• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.11
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• pp.520-527
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• 2001

This paper suggests a new methodology of evolutionary computations - an Adaptive Evolutionary Algorithm (AEA) for solving the Economic Load Dispatch (ELD) problem which has piecewise quadratic cost functions and prohibited operating zones with many local minima. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations: global search capability of GA and local search capability of ES. In the reproduction procedure, proportions of the population by GA and the population by ES are adaptively modulated according to the fitness. Case studies illustrate the superiority of the proposed methods to existing conventional methods in power generation cost and computation time. The results demonstrate that the AEA can be applied successfully in the solution of ELD with piecewise quadratic cost functions and prohibited operating zones

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.73-85
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• 2024

Predicting water quality of rivers and reservoirs is necessary for the management of water resources. Artificial Neural Networks (ANNs) have been used in many studies to predict water quality with high accuracy. Previous studies have used Gradient Descent (GD)-based optimizers as an optimizer, an operator of ANN that searches parameters. However, GD-based optimizers have the disadvantages of the possibility of local optimal convergence and absence of a solution storage and comparison structure. This study developed improved optimizers to overcome the disadvantages of GD-based optimizers. Proposed optimizers are optimizers that combine adaptive moments (Adam) and Nesterov-accelerated adaptive moments (Nadam), which have low learning errors among GD-based optimizers, with Harmony Search (HS) or Novel Self-adaptive Harmony Search (NSHS). To evaluate the performance of Long Short-Term Memory (LSTM) using improved optimizers, the water quality data from the Dasan water quality monitoring station were used for training and prediction. Comparing the learning results, Mean Squared Error (MSE) of LSTM using Nadam combined with NSHS (NadamNSHS) was the lowest at 0.002921. In addition, the prediction rankings according to MSE and R² for the four water quality indices for each optimizer were compared. Comparing the average of ranking for each optimizer, it was confirmed that LSTM using NadamNSHS was the highest at 2.25.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.209-212
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• 2013

본 논문에서 적응적 탐색 영역(Adaptive Search Range)을 이용하여 개선한 제한된 1비트 변환 알고리즘을 제안하였다. 이 변환은 전역 검색 알고리즘 (Full Search Algorithm)을 사용한다. 그러나 이것은 매우 많은 연산량과 복잡도를 가진다. 제안된 알고리즘에서는 각 블록의 탐색범위를 결정하기 위한 움직임 벡터 (Motion Vector)와 함께 제한된 1비트 변환 알고리즘의 제한된 마스크 (Constrained Mask)를 사용한다. 실험결과를 통해 제안된 알고리즘은 움직임 예측의 정확도에 대한 성능을 비슷하게 유지하면서 평균적으로 Search Point의 수를 84% 줄일 수 있음을 보여준다.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.210-223
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• 2020

Since constrained optimization algorithms are easy to fall into local optimum and their ability of searching are weak, an improved symbiotic organisms search algorithm with mixed strategy based on adaptive ε constrained (ε_SOSMS) is proposed in this paper. Firstly, an adaptive ε constrained method is presented to balance the relationship between the constrained violation degrees and fitness. Secondly, the evolutionary strategies of symbiotic organisms search algorithm are improved as follows. Selecting different best individuals according to the proportion of feasible individuals and infeasible individuals to make evolutionary strategy more suitable for solving constrained optimization problems, and the individual comparison criteria is replaced with population selection strategy, which can better enhance the diversity of population. Finally, numerical experiments on 13 benchmark functions show that not only is ε_SOSMS able to converge to the global optimal solution, but also it has better robustness.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.285-286
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• 2008

In this paper, we propose adaptive search channel estimate algorithm. The proposed algorithm is modified LMS algorithm which has a variable step size and parallel convolution. In simulation result, a error estimate accuracy of the proposed algorithm is about -20 dB and general LMS algorithm is about 10 dB. The proposed algorithm is better error estimate accuracy than general LMS algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.74-79
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• 2008

In an H.264/AVC video encoder, the motion estimation at fractional pixel accuracy improves a coding efficiency and image quality. However, it requires additional computation overheads for fractional search and interpolation, and thus, reducing the computation complexity of fractional search becomes more important. This paper proposes fast fractional search algorithms by combining the SASR(Simplified Adaptive Search Range) and the MSDSP(Mixed Small Diamond Search Pattern) with the predicted fractional motion vector. Compared with the full search and the prediction-based directional fractional pixel search, the proposed algorithms can reduce up to 93.2% and 81% of fractional search points, respectively with the maximum PSNR lost less than 0.04dB. Therefore, the proposed fast search algorithms are quite suitable for mobile applications requiring low power and complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2468-2487
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• 2015

In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4
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• pp.293-298
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• 2003

In this paper. we propose a fast search algorithm for nearest neighbor vector quantization (NNVQ). The proposed algorithm rejects those codewords which can not be the nearest codeword and reduces the search range of codebook. Hence it reduces computational time and complexity in encoding process, while it provides the same SD performance as the conventional full search algorithm. We apply the proposed algorithm to the adaptive multi-rate (AMR) speech coder and a general vector quantizer designed by LBG. algorithm. Simulation results show effectiveness of the proposed algorithm.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.9-14
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• 2011

This paper presents a new fast motion estimation method where search ranges are determined by the probabilities of motion vector differences (MVDs), which is an adaptive/dynamic search range (ASR) method. The MVDs' distribution is investigated and its parameter is estimated by the maximum likelihood estimator. With the estimated distribution, we show that the search ranges can be efficiently restricted by a prefixed probability for MVDs. Experimental results showed that the performance of the proposed method is very similar to that of the full search algorithm in PSNR but it enables significant reduction in the computational complexity. In addition, they revealed that the proposed method determine the search ranges much more efficiently than the conventional ASR methods.