• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.591-596
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• 2019

In this paper, we implemented a deep learning operation structure with less influence of local PC performance. In general, the deep learning model has a large amount of computation and is heavily influenced by the performance of the processing PC. In this paper, we implemented deep learning operation using AWS and streaming server to reduce this limitation. First, deep learning operations were performed on AWS so that deep learning operation would work even if the performance of the local PC decreased. However, with AWS, the output is less real-time relative to the input when computed. Second, we use streaming server to increase the real-time of deep learning model. If the streaming server is not used, the real-time performance is poor because the images must be processed one by one or by stacking the images. We used the YOLO v3 model as a deep learning model for performance comparison experiments, and compared the performance of local PCs with instances of AWS and GTX1080, a high-performance GPU. The simulation results show that the test time per image is 0.023444 seconds when using the p3 instance of AWS, which is similar to the test time per image of 0.027099 seconds on a local PC with the high-performance GPU GTX1080.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.327-330
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• 2007

This article discusses a capacity planning method in QoS-guaranteed IP networks such as BcN (Broadband convergence Network). Since IP based networks have been developed to transport best-effort data traffic, the introduction of multi-service component in BcN requires fundamental modifications in capacity planning and network dimensioning. In this article, we present the key issues of the capacity planning in multi-service IP networks. To provide a foundation for network dimensioning procedure, we describe a systematic approach for classification and modeling of BcN traffic based on the QoS requirements of BcN services. We propose a capacity planning framework considering data traffic and real-time streaming traffic separately. The multi-service Erlang model, an extension of the conventional Erlang B loss model, is introduced to determine required link capacity for the call based real-time streaming traffic. The application of multi-service Erlang model can provide significant improvement in network planning due to sharing of network bandwidth among the different services.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.373-375
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• 2000

본 논문에서는 통합 멀티미디어 스트리밍 프레임워크(ISSA)의 주요 모듈인 전송 및 세션 관리자에 대한 개발 경험을 소개한다. ISSA(Integrated Streaming Service Architectur)[1][2]는 유니캐스팅/멀티캐스팅 환경의 VOD 시스템과 실시간 방송시스템(라이브캐스팅)과 같은 통합 멀티미디어 스트리밍 서비스 응용을 개발하기 위한 스트리밍 프레임워크이며 RTP(Real-Time Protocol)/RTCP(Real-Time Control Protocol)[3][4], RTSP 등의 표준 실시간 전송 프로토콜을 사용함으로써 사용자에게 범용성을 제공한다. ISSA는 다양한 형태의 미디어를 지원하며, 이기종 운영체제와 네트워크에 독립적이며, 실시간 멀티미디어 데이터베이스와 연동하여 사용자에게 데이터베이스 서비스를 제공할 수 있다. 세션 관리자는 미디어 채널의 생성과 제어를 담당하는 기능과 멀티미디어 데이터베이스를 위한 트랜잭션 전송기능을 담당하는 기능을 제공하며 각각 RTSP와 RTTP 프로토콜을 이용하여 구현되었다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.445-447
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• 2001

인터넷 이용의 기하급수적인 증가와 정보통신 기술의 비약적인 발전으로 인하여 과거의 정적인 정보의 표현 및 전달에 비해 택스트, 이미지, 오디오 및 비디오가 결합된 멀티미디어 서비스의 요구가 급격히 증가하고 있다. 특히 최근 초고속 통신망의 발달로 인한 인터넷 망 (network)상에서의 방송과 통신이 융합되는 추세 또한 가속화되고 있다. 이에 스트리밍(Streaming) 기술을 기반으로 한 멀티미디어 데이터의 서비스와 데이터 정보의 푸시(push) 기술을 결합하여 인터넷과 고속망상에서의 원활한 실시간 전송 서비스를 위하여 3-tier 방식의 인터넷 방송 서버 시스템을 제시하였다. 본 논문에서는 JMF(Java Media Framework)와 RTP를 이용하여 전체 미디어 서비스를 제공하여, VOD(Video on Demand) 및 실시간 방송 서비스를 제공하는 분산 멀티미디어 스트리밍 전송 시스템을 구현하였다. VOD(Video on Demand) 및 실시간 방송 서비스를 제공하는 분산 멀티미디어 스트리밍 전송 시스템을 구현하였다. VOD 서버, 실시간 방송 서버 및 푸시 서버에서 멀티미디어 컨텐츠를 제공하고, 클라이언트로의 효율적이고 안정적인 서비스를 위하여 미들웨어에서의 부하분배 및 채널 정보의 관리를 가능하게 하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.247-249
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• 2001

