• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.363-369
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• 2010

Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.268-277
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• 2003

A document type definition(DTD) which defines tags for a document is a XML document grammar that defines syntactic structure of a document. An XML document keeps the rules and must be parsed to check validation. To parse XML document, the deterministic parsing method of programming language is irrelevant because it does not satisfy the definition of deterministic content model in element declaration. In this paper, we consider editing of a valid XML document in syntax-directed editing environment, and we suggest the internal storage representations of syntax in DTD and theirs algorithms. The consequence is that a syntactic structure of textual DTD is transformed into graph and table structures. The table structure of DTD is interpreted the context free grammar which has attribute values and is used in syntax-directed editor for XML. We called this the XML abstract grammar and showed generated results and examples.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Asia Marketing Journal
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• v.10 no.2
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• pp.125-151
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• 2008

Both companies and consumers are highly interested in on-line customer reviews which enable consumers to share their experience and knowledge about products. In this study, after classifying real reviews into context units and deriving categories, we analyzed differences between categories based on channel(manufacturers' homepage/ shopping mall), product attribute(search/experience) and price(high/low). The method to derive categories is based on roughly adopting constructs of ACSI model and elaborate and repetitive classification of real reviews. We set up the classification category with 3 levels. Level 1 consists of product and service, level 2 consists of function, design, price, purchase motive, suggestion/user-tip and recommendation/repurchase in product and AS/up-grade and delivery/others in service and level 3 is composed of details of level 2 of category. We could find remarkable differences between channels in all 8 items of level 2 of category. As the number of context units in homepage is more than in shopping mall, we found reviews in homepage is more concrete. Moreover, overall satisfaction in review was higher at homepage's. Also, in product attribute dimension, we found different patterns of reviews in design, purchase motive, suggestion/user-tip, recommendation/repurchase, AS/up-grade and delivery/others and no difference in overall customer's satisfaction. In price dimension, we found differences between high and low price in design, price and AS/up-grade and no difference in overall customer's satisfaction.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.810-819
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• 2013

FCM (Fuzzy C-Means) clustering algorithm, a typical split-based clustering algorithm, has been successfully applied to the various fields. Nonetheless, the FCM clustering algorithm has some problems, such as high sensitivity to noise and local data, the different clustering result from the intuitive grasp, and the setting of initial round and the number of clusters. To address these problems, in this paper, we determine fuzzy numbers which project the FCM clustering result on the axis with the specific attribute. And we propose a model that the fuzzy numbers apply to FDT (Fuzzy Decision Tree). This model improves the two problems of FCM clustering algorithm such as elevated sensitivity to data, and the difference of the clustering result from the intuitional decision. And also, this paper compares the effect of the proposed model and the result of FCM clustering algorithm through the experiment using real traffic and rainfall data. The experimental results indicate that the proposed model provides more reliable results by the sensitivity relief for data. And we can see that it has improved on the concordance of FCM clustering result with the intuitive expectation.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.291-300
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• 2005

The Ubiquitous computer environment is thing which invisible computer that is not shown linked mutually through network so that user may use computer always is been pervasive. Intend computing environment that can use easily as user wants and it is the smart environment that user provides context awareness that is wanting computing environment. This Ubiquitous computing contains much specially weak side in security. Masquerade attack of that crawl that is quoted to user or server among device that is around user by that discrete various computing devices exist everywhere among them become possible. Hereupon, in this paper, proposed method that have following characteristic. Present authentication model through transfer or device. Suggest two method that realize authentication through device in case of moved to method(MD: Multi Domain) and user ownself space(SD: Single Domain) that realize authentication through device in case of moved user's direct path who device differs.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.688-698
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• 2009

The market for Internet fashion/coordination shopping malls has been enormously increased year by year. However, online shoppers feel inconvenient because most of Internet shopping malls still rely on item classifications by category and do not provide the functionality in terms of which shoppers can find clothes they want. In an effort to build a fashion/coordination system for women's dress adopting the Heuristic-based method, one of the Context-based methods, we present a method for defining characteristics of a woman's dress as attributes and their inheritance relations, which can be input by a product manager. We also compare and analyze various methods for recommending the most similar clothes.

• Korean Institute of Interior Design Journal
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• v.24 no.4
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• pp.61-69
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• 2015

The purpose of study is not only analyzes the space images expressed with mass-produced furniture of the time and economy throughout the cover pages of IKEA published catalogs since 1951 to 2015 but also is set to deduct the characteristics of spatial expression regarding the usage of digital media applied catalogs which is to be published along with the 2015 catalog. Thus, throughout research of basic literature such as domestic and foreign academic material, books, and websites, the theoretical consideration of the meaning of IKEA's pursuit of selling manufactured furnitures is foregone. Based on the above, Analyze and organize periodically applied interior space in accordance with context characteristic by understanding the attribute of catalogue from expansion of media space applied new digital media. The importance of research on IKEA is not only the quality of products designed by them, but emotional aspect that deeply penetrate customer's actual daily life. Implication of digital media message to advertisement is a important role in society that shares digitalized information. IKEA's innovative attempt of connection to new world using traditional method of marketing expression and smart device may help people better understand space.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.504-512
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• 2016

In this paper, we propose a personalized event recommendation scheme using user's activity analysis and collaborative filtering in social network environments. The proposed scheme predicts un-evaluated attribute values through analysis of user activities, relationships, and collaborative filtering. The proposed scheme also incorporates a user's recent preferences by considering the recent history for the user or context-aware information to precisely grasp the user's preferences. As a result, the proposed scheme can recommend events to users with a high possibility to participate in new events, preventing indiscriminate recommendations. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.