• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.43-52
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• 2004

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• Journal of the Korean Society for Railway
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• v.9 no.1 s.32
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• pp.63-68
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• 2006

This paper proposes a fast Fourier transform(FFT)-based spectral analysis method(SAM) for the dynamic responses of the linear discrete dynamic models with non-proportional damping. The SAM was developed by using discrete Fourier transform(DFT)-theory. To verify the proposed SAM, a three-DOF system with non-proportional viscous damping is considered as an illustrative example. The present SAM is evaluated by comparing the dynamic responses obtained by SAM with those obtained by Runge-Kutta method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.473-478
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• 2001

Image processings using the two-dimensional wavelet transform (2DWT) have been a very active research area in recent years because the 2DWT possess many good properties. However, the discrete 2DWT can not be used for pattern recognition directly because it does not have the translation property. In this paper, we show why conventional discrete two-dimensional wavelet transforms cannot be used for pattern recognitions directly. Then, we propose a new method that makes it possible to use discrete 2DWT to pattern recognition without modification of standard pyramidal algorithms. The main idea of our method is to postprocess the wavelet transformed images using the second-order neural network. To justify the validity of the method, evaluations with test images were performed. The effectiveness of the method can be shown by the evaluation results.

• ETRI Journal
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• v.40 no.6
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• pp.726-735
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• 2018

Femtocells are good examples of the ultimate networking technology, offering enhanced indoor coverage and higher data rate. However, the dense deployment of femto base stations (FBSs) and the exploitation of subcarrier reuse between macrocell base stations and FBSs result in significant co-tier and cross-tier interference, thus degrading system performance. Therefore, appropriate resource allocations are required to mitigate the interference. This paper proposes a discrete bacterial foraging optimization (DBFO) algorithm to find the optimal resource allocation in two-tier networks. The simulation results showed that DBFO outperforms the random-resource allocation and discrete particle swarm optimization (DPSO) considering the small number of steps taken by particles and bacteria.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.45-56
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• 2021

This document traces the new technologies development based on a deep classical Hill method improvement. Based on the chaos, this improvement begins with the 256 element body construction, which is to replace the classic ring used by all encryption systems. In order to facilitate the application of algebraic operators on the pixels, two substitution tables will be created, the first represents the discrete logarithm, while the second represents the discrete exponential. At the same time, a large invertible matrix whose structure will be explained in detail will be the subject of the advanced classical Hill technique improvement. To eliminate any linearity, this matrix will be accompanied by dynamic vectors to install an affine transformation. The simulation of a large number of images of different sizes and formats checked by our algorithm ensures the robustness of our method.

• Steel and Composite Structures
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• v.47 no.3
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• pp.365-374
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• 2023

This paper presents a new scheme for constructing locking-free finite elements in thick and thin plates, called Discrete Shear Gap element (DSG), using multiphase material topology optimization for triangular elements of Reissner-Mindlin plates. Besides, common methods are also presented in this article, such as quadrilateral element (Q4) and reduced integration method. Moreover, when the plate gets too thin, the transverse shear-locking problem arises. To avoid that phenomenon, the stabilized discrete shear gap technique is utilized in the DSG3 system stiffness matrix formulation. The accuracy and efficiency of DSG are demonstrated by the numerical examples, and many superior properties are presented, such as being a strong competitor to the common kind of Q4 elements in the static topology optimization and its computed results are confirmed against those derived from the three-node triangular element, and other existing solutions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.747-752
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• 1999

In the fast sampling limit, the delta operator model tends to the analog system model. This fundamental property of the delta operator model unifies continuous and discrete time control system. In this paper, we study robust linear optimal model following servo system in the presence of disturbances and parameter perturbations. A technique to directly design the generalized differential operator based unified control system that convers both differential operator based continuous time and delta operator based discrete time case is presented. The quadratic criterion function for a linear system is used to design the robust unified servo control. The characteristics of the proposed servo system are analysed and simulated to verify the robustness.

• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1633-1642
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• 2002

Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today's highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system using electro-hydraulic manipulator because hydraulic manipulators have the advantage of electric insulation. Meanwhile it is relatively difficult to realize autonomous assembly tasks particularly in the case of manipulating flexible objects such as electric lines. In this report, a discrete event control system is introduced for automatic assembly task of electric lines into sleeves as one of the typical task of active electric power lines. In the implementation of a discrete event control system, LVQNN (linear vector quantization neural network) is applied to the insertion task of electric lines to sleeves. In order to apply these proposed control system to the unknown environment, virtual learning data for LVQNN is generated by fuzzy inference. By the experimental results of two types of electric lines and sleeves, these proposed discrete event control and neural network learning algorithm are confirmed very effective to the insertion tasks of electric lines to sleeves as a typical task of active electric power maintenance tasks.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.2
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• pp.76-81
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• 2011

Power system frequency is the main index of power quality indicating an abnormal state and disturbances of systems. The nominal frequency is deviated by sudden change in generation and load or faults. Power system is used as frequency relay to detection for off-nominal frequency operation and connecting a generator to an electrical system, and V/F relay to detection for an over-excitation condition. Under these circumstances, power system should maintain the nominal frequency. And frequency and frequency deviation should accurately measure and quickly estimate by frequency measurement device. The well-known classical method, frequency estimation technique based on the DFT, could be produce the gain error in accuracy. To meet the requirements for high accuracy, recently Wavelet transforms and analysis are receiving new attention. The Wavelet analysis is possible to calculate the time-frequency analysis which is easy to obtain frequency information of signals. However, it is difficult to apply in real-time implementation because of heavy computation burdens. Nowadays, the computational methods using the Wavelet function and transformation techniques have been searched on these fields. In this paper, we apply the Recursive Discrete Wavelet Transform (RDWT) for the frequency estimation. In order to evaluate performance of the proposed technique, the user-defined arbitrary waveforms are used.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1183-1190
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• 2007

This paper proposes the improved fault decision algorithm using DWT(Discrete Wavelet Transform) and ANNs for the FRTU(Feeder Remote Terminal Unit) on the feeder in the power distribution system. Generally, the FRTU has the fault decision scheme detecting the phase fault, the ground fault. Especially FRTU has the function for 2000ms. This function doesn't operate FI(Fault Indicator) for the Inrush current generated in switching time. But it has a defect making it impossible for the FI to be operated from the real fault current in inrush restraint time. In such a case, we can not find the fault zone from FI information. Accordingly, the improved fault recognition algorithm is needed to solve this problem. The DWT analysis gives the frequency and time-scale information. The neural network system as a fault recognition was trained to distinguish the inrush current from the fault status by a gradient descent method. In this paper, fault recognition algorithm is improved by using voltage monitoring system, DWT and neural network. All of the data were measured in actual 22.9kV power distribution system.