• Title/Summary/Keyword: Meta-data

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Design and Implementation of a 3-dimensional GIS Data Provider System (3차원 GIS 데이터제공자 시스템의 설계 및 구현)

  • 남광우;이성호;박종현
    • Spatial Information Research
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    • v.12 no.1
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    • pp.1-12
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    • 2004
  • The 3-dimensional GIS data provider system manages and retrieves 2, 3-dimensional spatial and time data. This system provides the standard interfaces which help developers to omit additional works to do modify or convert a format of 2 or 3-dimensional spatial data, which has been already accumulated, into that dependent on a specified system. This system consists of a data provider and data access component. The former deals with the connections with some databases and manages the meta data, so that the various GIS or software can access to 3-dimensional spatial and time data via the same method, and the latter takes charge of index management and spatial operations on GIS data for consumers. The system offers the diverse spatial operations and analysis functions for 3-dimensional GIS.

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Formal Modeling and Verification of an Information Retrieval System using SMV

  • Kim, Jong-Hwan;Park, Hea-Sook;Baik, Doo-Kwon
    • Proceedings of the Korea Society for Simulation Conference
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    • 2001.10a
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    • pp.141-146
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    • 2001
  • An Information Retrieval System offers the integrated view of SCM(Supply Chain Management) information to the enterprise by making it possible to exchange data between regionally distributed heterogeneous computers and also to enable these computers to access various types of databases. The Information Retrieval System is modeled using Data Registry Model based on X3.285. We only verify the MetaData Registry Manager(MDR Manager) among the core parts using SMV(Symbolic Model Verifier) in order to verify whether our model satisfies the requirements under the given assumptions.

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Automatic conversion of machining data by the recognition of press mold (프레스 금형의 특징형상 인식에 의한 가공데이터 자동변환)

  • 최홍태;반갑수;이석희
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1994.04a
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    • pp.703-712
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    • 1994
  • This paper presents an automatic conversion of machining data from the orthographic views of press mold by feature recognition rule. The system includes following 6 modules : separation of views, function support, dimension text recognition, feature recognition, dimension text check and feature processing modules. The characteristic of this system is that with minimum user intervention, it recognizes basic features such as holes, slots, pockets and clamping parts and thus automatically converts CAD drawing details of press mold into machining data using 2D CAD system instead of using an expensive 3D Modeler. The system is developed by using IBM-PC in the environment of AutoCAD R12, AutoLISP and MetaWare High C. Performance of the system is verified as a good interfacing of CAD and CAM when applied to a lot of sample drawings.

Knowledge Representation and Extraction of Biological Data using RDFS + OWL (RDFS + OWL을 이용한 생물학적 데이터의 지식 표현과 추출)

  • Lee Seung Hui;Sin Mun Su;Jeong Mu Yeong
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2003.05a
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    • pp.1136-1141
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    • 2003
  • Due to the lack of digitally usable standards, it has been known to be difficult to handle the biological data. For example, the name of genes and proteins changes over time or has several synonyms indicating different entities. To cope with these problems, several communities, including the Gene Ontology Consortium and PubGene are making their efforts to move science toward the semantic web vision. Although some progress has been made, its expressivity is not sufficient for full-fledged ontological modeling and reasoning. This paper suggests a methodology for representing and extracting biological knowledge by using Web Ontology Language (OWL) as an extension of Resource Description Framework Schema (RDFS). Some benefits of our approach are: (1) to ensure extended sharing of biological meta data on the Web, and (2) to enrich additional expressivity and the semantics of RDFS+OWL.

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Study of MetaData for Natural Language Query Processing (퍼지질의 처리를 위한 메타데이터에 관한 연구)

  • 신세영;박순철;이상범
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.40 no.5
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    • pp.259-265
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    • 2003
  • It leads to develop the query system with artificial intelligent technologies to handle inaccurate query. To develop the query system, metadata is essential to control a uncertain data, providing information about uncertainty of the data, and the classification system of metadata are necessary. This paper shows a classification of metadata based on fuzzy theory and the implementation processing to process the fuzzy query in a relational database system.

