis of Cyclotron to 17.47 mSv work showed the highest radiation dose, Gamma camera Cinema Room 7.24 mSv, PET/CT Cinema Room service is 7.60 mSv, 2.04 mSv in order of intervention high, were analyzed. Working period, according to domain-specific average annual dose of radiation dose from 10 to 14 in oral and maxillofacial radiology practitioners as high as 1.01~3.00 mSv average dose showed the Department of Radiology, 1-4 years, 5-9 years, respectively, 1.01 workers~8.00 mSv in the range of the most high-dose region showed the distribution, nuclear medicine, and the 1-4 years, 5-9 years 3.01~19.05 mSv, respectively, workers of the highest dose showed the distribution of the area in the range of 10 to 14 years, Workers at 15-19 3.01~15.00 mSv, respectively in the range of the high-dose region were distributed. Conclusion: These results suggest that medical radiation workers working in Nuclear Medicine radiation safety management of the majority of the current were carried out in the effectiveness, depending on job characteristics has been found that many differences. However, this requires efforts to minimize radiation exposure, and systematic training for them and for reasonable radiation exposure management system is needed.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.9-16
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• 2010

Purpose: With increasing medical use of radiation and radioactive isotopes, there is a need to better manage the risk of radiation exposure. This study aims to grasp and analyze the individual radiation exposure situations of radiation-related workers in a medical facility by specific job, in order to instill awareness of radiation danger and to assist in safety and radiation exposure management for such workers. Materials and Methods: 1 January 2007 to 31 December 2009 to work in medical institutions are classified as radiation workers Nuclear personal radiation dosimeter regularly, continuously administered survey of 40 workers in three years of occupation to target, Imaging Unit beautifully, age, dose sector, job function-related tasks to identify the average annual dose for a deep dose, respectively, were analyzed. The frequency analysis and ANOVA analysis was performed. Results: Imaging Unit beautifully three years the annual dose PET and PET/CT in the work room 11.06~12.62 mSv dose showed the highest, gamma camera injection room 11.72 mSv with a higher average annual dose of occupation by the clinical technicians 8.92 mSv the highest, radiological 7.50 mSv, a nurse 2.61 mSv, the researchers 0.69 mSv, received 0.48 mSv, 0.35 mSv doctors orderly, and detail work employed the average annual dose of the PET and PET/CT work is 12.09 mSv showed the highest radiation dose, gamma camera injection work the 11.72 mSv, gamma camera imaging work 4.92 mSv, treatment and safety management and 2.98 mSv, a nurse working 2.96 mSv, management of 1.72 mSv, work image analysis 0.92 mSv, reading task 0.54 mSv, with receiving 0.51 mSv, 0.29 mSv research work, respectively. Dose sector average annual dose of the study subjects, 15 people (37.5%) than the 1 mSv dose distribution and 5 people (12.5%) and 1.01~5.0 mSv with the dose distribution was less than, 5.01~10.0 mSv in the 14 people (35.0%), 10.01~20.0 mSv in the 6 people (15.0%) of the distribution were analyzed. The average annual dose according to age in occupations that radiological workers 25~34 years old have the highest average of 8.69 mSv dose showed the average annual dose of tenure of 5~9 years in jobs radiation workers in the 9.5 mSv The average was the highest dose. Conclusion: These results suggest that medical radiation workers working in Nuclear Medicine radiation safety management of the majority of the current were carried out in the effectiveness, depending on job characteristics has been found that many differences. However, this requires efforts to minimize radiation exposure, and systematic training for them and for reasonable radiation exposure management system is needed.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.1
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• pp.40-53
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• 2015

