• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.133-142
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• 2011

Overhead facility design problem(OFDP) is one of the shortest rectilinear flow network problem(SRFNP)[4]. Genetic algorithm(GA), artificial immune system(AIS), population management genetic algorithm (PM) and greedy randomized adaptive search procedures (GRASP) were introduced to solve OFDP. A path matrix formed individual was designed to represent rectilinear path between each facility. An exchange crossover operator and an exchange mutation operator were introduced for OFDP. Computer programs for each algorithm were constructed to evaluate the performance of algorithms. Computation experiments were performed on the quality of solution and calculations time by using randomly generated test problems. The average object value of PM was the best of among four algorithms. The quality of solutions of AIS for the big sized problem were better than those of GA and GRASP. The solution quality of GRASP was the worst among four algorithms. Experimental results showed that the calculations time of GRASP was faster than any other algorithm. GA and PM had shown similar performance on calculation time and the calculation time of AIS was the worst.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.190-196
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• 1999

Continuation power flow has been developed to remove the ill-condition problem caused by singularity of power flow Jacobian at and near at steady-state voltage instability point in conventional power flow. Continuation power flow consists of predictor and corrector. In prddictor, the direction vector at the resent solution is caluculated and the initial guess of next solution is determined at the distance of step length. The selection of step length is a very important part, since computational speed and convergence performance are both greatly affected by the choice of the step length. This paper presents the practical step length selection algorithm using the reactive power generation sensitivith. In numulation, the proposed algorithm is compared with step length selection algorithm using TVI(tangent vector index).

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.1
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• pp.105-116
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• 1989

This study presents an algorithm that determines the optimal configuration of the FMSs with limited local buffers. The algorithm finds the lowest cost configuration, i.e. the number of tools, the number of pallets as well as the number of buffers to be installed in front of each machine in the system. Thus it assures a given production ratio with a minimum cost. In the algorithm, FMSs are considered as the closed queueing network with limited queue length. System performance evaluation is performed using the Block-&-Recirculation model developed by Yao and Buzacott. The algorithm is composed with three steps. The steps are namely i) determination of a lower configuration, ii) derivation of an heuristic solution, and iii) obtaining the optimal solution. The computational efforts required in the algorithm usually lies within the capability of personal computers.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.417-429
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• 1998

This paper considers two-sided (left and right side) assembly lines which are often used, especially in assembling large-sized products such as trucks and buses. A large number of exact algorithms and heuristics have been proposed to balance one-sided lines. However, little attention has been paid to balancing two-sided assembly lines. We present an efficient algorithm based on a branch and bound for balancing two-sided assembly lines. The algorithm involves a procedure for generating an enumeration tree. To efficiently search for the near optimal solutions to the problem, assignment rules are used in the method. New and existing bound strategies and dominance rules are else employed. The proposed algorithm can find a near optimal solution by enumerating feasible solutions partially. Extensive computational experiments are carried out to make the performance comparison between the proposed algorithm and existing ones. The computational results show that our algorithm is promising and robust in solution quality.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.869-874
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• 2005

This paper proposes the Pareto artificial life algorithm for a multi-objective function optimization problem. The artificial life algorithm for a single objective function optimization problem is improved through incorporating the new method to estimate the fitness value fur a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm is applied to the optimum design of a Journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application are reported to present the possible solutions to a decision maker or a designer.

• Kyungpook Mathematical Journal
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• v.62 no.3
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• pp.533-555
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• 2022

In this paper, we introduce a new algorithm for finding a solution of an equilibrium problem in a real Hilbert space. Our paper extends the single projection method to pseudomonotone variational inequalities, from a 2018 paper of Shehu et. al., to pseudomonotone equilibrium problems in a real Hilbert space. On the basis of the given algorithm for the equilibrium problem, we develop a new algorithm for finding a common solution of a equilibrium problem and fixed point problem. The strong convergence of the algorithm is established under mild assumptions. Several of fundamental experiments in finite (infinite) spaces are provided to illustrate the numerical behavior of the algorithm for the equilibrium problem and to compare it with other algorithms.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.2-43.2
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• 2019

Tomography is a method to reconstruct three-dimensional structure of an optically thin object. We can obtain the three-dimensional information by combining a number of projected images at different angles. Solar rotational tomography (SRT) is the tomographic method to estimate the coronal structures using the solar rotation. There are a few practical difficulties in solar coronal observation. One of the most crucial difficulty is handling the blocking area by the occulter or the Sun itself. So we have to use the iterative reconstruction for the SRT which can resolve that problem by using the forward modeling. In this study, we propose an alternative method to reconstruct the solar coronal structure: the filtered backprojection (FBP) algorithm. The FBP algorithm is based on the simple analytic solution. Thus it is easy to understand, and the computing cost is much cheaper than that of the iterative reconstruction. Recently we found a solution for the FBP algorithm to the problem of the blocking area in the solar EUV observations. We introduce how to apply the FBP algorithm to the SRT, and show the initial results of the performance test.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.119-126
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• 2006

The task scheduling in multiprocessor system is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost practical cases, an NP-hard problem. Consequently algorithms based on various modern heuristics have been proposed for practical reason. This paper proposes a new task scheduling algorithm using Genetic Algorithm which combines simulated annealing (GA+SA) in multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the result of proposed algorithm is better than that of any other algorithms.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.10
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• pp.556-564
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• 2004

This paper presents an application of parallel Genetic Algorithm-Tabu Search(GA-TS) algorithm to search an optimal solution of a reconfiguration in distribution system. The aim of the reconfiguration of distribution systems is to determine switch position to be opened for loss minimization in the radial distribution systems, which is a discrete optimization problem. This problem has many constraints and very difficult to solve the optimal switch position because it has many local minima. This paper develops parallel GA-TS algorithm for reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10% of the population to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node aster predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium Ⅳ CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a distribution systems in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution qualify. speedup. efficiency and computation time.