• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.2
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• pp.219-243
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• 2017

This study aims to provide alternative strategies for promoting active engagement of users in participatory archives. It focuses on users and their active participation in digital archives beyond providing simple participation opportunities. In doing so, the study reviewed relevant literature that analyzes interpretation and development of participatory digital archives. Moreover, it examined several cases of participatory digital archives as to how they apply for user participation, policy, and service. As a general property, main participants, duration, and technology were examined. Technology was further subdivided into open source software, availability of Open API, availability of mobile web, and offline archives. Participation method was divided into active participation, hub participation, and passive participation according to degree of user participation, and the participation functions provided by each archive were compared and analyzed. In policy area, terms of use, personal information processing policy, copyright policy, collection policy, major collections, scope of collections, classification methods, and descriptive elements of each archive were discussed. Services were divided into content, search, and communication area. Based on such analysis, this study proposed ways for promoting active engagement of users in participatory digital archives in terms of participation, policy, content service, and communication service.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.411-418
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• 2021

In this study, information search methods and user interface types provided to users were investigated for the top 100 e-commerce services selected by Statista and the National Retail Federation. And the characteristics of each type were derived by analyzing the interaction method of the user's manipulation with the visualization elements constituting the interface. The research results are as follows. First, as the information provision method, spread format was more often used as the number and hierarchy of information increased, and drop-down and mega menu methods were used more often as the number and hierarchy of information decreased. Second, as a visual classification method according to the information hierarchy, the background color, font change, and line were often used, and there were many cases where the background color and line were used at the same time. Third, there were various elements such as background color, text color, and line as an interaction method for user manipulation, and two or more of them were applied at the same time the most. This study is meaningful in that it defines the characteristics of each type through the analysis of the types of interfaces for e-commerce information search and items that can be the selection criteria for detailed elements.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.1-6
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• 2019

In modern society, lifestyle and individuality are important, and personalized lifestyle and patterns are emerging. The number of people with articulation diseases is increasing due to wrong living habits. In addition, as the number of households increases, there is a case where emergency care is not received at the appropriate time. We need information that can be managed by ourselves through accurate analysis according to the individual's condition for health and disease management, and care appropriate to the emergency situation. It is effectively used for classification and prediction of data using CNN in deep learning. CNN differs in accuracy and processing time according to the data features. Therefore, it is necessary to improve processing speed and accuracy for real-time healthcare. In this paper, we propose motion monitoring using Mask R-CNN for articulation disease management. The proposed method uses Mask R-CNN which is superior in accuracy and processing time than CNN. After the user's motion is learned in the neural network, if the user's motion is different from the learned data, the control method can be fed back to the user, the emergency situation can be informed to the guardian, and appropriate methods can be taken according to the situation.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.861-869
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• 2020

We introduce the application that automatically makes several images stored in user's device into one video by using the different climax patterns appearing for each film genre. For the classification of the genre characteristics of movies, a climax pattern model style was created by analyzing the genre of domestic movie drama, action, horror and foreign movie drama, action, and horror. The climax pattern was characterized by the change in shot size, the length of the shot, and the frequency of insert use in a specific scene part of the movie, and the result was visualized. The model visualized by genre developed as a template using Firebase DB. Images stored in the user's device were selected and matched with the climax pattern model developed as a template for each genre. Although it is a short video, it is a feature of the proposed application that it can create an emotional story video that reflects the characteristics of the genre. Recently, platform operators such as YouTube and Naver are upgrading applications that automatically generate video using a picture or video taken by the user directly with a smartphone. However, applications that have genre characteristics like movies or include video-generation technology to show stories are still insufficient. It is predicted that the proposed automatic video editing has the potential to develop into a video editing application capable of transmitting emotions.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.3
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• pp.9-20
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• 2008

The government is planning to link a small park with the soon to be ready subway line 9 as a part of Seoul's policy, "The standard or plan for making a water-friendly space by water use" (2007). However, the specified concepts and instructions of the small parks need further work. Therefore, the policy is expected to expand to neighboring small parks near the subway station, but these are not supported by research or data which suggests the needs or actual user behavior and utilization of small parks. our country added the specified concept of small parks and theme parks to the classification of urban parks in the Urban Park Act Revision (2005.3.31), but the concept of small parks is not clearly settled in the law in the scopes of its function, scale, promotion nor particularly defined plans for small park projects. This study examines as small park near a subway station. The characteristics of there region and users vary from the characteristics of the station and region. In the "directions for concrete standards under the types of urban parks and green zones" (2007.2) the types of small parks are classified by "regional characteristics" and "user characteristics". Therefore, this study classifies the subject of neighboring small parks near subway stations as the neighborhood and small urban parks according to the Urban Park Act. The study was paralleled with observation and questionnaires on the analysis of the neighborhood and small urban parks. The actual conditions of park utilization and user behavioral characteristics were deducted by observation, while the questionnaire determined actual user utilization, importance and satisfaction level as well as the small park environment. This study largely focused on three aspects: park facility, design of this sentence isn't even complete. The second aspect isn't finished and the third isn't here.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.2 no.1
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• pp.178-210
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• 1974

