• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1261-1270
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• 2020

The physical security layer of industrial wireless sensor networks in the event of an eavesdropping attack has been investigated in this paper. An optimal sensor selection scheme based on the maximum channel capacity is proposed for transmission environments that experience Nakagami fading. Comparing the intercept probabilities of the traditional round robin (TRR) and optimal sensor selection schemes, the system secure performance is analyzed. Simulation results show that the change in the number of sensors and the eavesdropping ratio affect the convergence rate of the intercept probability. Additionally, the proposed optimal selection scheme has a faster convergence rate compared to the TRR scheduling scheme for the same eavesdropping ratio and number of sensors. This observation is also valid when the Nakagami channel is simplified to a Rayleigh channel.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.1
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• pp.22-30
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• 2008

This paper deals with the nonlinear optimal control approach to helicopter maneuver problems using the indirect method. We apply a penalty function to the deviation from a prescribed trajectory to convert the system optimality to an unconstrained optimal control problem. The resultant two-point boundary value problem has been solved by using the multiple-shooting method. This paper focuses on the effect of the number of shooting nodes and initialization methods on the numerical solution in order to define the minimum number of shooting nodes required for numerical convergence and to provide a method increasing convergence radius of the indirect method. The results of this study can provide an approach to improve numerical stability and convergence of the indirect method when we solve the optimal control problems of an inherently unstable helicopter system.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1512-1518
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• 2004

Rescaled Simulated Annealing (RSA) has been adapted to solve combinatorial optimization problems in which the available computational resources are limited. Simulated Annealing (SA) is one of the most popular combinatorial optimization algorithms because of its convenience of use and because of the good asymptotic results of convergence to optimal solutions. However, SA is too slow to converge in many problems. RSA was introduced by extending the Metropolis procedure in SA. The extension rescales the state's energy candidate for a transition before applying the Metropolis criterion. The rescaling process accelerates convergence to the optimal solutions by reducing transitions from high energy local minima. In this paper, structural optimization examples using RSA are provided. Truss structures of which design variables are discrete or continuous are optimized with stress and displacement constraints. The optimization results by RSA are compared with the results from classical SA. The comparison shows that the numbers of optimization iterations can be effectively reduced using RSA.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.5
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• pp.34-40
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• 2015

This paper presents an application of Differential Search (DS) meta-heuristic optimization algorithm for optimal operation of micro grid system. DS algorithm has the benefit of high convergence rate and precision compared to other optimization methods. The micro grid system consists of a wind turbine, a diesel generator, and a fuel cell. The simulation is applied to micro grid system only. The wind turbine generator is modeled by considering the characteristics of variable output. One day load data which is divided every 20 minute and wind resource for wind turbine generator are used for the study. The method using the proposed DS algorithm is easy to implement, and the results of the convergence performance are better than other optimization algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.22-29
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• 2005

A material cloud method, which is a new topology optimization method, is presented. In MCM, an optimal structure can be found out by manipulating sizes and positions of material clouds, which are lumps of material with specified properties. A numerical analysis for a specific distribution of material clouds is carried out using fixed background finite element mesh. Optimal material distribution can be element-wisely extracted from material clouds' distribution. In MCM, an expansion-reduction procedure of design domain for finding out better optimal solution can be naturally realized. Also the convergence of material distribution is faster and well-defined material distribution with fewer intermediate densities can be obtained. In addition, the control of minimum-member sizes in the material distribution can be realized to some extent. In this paper, basic concept of MCM is introduced, and formulation and optimization results of MCM are compared with those of the traditional density distribution method(DDM).

• Journal of the Korean Statistical Society
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• v.20 no.1
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• pp.29-43
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• 1991

Two data-based bandwidth selectors which are optimal in the sense that they achieve n$\^$-$\frac{1}{2}$/ rate of convergence in kernel density estimation are proposed. The proposed bandwidth selectors are constructed by modifying Park and Marron's plug-in method. The first modification is taking Taylor expansion of the mean integrated squared error to two more terms than in the case of plug-in method. The second is estimating more accurately the functionals of the unknown density appeared in the minimizer of the expansion by using higher order kernels. The proposed bandwidth selectors were proved to be optimal in terms of convergence rate. According to small-sample Monte Carlo studies, the proposed bandwidth selectors showed better performance than all the other bandwidth selectors considered in the simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_1
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• pp.985-991
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• 2023

This study proposes a solution to the voltage drop in electric vehicle chargers, due to the parasitic resistance and inductance of power cables when the chargers are separated by large distances. A method using multi-level electric vehicle chargers that can output power in stages, without installing an additional energy supply source such as a reactive power compensator or an energy storage system, is proposed. The voltage drop over the power cables, to optimize the charging scheduling, is derived. The obtained voltage drop equation is used to formulate the constraints of the optimization process. To validate the effectiveness of the obtained results, an optimal charging scheduling is performed for each period in a case study based on the assumed charging demands of three connected chargers. From the calculations, the proposed method was found to generate an annual profit of $20,800 for a $12,500 increase in installation costs.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.306-309
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• 2005

We consider the multi-objective optimization of a multi-service arrayed-waveguide grating-based single-hop metro WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. We develop and evaluate a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Our methodology provides the network architecture and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with our methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.2
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• pp.67-74
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• 2015

Two-wheeled driying mobild robots are precise controlled in terms of linear contol methods without considering the nonlinear dynamical characteristics. However, in the high maneuvering situations such as fast turn and abrupt start and stop, such neglected terms become dominant and heavy influence the overall driving performance. This study describes the nonlinear optimal control method to take advantage of the exact nonlinear dynamics of the balancing robot. Simulation results indicate that the optimal control outperforms in the respect of transient performance and required wheel torques. A design example is suggested for the state matrix that provides design flexibility in the control. It is shown that a well-planned state matrix by reflecting the physics of a balancing robot greatly conrtibutes to the driving performance and stability.