최근 VOD(Video On Demand)나 AOD(Audio On demand) 서비스가 급증하면서 멀티미디어 스트리밍 기술에 대한 관심이 커지고 있다. 기존의 스트리밍 서비스는 정적으로 멀티미디어를 전송하므로 네트워크 환경의 대역폭(bandwidth) 변화에 대처하는데 한계가 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 실시간 스트리밍 서비스(RTSS; Real Time Streaming Service) 시스템을 제안한다. RTSS 시스템은 동화상의 끊김없는 전송보장을 위해 미디어 스케일링 방식을 사용하고, 대역폭 변화에 능동적으로 대처할 수 있도록 동적인 멀티미디어 전송을 이용한다. 기존의 스트리밍 서비스의 버퍼링 방식의 단점을 해결하기 위하여 본 논문에서는 프레임 조정방식과 화질 조정방식을 동시에 채택하여 적용함으로써 고화질이 보장되는 전송환경과 느린 회선에서도 끊김없이 안정적인 스트리밍 서비스를 제공받을수 있도록 한다.

• Journal of Information Science Theory and Practice
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• v.4 no.2
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• pp.6-20
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• 2016

In the last few years, a new type of synchronous social networking services (SNSs) has emerged—social live streaming services (SLSSs). Studying SLSSs is a new and exciting research field in information science. What information behaviors do users of live streaming platforms exhibit? In our empirical study we analyzed information production behavior (i.e., broadcasting) as well as information reception behavior (watching streams and commenting on them). We conducted two quantitative investigations, namely an online survey with YouNow users (N = 123) and observations of live streams on YouNow (N = 434). YouNow is a service with video streams mostly made by adolescents for adolescents. YouNow users like to watch streams, to chat while watching, and to reward performers by using emoticons. While broadcasting, there is no anonymity (as in nearly all other WWW services). Synchronous SNSs remind us of the film The Truman Show, as anyone has the chance to consciously broadcast his or her own life real-time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.524-526
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• 2022

Considering QoS only considering real-time multimedia service, it is possible to adjust the number of terminals and ensure them appropriately, but this study considers complex services considering real-time multimedia service and general data service. Since the amount of physical network resources is limited, the guarantee of the desired QoS can not be achieved unless the appropriate CAC is done. However, given the traffic profile and QoS spec of the entire network resource and the current service being provided, and the traffic profile and QoS spec of the newly requested service, it is quite difficult to determine exactly whether the new service request is acceptable from this. To do this, it is necessary to study in various directions from mathematical analysis to various simulations and statistical research based on data obtained from actual network operation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10A
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• pp.886-895
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• 2005

As the networked media technology have been grown in recent, there have been many research works to deliver high-quality video such as HDV and HDTV over the Internet. To realize high-quality media service over the Internet, however, the network adaptive streaming scheme is required to adopt to the dynamic fluctuation of underlying networks. In this paper, we design and implement the network-adaptive HD(high definition) MPEG-2 streaming system employing the frame-based prioritized packetization. Delivered video is inputted from the JVC HDV camera to the streaming sewer in real-time. It has a bit-rate of 19.2 Mbps and is multiplexed to the MPEG-2 TS (MPEG-2 MP@HL). For the monitoring of network status, the packet loss rate and the average jitter are measured by using parsing of RTP packet header in the streaming client and they are sent to the streaming server periodically The network adaptation manager in the streaming server estimates the current network status from feedback packets and adaptively adjusts the sending rate by frame dropping. For this, we propose the real-time parsing and the frame-based prioritized packetization of the TS packet. The proposed system is implemented in software and evaluated over the LAN testbed. The experimental results show that the proposed system can enhance the end-to-end QoS of HD video streaming over the best-effort network.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.54-58
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• 2007

A real-time frame-layer rate control algorithm with a token bucket traffic shaper is proposed for distortion variation minimization. The proposed rate control method uses a non-iterative optimization method for low computational complexity, and performs bit allocation at the frame level to minimize the average distortion over an entire sequence as well as variations in distortion between frames. The proposed algorithm does not produce time delay from encoding, and is suitable for real-time low-complexity video encoder. Experimental results indicate that the proposed control method provides better visual and PSNR performances than the existing rate control method.

• Journal of KIISE
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• v.44 no.1
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• pp.84-94
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• 2017

Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this paper, we propose a Squall framework using Time-triggered Message-triggered Object (TMO) technology, a model that is widely used for processing real-time big data. Moreover, we provide a description of Squall framework and its operations under a single node. TMO is an object model that supports the non-regular real-time processing method for certain conditions as well as regular periodic processing for certain amount of time. A Squall framework can support the real-time event stream of big data and micro-batch processing with outstanding performances, as compared to Apache storm and Spark Streaming. However, additional development for processing real-time stream under multiple nodes that is common under most frameworks is needed. In conclusion, the advantages of a TMO model can overcome the drawbacks of Apache storm or Spark Streaming in the processing of real-time big data. The TMO model has potential as a useful model in real-time big data processing.