• 대한임베디드공학회논문지
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• 제4권2호
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• pp.79-83
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• 2009

Streaming Technology can support the real-time playback without downloading and storing multimedia data in local HDD. So, client browser or plug-in can represent multimedia data before the end of file transmission using streaming technology. Recently, the demand for efficient real-time playback and transmission of large amounts of multimedia data is growing rapidly. But most users' connections over network are not fast and stable enough to download large chunks of multimedia data. In this paper, we propose an intelligent IP streaming system based on personalized media service. The proposed IP streaming system enables users to get an intelligent recommendation of multimedia contents based on the user preference information stored on the streaming server or the home media server. The supposed intelligent IP streaming system consists of Server Metadata Agent, Pumping Server, Contents Storage Server, Client Metadata Agent and Streaming Player. And in order to implement the personalized media service, the user information, user preference information and client device information are managed under database concept. Moreover, users are assured of seamless access of streamed content event if they switch to another client device by implementing streaming system based on user identification and device information. We evaluate our approach with manufacturing home server system and simulation results.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2010년도 하계학술대회
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• pp.360-363
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• 2010

Streaming Technology can support the real-time playback without downloading and storing multimedia data in local HDD. So, client browser or plug-in can represent multimedia data before the end of file transmission using streaming technology. Recently, the demand for efficient real-time playback and transmission of large amounts of multimedia data is growing rapidly. But most users' connections over network are not fast and stable enough to download large chunks of multimedia data. In this paper, we propose an intelligent IP streaming system based on personalized media service. The proposed IP streaming system enables users to get an intelligent recommendation of multimedia contents based on the user preference information stored on the streaming server or the home media server. The supposed intelligent IP streaming system consists of Server Metadata Agent, Pumping Server, Contents Storage Server, Client Metadata Agent and Streaming Player. And in order to implement the personalized media service, the user information, user preference information and client device information are managed under database concept. Moreover, users are assured of seamless access of streamed content event if they switch to another client device by implementing streaming system based on user identification and device information. We evaluate our approach with manufacturing home server system and simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.515-517
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• 2006

Recently, there is a rapidly growing demand for efficient real-tine playback and transmission of large amounts of multimedia data. But most users' connections are not fast enough to download large chunks of multimedia data. Therefore a streaming technology is needed in which users enable the real-time playback of multimedia date without having to download the whole of the multimedia date. In this paper, we propose a personalized IP streaming system. The proposed IP streaming system enables users to got an intelligent recommendation of multimedia contents based on the user preference information stored on the streaming server or the home media server. Moreover, users are assured of seamless access of streamed content event if they switch to another client device by implementing streaming system based on user identification and device information. We evaluate our approach with simulation results.

• 한국지능시스템학회논문지
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• 제20권1호
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• pp.94-100
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• 2010

데이터 마이닝(Data Mining)은 환경으로부터 수집된 데이터에서 패턴을 추출하고 의미 있는 정보를 발견하기 위하여 주로 사용된다. 하지만, 기존의 방법은 데이터의 수집이 완료된 상태에서 분석하는 것을 기반으로 하고 있으며, 시간의 흐름에 따른 패턴의 변화를 반영하기 어렵다. 본 논문은 연속성(Continuity data), 대량성(Large scale) 그리고 패턴의 가변성(Changed pattern)과 같은 특성을 가지는 스트림 데이터(Stream Data)의 분석을 위한 스트리밍 의사결정 나무(Streaming Decision Tree : SDT) 방법을 소개한다. SDT는 연속적으로 발생하는 데이터를 블록으로 정의하고, 각 블록은 의사결정나무 학습 방법을 이용하여 규칙을 추출한다. 추출된 규칙은 발생 시간, 빈도 그리고 모순 등을 고려하여 결합하였다. 실험에서는 시계열 데이터를 이용하여 분석하였고, 적절한 결과를 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3465-3479
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• 2017

Information-Centric Vehicular Ad Hoc Network (IC-VANET) is a promising network architecture for the future intelligent transport system. Video streaming applications over IC-VANET not only enrich infotainment services, but also provide the drivers and pedestrians real-time visual information to make proper decisions. However, due to the characteristics of wireless link and frequent change of the network topology, the packet loss seriously affects the quality of video streaming applications. In this paper, we propose a REceiver-Driven loss reCOvery Mechanism (REDCOM) to enhance video dissemination over IC-VANET. A Markov chain based estimation model is introduced to capture the real-time network condition. Based on the estimation result, the proposed REDCOM recovers the lost packets by requesting additional forward error correction packets. The REDCOM follows the receiver-driven model of IC-VANET and does not require the infrastructure support to efficiently overcome packet losses. Experimental results demonstrate that the proposed REDCOM improves video quality under various network conditions.

