• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12A
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• pp.1267-1275
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• 2007

An improved backoff algorithm for retransmission randomization for OFDMA/CDMA/slotted ALOHA used in the ranging subchannel of IEEE 802.16 network is proposed. Exploiting the fact that a base station coordinates channel access using UL-/DL-MAP in the IEEE 802.16 networks, we propose a minor modification of the existing IEEE 802.16 in order to increase throughput, decrease delay variation and achieve a graceful performance degradation in case of overload channel condition of the random access protocol. The algorithm basically estimates the number of backlogged users and arrival rate using which, the BS calculates retransmission probability for the subscriber stations involved in a collision. Computer simulation is performed to demonstrate the effectiveness of the proposed algorithm and to compare the performance with existing binary exponential backoff algorithm.

• Journal of the Korean Society for Railway
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• v.15 no.5
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• pp.454-458
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• 2012

The CBTC(Communication based Train Control) and WPAN(Wireless Personal Area Network) systems employed on 2.4GHz ISM band wireless networks provide complementary service within the same environment. Coexistence between the networks will be impaired if the mutual packets are uncertainty associated the timing or gaussian distance. This paper analyzes the impact of the mutual interference and proposed method to minimize the effect of the WPAN system signals on CBTC system. The performance analysis is illustrated by examining the symbol error rate versus signal to noise interference ratio in terms of carrier frequency offset. The method contributes to reduce the symbol error rate drastically. However it can't be a fundamental solution for the impact on the interference problem. It may be needed for railway only frequency after checking the problems of the current railway frequency.

• Journal of KIISE
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• v.42 no.12
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• pp.1600-1610
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• 2015

While today's networks have been shown to exhibit HBDP (High Bandwidth Delay Product) characteristics, the legacy TCP increases the size of the congestion window slowly and decreases the size of the congestion window drastically such that it is not suitable for HBDP Networks. In order to solve this problem with the legacy TCP, many congestion control TCP mechanisms have been proposed. C-TCP (Compound-TCP) is a hybrid TCP which is a synergy of delay-based and loss-based approaches. C-TCP adapts the decreasing rate of the delay window without considering the congestion level, leading to degradation of performance. In this paper, we propose a new scheme to improve the performance of C-TCP. By controlling the increasing and decreasing rates according to the congestion level of the network, our proposed scheme can improve the bandwidth occupancy and fairness of C-TCP. Through performance evaluation, we show that our proposed scheme offers better performance in HBDP networks as compared to the legacy C-TCP.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.451-458
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• 2010

Unlike most aerodynamic wind-tunnel, Scramjet Engine Test Facility(SETF) of Korea Aerospace Research Institute should simulate enthalpy condition at a flight condition. SETF is a blow-down type, high-enthalpy wind tunnel. To attain a flight condition, a highly stagnated air comes into the test cell through a supersonic nozzle. Also, an air ejector of the SETF is used for simulating altitude conditions of the engine, and facility starting. SETF has a free-jet type test cell and this free-jet type test cell can simulate a boundary layer effect between an airplane and engine using facility nozzle, but it is too difficult to predict the nature of the facility. Therefore it is required to understand the starting characteristics of the facility by experiments. In this paper, the starting characteristics of the SETF and modifications of the ejector are described.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.12
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• pp.975-985
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• 2018

The drones have been developed for military purposes and are now used in many fields such as logistics, communications, agriculture, disaster, defense and media. As the range of use of drones increases, cases of abuse of drones are increasing. It is necessary to develop anti-drone technology to detect the position of unwanted drones using the physical phenomena that occur when the drones fly. In this paper, we estimate the DOA(direction of arrival) of the drone by using the acoustic signal generated when the drone is flying. In addition, the dynamics model of the drones was applied to the unscented kalman filter to improve the microphone array detection performance and reduce the error of the position estimation. Through simulation, the drone detection performance was predicted and verified through experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.987-994
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• 2020

In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.351-360
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• 2022

Image blurring is a phenomenon that occurs due to factors such as movement of a subject and shaking of a camera. Recently, the research for image deblurring has been actively conducted based on convolution neural networks. In particular, the method of guiding the restoration process via the difference between blur and sharp images has shown the promising performance. This paper proposes a novel method for improving the deblurring performance based on the distortion information. To this end, the transformer-based neural network module is designed to guide the restoration process. The proposed method efficiently reflects the distorted region, which is predicted through the global inference during the deblurring process. We demonstrate the efficiency and robustness of the proposed module based on experimental results with various deblurring architectures and benchmark datasets.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.93-100
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• 2022

In the era of information overload on the Internet, the recommendation system, which is an indispensable function, is a service that recommends products that a user may prefer, and has been successfully provided in various commercial sites. Recently, studies to reflect the rating time of items to improve the performance of collaborative filtering, a representative recommendation technique, are active. The core idea of these studies is to generate the recommendation list by giving an exponentially lower weight to the items rated in the past. However, this has a disadvantage in that a time function is uniformly applied to all items without considering changes in users' preferences according to the characteristics of the items. In this study, we propose a time-aware collaborative filtering technique from a completely different point of view by developing a new similarity measure that integrates the change in similarity values between items over time into a weighted sum. As a result of the experiment, the prediction performance and recommendation performance of the proposed method were significantly superior to the existing representative time aware methods and traditional methods.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1302-1308
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• 2005

Propose Cellular-IP/PRC network with united paging and Cellular IP special duality that use roof information administration cache to secure lake acceptance method in wireless Internet environment and QoS in lesser extent cell environment. When speech quality is secured considering increment of interference to receive in case of suppose that proposed acceptance method grooves base radio station capacity of transfer node is plenty, and moat of contiguity cell transfer node was accepted at groove base radio station with a blow, groove base radio station new trench lake acceptance method based on transmission of a message electric power estimate of transfer node be. Do it so that may apply composing PC(Paging Cache) and RC(Routing Cache) that was used to manage paging and router in radio Internet network in integral management and all nodes as one PRC(Paging Router Cache), and add hand off state machine in transfer node so that can manage hand off of transfer node and Roaming state efficiently, and studies so that achieve connection function at node. Analyze benevolent person who influence on telephone traffic in system environment and forecasts each link currency rank and imbalance degree, forecast most close and important lake interception probability and lake falling off probability, GoS(Grade of Service), efficiency of cell capacity in QoS because applies algorithm proposing based on algorithm use gun send-receive electric power that judge by looking downward link whether currency book was limited and accepts or intercept lake and handles and displays QoS performance improvement.