It needs to put into practice the systems analysis in the analysis of some operations and status of library for the purpose of systematizing the work of reforming in the new easier form to process, to storage, to retrieve and to make use of the increasing informations and data of library. In this study, some of systems which are generally using in every library was caught in the case study of K university library. Having analyzed them with the two methods of the flowcharting and mathematical analysis, we found the obstructive factors in operation. As the result of this research, it was gained the new system as the alternative one. A. Alternative System B. Advantages of alternative systems 1. In the reference room When it converts the present system into the new system, it can profit 6.771 won/user (13.815won-7.044won=6.771 won). Therefore, a half the average required cost of the present system can be saved. If this saving would be alloted for the cost 33,000won required to make the cataloging cards, it would be taken for 94 days (33,000 won ${\div}$ 6,771 won/user=4,874users. 4,874users ${\div}$ 52users/day=94days) to get it. The saving cost/year by the new system will be 95,417 won, and in the first year the initial cost (33,000won) reduces the saving cost to 62,417won. 2. In the periodical room The average required time for using the materials of the present system is 17 minutes/user and the average required cost/user is 23.775won, while the average required time of the new system is 4 minutes and the average required cost/user is 5.33won. Therefore, the new system has profit 4 times of the present system. Accordingly, it occurs when the dispersed periodical materials get together. 3. In the classification and cataloging When one processes - the oriental books - by the Linear Programming Technique, the maximum of the process can be increased from 11.6 volumes per librarian of the present system to 12 volumes per librarian of the new system increased 0.4 volume in a day, and cataloging by the manual printer can be shorten from 3 minutes per card of the present system to 1.5 minutes per card of the new system. Consequently, we can complete the other operations (books equipment, updating of cataloging cards, etc.) with 141 minutes which are saved in the course of the afore-mentioned works. 4. In the status of collections The average growth rate of 4 years from 1968 to 1971 is 9.825 %, and that of the purchased materials is 6.2% similar to the advanced nations, but it has the different position from 215,000 volumes by the Standard Degree for Establishment of College and University, and the difference between the total collections 151,671 volumes and Dunns' growth model ($N_t=N_oe^{-at}$) claimed by Leimkuhler 155,297 volumes in 1971 is 3,626 volumes, and for the purpose of compensation the difference, we found the fact that it needs to have the increased budget of 24~30% per year, Thus, if the budget of 24~30 % per year. Thus, if the budget would be increased per year as the rate of the afore-mentioned figure, it would be reached at the Standard Degree for Establishment of College and University in 1975, and thereafter, it can be decreased to the lebel which is able to maintain the growth rate of 5~6% per year.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.19-33
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• 2013

In this research, a proposed Dynamic Virtual Ontology using Tags (DyVOT) supports dynamic search of resources depending on user's requirements using tags from social web driven resources. It is general that the tags are defined by annotations of a series of described words by social users who usually tags social information resources such as web-page, images, u-tube, videos, etc. Therefore, tags are characterized and mirrored by information resources. Therefore, it is possible for tags as meta-data to match into some resources. Consequently, we can extract semantic relationships between tags owing to the dependency of relationships between tags as representatives of resources. However, to do this, there is limitation because there are allophonic synonym and homonym among tags that are usually marked by a series of words. Thus, research related to folksonomies using tags have been applied to classification of words by semantic-based allophonic synonym. In addition, some research are focusing on clustering and/or classification of resources by semantic-based relationships among tags. In spite of, there also is limitation of these research because these are focusing on semantic-based hyper/hypo relationships or clustering among tags without consideration of conceptual associative relationships between classified or clustered groups. It makes difficulty to effective searching resources depending on user requirements. In this research, the proposed DyVOT uses tags and constructs ontologyfor effective search. We assumed that tags are extracted from user requirements, which are used to construct multi sub-ontology as combinations of tags that are composed of a part of the tags or all. In addition, the proposed DyVOT constructs ontology which is based on hierarchical and associative relationships among tags for effective search of a solution. The ontology is composed of static- and dynamic-ontology. The static-ontology defines semantic-based hierarchical hyper/hypo relationships among tags as in (http://semanticcloud.sandra-siegel.de/) with a tree structure. From the static-ontology, the DyVOT extracts multi sub-ontology using multi sub-tag which are constructed by parts of tags. Finally, sub-ontology are constructed by hierarchy paths which contain the sub-tag. To create dynamic-ontology by the proposed DyVOT, it is necessary to define associative relationships among multi sub-ontology that are extracted from hierarchical relationships of static-ontology. The associative relationship is defined by shared resources between tags which are linked by multi sub-ontology. The association is measured by the degree of shared resources that are allocated into the tags of sub-ontology. If the value of association is larger than threshold value, then associative relationship among tags is newly created. The associative relationships are used to merge and construct new hierarchy the multi sub-ontology. To construct dynamic-ontology, it is essential to defined new class which is linked by two more sub-ontology, which is generated by merged tags which are highly associative by proving using shared resources. Thereby, the class is applied to generate new hierarchy with extracted multi sub-ontology to create a dynamic-ontology. The new class is settle down on the ontology. So, the newly created class needs to be belong to the dynamic-ontology. So, the class used to new hyper/hypo hierarchy relationship between the class and tags which are linked to multi sub-ontology. At last, DyVOT is developed by newly defined associative relationships which are extracted from hierarchical relationships among tags. Resources are matched into the DyVOT which narrows down search boundary and shrinks the search paths. Finally, we can create the DyVOT using the newly defined associative relationships. While static data catalog (Dean and Ghemawat, 2004; 2008) statically searches resources depending on user requirements, the proposed DyVOT dynamically searches resources using multi sub-ontology by parallel processing. In this light, the DyVOT supports improvement of correctness and agility of search and decreasing of search effort by reduction of search path.