• Journal of Power Electronics
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• v.9 no.3
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• pp.418-429
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• 2009

In this paper an optimal damped input filter is designed based on a Genetic Algorithm (GA) for an electrolytic capacitor-less AC-AC converter. Sufficient passive damping and minimum losses in passive damping elements, minimization of the filter output impedance at the filter cut-off frequency, minimization of the DC-link voltage and input current fluctuations, and minimization of the filter costs are the main objectives in the multi-objective optimization of the input filter. The proposed filter has been validated experimentally using an induction motor drive system employing an electrolytic capacitor-less AC-AC converter.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.559-563
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• 2005

The wafer positioning robot used in the semiconductor industry is required to operate at high speed for the improvement of productivity. However, the residual vibration produced by the high speed of the wafer positioning robot makes the life of the robot shorter and the cycle time longer. In this study, the input shaping and the path of the system are designed for the reduction of the residual vibration and the optimization of the cycle time. The followings are the process for the reduction and the optimization; 1)System modeling of wafer positioning robot, 2)Verification of dynamic characteristic of wafer positioning robot, 3)Input shaping plan using impulse response reiteration, 4)Simulation test using simulink, 6)Analysis of result.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.2-42
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• 2002

We discuss a neural network solver for the inverse optimization problem. The problem is that find functional relations between input and output data, which are include defects. Finding the relations, predictions of the defect parts are also required. The part of finding the defects in the input data is an inverse problem . We consider the meanings to solve the problem on the neural network system at first. Next, we consider the network structure of the system, the learning scheme of the network, and at last, examine the precision on the numerical calculations. In the paper, we proposed the high-precision learning method for plural three-layer neural network system that is series-connect...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.239-239
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• 2000

This paper present experimental results on temperature trajectory tracking. The benefits of precalculated feedforward input together with PID feedback control are demonstrated by experimental results. To find the feedforward input, the plant (autoregresiive) model is first identified and convex optimization procedure is applied. PID controller is then implemented based on Ziegler-Nickels tuning rule to reduce effects of disturbances and modeling errors. Experimental results show an improvement in slope tracking performance over the fully PID controller.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.458-464
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• 2016

This paper proposes the compensation method which decreases the radiation pattern distortion caused by the mutual coupling in an array antenna. If the element distance of an array antenna decreases, the radiation pattern could be distorted by the strong mutual coupling, which changes the magnitude and phase of input signals and causes an unwanted radiation pattern. To remove the pattern distortion, compensated input signals are inserted in an array antenna. The magnitude and phase of input signals are determined by Particle Swarm Optimization (PSO) algorithm. A $4{\times}1$ dipole array antenna with omnidirectional elements is used to confirm the validity of the algorithm, where each element is placed in 0.2 wavelength to evoke the strong coupling. After input signals are optimized by PSO, it is found that the compensated radiation results in the same as the ideal case.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1108-1121
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• 2019

The non-inverting buck-boost converter (NIBB) is a step-up and step-down DC-DC converter suitable for wide-input-voltage-range applications. However, when the input voltage is close to the output voltage, the NIBB needs to operate in the buck-boost mode, causing a significant efficiency reduction since all four switches operates in the PWM mode. Considering both the current stress limitation and the efficiency optimization, a novel design methodology for the optimal phase-shift modulation of a NIBB in the buck-boost mode is proposed in this paper. Since the four switches in the NIBB form two bridges, the shifted phase between the two bridges can serve as an extra degree of freedom for performance optimization. With general phase-shift modulation, the analytic current expressions for every duty ratio, shifted phase and input voltage are derived. Then with the two key factors in the NIBB, the converter efficiency and the switch current stress, taken into account, an objective function with constraints is derived. By optimizing the derived objective function over the full input voltage range, an offline design methodology for the optimal modulation scheme is proposed for efficiency optimization on the premise of current stress limitation. Finally, the designed optimal modulation scheme is implemented on a DSPs and the design methodology is verified with experimental results on a 300V-1.5kW NIBB prototype.

• Agricultural and Biosystems Engineering
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• v.1 no.1
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• pp.7-15
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• 2000

Optimization was considered from three perspectives : minimum grain loss, minimum damaged grain loss, and minimum power consumption. Factors affecting combine performance were classified as control, adjustable, and environmental. Control and adjustable factors were optimized by the parameter design developed by Taguchi. Environmental factors were used as input for optimization. Optimum range for control and adjustable factors are presented. Parameter design was adequate to obtain the optimum levels of control factors and optimum range of adjustable factors.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.714-726
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• 1993

Optimization was considered from three perspectives ; minimum grain loss, minimum damaged grain loss, and minimum power consumption. Factors affecting combine performance were classified as control , adjustable , and environmental. Control and adjustable factors were optimized by the parameter design developed by Tajuchi. Environmental factors were used as input for optimization Optimum range for control and adjustable factors are presented. Parameter design was adequate to obtain the optimum levels of control factors and optimum range of adjustable factors.