• Journal of KIISE:Information Networking
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• v.36 no.6
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• pp.492-504
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• 2009

The delivery of multimedia that efficiently adapts its bit-rate to changing network characteristics and conditions is one of the important challenging tasks in the design of today's real-time multimedia streaming systems such as IPTV, Mobile IPTV and so on. In these work, the primary focus is on network congestion, to improve network stability and inter-protocol fairness. However, these existing works have problems which do not support QoE (Quality of Experience), because they did not consider essential characteristics of contents playback such as the media continuity. In this paper, we propose a novel rate control scheme for improving the QoE of multimedia streaming service in the Internet congestion, called NCAR (Network and Client-Aware Rate control), which is based on network-aware congestion control and client-aware flow control scheme. Network-aware congestion control of the NCAR offers an improving reliability and fairness of multimedia streaming, and reduces the rate oscillation as well as keeping high link utilization. Client-aware flow control of NCAR offers a removing the media discontinuity and a suitable receiver buffer allocation, and provides a good combination of low playback delay. Simulation results demonstrate the effectiveness of our proposed schemes.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.119-125
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• 2015

This research has conducted high availability system modeling to assure the reliability of shipboard combat systems. Shipboard combat system is a way for efficient execution of duty and a crucial battlefield management system that determines the outcome of battle in the modern war. Especially in regard to a network-centric operational environment in the future, even 1% of malfunction can lead to fatal consequences for the outcome of war. So combat system should be designed by high availability system which is a "always-on" service. In this point of view, This work describes an architecture-based various high availability model and proposed alternative high available systems that can achieve more than 99.9999% accuracy at a minimum. This paper also provides an applicable model with which system engineers analyze out system failure and recovery process by employing computerized tools.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.425-436
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• 2009

In recent years, the user demands have increased for multimedia service of high quality over the broadband convergence network. These rising demands for high quality multimedia service led the popularization of various user terminals and large scale display equipments, which needs a variety type of QoS (Quality of Service). In order to support demands for QoS, numerous research projects are in progress both from the perspective of network as well as end system; For example, at the network perspective, QoS guaranteeing by improving of internet performance such as Active Queue Management, while at the end system perspective, SVC (Scalable Video Coding) encoding scheme to guarantee media quality. However, existing AQM algorithms have problems which do not guarantee QoS, because they did not consider the essential characteristics of video encoding schemes. In this paper, it is proposed to solve this problem by deploying the TS- AQM (Temporal Scalability Active Queue Management) which employs the differentiated packet dropping for dependency of the temporal level among the frames, based on SVC encoding characteristics by exploiting the TID (Temporal ID) field of the SVC NAL unit header. The proposed TS-AQM guarantees multimedia service quality through video decoding reliability for SVC streaming service, by differentiated packet dropping when congestion exists.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.5 s.347
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• pp.35-44
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• 2006

Reflective semiconductor optical amplifiers(R-SOAs) were designed with high gain, wide optical bandwidth, high thermal reliability and wide modulation bandwidth in TO-can package for the transmitter of wavelength division multiplexed-passive optical network(WDM-PON) application. Double trench structure and current block layer were introduced in designing the active layer of R-SOA to enable high speed modulation. The injection power requirement and the viable temperature range of WDM-PON system are experimentally analysed in based on Amplified Spontaneous Emission(ASE)-injected R-SOAs. The effect of the different injection spectrum in the gain-saturated R-SOA was experimentally characterized based on the measurements of excessive intensity noise, Q factor, and BER. The proposed spectral pre-composition method reduces the bandwidth of injection source below the AWG bandwidth and thereby avoids spectrum distortion impeding the intensity noise reduction originated from the amplitude squeezing.

• Tunnel and Underground Space
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• v.19 no.3
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• pp.203-212
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• 2009

The prediction of near field behavior around an underground high-level radioactive waste repository is important for the repository design as well as the safety assessment. In this study, a sensitivity analysis for seven parameters consisted of design parameters and material properties was carried out using a three-dimensional finite difference code. From the sensitivity analysis, it was found that the effects of borehole spacing, tunnel spacing, cooling time and rock thermal conductivity were more significant than the other parameters. For getting a statistical distribution of buffer and rock temperatures around the repository, an artificial neural network, backpropagation, was applied. The reliability of the trained neural network was tested with the cases with randomly chosen input parameters. When the parameter variation is within ${\pm}10%$, the prediction from the network was found to be reliable with about a 1% error. It was possible to calculate the temperature distribution for many cases quickly with the trained neural network. The buffer and rock temperatures showed a normal distribution with means of $98^{\circ}C$ and $83.9^{\circ}C$ standard deviations of $3.82^{\circ}C$ and $3.67^{\circ}C$, respectively. Using the neural network, it was also possible to estimate the required change in design parameters for reducing the buffer and rock temperatures for $1^{\circ}C$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.18-25
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• 2020

