• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1084-1091
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• 2014

When demand for transmitting multimedia data increases, network congestion is more likely to occur and users will suffer high loss rate as well as high delay. In order to enhance quality-of-service (QoS) of video multicasting, we need to raise transmission reliability and reduce end-to-end delay. This paper proposes a routing mechanism for a OpenFlow-based small-scale network in order to multicast video reliably with low delay. In our method, multipath routing will be applied to Multiple Description (MD) Coded video to exploit its multi-description property. Through performance evaluation, our method shows improvement on loss rate, delay and video distortion.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1-6
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• 2011

In Korea, high-speed train(HEMU-400X) system with distributed power is being developed, and the highest test speed of this train is 400km/h. This developed the first high-speed train as a distributed power train in Korea, as well as for the development of train system and evaluation system included evaluation technology is needed. Accordingly, a comprehensive and efficient OMS(Overall Measurement System) was build up for verifying the performance of HEMU-400X. By the case study of the G7 and TTX, OMS of HEMU-400X was improved the test performance and maintainability. In particular, the existing Window-based systems has a limit to the control, due to this reason OMS applying enhanced RT(Real-time) system. In this study, compare the advantages and disadvantages of RT systems with Window-based systems, as well as the configuration of the RT systems.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.265-276
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• 2004

Genetic Algorithm(GA), that is shown stable performance to find an optimal solution, has been used as a method of solving large-scaled optimization problems with complex constraints in various applications. Since it takes so much time to execute a long computation process for iterative evolution and adaptation. In this paper, a hardware-based adaptive GA was proposed to reduce the serious computation time of the evolutionary process and to improve the accuracy of convergence to optimal solution. The proposed GA, based on steady-state model among continuos generation model, performs an adaptive mutation process with consideration of the evolution flow and the population diversity. The drawback of the GA, premature convergence, was solved by the proposed adaptation. The Performance improvement of convergence accuracy for some kinds of problem and condition reached to 5-100% with equivalent convergence speed to high-speed algorithm. The proposed adaptive GAP(Genetic Algorithm Processor) was implemented on FPGA device Xilinx XCV2000E of EHW board for face recognition.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.69-74
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• 1999

Adaptive algorithms based on gradient adaptation have been extensively investigated and successfully joined with active noise/vibration control applications. The Filtered-X LMS algorithm became one of the basic feedforward algorithms in such applications, but is not fully understood yet. Effects of cancellation path model on the Filtered-X LMS algorithm have investigated and some useful properties related to stability were discovered. Most of the results stated that the error in the cancellation path model is undesirable to the Filtered X LMS. However, we started convergence analysis of Filtered-X LMS based on the assumption that erroneous model does not always degrade its performance. In this paper, we present a way of optimizing the cancellation path modern in order to enhance the convergence speed by introducing intentional phase error. Carefully designed intentional phase error enhances the convergence speed of the Filtered X LMS algorithm for pure tone noise suppression application without any performance loss at steady state.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1588-1603
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• 2014

In space diversity combining, conventional methods such as maximal ratio combining (MRC), equal gain combining (EGC) and selection combining (SC) are commonly used to improve the output signal-to-noise ratio (SNR) provided that the channel is perfectly estimated at the receiver. However, in practice, channel estimation is often imperfect and this indeed deteriorates the system performance. In this paper, diversity combining techniques based on two evolutionary algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO) are proposed and compared. Numerical results indicate that the proposed methods outperform the conventional MRC, EGC and SC methods when the channel estimation is imperfect while it shows similar performance as that of MRC when the channel is perfectly estimated.

• ETRI Journal
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• v.33 no.3
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• pp.458-461
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• 2011

With the introduction of the new generation RFID technology, EPCglobal Class-1 Generation-2, there is considerable interest in improving the performance of the framed slotted Aloha (FSA)-based tag collision arbitration protocol. We suggest a novel time-optimal anti-collision algorithm for the FSA protocol. Our performance evaluation demonstrates that our algorithm outperforms other tag collision arbitration schemes.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.665-674
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• 2006

This paper presents the optimal design of a high-speed (200[km/h]) Linear Synchronous Motor which will be used as a propulsion system of a dynamic tester for catenary-current collection used in railways. Motor performance, especially detent force minimization on various design schemes has been investigated in detail by using FEM (Finite Element Method). Simulation-based DOE (Design of Experiments) method is also applied in order to reduce the large number of analysis according to each design variable and consider the effect among variables. The optimal design in all aspects is proposed by an optimization algorithm using a regression equation derived from the simulation-based DOE and the performance is verified by FEM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.420-426
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• 1998

Evolutionary algorithms are probabilistic optimization algorithms based on the model of natural evolution. Recently the efforts to improve the performance of evolutionary algorithms have been made extensively. In this paper, we introduce the research for improving the convergence rate and search faculty of evolution algorithms by using reinforcement learning. After providing an introduction to evolution algorithms and reinforcement learning, we present adaptive genetic algorithms, reinforcement genetic programming, and reinforcement evolution strategies which are combined with reinforcement learning. Adaptive genetic algorithms generate mutation probabilities of each locus by interacting with the environment according to reinforcement learning. Reinforcement genetic programming executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. Reinforcement evolution strategies use the variances of fitness occurred by mutation to make the reinforcement signals which estimate and control the step length.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.132-139
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• 2008

This paper proposes a scatternet topology formation, self-healing, and self-routing path optimization algorithm based on Relative Neighborhood Graph. The performance of the algorithm using ns-2 and extensible Bluetooth simulator called blueware shows that even though RNG-FHR does not have superior performance, it is simpler and easier to implement in deploying the Ad-Hoc network in the distributed dynamic environments due to the exchange of fewer messages and the only dependency on local information. We realize that our proposed algorithm is more practicable in a reasonable size network than in a large scale.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1267-1269
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• 2005

This paper presents the design for improving performance of the high-precision Magnetic levitation system. Motor performance on various design schemes such as thickness and magnetizing patterns of the permanent magnets, pole pitch, length of air gap, turn number of windings, and thickness of the aluminum-core has been investigated in detail by using FEM(Finite Element Method) Simulation-based DOE(Design of Experiments) method is also applied in order to reduce the large number of analysis according to each design variable and consider the effect among variables. The design in all aspects is proposed by an optimization algorithm using regression equation derived from the simulation-based DOE.