• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1444-1451
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• 2016

This paper proposes a Tabu search algorithm to minimize the required energy to send data between a source and a destination using the cooperative communication in wireless ad hoc networks. As the number of nodes in wireless ad hoc networks increases, the amount of calculation for establishing the path between nodes would be too much increased. To obtain the optimal cooperative path within a reasonable computation time, we propose a new Tabu search algorithm for a high-density wireless network. In order to make a search more efficient, we propose some efficient neighborhoods generating operations of the Tabu search algorithm. We evaluate those performances through some experiments in terms of the minimum energy required to send data between a source and a destination as well as the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.83-90
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• 2015

This paper proposes a Tabu search algorithm to maximize the connectivity between the router nodes and the client nodes in wireless mesh networks. As the number of the router nodes and the client nodes in the networks increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time for a high-density network, we propose a Tabu search algorithm to obtain the optimal solution for maximizing the connectivity. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the Tabu search algorithm. We evaluate those performances through some experiments in terms of the maximum number of the connectivity and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.

• Journal of KIISE
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• v.42 no.8
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• pp.961-971
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• 2015

As a representative area-decomposed algorithm, an area-based quad-trie (AQT) has an issue of search performance. The search procedure must continue to follow the path to its end, due to the possibility of the higher priority-matching rule, even though a matching rule is encountered in a node. A leaf-pushing AQT improves the search performance of the AQT by making a single rule node exist in each search path. This paper proposes a new algorithm to further improve the search performance of the leaf-pushing AQT. The proposed algorithm implements a leaf-pushing AQT using a hash table and an on-chip Bloom filter. In the proposed algorithm, by sequentially querying the Bloom filter, the level of the rule node in the leaf-pushing AQT is identified first. After this procedure, the rule database, which is usually stored in an off-chip memory, is accessed. Simulation results show that packet classification can be performed through a single hash table access using a reasonable sized Bloom filter. The proposed algorithm is compared with existing algorithms in terms of the memory requirement and the search performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.27-35
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• 2009

Integer programming is a very effective technique for searching optimal solution of combinatorial optimization problems. However, its applicability is limited to linear models. In this paper, I propose an effective method for solving a nonlinear optimization problem by integrating the powerful search performance of integer programming and the flexibility of neighborhood search algorithms. In the first phase, integer programming is executed with subproblem which can be represented as a linear form from the given problem. In the second phase, a neighborhood search algorithm is executed with the whole problem by taking the result of the first phase as the initial solution. Through the experimental results using a nonlinear maximal covering problem, I confirmed that such a simple integration method can produce far better solutions than a neighborhood search algorithm alone. It is estimated that the success is primarily due to the powerful performance of integer programming.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.304-315
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• 2018

Harmony Search (HS) is a recently developed metaheuristic algorithm that is widely known to many researchers. However, due to the increasing complexity of optimization problems, the optimal solution cannot be efficiently found by HS. To overcome this problem, there have been many studies that have improved the performance of HS by modifying the parameter settings and incorporating other metaheuristic algorithms. In this study, Copycat HS (CcHS) is suggested, which improves the parameter setting method and the performance of searching for the optimal solution. To verify the performance of CcHS, the results were compared to those of HS variants with a set of well-known mathematical benchmark problems. The effectiveness of CcHS was proven by finding final solutions that are closer to the global optimum than other algorithms in all problems. To analyze the applicability of CcHS to engineering optimization problems, it was applied to a design problem for Water Distribution Systems (WDS), which is widely applied in previous research. As a result, CcHS proposed the minimum design cost, which was 21.91% cheaper than the cost suggested by simple HS.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Interdisciplinary Bio Central
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• v.1 no.3
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• pp.9.1-9.6
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• 2009

Introduction: In the mass spectrometry-based proteomics, biological samples are analyzed to identify proteins by mass spectrometer and database search. Database search is the process to select the best matches to the experimental mass spectra among the amino acid sequence database and we identify the protein as the matched sequence. The match score is defined to find the matches from the database and declare the highest scored hit as the most probable protein. According to the score definition, search result varies. In this study, the difference among search results of different search engines or different databases was investigated, in order to suggest a better way to identify more proteins with higher reliability. Materials and Methods: The protein extract of human mesenchymal stem cell was separated by several bands by one-dimensional electrophorysis. One-dimensional gel was excised one by one, digested by trypsin and analyzed by a mass spectrometer, FT LTQ. The tandem mass (MS/MS) spectra of peptide ions were applied to the database search of X!Tandem, Mascot and Sequest search engines with IPI human database and SwissProt database. The search result was filtered by several threshold probability values of the Trans-Proteomic Pipeline (TPP) of the Institute for Systems Biology. The analysis of the output which was generated from TPP was performed. Results and Discussion: For each MS/MS spectrum, the peptide sequences which were identified from different conditions such as search engines, threshold probability, and sequence database were compared. The main difference of peptide identification at high threshold probability was caused by not the difference of sequence database but the difference of the score. As the threshold probability decreases, the missed peptides appeared. Conversely, in the extremely high threshold level, we missed many true assignments. Conclusion and Prospects: The different identification result of the search engines was mainly caused by the different scoring algorithms. Usually in proteomics high-scored peptides are selected and low-scored peptides are discarded. Many of them are true negatives. By integrating the search results from different parameter and different search engines, the protein identification process can be improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.55-65
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• 2006

Most of fast block motion estimation algorithms reported so far in literatures aim to reduce the computation in terms of the number of search points, thus do not fit well with multimedia processors due to their irregular data flow. For multimedia processors, proper reuse of data is more important than reducing number of absolute difference operations because the execution cycle performance strongly depends on the number of off-chip memory access. Therefore, in this paper, we propose a Hexagon-shape line search (HEXSLS) algorithm using line search pattern which can increase data reuse from on-chip local buffer, and check sub-sampling points in line search pattern to reduce unnecessary SAD operation. Our experimental results show that the prediction error (MAE) performance of the proposed HEXSLS is similar to that of the full search block matching algorithm (FSBMA), while compared with the hexagon-based search (HEXBS), the HEXSLS outperforms. Also the proposed HEXSLS requires much lesser off-chip memory access than the conventional fast motion estimation algorithm such as the hexagon-based search (HEXBS) and the predictive line search (PLS). As a result, the proposed HEXSLS algorithm requires smaller number of execution cycles on media processor.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.13-27
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• 2012

Integer programming-based local search(IPbLS) is a kind of local search based on simple hill-climbing search and adopts integer programming for neighbor generation unlike general local search. According to an existing research [1], IPbLS is known as an effective method for the multidimensional knapsack problem(MKP) which has received wide attention in operations research and artificial intelligence area. However, the existing research has a shortcoming that it verified the superiority of IPbLS targeting only largest-scale problems among MKP test problems in the OR-Library. In this paper, I verify the superiority of IPbLS more objectively by applying it to other problems. In addition, unlike the existing IPbLS that combines simple hill-climbing search and integer programming, I propose methods combining other local search algorithms like hill-climbing search, tabu search, simulated annealing with integer programming. Through the experimental results, I confirmed that IPbLS shows comparable or better performance than the best known heuristic search also for mid or small-scale MKP test problems.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.