• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.17-26
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• 2002

In this paper, a new design methodology of the fuzzy controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors. The design procedure is based on evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm to adjust and estimate scaling factors respectively. The tuning of the soiling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as HCM (Hard C-Means) and Neuro-Fuzzy model[7]. The validity and effectiveness of the proposed estimation algorithm for the fuzzy controller are demonstrated by the inverted pendulum system.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1343-1348
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• 2014

The low frequency noise in Silicon Nanowire Field Effect Transistors is analyzed by characterizing the gate electrode dependence on various geometrical parameters. It shows that gate electrodes have a strong impact in the flicker noise of Silicon Nanowire Field effect transistors. Optimization of gate electrode was done by comparing different performance metrics such a DIBL, SS, $I_{on}/I_{off}$ and fringing capacitance using TCAD simulations. Molybdenum based gate electrode showed significant improvement in terms of high drive current, Low DIBL and high $I_{on}/I_{off}$. The noise power sepctral density is reduced by characterizing the device at higher frequencies. Silicon Nanowire with Si3N4 spacer decreases the drain current spectral density which interms reduces the fringing fields there by decreasing the flicker noise.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.379-389
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• 2014

Low frequency oscillations (LFOs) are load angle oscillations that have a frequency between 0.1-2.0 Hz. Power system stabilizers (PSSs) are very effective controllers in improvement of the damping of LFOs. PSSs are designed by linearized models of the power system. This paper presents a new model of the power system that has the advantages of the Single Machine Infinite Bus (SMIB) system and the multi machine power system. This model is named a single machine normal-bus (SMNB). The equations that describe the proposed model have been linearized and a lead PSS has been designed. Then, particle swarm optimization technique (PSO) is employed to search for optimum PSS parameters. To analysis performance of PSS that has been designed based on the proposed model, a few tests have been implemented. The results show that designed PSS has an excellent capability in enhancing extremely the dynamic stability of power systems and also maintain coordination between PSSs.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.80-83
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• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1007-1008
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• 2007

This paper is proposed an efficiency optimization control algorithm for IPMSM which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy learning control-fuzzy neural networks(AFLC-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the AFLC-FNN controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.4
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• pp.344-348
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• 2002

As microelectronics technology continues to progress, there is also a continuous demand on highly integration and miniaturization of systems. For example, it is desirable to package several integrated circuits together in multilayer structure, such as multichip modules, to achieve higher levels of compactness and higher performance. Passive components (i.e., capacitors, resistors, and inductors) are very important fort many MCM applications. In addition, the low-temperature co-fired ceramic (LTCC) process has considerable potential for embedding passive components in a small area at a low cost. In this paper, we investigate a method of statistically modeling integrated passive devices from just a small number of test structures. A set of LTCC inductors is fabricated and their scattering parameters (s-parameters) are measured for a range of frequencies from 50MHz to 5GHz. An accurate model for each test structure is obtained by using a building block based modeling methodology and circuit parameter optimization using the HSPICE circuit simulator.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1459-1461
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• 2005

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on fuzzy-neural networks(FNN) controller that is implemented using fuzzy control and neural networks. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. Simulation results are presented to show the validity of the proposed algorithm

• Nuclear Engineering and Technology
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• v.36 no.2
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• pp.127-141
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• 2004

Various types of integrated steam generators, which integrate IHTS and a steam generator into a single unit of equipment for an LMR, were analyzed using an analytic solution with some simplification. The analysis showed that the undesirable reversed heat transfer, of which occurrence was previously observed only in an integrated single-region bundle type, can also occur in an integrated double-region bundle type. The mechanism of the reversed heat transfer occurrence in the double-region type is explained and it is shown the mechanism in the double-region type is completely different from that in the single-region type. Based on this finding, a method for preventing the aforementioned heat transfer is suggested. The performance of the four types of the integrated steam generators is assessed. For this assessment, a SG is actually designed for each type and the optimization in the geometric parameters and flow rate are optimized.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.55-60
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• 2021

We applied an artificial neural network (ANN) and evaluated surface roughness prediction in lateral milling using an endmill. The selected workpiece was AL6061-T4 to obtain data of surface roughness measurement based on the spindle speed, feed, and depth of cut. The Bayesian optimization algorithm was applied to the number of nodes and the learning rate of each hidden layer to optimize the neural network. Experimental results show that the neural network applied to optimize using the Expected Improvement(EI) algorithm showed the best performance. Additionally, the predicted values do not exactly match during the neural network evaluation; however, the predicted tendency does march. Moreover, it is found that the neural network can be used to predict the surface roughness in the milling of aluminum alloy.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.