• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.114-126
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• 2011

Video streaming over wireless multi-hop networks(WMNs) contains the following challenges from channel fading and variable bandwidth of wireless channel, and it cause degradation of video streaming performance. To overcome the challenges, currently, WMNs can use Forward Error Correction (FEC) mechanism. In WMNs, traditional FEC schemes, E2E-FEC and HbH-FEC, for video streaming are applied, but it has long transmission delay, high computational complexity and inefficient usage of resource. Also, to distinguish network status in streaming path, it has limitation. In this paper, we propose monitoring-based coordination of network-adaptive hop-to-end(H2E) FEC scheme. To enable proposed scheme, we apply a centralized coordinator. The coordinator has observing overall monitoring information and coordinating H2E-FEC mechanism. Main points of H2E-FEC is distinguishing operation range as well as selecting FEC starting node and redundancy from monitored results in coordination. To verify the proposed scheme, we perform extensive experiment over the OMF(Orbit Measurement Framework) and IEEE 802.1la-based multi-hop WMN testbed, and we carry out performance improvement, 17%, from performance comparison by existing FEC scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3B
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• pp.85-94
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• 2005

In this paper, we propose a reliable high-speed UDP-based media transport with an adaptive error control. The proposed adaptive transport scheme controls the amount of redundancy by monitoring the network in order to adapt to network fluctuations efficiently. The feedback of receiver enables the sender to be aware of current reception status (i.e., rate and type of packet loss) and to estimate the expected network status. Based on this, the proposed transport attempts to enable reliable transport by adaptively controlling the amount of both whole sending rate and the ratio for adaptive FEC code. Experiment with high-speed network has been conducted to verify the performance of the proposed system that demonstrates the enhanced reliability of the proposed transport at the speed of up to several hundred Mbps.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.67-72
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• 2021

To ensure the safety and functionality of a railroad bridge, maintaining the integrity of the bridge via continuous structural health monitoring is important. However, most structural integrity monitoring methods proposed to date are based on modal responses which require the extracting process and have limited availability. In this paper, the applicability of the existing damage identification method based on free-vibration reponses to time-domain deflection shapes due to moving train load is investigated. Since the proposed method directly utilizes the time-domain responses of the structure due to the moving vehicles, the extracting process for modal responses can be avoided, and the applicability of structural health evaluation can be enhanced. The feasibility of the presented method is verified via a numerical example of a simple plate girder bridge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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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.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.7
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• pp.57-64
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• 2011

To adjust the difference of bandwidth between host interface and NAND flash memory, DRAM is adopted as the buffer management in SSD (Solid-state Disk). In this paper, we propose cost-effective techniques to enhance SSD performance instead of using expensive high bandwidth DRAM. The SSD data can be classified into three groups such as user data, meta data for handling user data, and FEC(Forward Error Correction) parity/ CRC(Cyclic Redundancy Check) for error control. In order to improve the performance by considering the features of each data, we devise a flexible burst control method through monitoring system and a page based FEC parity/CRC application. Experimental results show that proposed methods enhance the SSD performance up to 25.9% with a negligible 0.07% increase in chip size.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.859-866
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• 2010

Digital vessels can occur large a disaster at sea because vessels in fire and collision in case of certain unforeseen circumstances. In this paper, We propose digital vessel context monitoring system through risk analysis. We propose environment information analysis system using wireless sensor that have to acquire marine environment and context of marine digital vessel. For conducting simulation, we chose 300 data sets to train the neural network. As a result, we obtained about 96% accuracy for fire risk context and we obtained 88.7% accuracy for body of vessel risk context. To improve the accuracy of the system, we implement a FEC (Forward Error Correction) block. We implemented digital vessel context monitoring system that transmitted to diagnosis result in CDMA.