• 한국통신학회논문지
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• 제31권3C
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• pp.293-301
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• 2006

본 논문에서는 Ubi-Home에서의 지능적 멀티미디어 스트리밍을 지원하는 콘텐츠 보호 및 관리 시스템(UHSMS-DRM: Ubi-Home Smart Multimedia Streaming-Digital Right Management)을 설계 및 구현하였다. 제안 시스템은 Ubi-Home에서 디지털 콘텐츠의 저작권 보호 및 관리를 위한 유연한 유통 플랫폼을 제공하며, PC, STB, PDA, Portable device 등 다양한 디바이스의 인증을 통해 정당한 사용자에게 Multimedia Steaming Service를 제공한다. 그리고, 도메인 인증개념을 적용하여 Ubi-Home의 모든 디바이스에 대한 라이센스 관리의 효율성을 높인다. 또한, Ubi-Home에서 intelligent Service를 위해 사용자의 위치를 인지하기 위한 알고리즘을 제안 및 적용한다.

• 한국지능시스템학회논문지
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• 제19권4호
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• pp.485-492
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• 2009

모바일 서비스 환경에서 시간 동기화에 의해 생성된 패턴들은 데이터 스트리밍으로 인하여 인스턴스 값들이 다르게 스트리밍 된다. 본 논문에서는 유연한 클러스터링을 지원하기 위해 가변클러스터링 관리 기법을 제안하며, 이 구조는 다중 데이터 스트리밍을 동적으로 관리하도록 지원한다. 제안되는 기법은 일반적인 스트리밍기법과 달리 데이터 스트림 환경에서 동기화를 효율적으로 지원하는 기능을 수행하며, 구조적 표현단계와 적합성 표현단계를 거쳐 클러스터링 스트리밍이 관리된다. 구조적 표현 단계는 레벨정합과 누적정합을 수행하여 스트림 구조가 표현되며, 동적 세그먼트와 정적세그먼트 관리를 통해서 클러스터링 관리가 가변적으로 수행되도록 하였다. 제안된 기법의 성능 평가를 위해서 k-means 기법, C/S 서버기법 그리고 CDN 기법과 시뮬레이션평가를 수행하였으며 그 결과 제안된 기법의 성능이 효율적임을 알 수 있었다.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.188-198
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• 2018

With recent development of high-speed wide-area wireless networks and wide spread of highperformance wireless devices, the demand on seamless video streaming services in Long Term Evolution (LTE) network environments is ever increasing. To meet the demand and provide enhanced Quality of Experience (QoE) with mobile users, the Dynamic Adaptive Streaming over HTTP (DASH) has been actively studied to achieve QoE enhanced video streaming service in dynamic network environments. However, the existing DASH algorithm to select the quality of requesting video segments is based on a procedural algorithm so that it reveals a limitation to adapt its performance to dynamic network situations. To overcome this limitation this paper proposes a novel quality selection mechanism based on a Deep Q-Network (DQN) model, the DQN-based DASH ABR($DQN_{ABR}$) mechanism. The $DQN_{ABR}$ mechanism replaces the existing DASH ABR algorithm with an intelligent deep learning model which optimizes service quality to mobile users through reinforcement learning. Compared to the existing approaches, the experimental analysis shows that the proposed solution outperforms in terms of adapting to dynamic wireless network situations and improving QoE experience of end users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.579-603
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• 2018

In this paper we propose a QoS-enhanced intelligent stochastic optimal fair real-time packet scheduler, QUEST, for 4G LTE traffic in routers. The objective of this research is to maximize the system QoS subject to the constraint that the processor utilization is kept nearly at 100 percent. The QUEST has following unique advantages. First, it solves the challenging problem of starvation for low priority process - buffered streaming video and TCP based; second, it solves the major bottleneck of the scheduler Earliest Deadline First's failure at heavy loads. Finally, QUEST offers the benefit of arbitrarily pre-programming the process utilization ratio.Three classes of multimedia 4G LTE QCI traffic, conversational voice, live streaming video, buffered streaming video and TCP based applications have been considered. We analyse two most important QoS metrics, packet loss rate (PLR) and mean waiting time. All claims are supported by discrete event and Monte Carlo simulations. The simulation results show that the QUEST scheduler outperforms current state-of-the-art benchmark schedulers. The proposed scheduler offers 37 percent improvement in PLR and 23 percent improvement in mean waiting time over the best competing current scheduler Accuracy-aware EDF.