Automatic Conversion of Machining Data by the Feature Recognition of Press Mold (프레스 금형의 특징형상 인식에 의한 가공데이타 자동변환)

  • Choi, Hong-Tae;Bahn, Kab-Soo;Lee, Seok-Hee
    • IE interfaces
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    • v.7 no.3
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    • pp.181-191
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    • 1994
  • This paper presents an automatic conversion of machining data from the orthographic views of press mold by feature recognition rule. The system includes following 6 modules : separation of views, function support, dimension text check and feature processing modules. The characteristic of this system is that with minimum user intervention, it recognizes basic features such as holes, slots, pockets and clamping parts and thus automatically converts CAD drawing details of press mold into machining data using 2D CAD system instead of using an expensive 3D Modeler. The system is developed by using IBM-PC in the environment of AutoCAD R12, AutoLISP and MetaWare High C. Performance of the system is verified as a good interfacing of CAD and CAM when applied to a lot of sample drawing.

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Classification for Imbalanced Breast Cancer Dataset Using Resampling Methods

  • Hana Babiker, Nassar
    • International Journal of Computer Science & Network Security
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    • v.23 no.1
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    • pp.89-95
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    • 2023
  • Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.

Genetic classification of various familial relationships using the stacking ensemble machine learning approaches

  • Su Jin Jeong;Hyo-Jung Lee;Soong Deok Lee;Ji Eun Park;Jae Won Lee
    • Communications for Statistical Applications and Methods
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    • v.31 no.3
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    • pp.279-289
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    • 2024
  • Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

Study on Enhancement of Data Processing Algorithm in SaaS Cloud Infrastructure to Monitor Wind Turbine Condition (풍력발전기 상태 감시를 위한 SaaS 클라우드 인프라 내 데이터 처리 알고리즘 개선 연구)

  • Lee, Gwang-Se;Choi, Jungchul;Kang, Minsang;Park, Sail;Lee, JinJae
    • New & Renewable Energy
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    • v.16 no.1
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    • pp.25-30
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    • 2020
  • In this study, an SW for the analysis of the wind-turbine vibration characteristics was developed as an application of SaaS cloud infrastructure. A measurement system for power-performance, mechanical load, and gearbox vibration as type-test class was installed at a target MW-class wind turbine, and structural meta and raw data were then acquired into the cloud. Data processing algorithms were developed to provide cloud data to the SW. To operate the SW continuously, raw data was downloaded consistently based on the algorithms. During the SW test, an intermittent long time-delay occurred due to the communication load associated with frequent access to the cloud. To solve this, a compression service for the target raw data was developed in the cloud and more stable data processing was confirmed. Using the compression service, stable big data processing of wind turbines, including gearbox vibration analysis, is expected.

Design of a Coordinating Mechanism for Multi-Level Scheduling Systems in Supply Chain

  • Lee, Jung-Seung;Kim, Soo
    • Journal of Information Technology Applications and Management
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    • v.19 no.1
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    • pp.37-46
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    • 2012
  • The scheduling problem of large products like ships, airplanes, space shuttles, assembled constructions, and automobiles is very complex in nature. To reduce inherent computational complexity, we often design scheduling systems that the original problem is decomposed into small sub-problems, which are scheduled independently and integrated into the original one. Moreover, the steep growth of communication technology and logistics makes it possible to produce a lot of multi-nation corporation by which products are produced across more than one plant. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. In this research, we suggest an agent-based coordinating mechanism for multi-level scheduling systems in supply chain. For design of a general coordination mechanism, at first, we propose a grammar to define individual scheduling agents which are responsible to their own plants, and a meta-level coordination agent which is engaged to supervise individual scheduling agents. Second, we suggest scheduling agent communication protocols for each scheduling agent topology which is classified according to the system architecture, existence of coordinator, and direction of coordination. We also suggest a scheduling agent communication language which consists of three layers : Agent Communication Layer, Scheduling Coordination Layer, Industry-specific Layer. Finally, in order to improve the efficiency of communication among scheduling agents we suggest a rough capacity coordination model which supports to monitor participating agents and analyze the status of them. With this coordination mechanism, we can easily model coordination processes of multiple scheduling systems. In the future, we will apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly-plant-scheduling agents, and a meta-level coordination agent. A series of experiment using the real-world data will be performed to examine this mechanism.