• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.4
• /
• pp.711-725
• /
• 2013

A heuristic with multi-byte suffix matching plays an important role in real pattern matching algorithms. By skipping many characters at a time in the process of comparing a given pattern with the text, the pattern matching algorithm based on a heuristic with multi-byte suffix matching shows a faster average search time than algorithms based on deterministic finite automata. Based on various experimental results and simulations, the previous works show that the pattern matching algorithms with multi-byte suffix matching performs well. However, there have been limited studies on the mathematical model for analyzing the performance in a standard manner. In this paper, we propose a new probabilistic model, which evaluates the performance of a heuristic with multi-byte suffix matching in an average-case search. When the theoretical analysis results and experimental results were compared, the proposed probabilistic model was found to be sufficient for evaluating the performance of a heuristic with suffix matching in the real pattern matching algorithms.

• Smart Structures and Systems
• /
• v.7 no.2
• /
• pp.117-132
• /
• 2011

This paper is aimed at presenting two methods on the basis of pattern search and genetic algorithms to detect and estimate damage in plates using the modal data of a damaged plate. The proposed methods determine the damages of plate structures using optimization of an objective function by pattern search and genetic algorithms. These methods have been applied to two numerical examples, namely four-fixed supported and cantilever plates with and without noise in the modal data and containing one or several damages. The obtained results clearly reveal that the proposed methods can be viewed as a powerful and reliable method for structural damage detection in plates using the modal data.

• The Journal of the Korea Contents Association
• /
• v.7 no.1
• /
• pp.58-64
• /
• 2007

In pattern recognition field, there are many optimization problems having exponential search spaces. To solve of sequential search algorithms seeking sub-optimal solutions have been used. The algorithms have limitations of stopping at local optimums. Recently lots of researches attempt to solve the problems using genetic algorithms. This paper explains the huge search spaces of typical problems such as feature selection, classifier ensemble selection, neural network pruning, and clustering, and it reviews the genetic algorithms for solving them. Additionally we present several subjects worthy of noting as future researches.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.17 no.6
• /
• pp.751-763
• /
• 2014

The paper is written to determine the optimal search pattern through search effects assessment on underwater targets. 5 types of search patterns are introduced such as, M-type pattern, W-type pattern, rectangular pattern, 4-type pattern and square pattern, In addition, Operational effectiveness analysis model is developed to obtain the optimum search pattern. The algorithms and mathematical models are also suggested to analyze the required search times, AUV's movement patterns, moving distances, overlapping areas and so on.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.15 no.4
• /
• pp.267-275
• /
• 2011

This paper proposes a new quickly convergent pattern search quasi-Newton algorithm that employs the multi-step version of the Symmetric Rank One (SRI). The new algorithm works on the factorizations of the inverse Hessian approximations to make available a sequence of convergent positive bases required by the pattern search process. The algorithm, in principle, resembles that developed in [1] with multi-step methods dominating the dervation and with numerical improvements incurred, as shown by the numerical results presented herein.

• Journal of Broadcast Engineering
• /
• v.10 no.3
• /
• pp.357-371
• /
• 2005

In this Paper, we propose a new motion search algorithm called CDTS (Cross-Diamond-Triangle Search algorithm) that uses optimal search pattern according to the position of a search area to improve the performance of CDS(Cross-Diamond Search algorithm) as well as CDHSs(Cross-Diamond-Hexagonal Searches algorithms). We analyze motion distributions in various test video sequences to apply optimal search pattern according to a position of search area. Based on the result of this analysis, we propose a new triangle-shaped search pattern whose structure is asymmetric while previous search patterns are generally symmetric in conventional algorithms. In CDTS, we apply cross- and diamond-shaped search patterns to central search areas, and triangle- and diamond-shaped patterns to the other areas. Applying CDTS to test video sequences, the proposed scheme can reduce search points more than CDS and CDHSs by 16.22$\%$ and 3.09$\%$, respectively, without any visual quality degradation.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.2
• /
• pp.73-78
• /
• 2004

By modeling the block matching algorithm as a function of the correlation of image blocks, we derive search patterns for fast block matching motion estimation. The proposed approach provides an analytical support lot the diamond-shape search pattern, which is widely used in fast block matching algorithms. We also propose a new fast motion estimation algorithm using adaptive search patterns and statistical properties of the object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the block differences. By changing the search pattern adaptively, we improve motion prediction accuracy while reducing required computational complexity compared to other fast block matching algorithms.

• The KIPS Transactions:PartB
• /
• v.10B no.7
• /
• pp.811-814
• /
• 2003

In this paper, we propose a fast block-matching motion estimation method using the cross pattern and the hexagonal pattern. For the block-matching motion estimation method, full search finds the best motion estimation, but it requires huge search time because it has to check every search point within the search window. The proposed method makes use of the fact that most of motion vectors lie near the center of block. The proposed method first uses the cross pattern to search near the center of block, and then uses the hexagonal pattern to search larger motion vectors. Experimental results show that our method is better than recently proposed search algorithms in terms of mean-square error performance and required search time.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.339-342
• /
• 2005

We present an efficient block-based motion estimation algorithm with motion analysis. The motion analysis determines a size of search pattern and a maximum repeated count of search pattern. In case of large movement in large image, we reduce search points and the local minimum which caused by low performance. The proposed algorithm employs with searching step of 2. The first step determines an initial search point with neighbor block vector and a size of initial search pattern. The second step determines a size of search pattern and a maximum repeated count with motion analysis. We improve motion prediction accuracy while reducing required computational complexity compared to other fast block-based motion estimation algorithms.

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