With the development of water internet technology, data communication between objects is expanding. Research related to data communication technology between vehicles that incorporates related technologies into vehicles has been actively conducted. For data communication between mobile terminals, data stability, reliability, and real-time performance must be guaranteed. The 5 GHz Wi-Fi band, which is advantageous in bandwidth, communications speed, and wireless saturation of the wireless network, was selected as the data communications network between vehicles. This study analyzes how to design and implement a 5 GHz Wi-Fi network in a vehicle network. Considering the characteristics of the mobile communication terminal device, a continuous variable communications structure is proposed to enable high-speed data switching. We simplify the access point access procedure to reduce the latency between wireless terminals. By limiting the Transmission Control Protocol Internet Protocol (TCP/IP)-based Dynamic Host Configuration Protocol (DHCP) server function and implementing it in a broadcast transmission protocol method, communication delay between terminal devices is improved. Compared to the general commercial Wi-Fi communication method, the connection operation and response speed have been improved by five seconds or more. Utilizing this method can be applied to various types of event data communication between vehicles. It can also be extended to wireless data-based intelligent road networks and systems for autonomous driving.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1415-1429
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• 2019

Nowadays object tracking process becoming one of the most challenging task in Computer Vision filed. A CSR-DCF (channel spatial reliability-discriminative correlation filter) tracking algorithm have been proposed on recent tracking benchmark that could achieve stat-of-the-art performance where channel spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process with only two simple standard features, HoGs and Color names. However, there are some cases where this method cannot track properly, like overlapping, occlusions, motion blur, changing appearance, environmental variations and so on. To overcome that kind of complications a new modified version of CSR-DCF algorithm has been proposed by integrating deep learning based object detection and CSRT tracker which implemented in OpenCV library. As an object detection model, according to the comparable result of object detection methods and by reason of high efficiency and celerity of Faster RCNN (Region-based Convolutional Neural Network) has been used, and combined with CSRT tracker, which demonstrated outstanding real-time detection and tracking performance. The results indicate that the trained object detection model integration with tracking algorithm gives better outcomes rather than using tracking algorithm or filter itself.

• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.61-70
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• 2001

The origin and destination(O-D) matrix is one of the most important elements in transportation planning process. Traditionally, transport planners survey the O-D movements in order to estimate the O-D matrix. Even though the cost of the O-D survey requires high amounts of resources, the accuracy is relatively low. Therefore, many researchers have studied the estimation of the O-D matrix for automobile from traffic counts. however, there is a little attention for the application on the transit O-D matrix estimation from traffic counts. The objective of this study is therefore the estimation of the transit O-D matrix from traffic counts using Gradient method. which is verified by the reliability analysis using a contrived small example network.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.2
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• pp.178-185
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• 2014

This paper describes a fabrication multiplexing sensor probe that employs a fiber Bragg grating(FBG) based on multiple measurements to determine the blade deflection of a wind power generator the reliability analysis of this probe is also presented. To diminish the temperature sensitivity of the FBG sensor, we form multiple CFRPs onto the upper and lower layers of the FBG and package it with an epoxy resin. As a result, the depth of the CFRP is 1mm, and the temperature sensitivity is $2.39pm/^{\circ}C$. We construct a sensor network utilizing the fabricated sensor with a blade beam model. As the number of pendulums is increased on the fore-end of the beam, the strain value is measured. The strain variation is calculated from the measurement of the load on the blade beam model by monitoring the strain of the FBG sensor. When the linear equation is applied, the strain error is 0.4% and when the finite difference method is used, the tip deflection error is 3.3%. The displacement error derived from the strain value of the FBG sensor is 4.39%. The calculated result between the measured value of the dead-end of the beam and the strain is less than 2.46% tip distortion error. Therefore, our proposed multiplexing sensor probe is a low-cost and high-reliability solution for a commercial wind power generator.