• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.255-263
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• 2009

In this paper, we propose an adaptive beacon scheduling algorithm to control a reception period of actual data according to variation of amount of traffic in IEEE 802.15.4 LR-WPAN(Low Rate-Wireless Personal Area Network) with the cluster-tree topology. If a beacon interval is shortened, the amount of the traffic a node receives can be increased while consumption of the energy can be also increased. In this sense, we can achieve optimal on orgy consumption by controlling the beacon interval when the amount of data to be received is being decreased. The result of simulation using NS-2 shows that the proposed algorithm improves performances in terms of packet loss rate and end-to-end delay compared with algorithm using a fixed beacon interval. For a design of cluster-tree based LR-WPAN managing delay-sensitive services, the proposed algorithm and the associated results can be applied usefully.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.251-254
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• 2008

H.264/AVC의 확장 표준으로 제정된 SVC는 공간적 확장성의 압축 효율을 높이기 위해 기존 H.264/AVC에서 제공하는 인트라 예측과 인터 예측뿐만 아니라 계층 간 예측을 추가로 수행한다. SVC 표준의 인트라 예측 과정은 부호화가 가능한 모든 모드를 부호화한 후에 최적의 RD(Rate Distortion) 값을 갖는 모드를 선택하기 때문에 계층 간 예측이 추가되어 연산량이 더욱 증가되는 문제점이 있다. 본 논문에서는 공간적 향상 계층에서 인트라 예측 시 연산량을 효과적으로 감소시킬 수 있는 빠른 인트라 예측 모드 결정 방법을 제안한다. 매크로블록 내 경계의 평탄 여부를 조사하여 미리 Intra_BL 모드를 결정하는 방법으로 모드 선택에 따른 RD 값 비교 과정을 줄임으로써 SVC 표준의 인트라 예측 방법보다 연산량이 크게 감소되었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.413-417
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• 2009

The use of scalable video coding scheme has been regarded as a promising solution for guaranteeing the quality of service of the video streaming over the Internet because it is a capable coding scheme to perform quality adaptation depending on network conditions. In this paper, we use a streaming model that transmits base layer using TCP and enhancement layers using DCCP, which try to provide transmission reliability of the BL and TCP friendliness. Unlike pervious works, the proposed algorithm performs rate adaptation based on playout buffer status. The PoB status of the client is sent back periodically to the server and serves as a network congestion indicator. Experimental results show that our scheme improves streaming quality comparing with pervious scheme in the case of not only constant/dynamic background flows but also VBR-encoded video sequence.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.467-475
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• 2009

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.