• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.247-260
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• 2021

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims to provide richer expressiveness and incorporate a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate the viewing of connections and ideas on a database. The described structure of the data is often represented in an entity–relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes, and relationships. This paper attempts to examine the basic ER modeling notions in order to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships, and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

• Korean Journal of Computational Design and Engineering
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• v.12 no.1
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• pp.74-85
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• 2007

During the lifecycle of a nuclear power plant many organizations are involved in KOREA. Korea Plant Engineering Co. (KOPEC) participates in the design stage, Korea Hydraulic and Nuclear Power (KHNP) operates and manages all nuclear power plants in KOREA, Dusan Heavy Industries manufactures the main equipment, and a construction company constructs the plant. Even though each organization has a digital data management system inside and obtains a certain level of automation, data sharing among organizations is poor. KHNP gets drawing and technical specifications from KOPEC in the form of paper. It results in manual re-work of definition and there are potential errors in the process. A data warehouse based on a neutral model has been constructed in order to make an information bridge between design and O&M phases. GPM(generic product model), a data model from Hitachi, Japan is addressed and extended in this study. GPM has a similar architecture with ISO 15926 "life cycle data for process plant". The extension is oriented to nuclear power plants. This paper introduces some of implementation results: 1) 2D piping and instrument diagram (P&ID) and 3D CAD model exchanges and their visualization; 2) Interface between GPM-based data warehouse and KHNP ERP system.

• Journal of Korea Multimedia Society
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• v.1 no.2
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• pp.131-141
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• 1998

Video data occurred in multimedia documents consist of a large scale of irregular data including audio-visual, spatial-temporal, and semantic information. In general, it is difficult to grasp the exact meaning of such a video information because video data apparently consist of unmeaningful symbols and numerics. In order to relieve these difficulties, it is necessary to develop an integrated manager for complex structures of video data and provide users of video digital libraries with easy, systematic access mechanisms to video informations. This paper proposes a generic integrated video information model(GIVIM) based on an extended Dublin Core metadata system to effectively store and retrieve video documents in digital libraries. The GIVIM is an integrated mo이 of a video metadata model(VMN) and a video architecture information model(VAIM). We also present design and implementation results of a video document management system(VDMS) based on the GIVIM.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.603-614
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• 1999

As multimedia application services on high-speed information network have been rapidly developed, the need for the video information management system that provides an efficient way for users to retrieve video data is growing. In this paper, we propose a video data model that integrates free annotations, image features, and spatial-temporal features for video purpose of improving content-based retrieval of video data. The proposed video data model can act as a generic video data model for multimedia applications, and support free annotations, image features, spatial-temporal features, and structure information of video data within the same framework. We also propose the video query language for efficiently providing query specification to access video clips in the video data. It can formalize various kinds of queries based on the video contents. Finally we design and implement the query processing engine for efficient video data retrieval on the proposed metadata model and the proposed video query language.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.393-400
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• 2016

Recently, there have been various researches on efficient and automatic analysis on urban environment methods that utilize the computer vision and machine learning technology. Among many new analyses, urban safety analysis has received a major attention. In order to predict more accurately on safety score and reflect the human visual perception, it is necessary to consider the generic and local information that are most important to human perception. In this paper, we use Double-column Convolutional Neural network consisting of generic and local columns for the prediction of urban safety. The input of generic and local column used re-sized and random cropped images from original images, respectively. In addition, a new learning method is proposed to solve the problem of over-fitting in a particular column in the learning process. For the performance comparison of our Double-column Convolutional Neural Network, we compare two Support Vector Regression and three Convolutional Neural Network models using Root Mean Square Error and correlation analysis. Our experimental results demonstrate that our Double-column Convolutional Neural Network model show the best performance with Root Mean Square Error of 0.7432 and Pearson/Spearman correlation coefficient of 0.853/0.840.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.753-758
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• 2005

This study attempts at building an integrated GIS computing framework for evaluating space-time accessibility of an individual with the approach of time geography model. The proposed method is based on the integration of mobile GIS and object-relational spatio-temporal database. Three components are central to our system: ( i ) mobile GIS application that transmits spatio-temporal trajectory data of an individual; ( ii ) spatio-temporal database server that incorporates the time geography model; and (iii) geovisualization client that provides time geographic queries to the spatio-temporal database. As for the mobile GIS application, spatio-temporal trajectory data collected by GPS-PDA client is automatically transmitted to the database server through mobile data management middleware. The spatio-temporal database server implemented by extending a generic DBMS provides spatio-temporal objects, functions and query languages. The geovisualization client illustrates 3D visual results of the queries about space-time path. space-time prism and space-time accessibility. This study shows a method of integrating mobile GIS and DBMS for time geography application, and presents an appropriate spatio-temporal data model for evaluating space-time accessibility of an individual.

• Smart Structures and Systems
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• v.30 no.3
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• pp.255-261
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• 2022

When no extensometer is available in a generic tensile-compression test carried out by a universal testing machine (for instance the model BIONIX from Material Testing Systems (MTS)), the test results only provide the relative displacement between the machine grips. The test does not provide any information on the local behaviour of the material. This contribution presents the potential of an application of Digital Image Correlation (DIC) toward the reconstruction of the behaviour along the specimen. In particular, the authors test a Ni-Ti shape memory alloys (SMA) specimen with emphasis on the coupling of the two measurement techniques.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.57.5-57
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• 2001

This paper presents an enhanced web-based distributed system with Single Access Object Protocol(SOAP), which uses HTTP as a communication protocol and XML as a data presentation. The suggested system is applied into a Generic Equipment Model (GEM) for exchanging specified messages(SECS-II), which are encoded into XML presentation, between Equipment and Host via the web sorrel. In this paper, the designed system architecture and its soap components are presented.

• The Journal of the Korea Contents Association
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• v.8 no.4
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• pp.48-54
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• 2008

This paper analyzes cost tradeoffs between memory usage and computation for window-based operators in data stream environments. It identifies generic operator network constructs, and sets up a cost model for the estimation of the expected memory reduction and the computation overheads when window memory relocations are applied to each operator network construct. This cost model helps to identify the utility of window memory relocations. It also helps to apply window memory relocation to improve a query execution plan to save memory usage. The proposed approach contributes to expand the scope of query processing and optimization in data stream environments. It also provides a basis to develop a cost estimation model for the query optimization using window memory relocations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.669-674
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• 2018

Data augmentation is an efficient way to reduce overfitting on models and to improve a performance supplementing extra data for training. It is more important in deep learning based industrial machine vision. Because deep learning requires huge scale of learning data to learn a model, but acquisition of data can be limited in most of industrial applications. A very generic method for augmenting image data is to perform geometric transformations, such as cropping, rotating, translating and adjusting brightness of the image. The effectiveness of data augmentation in image classification has been reported, but it is rare in defect inspections. We explore and compare various basic augmenting operations for the metal surface defects. The experiments were executed for various types of defects and different CNN networks and analysed for performance improvements by the data augmentations.