• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 춘계학술발표대회논문집
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• pp.154-158
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• 2003

RUP의 Unified Process Model에는 Use Case Model, Analysis Model, Design Model, Deployment Model, Implementation model, Test Model의 6가지 모델이 있다. 이 모델들은 모두 일관성이 있다. 한 모델에서 나오는 요소들은 전, 후 단계에서 다른 모델들과 Trace Dependencies를 갖는다. 이러한 관계들은 각각의 요소들 사이의 진행, 기록 관계를 나타낸다 그러나 일반적인 데이터간의 관계와 데이터 모델 설계는 이러한 관계없이 설계되어져 전체적인 일관성을 이루지 못 하였다 본 논문에서는 이러한 관계를 유지하면서 요구사항에 맞는 데이터 모델을 설계하고자 한다. 따라서 본 논문에서는 대학 종합정보시스템 구축의 일부분인 자산관리 시스템을 분석하여 데이터 모델을 제시한다.

• 한국멀티미디어학회논문지
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• 제21권7호
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• pp.779-786
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• 2018

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.245-254
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• 2021

We proposed the research on an analysis and prediction model that allows the identification of outliers or abnormality in the data followed by effective and rapid imputation of missing values was conducted. This model is expected to analyze efficiently the problems in the data based on the calibrated raw data. As a result, a system that can adequately utilize the data was constructed by using the introduced KNN + MLE algorithm. With this algorithm, the problems in some of the existing KNN-based missing data imputation algorithms such as ignoring the missing values in some data sections or discarding normal observations were effectively addressed. A comparative evaluation was performed between the existing imputation approaches such as K-means, KNN, MEI, and MI as well as the data missing mechanisms including MCAR, MAR, and NI to check the effectiveness/efficiency of the proposed algorithm, and its superiority in all aspects was confirmed.

• 한국멀티미디어학회논문지
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• 제1권2호
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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.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1300-1312
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• 2019

BSTRACT In this paper, PM10 forecast model using DNN(Deep Neural Network) is developed for Seoul region. The previous Julian forecast model has been developed using weather and air quality data of Seoul region only. This model gives excellent results for accuracy and false alarm rates, but poor result for POD(Probability of Detection). To solve this problem, an WA(Wide Area) forecasting model that uses Chinese data is developed. The data is highly correlated with the emergence of high concentrations of PM10 in Korea. As a result, the WA model shows better accuracy, and POD improving of 3%(D+0), 21%(D+1), and 36%(D+2) for each forecast period compared with the Julian model.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2001년도 춘계학술발표논문집
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• pp.192-195
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• 2001

MPEG-4 content is streaming media that are composed of different types of media objects, organized in a hierarchical fashion. This paper proposes scene composition model for authoring MPEG-4 contents which can support object based interactions. And we have developed MPEG-4 contents authoring tool applied the proposed scene composition model as intermediate data structure. Particularly, for supporting interoperability between multimedia contents, the scene composition model should be used independent of file format. So visual scene composed of media objects on the from of scene composition tree can be transformed variable data format including BIFS, scene description from proposed by MPEG-4 standard and also support the extension of capability.

• Journal of Multimedia Information System
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• 제4권3호
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• pp.115-122
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• 2017

Over the years, there has been much research in blended learning. However, research regarding its use and evaluation is inconsistent, not following any specific evaluation method, and may not be applicable to local students. In this research, a case study was conducted to evaluate the environment based on three levels of Kirkpatrick's model. Methodological triangulation was the principle of data collection used in which multiple sources of evidence were triangulated to provide insights into this study. Instruments used include surveys, interviews, questionnaires and pre- and post-tests that are guided by Kirkpatrick's model. The results revealed that students were positive with the learning environment. Students enjoyed learning with multimedia and motivated to learn as well as engaged in the environment. The tests showed significant difference in their learning. Students also perceived that they have transferred their learning from face-to-face lecture into problem-based learning and learning outcome. This research contributes to the field by providing deeper insights into assessments in multimedia-based blended learning environment and empirical evidence on views, attitudes, learning and knowledge transfer of students in higher education.

• 한국멀티미디어학회논문지
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• 제23권6호
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• pp.747-755
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• 2020

In this paper, we deal with outlier data problems that occur when constructing a PM_2.5 fine dust forecasting system using a neural network. In general, when learning a neural network, some of the data are not helpful for learning, but rather disturbing. Those are called outlier data. When they are included in the training data, various problems such as overfitting occur. In building a PM_2.5 fine dust concentration forecasting system using neural network, we have found several outlier data in the training data. We, therefore, remove them, and then make learning 3 ways. Over_outlier model removes outlier data that target concentration is low, but the model forecast is high. Under_outlier model removes outliers data that target concentration is high, but the model forecast is low. All_outlier model removes both Over_outlier and Under_outlier data. We compare 3 models with a conventional outlier removal model and non-removal model. Our outlier removal model shows better performance than the others.

• 한국멀티미디어학회논문지
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• 제22권7호
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• pp.749-758
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• 2019

This paper proposes a data augmentation algorithm to improve the performance of DNN(Deep Neural Network) based speech enhancement. Many deep learning models are exploring algorithms to maximize the performance in limited amount of data. The most commonly used algorithm is the data augmentation which is the technique artificially increases the amount of data. For the effective data augmentation algorithm, we used a formant enhancement method that assign the different weights to the formant frequencies. The DNN model which is trained using the proposed data augmentation algorithm was evaluated in various noise environments. The speech enhancement performance of the DNN model with the proposed data augmentation algorithm was compared with the algorithms which are the DNN model with the conventional data augmentation and without the data augmentation. As a result, the proposed data augmentation algorithm showed the higher speech enhancement performance than the other algorithms.