• 한국통신학회논문지
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• 제18권8호
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• pp.1176-1189
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• 1993

본 논문에서는 객체 지향 멀티미디어 데이타베이스 모델 하에서 다중 키워드를 이용한 검색 기법을 제안하였다. 멀티미디어 데이타 검색에서의 부분 매칭 문제점을 가급적 줄이기 위한 다중 키워드 등록 띤 검색알고리즘을 개발하였으며, 이를 위해 적절한 탐색 테이블의 저장 구조를 설계하였다. 또한 미디어 데이타 화일을 B+ 트리로 구성하여 검색 시간이 일정하도록 하였다.

• 한국전자거래학회지
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• 제1권1호
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• pp.159-171
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• 1996

Nowadays, multimedia and Hypermedia become hot topics in information industry. Due to high capacity of media storage and fast communication network, it is possible to exchange text data as well as image, moving picture and voice. Especially to apply hypermedia under CALS standard environment, the relation between international standard and CALS standard needs to be considered. This study introduces conceptual background and processing model of HyTime (Hypermedia Time-based Structuring Language) which is a specification of hypermedia exchange, Hyper ODA (Hyper Open Document Architecture) which is a major multimedia communication basis, MMCF (Multimedia Communication Forum), AHM(Amsterdam Hypermedia Model), and DSRM(DAVIC System Reference Model) reference model which helps determination of hypermedia communication specification Although they are international standard, provisional standard or non-standard, it discusses the Possibility of adopting them as CALS standard. Hence, this paper chooses the best recommend for CALS among these models.

• 한국정보처리학회논문지
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• 제7권2S호
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• pp.711-720
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• 2000

BADA-IV/XML is a system designed specifically for managing XML. Documents and is essential to various electronic document applications as a fundamental system. BADA-IV/XML supports all of aspects of data model, querying and manipulation operations for managing XML documents. This paper provides an overview of these aspects of the BADA-IV/XML, as well as defines schema classes for stroing, querying and maintaining hierarchical semantics of multimedia documents and structural semantics of complex documents linked with each other, Also a multimedia document query language is designed and implemented to support essential operations for efficient searching and managing multimedia documents. Finally, some simulation results show the performance of the paged VF(Virtual Fragmentation) model and the search model using element's identifier as compared with a general model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권6호
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• pp.1707-1717
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• 2012

Digital textbooks, considered as core textbooks tailored to the abilities and interests of students, provide them with a combination of textbooks, reference books, workbooks, dictionaries and multimedia contents. In this paper, we employ a modified version of the Technology Acceptance Model(TAM), a model which has been widely used and empirically validated to explain why individuals use a particular information technology(IT). This paper proposes that additional variables, such as educational impacts, contents quality, interaction and enjoyment, enhance our understanding of behaviors of pilot school teachers in using multimedia-based digital textbooks as a substitute for traditional paper-version of textbooks. A structural equation model is employed with survey data from 157 pilot school teachers in pilot program supported by Korean government and KERIS(Korea Education & Research Information Service), to empirically assess the strength of the relationships in the proposed model. It is hoped that the results from this study provide meaningful insight into the development and delivery of high quality digital textbooks.

• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.913-922
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• 2019

The Internet of Things (IoT) envisions smart objects collecting and sharing data at a massive scale via the Internet. One challenging issue is how to disseminate data to relevant data consuming objects efficiently. In such a massive IoT network, Mission critical data dissemination imposes constraints on the message transfer delay between objects. Due to the low power and communication range of IoT objects, data is relayed over multi-hops before arriving at the destination. In this paper, we propose a quorum-based adaptive dissemination algorithm (QADA) for the critical data in the monitoring-based applications of massive IoTs. To design QADA, we first design a new stepped-triangular grid structures (sT-grid) that support data dissemination, then construct a triangular grid overlay in the fog layer on the lower IoT layer and propose the data dissemination algorithm of the publish/subscribe model that adaptively uses triangle grid (T-grid) and sT-grid quorums depending on the mission critical in the overlay constructed to disseminate the critical data, and evaluate its performance as an analytical model.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2002년도 추계학술발표논문집
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• pp.359-362
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• 2002

