• 한국산업정보학회논문지
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• 제5권1호
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• pp.16-24
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• 2000

분산 멀티미디어 문서 시스템은 네트워크로 연결된 여러 서버에 있는 미디어들은 검색하여 제시된 시간 관계에 따라 미디어들을 프리젠테이션한다. 효과적인 프리젠테이션을 위해서는 동기화가 지원되어야 하며, 특히, 분산 환경에서의 프리젠테이션은 네트워크 대역폭과 지연시간 등에 의해 영향을 받기 때문에, 그러한 요소들이 고려되어 동기화가 지원되어야 한다. 본 연구에서는 미디어들이 각 서버로부터 전송 될때 네트워크의 상태와 자원을 검사하여 그에 따른 변화를 서버에 피드백시켜, 서버로부터 전송되는 데이터의 양을 조절함으로서 동기화를 지원하는 분산 멀티미디어 프리젠테이션 모델을 제안한다.

• 한국멀티미디어학회논문지
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• 제6권7호
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• pp.1219-1230
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• 2003

본 논문에서는 음성, 스트림-데이터 및 패킷-데이터 트래픽이 혼합된 환경에서 전체 채널을 음성 우선 채널과 음성 비우선 채널로 구분하여 공유하는 CDMA 시스템을 마르코프 체인으로 해석한다. 실시간 전송이 요구되는 음성 서비스는 음성 우선 채널에 대해서는 선점 우선권(preemptive priority)을 가지고 사용하며, 음성 비우선 채널에 대해서는 스트림-데이터 서비스와 동등한 순위를 지니고 사용한다. 지연에 다소 민감하지 않은 스트림-데이터 서비스에 대해서는 버퍼를 사용한다. 또한, 전자우편 등과 같은 패킷-페이터 서비스는 타 서비스에 의해 사용되고 있지 않는 잔여 채널을 이용하여 최선의 시도를 통해 전송하도록 한다. 본 논문에서는 채널의 선점 우선권을 지니는 음성 서비스 및 버퍼가 적용된 스트림-데이터 서비스를 2-차원 마르코프 체인으로 모델링하고 분석하였으며, 잔여 용량(residual capacity)을 기초로 하여 패킷 데이터 트래픽에 대한 수학적 분석을 수행하였다.

• 기술사
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• 제34권4호
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• pp.36-40
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• 2001

Multimedia service via satellite Is supported voice, data, Image and video signals. The representation case model of satellite multimedia are satellite TV. satellite Internet. In the early 1990s, satellite communication and broad casting services successfully expanded form C/Ku band to Ka band. The benefits of operation at Ka-band are greater bandwidth available to accommodate the increased demand for high-speed Information exchange. By the early years of the 21s1 century, millions of households worldwide with dual Ku / Ka-band dishes Satellite multimedia systems receive hundreds of TV channels, originating from around the world, and delivering entertainment, information and education. Many Ku-band satellites have been ordered, but few Ka-band systems are moving into production. So Ka-band systems are characterized that low-cost access to low and high peed, two-way voice, data, and video communications.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1345-1360
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• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.1059-1065
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• 2020

In hospitals, nurses are subjectively determining the urine status to check the kidneys and circulatory system of patients whose statuses are related to patients with kidney disease, critically ill patients, and nursing homes before and after surgery. To improve this problem, this paper proposes a urine spectrum analysis system which clusters urine test results based on a hybrid machine learning model consists of unsupervised learning and supervised learning. The proposed system clusters the spectral data using unsupervised learning in the first part, and classifies them using supervised learning in the second part. The results of the proposed urine spectrum analysis system using a mixed model are evaluated with the results of pure supervised learning. This paper is expected to provide better services than existing medical services to patients by solving the shortage of nurses, shortening of examination time, and subjective evaluation in hospitals.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1347-1355
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• 2019

Deep learning approach based on convolution neural network (CNN) has extensively studied in the field of computer vision. However, periocular feature extraction using CNN was not well studied because it is practically impossible to collect large volume of biometric data. This study uses the ResNet model which was trained with the ImageNet dataset. To overcome the problem of insufficient training data, we focused on the training of multi-layer perception (MLP) having simple structure rather than training the CNN having complex structure. It first extracts features using the pretrained ResNet model and reduces the feature dimension by principle component analysis (PCA), then trains a MLP classifier. Experimental results with the public periocular dataset UBIPr show that the proposed method is effective in person authentication using periocular region. Especially it has the advantage which can be directly applied for other biometric traits.

• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.225-234
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• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.