• Journal of the Korea Computer Industry Society
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• v.3 no.1
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• pp.85-94
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• 2002

EMRCA(Explicit Max_min Rate Control Algorithm) switch, which has been proposed in the ATM(Asychronous Transfer Mode) standard, controls the ABR(Available Bit Rate) service traffic in the ATM networks. The ABR service class of ATM networks uses a feedback control mechanism to adapt to varying link capacities. The VS/VD(Virtual Source/Virtual Destination) technique offers the possibility to segment the otherwise end-to-end ABR control loop into separate loops. The improved feedback delay and the control of ABR traffic inside closed segments provide a better performance and QoS(Quality of Service) for ABR connections with respect to throughput, delay, and jitter. This paper is study of an ABR VS/VD flow control method. Linear control theory offers the means to derive correct choices of parameters and to assess performance issues, like stability of the system, during the design phase. The performance goals are a high link utilization, fair bandwidth distribution and robust operation in various environments, which are verified by discrete event simulations. The major contribution of this work is the use of linear control theory to model and design an ABR flow control method tailored for the special layout of a VS/VD switch, the simulation shows that this techniques better than conventional method.

• The KIPS Transactions:PartC
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• v.18C no.6
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• pp.433-440
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• 2011

In this paper, we propose a new scheduling method that can guarantee reliable jitter by minimizing the queue length variation in the streaming service provisioning such as IPTV and VoD. The amount of traffic to be delivered within a certain time is very fluid because MPEG-4 and H.264 encoders use VBR(Variable Bit Rate) for delivering video streaming traffic. This VBR characteristic increases the end-to-end propagation delay variation when existing scheduling methods are used for delivering video frames. Therefore, we propose the new scheduling method that can minimize change rate of queue length by adaptively controling service rate taking into account the size of bulk incoming packets and arrival rate for bulk streaming traffic. Video frames can be more reliably transmitted through the minimization of the queue length variation using the proposed method. We use the queueing model and also carry out OPNET simulation to validate the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.419-426
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• 2022

In this paper, we propose a method to improve the accuracy of 3D human pose estimation model in various move motions. Existing human pose estimation models have some problems of jitter, inversion, swap, miss that cause miss coordinates when estimating human poses. These problems cause low accuracy of pose estimation models to detect exact coordinates of human poses. We propose a method that consists of detection and correction methods to handle with these problems. Deep learning-based outlier detection method detects outlier of human pose coordinates in move motion effectively and rule-based correction method corrects the outlier according to a simple rule. We have shown that the proposed method is effective in various motions with the experiments using 2D golf swing motion data and have shown the possibility of expansion from 2D to 3D coordinates.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.1976-1985
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• 2015

The amount of packets in palyout buffer of video streaming receiver can be changed by network condition, and saturated and exhausted by the delay and jitter. Especially, if the amount of incoming video traffic exceeds the maximum allowed playout buffer, buffer overflow problem can be generated. It makes the deterioration of video image and the discontinuity of playout by skip phenomenon. Also, if the incoming packets are delayed by network confusion, the stop phenomenon of video image is made by buffering due to buffer underflow problem. To solve these problems, this paper proposes the video streaming receiver with token bucket scheme which automatically establishes the important parameters like token generation rate r and bucket maximum capacity c adapting to the pattern of video packets. The simulation results using network simulator-2 (NS-2) and joint scalable video model (JSVM) show that the proposed token bucket scheme with automatic establishment parameter provides better performance than the existing token bucket scheme with manual establishment parameter in terms of the generation number of overflow and underflow, packer loss rate, and peak signal to noise ratio (PSNR) in three test video sequences.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.