• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.97-103
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• 2010

There are many algorithms for 2D to 3D graphic conversion technology which have the high complexity and large scale of iterative computation. So in this paper propose parallel algorithm and high speed graphics accelerator architecture using Park's MAMS(Multiple Access Memory System) for Phong Shading, one of many 3D algorithms. The Proposed SIMD processor architecture is simulated by HDL and simulated and got 30 times faster result. It means any kinds of 3D algorithm can make parallel algorithm and accelerated by SIMD processor with Park's MAMS for real time processing.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.1
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• pp.88-97
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• 2000

We have proposed and analysed a novel Lookup Algorithm which had a short switching speed and tiny memory size for IP router. This algorithm could simply be implemeted by a hardware with SRAM because of simple structure. This Lookup scheme needs 1${\sim}$3 memory access times. When we simulated with 40,000 routing record obtained from IPMA Website, the maximum memory size of this algorithm was 316KB(the offset threshold for compression algorithm was 8). When we simulated by HDL using ALTERA EPM7256 series and 100MHz clock and SRAM of 10ns access time, the total lookup time was 45ns for two memory access, 175ns for three memory access.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.145-155
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• 2004

The design of a communication network has long been a challenging optimization problem. Since the optimal design of a network topology is a well known as a NP-complete problem, many researches have been conducted to obtain near optimal solutions in polynomial time instead of exact optimal solutions. All of these researches suggested diverse heuristic algorithms that can be applied to network design problems. Among these algorithms, a simulated annealing algorithm has been proved to guarantee a good solution for many NP-complete problems. in applying the simulated annealing algorithms to network design problems, generating mechanisms for initial solutions and candidate solutions play an important role in terms of goodness of a solution and efficiency. This study aims at analyzing these mechanisms through experiments, and then suggesting reliable mechanisms.

• Journal of Power System Engineering
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• v.8 no.2
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• pp.45-52
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• 2004

This paper describes the optimum design for journal bearings by using simulated annealing method. Simulated annealing algorithm is an optimization technique to calculate global and local optimum solutions. Dynamic characteristics of the journal bearing are calculated by using finite difference method (FDM), and these values are used for the procedure of journal bearing optimization. The objective is to minimize the resonance response (Q factor) of the simple rotor system supported by the journal bearings. Bearing clearance and length to diameter ratio are used as the design variables.

• Steel and Composite Structures
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• v.7 no.3
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• pp.201-217
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• 2007

Two combinatorial optimization algorithms, tabu search and simulated annealing, are presented for the minimum-weight design of geometrically non-linear steel plane frames. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) specification, maximum and interstorey drift constraints and size constraints for columns were imposed on frames. The stress constraints of AISC Allowable Stress Design (ASD) were also mounted in the two algorithms. The comparisons between AISC-LRFD and AISC-ASD specifications were also made while tabu search and simulated annealing were used separately. The algorithms were applied to the optimum design of three frame structures. The designs obtained using tabu search were compared to those where simulated annealing was considered. The comparisons showed that the tabu search algorithm yielded better designs with AISC-LRFD code specification.

• International Journal of CAD/CAM
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• v.12 no.1
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• pp.9-19
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• 2012

Generation of optimum planar B-spline curve in terms of minimum deviation and required fairness to approximate a target shape defined by a strip-shaped unorganized 2D point cloud is studied. It is proposed to use the location of control points as variables within the geometric optimization framework of point distance minimization. An adaptive simulated annealing heuristic optimization algorithm is developed to iteratively update an initial approximate curve towards the target shape. The new implementation comprises an adaptive cooling procedure in which the temperature change is adaptively dependent on the objective function evolution. It is shown that the proposed method results in an improved convergence speed when compared to the standard simulated annealing method. A couple of examples are included to show the applicability of the proposed method in the surface model reconstruction directly from point cloud data.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.381-389
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• 1996

A simple modification of the standard back-propagation algorithm to eliminate redundant connections(weights and biases) is described. It was motivated by speculations from the distribution of the magnitudes of the weights and the biases, analysis of the classification boundary, and the nonlinearity of the sigmoid function. After initial training, this algorithm eliminates all connections of which magnitude is below a threshold by setting them to zero. The algorithm then conducts retraining in which all weights and biases are adjusted to allow important ones to recover. In studies with Boolean functions, the algorithm reconstructed the theoretical minimum architecture and eliminated the connections which are not necessary to solve the functions. For simulated random signal classification problems, the algorithm produced the result which is consistent with the idea that easier problems require simpler networks and yield lower misclassification rates. Furthermore, in comparison, our algorithm produced better generalization than the standard algorithm by reducing over fitting and pattern memorization problems.