XML DBMS는 XML 문서의 직접적인 접근과 문서의 부분, 문서와 그 문서의 부분들을 통한 질의를 할수 있는 능력을 가져야 한다. 이러한 XML DBMS의 사용은 복잡한 계층적 관계의 범위로부터 데이터를 얻어내는데 특히 유용하게 사용될 수 있다. 일반적으로 XML DBMS를 구축하기 위해서는 다른 DBMS 위에 덧붙이거나, 처음부터 새로 만들거나, 새 제품을 구입할 수 있을 것이다. 처음부터 생성하는 것은 메모리 할당과 문서 조각의 효율적인 검색을 신중히 생각해야 한다. 또한 XML DBMS를 구입하는 것은 기존의 자료가 없고, 프로젝트가 현존하는 XML DBMS를 원할 경우에는 가장 좋은 솔루션이다. 다른 DBMS 위에 XML DBMS를 구축하는 것은 빠르게 구축할 수 있고, 하부를 구성하는 DBMS의 저장소를 사용할 수 있다는 이점이 있다. 또한 현재 존재하는 애플리케이션들과 기존의 자료와 통합이 쉬울 수 있다. 본 논문은 XML DBMS를 Generic Data Model을 기반으로 하여 설계 및 구현을 하였다. 이는 관계형 데이터베이스에 XML 문서를 저장, 삭제, 검색 할 수 있는 XML Repository system 위에 XML 어댑터를 접목시키는 방법을 사용하였다. 이 방법은 비용이 많이드는 XML DBMS를 사용하지 않고, 기존에 사용되는 DBMS를 이용하여 구축하기 때문에 적은 비용으로도 XML DBMS를 구현할 수 있다.

• Journal of Multimedia Information System
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• 제8권3호
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• pp.183-190
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• 2021

In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

• 한국멀티미디어학회논문지
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• 제25권7호
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• pp.903-911
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• 2022

A shrimp quality detection model using YOLOv4 deep learning algorithm is designed, which is superior in terms of network architecture, data processing and feature extraction. The shrimp images were taken and data expanded on their own, the LableImage platform was used for data annotation, and the network model was trained under the Darknet framework. Through comparison, the final performance of the model was all higher than other common target detection models, and its detection accuracy reached 93.7% with an average detection time of 47 ms, indicating that the method can effectively detect the quality of shrimp in the production process.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2000년도 춘계종합학술대회
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• pp.325-330
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• 2000

최근 들어, 컴퓨터 및 관련 기술의 발전은 통신망, 정보 처리 형태 및 통신 서비스의 발전을 동반하여 정보화 사회로의 진입을 가속화시키고 있다. 특히 인터넷의 WWW 등장으로 인하여 전자 신문, 전자 컨퍼런스, 멀티미디어 문서 검색 등 헤아릴 수 없는 많은 서비스들이 손쉽게 개발되었고, 이는 일반인들의 정보 통신망의 수요를 더 한층 증가시키고 있으며 실제적인 정보화 시대를 앞당기고 있다. 최근 연구되고 있는 주요 기반 기술들로는 정보의 형태가 멀티미디어 화에 따른 내용 기반 정보검색 기술 연구가 활발히 진행되고 있고, 멀티미디어 정보가 방대함에 따라 기존의 하드디스크 대신에 3차 저장 장치를 하부 저장 시스템의 일부로 확장시키는 연구가 한창이다. 또한 정보 검색 분야 외에도 정보 구축 기술로, 정보들을 체계적으로 관리할 수 있도록 모델링 기법에 대해 기술한다.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1407-1416
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• 2018

The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learning model for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice's similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice's similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone.