• The KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.235-240
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• 2006

Stream Control Transmission Protocol (SCTP) provides the multi-homing feature, which allows each SCTP endpoint to use two or more IP addresses for data transmission. In this paper, the SCTP multi-homing feature is experimented and analyzed in terms of throughput over Linux platforms based on the NISTNET network emulator. We perform the experimental analysis of SCTP throughputs by SCTP multi-homing for the various network conditions: different packet loss rates, network bandwidths, and transmission delays. From the experimental results, it is shown that the SCTP multi-homing gives much better throughout gun over the SCTP single-homing case in the networks with a high packet loss rate. In the meantime, the other factors including network bandwidth and transmission delay do not seem to give a significant impact on the performance of the SCTP multi-homing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.2
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• pp.161-171
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• 1993

The basin instantaneous unit hydrograph was derived by considering the network geometry and hillslope. The network geometry is quantified in a function, termed the width function, that reflects the distribution of runoff with flow distance from the outlet. The model using the derivation of the basin IUH consists of two components: the routing component of the initial distribution through the network by means of a simplified diffusion approximation and the hillslope component by means of a exponential distribution that is the probability density function of the travel time in the hillslope. The application of this method was tested on four observed flood data of Bocheong stream and Wi stream. The results show that the proposed method can be used for the analysis of the basin IUH.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.937-942
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• 2003

In this paper, we measured and examined RTT delays and packet losses according to the changes of stationary loads for two typical stream-type traffics, a DV and a MPGE2 on the R＆D Gigabit Network testbed, JGN. As the result of our actual measurements, we realized that the packet size of stationary load have no effects on a DV and a MPGE2 stream on the very high-speed network(50Mbps, IP over ATM). When its bandwidth and stationary load exceeds 95％ of network bandwidth, packet losses appeared and RTT delay increased rapidly. Also we realized that the number and size of Receive ＆ Transmit buffer on the end systems have no effects on packet losses and RTT delays.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.291-300
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• 2006

In this study a statistical water quality monitoring network design of Kyung-An stream is proposed. Water quality data for the design is obtained by QUAL2E model simulation. The observed monthly average water quality data from March to November in Kyung-An stream has been applied to this study. HEC-RAS model is also used for QUAL2E hydrauric parameter estimation. Before QUAL2E water quality parameter estimation, FORA is performed to reduce the number of parameters to be estimated, and then water quality parameters are calibrated with a observed monthly average data. Using these simulated water quality data, the number of gage station and its location are estimated by kriging theory and branch & boundary method. Such a network design is based on two case; average flow and low flow case, respectively. Next, proportional sampling method is applied to estimate the sampling frequency.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.34-42
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• 2008

This paper proposes the mechanism which improves the qualify of video on a limited network bandwidth by applying the ROI technique to an H.264 Scalable Extension technique. The network environment assumed in this parer is the next generation network convergence environment in which the mobile device has one or more network interfaces. Therefore, we allocate the priority to video packets as the hierarchy structure of H.264 SVC-encoded video stream and ROI information, and transmit those packets over appropriate network channel for using those multiple network interfaces. This paper shows two experiments first one is extracting and allocating the video stream on an appropriate network channel, second one is unequal packet transmission by allocated priorities (e.g. ROI). Performance evaluations show that this approach delivers an improved decoded video quality when compared with conventional transmission schemes, especially on device which has multiple network interfaces.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.66-74
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• 2018

Recently, there has been active studies on hand gesture recognition to increase immersion and provide user-friendly interaction in a virtual reality environment. However, most studies require specialized sensors or equipment, or show low recognition rates. This paper proposes a hand gesture recognition method using Deep Learning technology without separate sensors or equipment other than camera to recognize static and dynamic hand gestures. First, a series of hand gesture input images are converted into high-frequency images, then each of the hand gestures RGB images and their high-frequency images is learned through the DenseNet three-dimensional Convolutional Neural Network. Experimental results on 6 static hand gestures and 9 dynamic hand gestures showed an average of 92.6% recognition rate and increased 4.6% compared to previous DenseNet. The 3D defense game was implemented to verify the results of our study, and an average speed of 30 ms of gesture recognition was found to be available as a real-time user interface for virtual reality applications.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.101-112
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• 2013

Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) offers opportunities to make advances in many research areas including hydrology by providing near-global scale elevation measurements at a uniform resolution. Its wide coverage and complimentary online access especially benefits researchers requiring topographic information of hard-to-access areas. However, SRTM DEM also contains inherent errors, which are subject to propagation with its manipulation into analysis outputs. Sensitivity of hydrologic analysis to the errors has not been fully understood yet. This study investigated their impact on estimation of hydrologic derivatives such as slope, stream network, and watershed boundary using Monte Carlo simulation and spatial moving average techniques. Different amount of the errors and their spatial auto-correlation structure were considered in the study. Two sub-watersheds of Geum and Deadong River areas located in South and North Korea, respectively, were selected as the study areas. The results demonstrated that the spatial presentations of stream networks and watershed boundaries and their length and area estimations could be greatly affected by the SRTM DEM errors, in particular relatively flat areas. In the Deadong River area, artifacts of the SRTM DEM created sinks even after the filling process and then closed drainage basin and short stream lines, which are not the case in the reality. These findings provided an evidence that SRTM DEM alone may not enough to accurately figure out the hydrologic feature of a watershed, suggesting need of local knowledge and complementary data.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1320-1331
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• 2004

A video adaptation system translates the source video stream into appropriate video stream while satisfying the network and client constraints and maximizing the video quality as much as possible. This paper proposes a semi-dynamic video adaptation scheme, in which several intermediate video streams and the information for the measuring of video quality are generated statically. The intermediate video streams are generated by reducing the resolution of the video stream by a power of two several times, and they are stored as the intermediate video streams on the video server. The statically generated information for the input video stream consists of the degrees of smoothness for each frame rate and the degree of frame definition for each pixel bit rate. It helps to dynamically generate the target video stream according to the client's QoS at run-time as quickly as possible. Experimental result shows that the proposed adaptation scheme can generate the target video stream about thirty times faster while keeping the quality degradation as less than 2% comparing to the target video stream that is totally dynamically generated, although the extra storages for the intermediate video streams are required.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.163-174
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• 2006

In distributed wireless sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. We propose a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes input as a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a standard text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Bayesian classifier and SVM, and for unsupervised, we tested Jaccard, TFIDF Jaro and Jaro Winkler. In our experiments, SVM and TFIDF outperformed other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.