• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.3
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• pp.32-40
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• 1999

In this paper, we present a fast block matching motion estimation algorithm based on successive elimination algorithm (SEA). Based on the characteristic of center-biased motion vector distribution in the search area, the proposed method improves the performance of the SEA with a reduced the number of the search positions in the search area, In addition, to reduce the computational load, this method is combined with both the reduced bits mean absolute difference (RBMAD) matching criterion which can be reduced the computation complexity of pixel comparison in the block matching and pixel decimation technique which reduce the number of pixels used in block matching. Simulation results show that the proposed method provides better performance than existing fast algorithms and similar to full-search block motion estimation algorithm.

• Journal of KIISE:Information Networking
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• v.37 no.2
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• pp.162-165
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• 2010

In the paper, we propose an algorithm that significantly reduces unnecessary computations, while keeping prediction quality almost similar to that of the full search. In the proposed algorithm, we can reduces only unnecessary computations efficiently by taking different search patterns and error criteria of block matching according to distribution probability of motion vectors. Our algorithm takes only 20~30% in computational amount and has decreased prediction quality about 0~0.02dB compared with the fast full search of the H.264 reference software. Our algorithm will be useful to real-time video coding applications using MPEG-2/4 AVC standards.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.555-576
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• 2016

In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

• Journal of Power Electronics
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• v.16 no.1
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• pp.287-296
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• 2016

Conventional maximum power point tracking (MPPT) methods are ineffective under partially shaded conditions because multiple local maximum can be exhibited on power-voltage characteristic curve. This study proposes an improved cuckoo search (ICS) MPPT method after investigating the cuckoo search (CS) algorithm applied in solving multiple MPPT. The algorithm eliminates the random step in the original CS algorithm, and the conception of low-power, high-power, normal and marked zones are introduced. The adaptive step adjustment is also realized according to the different stages of the nest position. This algorithm adopts the large step in low-power and marked zones to reduce search time, and a small step in high-power zone is used to improve search accuracy. Finally, simulation and experiment results indicate that the promoted ICS algorithm can immediately and accurately track the global maximum under partially shaded conditions, and the array output efficiency can be improved.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.991-999
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• 2010

In this paper, we propose a fast motion estimation algorithm for video encoding. Conventional fast motion estimation algorithms have a serious problem of low prediction quality in some frames. However, full search based fast algorithms have low computational reduction ratio. In the paper, we propose an algorithm that significantly reduces unnecessary computations, while keeping prediction quality almost similar to that of the full search. The proposed algorithm uses distribution probability of motion vectors and adaptive search patterns and block matching criteria. By taking different search patterns and error criteria of block matching according to distribution probability of motion vectors, we can reduces only unnecessary computations efficiently. Our algorithm takes only 20~30% in computational amount and has decreased prediction quality about 0~0.02dB compared with the fast full search of the H.264 reference software. Our algorithm will be useful to real-time video coding applications using MPEG-2 or MPEG-4 AVC standards.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.86-94
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• 1998

This paper proposes a new fast block-matching algorithm, named MFSS(Modified Four-Step Search) algorithm, which has better performance and is more adequate for hardware realization than the existing fast algorithms. The proposed algorithm is suitable for hardware realization since it has a unique regularity during the search procedure. It is shown from simulation results that its performance is close to that of FS(Full Search) algorithm. This paper also proposes a VLSI architecture and presents some design results of a motion estimator and compensator which adopted the MFSS algorithm. The important aspects considered in designing a motion estimator and compensator are hardware complexity of design results, and total delay needed to generate the motion compensated data after finding the motion vectors. Hardware complexity is minimized by using just nine PE(Process Element)'s, and total delay is minimized by sharing search memory of the motion estimator and compensator.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2470-2476
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• 1997

We propose a new real-time search algorithm and provide experimental evaluation and comparison of the new algorithm with mini-min lookahead algorithm. Many other real-time heuristic-search approached often divide the problem space to several sub-problems. In this paper, the proposed algorithm guarantees not only the sub-problem deadline but also total deadline. Several heuristic real-time search algorithms such as $RTA^{\ast}$, SARTS and DYNORA have been proposed. The performance of such algorithms depend on the quality of their heuristic functions, because such algorithms estimate the search time based on the heuristic function. In real-world problem, however, we often fail to get an effective heuristic function beforehand. Therefore, we propose a new real-time algorithm that determines the sub-problem deadline based on the status of search space during sub-problem search process. That uses the cut-off method that is a dynamic stopping-criterion-strategy to search the sub-problem.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.129-133
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• 2015

In this paper, we propose a fast motion estimation algorithm which is important in the performance of video encoding. Conventional fast motion estimation algorithms have serious problems of low prediction quality in some frames and still much computation. In the paper, we propose an algorithm that reduces unnecessary computations only, while keeping prediction quality almost similar to that of the full search. The proposed algorithm uses distribution of probability of motion vectors, divides search range into several groups according to its importance, and applies adaptive block matching criteria for each group of search range. The proposed algorithm takes only 3~5% in computational amount and has decreased prediction quality about 0~0.01dB compared with the fast full search algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.85-94
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• 2017

In this paper, I proposed a sound model generation and a most frequent model search algorithm for recognizing animal vocalization. The sound model generation algorithm generates a optimal set of models through repeating processes such as the training process, the Viterbi Search process, and the most frequent model search process while adjusting HMM(Hidden Markov Model) structure to improve global recognition rate. The most frequent model search algorithm searches the list of models produced by Viterbi Search Algorithm for the most frequent model and makes it be the final decision of recognition process. It is implemented using MFCC(Mel Frequency Cepstral Coefficient) for the sound feature, HMM for the model, and C# programming language. To evaluate the algorithm, a set of animal sounds for 27 species were prepared and the experiment showed that the sound model generation algorithm generates 27 HMM models with 97.29 percent of recognition rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6A
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• pp.369-378
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• 2012

In this study, direct-search based optimization methods are applied for blade shape optimization of wind turbines and the optimization performances of several methods including conventional genetic algorithm, micro genetic algorithm and pattern search method are compared to propose a more efficient method. For this purpose, the currently available version of HARP_Opt (Horizontal Axis Rotor Performance Optimizer) code is enhanced to rationally evaluate the annual energy production value according to control strategies and to optimize the blade shape using pattern search method as well as genetic algorithm. The enhanced HARP_Opt code is applied to obtain the optimal turbine blade shape for 1MW class wind turbines. The results from pattern search method are compared with the results from conventional genetic algorithm and also micro genetic algorithm and it is found that the pattern search method has a better performance in achieving higher annual energy production and consistent optimal shapes and the micro genetic algorithm is better for reducing the calculation time.