• Journal of Digital Convergence
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• v.13 no.9
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• pp.235-242
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• 2015

Social network technology has been increasing interest in the big data service and development. However, the data stored in the distributed server and not on the central server technology is easy enough to find and extract. In this paper, we propose a big data management techniques to minimize the processing time of information you want from the content server and the management server that provides big data services. The proposed method is to link the in-group data, classified data and groups according to the type, feature, characteristic of big data and the attribute information applied to a hash chain. Further, the data generated to extract the stored data in the distributed server to record time for improving the data index information processing speed of the data classification of the multi-attribute information imparted to the data. As experimental result, The average seek time of the data through the number of cluster groups was increased an average of 14.6% and the data processing time through the number of keywords was reduced an average of 13%.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.360-366
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• 2008

The initial fuzzy partitions in fuzzy rule-based classification systems are determined by considering the domain region of each attribute with the given data, and the optimal classification boundaries within the fuzzy partitions can be discovered by tuning their parameters using various learning processes such as neural network, genetic algorithm, and so on. In this paper, we propose a selection method for fuzzy partition based on statistical information to maximize the performance of pattern classification without learning processes where statistical information is used to extract the uncertainty regions (i.e., the regions which the classification boundaries in pattern classification problems are determined) in each input attribute from the numerical data. Moreover the methods for extracting the candidate rules which are associated with the partition intervals generated by statistical information and for minimizing the coupling problem between the candidate rules are additionally discussed. In order to show the effectiveness of the proposed method, we compared the classification accuracy of the proposed with those of conventional methods on the IRIS and New Thyroid Cancer data. From experimental results, we can confirm the fact that the proposed method only considering statistical information of the numerical patterns provides equal to or better classification accuracy than that of the conventional methods.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.76-81
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• 2002

인터넷의 발달 등으로 많은 정보들이 문서화되기도 하고 그런 정보들이 공유되고 있는 지금, 언어학이나 전산학의 요구를 함께 충족시킬 수 있는 문법 모델 개발의 필요성이 극대화되고 있다. 이 글은 한국어 품사 분류에 대해서 국어학과 전산학에서의 처리 방법과 결과를 검토하고 정리하여 우리말의 특성을 잘 설명하면서도 국어를 전산 처리하는데도 도움을 줄 수 있는 품사분류를 제안하는데 그 목적이 있다. 한국어의 특성을 고려하여 음운, 형태, 통 어, 의미 정보를 함께 처리할 수 있는 어휘부 중심의 문법인 HPSG의 모형을 도입하여 한국어 품사 분류를 정보 전달에 기반을 두어 자질 체계와 통합 연산을 핵심으로 기술하려고 한다. 문법기술은 주로 자질 구조를 속성과 값의 행렬인 AVM(attribute-value matrices)으로 제시할 것이다.

• Journal of Service Research and Studies
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• v.6 no.2
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• pp.83-97
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• 2016

Smart car is a vehicle which maximizes convenience, safety, and user experience. The traditional vehicle style will be replaced by a smart car. The objective of this paper is to find essential quality attributes that consumers want. We provide a method to select a best smart car reflecting their preference based on the quality attributes. We derive Kano's quality attributes by an exploratory survey and show an example to implement their decision using the Analytic Hierarchy Process method. As a result, the quality attributes were classified into two groups of attractive quality and indifference quality. The respondents evaluated that the safety of smart cars was more important than the convenience and user experience. However, the smart car was required more functions related to the convenience criteria. These results will provide important implications for smart car design.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2106-2112
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• 1999

In decision-making, information is the thing manufactured as the useful type for decision -making. We can improve the efficiency of decision-making by elimination of unnecessary information. Rough set is the theory that can classify and reduce the unnecessary. But the reduction process of rough set becomes more complex according to the number of attribute and tuple. After eliminating of the dispensable attributes using $\chi$2 and rough set, the indispensable attributes are used for the units of input layers in neural network. This rough-neural network can support more correct decision-making of neural network.

• Journal of KIISE:Software and Applications
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• v.28 no.5
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• pp.412-421
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• 2001

본 논문은 문서 영상에 존재하는 개별 단어들에 대한 속성정보 추출 방법을 제안한다. 단어 단위의 속성 인식은 단어 영상 매칭의 정확도 및 속도 개선, OCR 시스템에서 인식률 향상, 문서의 재생산 등 다양한 응용 가치를 찾을 수 있으며, 메타정보(meta-information) 추출을 통해 영상 검색(image retrieval)이나 요약(summary) 생성 등에 활용할 수 있다. 제안하는 시스템에서 고려하는 단어 영상의 속성은 언어의 종류(한글, 영문), 스타일(볼드, 이탤릭, 보통, 밑줄), 문자 크기(10, 12, 14 포인트), 문자 개수 (한글: 2, 3, 4, 5, 영문: 4, 5, 6, 7, 8, 9, 10), 서체(명조, 고딕)의 다섯 가지 정보이다. 속성 인식을 위한 특징은, 언어 종류 인식에 2개, 스타일 인식에 3개, 문자 크기와 개수는 각각 1개, 한글 서체 인식은 1개, 영문 서체 인식은 2개를 사용한다. 분류기는 신경망, 2차형 판별함수(QDF), 선형 판별함수(LDF)를 계층적으로 구성한다. 다섯 가지 속성이 조합된 26,400개의 단어 영상을 사용한 실험을 통해, 제안된 방법이 소수의 특징만으로도 우수한 속성 인식 성능을 보임을 입증하였다.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.275-287
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• 2019

As game players have been more diverse and new features of digital games have been emerged in recent days, it is important to find out and understand how recent game players recognize and classify digital games. Thirty game players conducted Q-analysis of twenty-nine Q-statements extracted from previous studies on game typology. By using a QUANL program, three different types were revealed. For game classification, 'Physical Environment Centric Players' type highly values external game elements from the outside perspectives. 'Contents Centric Players' type considers internal game elements as the most important criterion. 'Emotional Experience Centric Players' type values his/her subjective feeling and thoughts. Based on this study, it is expected to make a contribution in developing a framework of game players with their perspectives on game classification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.1
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• pp.82-95
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• 2007

The paper focuses on how to classify as well as to define the Architecture Patterns which are popularly used in the design of software architecture. In order to achieve this purpose, we propose not only the revised methodology for Pattern-Oriented Software Architecture Design, but also new method of classification and definition for the Architecture Patterns. Especially, because the patterns are so diverse depending on the level of abstraction and types of applications, it was considered to have some different views of classification of the patterns in order to support convenient access to classified and stored patterns. The abstraction of the pattern is another important result of the research, which is devised for concrete expression of the patterns and for presentation of the interrelation among group of the patterns. The research also includes the extension of the quality model popularly adopted in the software domain, which enables the description of the patterns with the well defined quality attributes in terms of software architecture's point of view.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.133-142
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• 2021

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.