To train the manpower to meet the requirements of the industrial field, the introduction of the National Qualification Frameworks(hereinafter referred to as NQF) was determined in 2001 by National Competency Standards(hereinafter referred to as NCS) centrally of the Office for Government Policy Coordination. Also, for landscape architecture in the construction field, the "NCS -Landscape Architecture" pilot was developed in 2008 to be test operated for 3 years starting in 2009. Especially, as the 'realization of a competence-based society, not by educational background' was adopted as one of the major government projects in the Park Geun-Hye government(inaugurated in 2013) the NCS system was constructed on a nationwide scale as a detailed method for practicing this. However, in the case of the NCS developed by the nation, the ideal job performing abilities are specified, therefore there are weaknesses of not being able to reflect the actual operational problem differences in the student level between universities, problems of securing equipment and professors, and problems in the number of current curricula. For soft landing to practical curriculum, the process of clearly analyzing the gap between the current curriculum and the NCS must be preceded. Gap analysis is the initial stage methodology to reorganize the existing curriculum into NCS based curriculum, and based on the ability unit elements and performance standards for each NCS ability unit, the discrepancy between the existing curriculum within the department or the level of coincidence used a Likert scale of 1 to 5 to fill in and analyze. Thus, the universities wishing to operate NCS in the future measuring the level of coincidence and the gap between the current university curriculum and NCS can secure the basic tool to verify the applicability of NCS and the effectiveness of further development and operation. The advantages of reorganizing the curriculum through gap analysis are, first, that the government financial support project can be connected to provide quantitative index of the NCS adoption rate for each qualitative department, and, second, an objective standard is provided on the insufficiency or sufficiency when reorganizing to NCS based curriculum. In other words, when introducing in the subdivisions of the relevant NCS, the insufficient ability units and the ability unit elements can be extracted, and the supplementary matters for each ability unit element per existing subject can be extracted at the same time. There is an advantage providing directions for detailed class program and basic subject opening. The Ministry of Education and the Ministry of Employment and Labor must gather people from the industry to actively develop and supply the NCS standard a practical level to systematically reflect the requirements of the industrial field the educational training and qualification, and the universities wishing to apply NCS must reorganize the curriculum connecting work and qualification based on NCS. To enable this, the universities must consider the relevant industrial prospect and the relation between the faculty resources within the university and the local industry to clearly select the NCS subdivision to be applied. Afterwards, gap analysis must be used for the NCS based curriculum reorganization to establish the direction of the reorganization more objectively and rationally in order to participate in the process evaluation type qualification system efficiently.

• Journal of Preventive Medicine and Public Health
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• v.28 no.4 s.51
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• pp.809-844
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• 1995

The purposes of this study are to define the scope of occupational health management and to classify occupational management by review of related journals from 1945 to 1994 in Korea. The steps of this study were as follows: (1) Search of secondary reference; (2) Collection and review of primary reference; (3) Survey; and (4) Analysis and discussion. The results were as follows ; 1. Most of the respondents majored in occupational health(71.6%), and were working in university (68.3%), males and over the age 40. Seventy percent of the respondents agreed with the idea that classification of occupational health management is necessary, and 10% disagreed. 2. After integration of the idea of respondents, we reclassified the scope of occupational health management. It was defined 3 parts, that is , occupational health system, occupational health service and others (such as assessment, epidemiology, cost-effectiveness analysis and so on). 3. The number of journals on occupational health management was 510. It was sightly increased from 1986 and abruptly increased after 1991. The kinds of journals related to occupational health management were The Korean Journal of Occupational Medicine(18.2%), Several Kinds of Medical Colloge Journal(17.0%), The Korean Journal Occupational Health(15.1%), The Korean Journal of Preventive Medicine(15.1%) and others(34.6%). As for the contents, the number of journals on occupational health management systems was 33(6.5%) and occupational health services 477(93.5%). Of the journals on occupational health management systems, the number of journals on the occupational health resource system was 15(45.5%), occupational finance system 8(24.2%), occupational health management system 6(18.2%), occupational organization 3(9.1%) and occupational health delivery system 1 (3.0%). Of the journals on occupational health services, the number of journals on disease management was 269(57.2%), health management 116(24.7%), working environmental management 85(18.1%). As for the subjects, the number of journals on general workers was 185(71.1%), followed by women worker, white coiler workers and so on. 4. Respondents made occupational health service(such as health management, working environmental management and health education) the first priority of occupational health management. Tied for the second are quality analysis(such as education, training and job contents of occupational health manager) and occupational health systems(such as the recommendation of systems of occupational and general disease and occupational health organization). 5. Thirty seven respondents suggested 48 ideas about the future research of occupational health management. The results were as follows: (1) Study of occupational health service 40.5%; (2) Study of organization system 27.1%; (3) Study of occupational health system (e.g. information network) 8.3%; (4) Study of working condition 6.2%; and (5) Study of occupational health service analysis 4.2%.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Asia Marketing Journal
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• v.14 no.2
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• pp.177-207
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• 2012

