• KIEAE Journal
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• v.9 no.1
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• pp.31-38
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• 2009

The purpose of this study is to present direction in outdoors space planning and design after direction through user characteristic analysis through spectacle component establishment of culture art center outdoors space through on-the-site analysis and literature investigation to culture art center of Seoul city and capital region 17 places in this research. The data was collected from classification and bisection kind, subdivision kind, and great classification composed to 17 items. User satisfaction side and Variable that is looked below satisfaction than average appeared to bench, pergola, sculpture facilities, pavement facilities, border facilities. And these facilities were analyzed dissatisfaction. When see satisfaction model, when make up culture art center or similar facilities in local government hereafter because parking facilities and rest area cause big effect in satisfaction, is judged that is item to consider most preferentially. In most case, parking lot security from outdoors space, resting place security, security of field performance facilities etc. taking a serious view because tendency that users see performance or use most vehicles except neighborhood walking area for a rest, a walk etc.. is trend. But, is judged that physical side so that can feel satisfaction as space security of quantitative side is important but users utilize substantially and side that is the program are more important in hereafter.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.11-17
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• 2009

It's a trend that the proportion of using an internet messenger among on-line communication methods is getting increased. However, there are not many applications which efficiently utilize these messenger communication data. Messenger communication data have specific characteristics that reflect the user's linguistic habits. The linguistic habits are revealed through frequently used words and emoticons, and user's emotions can be grasped by these. This paper proposes the method that efficiently classifies the emotions of a messenger user using frequently used words or symbols. The emotion classifier from repeated experiments achieves high accuracy of more than 95%.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.703-720
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• 2022

Based on the love styles theory used in psychology, this paper classifies users(Passionate Love, Game-playing Love, Friendship Love, Practical Love, Possessive Love, Altruistic Love) and investigates satisfaction with the motivation for using TikTok love videos(Entertainment, Social Relationship, Love skills-learning, Self-verification, Problem-solving) according to the theory of use and satisfaction. First, 414 users were selected to conduct TikTok surveys to collect data. Then, through the analysis of the research results, among the six love types, game-playing type and possessive type have a positive (+) impact on entertainment motivation and love skill-learning motivation. Game-playing type also have a positive (+) impact on social relationship motivation and self-verification motivation. In addition, altruistic type and possessive type are also factors to strengthen the motivation of self-verification. The altruistic type, possessive type and practical type will improve the problem-solving motivation. Finally, through hierarchial multiple regression analysis, it is confirmed that game-playing love type, entertainment motivation, love skill-learning motivation and self-verification motivation can improve user satisfaction. The above results enrich the research of user classification as well as providing inspiration for improving the quality and communication efficiency of TikTok's video and enhancing user experience.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.807-810
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• 2003

This research uses high-resolution satellite images as a source of collecting farmland information. For effectively extract the paddy area, we use texture information and different classify methods to assist the satellite image classification. First, using maximum likelihood classifier to extract paddy information from images. The results show that User Accuracy and Procedure Accuracy of the paddy area can increase from 80.60% to 95.45% and 84.38% to 95.45%. Second, establishing a paddy Knowledge Base and using Knowledge Base Classifier to extract paddy area, and result shows the User Accuracy and Producer Accuracy to be 92.16% and 90.06%. Finally, The result shows we can effectively contribute to the paddy field information extraction from high-resolution satellite images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.6
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• pp.539-545
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• 2004

For question answering system that extracts an answer and output to user‘s natural language question, a process of question type classification from user’s natural language query is very important. This paper proposes a question and answer type classifier using the interrogatives and word semantic categories instead of complicated classifying rules and huge dictionaries. Synonyms and postfix information are also used for question type classification. Experiments show that the semantic categories are helpful for question type classifying without interrogatives.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.81-90
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• 2008

While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1413-1415
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• 2004

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.37-44
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• 2017

While IoT (Internet of Things) industry has been spreading, it becomes very important for object to recognize user's information by itself without any control. Above all, gender (male, female) is dominant factor to analyze user's information on account of social and biological difference between male and female. However since each gender consists of diverse face feature, face-based gender classification research is still in challengeable research field. Also to apply gender classification system to IoT, size of device should be reduced and device should be operated with low power. Consequently, To port the function that can classify gender in real-world, this paper contributes two things. The first one is new gender classification algorithm based on deep learning and the second one is to implement real-time gender classification system in embedded board operated by low power. In our experiment, we measured frame per second for gender classification processing and power consumption in PC circumstance and mobile GPU circumstance. Therefore we verified that gender classification system based on deep learning works well with low power in mobile GPU circumstance comparing to in PC circumstance.

• Journal of Korean Library and Information Science Society
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• v.11
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• pp.175-209
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• 1984

The purpose of this study is to build a general principle for the automatic book-classification which can be put to use in library operation, and to present a methodology of the automatic classification for the library. Since the enumerative classification scheme which exist as manual systems cannot be a n.0, pplied to the automation of classification, the principles of Colon Classification by S.R. Ranganathan is brought in and studied. The result of the study can be summarized as follows: (1) Automatic book-classification can be performed by the principles of faceted classification. (2) This study presents a general and an a n.0, pplication principles for the automatic book-classification. (3) File design for the automatic book-classification of a general classification is different from that of special classification, (4) The methodology is to classify the literature by inputting the title into a terminal. In addition, the expected Value from the Automatic Book-classification is as follows: (1) The prompt and accurate process of classification is possible. (2) Though a book is classified in any library it can have the same classification number. (3) The user can retrieve the classification code of a book for which he or she wants to search through the dialogue with the computer. (4) Since the concept coordination method is employed, even the representing of a multi-subject concept is made simple. (5) By performing automatic book-classification, the automation of library operation can be completed.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.