• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.6
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• pp.325-332
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• 2003

The loss minimization is one of the most important problems to save the operational cost in distribution systems. This paper presents an efficient method for optimal feeder reconfiguration of distribution systems. Chaos search algorithm (CSA) is used to reconfigure distribution systems so that active power losses are globally minimized with turning on/off sectionalizing switches. In optimization problem, the CSA searches the global optimal solution on the basis of regularity in chaotic motions and easily escapes from local or near optimal solution. The CSA is tested on 15 buses and 32 buses distribution systems, and the results indicate that it is able to determine appropriate switching options for global optimum reconfiguration.

• ETRI Journal
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• v.39 no.1
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• pp.13-20
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• 2017

A method is proposed for scheduling sensor accesses to the shared network in a networked control system. The proposed method determines the access order in which the sensors are granted medium access through minimization of the state estimation error covariance. Solving the problem by evaluating the error covariance for each possible ordered set of sensors is not practical for large systems. Therefore, a convex optimization problem is proposed, which yields approximate yet acceptable results. A state estimator is designed for the augmented system resulting from the incorporation of the optimally chosen communication sequence in the plant dynamics. A car suspension system simulation is conducted to test the proposed method. The results show promising improvement in the state estimation performance by reducing the estimation error norm compared to round-robin scheduling.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.660-667
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• 2003

In this study, a Multiple Objective Mixed Integer Programming (MOMIP) Model is developed for sewer rehabilitation planning by considering cost, inflow/infiltration. A sewer rehabilitation planning model is required to decide the economic life of the sewer by considering trade-off between cost and inflow/infiltration. And it is required to find the optimal rehabilitation timing, according to the cost effectiveness of each sewer rehabilitation within the budget. To develop such a model, a multiple objective mixed integer programming model is formulated based on network flow optimization. The network is composed of state nodes and arcs. The state nodes represent the remaining life and the arcs represent the change of the state. The model consider multiple objectives which are cost minimization and minimization of inflow/infiltration. Using the multiple objective optimization, the trade-off between the cost and inflow/infiltration is presented to the planner so that a proper sewer rehabilitation plan can be selected.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.318.2-318
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• 2002

Axial fans are widely used in heavy machines due to their ability to produce high flow rate fur cooling of engines. At the same time, the noise generated by these fans causes one of the most serious problems. This work is concerned with the low noise technique of discrete frequency noise. To calculate the unsteady resultant force over the fan blade in an unsymmetric engine room, Time-Marching Free-Wake Method is used. From the calculations of unsteady force on fan blades, noise signal of an engine cooling fan is calculated by using an acoustic similarity law. (omitted)

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.89-96
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• 2006

Nondestructive evaluation using surface waves needs an analytical solution for the reference value to compare with experimental data. Finite element analysis is very powerful tool to simulate the wave propagation, but has some defects. It is very expensive and high time-complexity for the required high resolution. For those reasons, it is hard to implement an optimization problem in the actual situation. The developed engine in this paper can substitute for the finite element analysis of surface waves propagation, and it accomplishes the fast analysis possible to be used in optimization. Including this artificial intelligence engine, most of soft computing algorithms can be applied on the special database. The database of surface waves propagation is easily constructed with the results of finite element analysis after reducing the dimensions of data. The principal wavelet-component analysis is an efficient method to simplify the transient wave signal into some representative peaks. At the end, artificial neural network based on the database make it possible to invent the artificial intelligence engine.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.201-219
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• 2014

This paper considers service deployment and priority optimization for multiple service-oriented applications sharing reusable services, which are deployed as multiple instances in the cloud. In order to handle variations in the workloads of the multiple applications, service instances of the individual reusable services are dynamically provisioned in the cloud. Also service priorities for each application in a particular reusable service are dynamically adjusted. In this paper, we propose an analytic performance model, based on a queueing network model, to predict the expected sojourn times of multiple service-oriented applications, given the number of service instances and priority disciplines in individual reusable services. We also propose a simple heuristic algorithm to search an optimal number of service instances in the cloud and service priority disciplines for each application in individual reusable services. A numerical example is also presented to demonstrate the applicability of the proposed performance model and algorithm to the proposed optimal decision problem.

• Annual Conference of KIPS
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• 2003.11b
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• pp.1137-1140
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• 2003

인터넷 mobility 기술의 발전으로 인해 Mobile IPv6 를 기반으로 하는 이동 네트워크(Mobile Network, NEMO)기술이 등장하였으며, MR(Mobile Router)와 HA(Home Agent)간의 bi-directional 터널을 통해 네트워크의 이동성을 지원한다. 그러나. 이동네트워크 안에 또 다른 이동네트워크가 존재하는 중첩된 이동네트워크에서는 bi-directional 터널이 중복되는 routing problem이 발생한다. 따라서 본 논문에서는 중첩된 이동네트워크가 계층적 구조를 가지는 것을 이용하여, 최상위 MR 이 지역 HA 역할을 수행하게 항으로써, 중첩된 이동네트워크내의 노드들을 위한 경로최적화와 마이크로 이동성을 동시에 지원할 수 잇도록 한다.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.232-235
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• 1999

This paper presents a HNN(Hopfield Neural Network) model to solve the ORP(Optimal Routing Problem) in DSP(Distribution System Planning). This problem is generally formulated as a combinatorial optimization problem with various equality and inequality constraints. Precedent study[3] considered only fixed cert, but in this paper, we proposed the capability of optimization by fixed cost and variable cost. And suggested the corrected formulation of energy function for improving the characteristics of convergence. The proposed algorithm has been evaluated through the sample distribution planning problem and the simmulation results are presented.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.