• 대한전기학회논문지:전력기술부문A
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• 제48권12호
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.81-84
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• 2000

Fractal image compression can reduce the size of image data by contractive mapping of original image. The mapping is affine transformation to find the block(called range block) which is the most similar to the original image. Fractal is very efficient way to reduce the data size. However, it has high distortion rate and requires long encoding time. In this paper, we present the simulation result of fractal and VQ hybrid systems which use different clustering algorithms, normal and improved competitive learning SOFM. The simulation results showed that the VQ hybrid fractal using improved competitive learning SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.23-27
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• 2007

Many trials have been used to model room impulse responses, all attempting to provide efficient representations of room acoustics. The traditional model designs for room impulse response seem to fail in accuracy, controllability, or computational efficiency. In time domain, a room impulse response is generally considered as the combination of three parts having different acoustic characteristics, initial time delay, early reflection, and late reverberation. This paper introduces new learning hybrid model for the room impulse response. In this proposed model, those three parts are modeled using different models with learning algorithms that determine the length or boundary of each model in the hybrid model. By the simulation with measured room impulse responses, it was examined that the performance of proposed model shows the best efficiency in views of both the parameter numbers and modeling error.

• 한국소음진동공학회논문집
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• 제18권3호
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• pp.361-367
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• 2008

Many trials have been used to model room impulse responses, all attempting to provide efficient representations of room acoustics. The traditional model designs for room impulse response seem to fail in accuracy, controllability, or computational efficiency. In the time domain, room impulse responses are generally considered as combination of the three Parts having different acoustic characteristics, initial time delay, early reflection, and late reverberation. This paper introduces new learning hybrid model for room impulse responses. In this proposed model, those three parts are modeled using different models with learning algorithms that determine the boundary of each model in the hybrid model. By the simulation with measured room impulse responses, the performance of proposed model shows the best efficiency in views of computational burden and modeling error.

• 한국컴퓨터정보학회논문지
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• 제21권4호
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• pp.17-24
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• 2016

In this paper, we propose a AH LSU(Advanced Hybrid QoS Routing Link State Update) Algorithm that improves the performance of Hybrid LSU(Hybrid QoS Link State State Update) Algorithm with statistical information of flow holding time in network. AH LSU algorithm has had both advantages of LSU message control in periodic QoS routing LSU algorithm and QoS routing performance in adaptive LSU algorithm. It has the mechanism that calculate LSU message transmission priority using the flow of statistical request bandwidth and available bandwidth and include MLMR(Meaningless LSU Message Removal) mechanism. MLMR mechanism can remove the meaningless LSU message generating repeatedly in short time. We have evaluated the performance of the MLMR mechanism, the proposed algorithm and the existing algorithms on MCI simulation network. We use the performance metric as the QoS routing blocking rate and the mean update rate per link, it thus appears that we have verified the performance of this algorithm.

• IEIE Transactions on Smart Processing and Computing
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• 제3권4호
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• pp.234-239
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• 2014

This paper presents an empirical study on selecting a small amount of useful unlabeled data to improve the classification accuracy of semi-supervised learning algorithms. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally-reinforced selection method was considered and evaluated empirically. The experimental results, which were obtained from well-known benchmark data sets using semi-supervised support vector machines, demonstrated that the hybrid method works better than the traditional ones in terms of the classification accuracy.

• 대한전기학회논문지:전력기술부문A
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• 제52권6호
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• pp.316-324
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• 2003

This paper applied Elite-based Simplex-GA hybrid approach combined with Muptipop-GA (ESGA) to determining the location, size and number of capacitors to improve voltage profile and minimize power losses in unbalanced distribution systems. One of the main obstacles in applying GA to complex problems has been the high computational cost due to their slow convergence rate. To alleviate this difficulty, ESGA approach was developed that combines Elite-based Simplex-GA hybrid approach with Muptipop-GA. The objective function formulated consists of two terms: cost for energy losses and cost related to capacitor purchase and capacitor installation. The cost function associated with capacitor placement is considered as a step function due to banks of standard discrete capacities. Its efficiency was proved through the application in IEEE 13 bus and 34 bus test systems and was compared with several methods using GA.

• 한국통신학회논문지
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• 제25권5A호
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• pp.726-742
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• 2000

A novel rte control algorithm consisting of two major components, i.e. a variable encoding frame rate method and a hybrid DCT/wavelet I-frame coding scheme, is proposed in this work for low bit rate video coding. Most existing rate control algorithms for low bit rate video focus on bit allocation at the macroblock level under a constant frame rate assumption. The proposed rate control algorithm is able to adjust the encoding frame rate at the expense of tolerable time-delay. Furthermore, an R-D optimized hybrid DCT/wavelet scheme is used for effective I-frame coding. The new rate-control algorithm attempts to achieve a good balance between spatial quality and temporal quality to enhance the overall human perceptual quality at low bit rates. It is demonstrated that the rate control algorithm achieves higher coding efficiency at low bit rates with a low additional computational cost. The variable frame rate method and hybrid I-frame coding scheme are compatible with the bi stream structure of H.263+.

• 제어로봇시스템학회논문지
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• 제16권2호
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• pp.194-201
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• 2010

This paper discuss a performance evaluation of the synchronization algorithm for hybrid networks in industrial environments. The proposed algorithms minimizes synchronization errors which were caused from channel, Propagation, and frequency delays. The modified RBS and offset synchronization methods can be operated by adjustment parameters. The differential BP (Back-off Period) adjustment can synchronize the local time of each node with master node's time in hybrid networks. For the performance analysis, the data transmission time between the wired and wireless devices are investigated. The experimental results show the performance evaluations in terms of the polling service time and an average end-to-end delay.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2908-2910
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• 1999

In this paper, a fuzzy model is identified and optimized using the hybrid algorithm and HCM clustering method. Here, the hybrid algorithm is carried out as the structure combined with both a genetic algorithm and the improved complex method. The one is utilized for determining the initial parameters of membership function, the other for obtaining the fine parameters of membership function. HCM clustering algorithm is used to determine the confined region of initial parameters and also to avoid overflow phenomenon during auto-tuning of hybrid algorithm. And the standard least square method is used for the identification of optimum consequence parameters of fuzzy model. Two numerical examples are shown to evaluate the performance of the proposed model.