Today's consumers perceive consumption as a representation of themselves. It is not simply an act that fulfills a consumer's physical and practical needs. Even in terms of life quality, consumers increasingly want to achieve an emotional and sensible experience through consumption. Consumers now make decisions based on their need to express their position in relation to other people, pursue emotional satisfaction, and try to improve the quality of life. Culture-art products that meet such internal and external demands of consumers have made significant improvements in both quantity and quality, because of the social interest and policy support. The recognition of personal and social values of culture and arts has brought about interest in and need for culture-art products. Businesses have agilely embraced such change and actively implemented various marketing strategies utilizing culture and arts. For example, businesses began to sponsor artists who produce culture-art products while building facilities for cultural and art performances or exhibitions. Businesses have also provided performances and exhibitions free-of-charge or at affordable prices. As a result, the supply in the market has started to exceed its demand as is often the case in many of other markets. However, such imbalance has occurred not because of over-supply but because of a lack of demand. Given these circumstances, the government and culture and art related organizations, which had mainly concentrated on the supply side, started to recognize the importance of creating personal and social values in culture and arts. As a result, the government and various organizations are now creating various strategies that include policy measures to achieve their new found goal. Unfortunately however, such efforts are not meeting the expectations. Focusing on above-mentioned circumstances and problems, this study aims to find measures to create demand for culture-art products in the internal conditions of those who consume culture-art products. In other words, given that the demand for culture-art products has not increased despite all external conditions to encourage consumption, this study aims to find the reasons in consumers' value judgment on culture-art products. Though there were recent studies on culture-art products that applied consumer behavior on marketing theories, most of them focused on peripheral aspects such as people's motivation for or satisfaction from watching culture-art events. Hence, there is a need to understand what kind of value consumers perceive from culture-art products and how such value cognition leads to consumption in a comprehensive manner. This study acts as follow-up to a separate study entitled "Qualitative Study about Value Cognition and Benefits of Consumer on Culture-Art Products". The current study aims to extend practical implications that enhance the effectiveness of marketing strategies among the producing and policy agencies in the industry. The purpose of this study is to investigate dimensions of value cognition, benefits and attributes of culture-art products, and identify the effects of consumer value cognition on benefits and attributes. The questionnaire was developed based on the conceptual structure of qualitative research and previous researches. It was composed of value cognition, benefits, attributes of culture-art products and demographic variables. This survey was conducted on-line and off-line among a total of 662 persons ranging from their teens to their 50's who were living in Seoul, Gyeonggi-do, various metropolitan cities, and small and medium-sized cities. The data collected was analyzed by factor analysis and path analysis using SPSS WIN 18.0 and AMOS 16.0. This empirical study found that the dimensions of value cognition of culture-art products were categorized into personal goods, aesthetic goods and public property. This shows that the consumers perceive culture-art products as products that are worthy enough to pay the costs not just for personal benefits but also for their social values. Also the formation of value cognition for culture-art products requires special conditions unlike that for physical consumer goods and services, which simply require marketing stimuli. The dimensions of benefits pursued by consuming culture-art products were found to be composed of four types - pursuit of aesthetic benefits, pursuit of actual benefits, pursuit of emotional benefits, and pursuit of conspicuous character. This result implies that people consume culture-art products not just to pursue pleasure from emotional and intelligent satisfaction as well as social relations, but also to seek the needs and benefits embodied at a social level. The dimensions of attributes of culture-art products had seven different factors, - environmental, price, evaluation, people, artwork, composition, and personal relations - which is plentiful. This is because the attributes of culture-art products are very complicated compared to other consumer goods or services. Since culture-art products include not just cultural or artistic works but also all physical, human, environmental, and systemic elements of the products in a comprehensive manner, consumers perceive everything they experience in the process of consuming culture-art products as part of the products. The dimensions of value cognition was found to affect attributes of the products, mostly using pursued benefits as a mediating factors. This result is consistent with the result of qualitative research, and proves that applying the means-end chain theory in the reverse direction is reasonable. The result can be interpreted that consumers' value cognitions for culture-art products turns into actual benefits leading to consumers' decisions. Furthermore, this result reveals that when consumers choose culture-art products, they take into account the attributes of culture-art products depending on the benefits they pursue. These results confirm that despite their conceptual and abstract attributes, culture-art products have values that contribute to actual benefits for individual consumers and society. Hence, value cognition generates benefits to be pursued and this in turn affects the consumers' choices of attributes on products. Based on the conceptual structure of consumers' value cognitions on culture-art products and its dimensions, it is possible to find detailed methods to provide opportunities for education and training to form and reinforce positive value cognition on culture-art products. And through those methods, it will be possible to develop attributes of culture-art products according to the dimensions of pursued benefits, and allow conceptual products become the subject to valuable consumption in real life. These results provide theoretical understanding of consumer behavior in culture marketing and useful information to culture-art producers, companies that use culture and art, and government agencies that use culture-art as a mean to improve the public perception of quality of life. As a follow up on this study, there should be experimental studies that can develop criteria visualizing the demands of consumers who purchase culture-art products and identify their detailed attributes. Studies that compare characteristics of different areas within the culture-art product category and in-depth studies on a specific area or genre will also be needed. In order to develop marketing strategies for culture-art products, studies on the formation and reinforcement of positive value cognition on culture-art products and education for the development of consumer demand as well as on the development and differentiation of attributes of culture-art products depending on types of consumer groups should also follow.