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민미경 서경대학교 산업기술연구소 2013 産業技術硏究所論文集 Vol.31 No.-
Sensor Network is a distributed network composed of a large number of sensor nodes which automatically collect information of objects and environments. Middleware for sensor network has emerged as an important research area to bridge the gap between applications and low-level constructs. Due to resource constraints of wireless sensor networks, a middleware layer should contain challenging technologies. This paper finds out design criteria and various technologies of sensor network middlewares. Also this paper presents a comparative study of several existing middlewares and how they deploy these technologies. In doing so, some characteristics and limitations of present sensor network middlewares are pointed out.
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기계학습 클러스터링을 이용한 승하차 패턴에 따른 서울시 지하철역 분류
민미경,Min, Meekyung 한국인터넷방송통신학회 2018 한국인터넷방송통신학회 논문지 Vol.18 No.4
In this study, we classify Seoul metro stations according to boarding and alighting patterns using machine earning technique. The target data is the number of boarding and alighting passengers per hour every day at 233 subway stations from 2008 to 2017 provided by the public data portal. Gaussian mixture model (GMM) and K-means clustering are used as machine learning techniques in order to classify subway stations. The distribution of the boarding time and the alighting time of the passengers can be modeled by the Gaussian mixture model. K-means clustering algorithm is used for unsupervised learning based on the data obtained by GMM modeling. As a result of the research, Seoul metro stations are classified into four groups according to boarding and alighting patterns. The results of this study can be utilized as a basic knowledge for analyzing the characteristics of Seoul subway stations and analyzing it economically, socially and culturally. The method of this research can be applied to public data and big data in areas requiring clustering. 본 연구에서는 기계학습을 이용하여 서울시 지하철역의 승하차 패턴에 따라 지하철역을 분류한다. 대상 데이터는 공공데이터 포탈에서 제공하는 2008년부터 2017년까지 서울 지하철 233개 역에서의 매일 매시간별 승차객 숫자와 하차객 숫자이다. 기계학습 기법으로는 가우시안 혼합 모델(GMM)과 K-평균 클러스터링을 사용한다. 이용객의 승차시간과 하차시간의 분포는 가우시안 혼합 모델로 모델링할 수 있으며, 이를 K-평균 클러스터링을 이용하여 비지도 학습시킨다. 학습결과 서울시 지하철역은 승하차 패턴에 따라 4개의 그룹으로 분류되었다. 본 연구의 결과는 서울시 지하철역의 특성을 파악하여 경제, 사회, 문화적으로 분석하기 위한 주요 기반 지식으로 활용될 수 있다. 본 연구의 방법은 클러스터링이 필요한 모든 공공데이터나 빅데이터에 적용할 수 있다.
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민미경 한국품질경영학회 2017 품질경영학회지 Vol.45 No.4
Purpose: The purpose of this paper is to evaluate the performance quality of the open source DBMSs. Performance quality is defined as processing time for Join queries. Query processing time is measured and compared in the most widely used open source DBMSs and commercial DBMS. Methods: By varying the number of tuples of two relations to be joined, the average processing time(seconds) of a Join query in each DBMS was obtained experimentally. ANOVA and Tukey HSD test were used in order to compare the performance quality of DBMSs. Results: There was a significant difference between the performance qualities of the three DBMSs at all experimental levels where the number of tuples was 100, 1,000, 2,000, 10,000, and 50,000. As a result of the Tukey HSD test, two open source DBMSs (MariaDB, MySQL) were classified in the same group only at the tuple level of 100. The commercial DBMS (MS-SQL Server) belonged to another group. At level of more than 1,000 tuples, all three DBMSs belonged to different groups. Conclusion: Within the open source DBMS group, MariaDB showed the better performance quality except for a small number of tuples. Thus the results show that MariaDB can be the alternative to MySQL which is currently most widely used. Between open source DBMS and commercial DBMS groups, MS-SQL Server always shows the best performance quality, but the less number of tuples, the less the difference.
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민미경 서경대학교 산업기술연구소 2013 産業技術硏究所論文集 Vol.31 No.-
This paper surveys expert systems using a literature review of recent articles. Based on the articles, structures of the knowledges in these expert systems are analyzed and classified. Knowledge structures are composed of rules, knowledges, fuzzy logics, neural networks, database and ontologies. Discussions are presented with the future development directions for expert system methodologies and applications. Keywords: Expert system, Knowledge, Rule, Fuzzy logic, Neural network, Ontology
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민미경 서경대학교 1995 論文集 Vol.23 No.2
지식기반 시스템의 응용분야가 크고 복잡해짐에 따라, 대량의 지식을 저장하고 공유하기 위하여 데이터베이스 시스템의 기능을 이용하는 기법이 활발히 연구되고 있다. 본 논문에서는 지식의 지속성을 제공하기 위하여 규칙기반 시스템을 객체지향 데이터베이스 시스템과 결합한 LKBS(Large Knowledge-Based System)라고 하는 결합 시스템 모델을 제안하고, 이를 구현하기 위한 알고리즘을 제시한다. LKBS는 시스템 전반에 걸쳐 객체지향 개념을 적용함으로써 결합을 효율적이고 용이하게 한다. LKBS 지식베이스의 사실 및 규칙은 객체지향 데이터베이스의 객체와 동일한 개념이며, 추론기관은 DBMS의 클래스 라이브러리에 내장되어 객체지향 방식에 의해 수행된다. 또한, 본 논문은 Sun Workstation 상에서 규칙기반 시스템인 HEXPERT와 객체지향 데이터베이스 시스템인 Obase를 결합하여 LKBS를 개발함으로써, 두 시스템의 결합에 관한 이론적인 기법들이 실제로 구현되었을 경우의 문제점을 살펴보고, 이를 해결할 수 있는 방법을 제시한다. As the application domains of knowledge-based systems become larger and more complicated, the integration of knowledge-based systems with database systems has become the topic of many research works. For persistencies of knowledge bases of knowledge-based systems, this paper suggests a model of an integrated system, called Large Knowledge-Based System (LKBS) and presents the algorithms to implement the LKBS. The integration method employed in LKBS is effectively done by applying object-oriented paradigm. In LKBS, facts and rules from the knowledge base and objects from the database are manipulated as a single concept. The inference engine of the LKBS is embedded in the class library of DBMS and executed by the object-oriented methods. The LKBS, developed on Sun Workstation, couples a rule-based system HEXPERT with an object-oriented database system Obase. Some implementation problems are pointed out and the methods to solve these problems are discussed in this paper.
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협동작업을 지원하는 객체지향 데이터베이스의 버전관리 기법
민미경 서경대학교 1998 論文集 Vol.26 No.-
This is paper presents a version management mechanism that supports cooperative works. The version model includes version derivation graph, versionable object and composite object. Some operations are supported to manipulate versions. In order to support cooperation, a check-in and check-out mechanism is proposed which is based on versioning. Also, various kinds of locks are introduced in order to improve sharability of database.
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