• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.2
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• pp.153-166
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• 2008

This paper concerns the development of a knowledge-based tunnel design system within the framework of artificial neural networks (ANNs). The system is aimed at expediting a routine tunnel design works such as determination of support patterns and stability analysis of selected support patterns. A number of sub-modules for determination of support patterns and stability assessment were developed and implemented to the system. It is shown that the ANNs trained with the results of 2D and 3D numerical analyses can be generalized with a reasonable accuracy, and that the ANN based tunnel design concept is a robust tool for tunnel design optimization. The details of the system architecture and the ANNs development are discussed in this paper.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1832-1837
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• 2011

This paper presents the optimal permanent magnet shape on the rotor of an interior permanent magnet motor to reduce the core losses and improve the performance. As permanent magnet has conductivity inherently, it causes huge amount of eddy current losses by the slot harmonics with concentrated winding. This loss is roughly 100 times larger than that of distributed winding in high speed operation and it cannot be ignored, especially on traction motors. Each eddy current loss on permanent magnet has been investigated in detail by using FEM(Finite Element Method) instead of EMCNM(Equivalent Magnetic Circuit Network Method) in order to consider saturation and non-linear magnetic property. Simulation-based DOE(Design Of Experiment) is also applied to avoid large number of analyses according to each design parameter and consider expected interactions among parameters. Consequently, the optimal design to reduce the core loss on the permanent magnet while maintaining or improving motor performance is proposed by an optimization algorithm using regression equation derived and lastly, the core loss reduction on the proposed shape of the permanent magnet is verified by FEM.

• Journal of Advanced Navigation Technology
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• v.5 no.1
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• pp.85-96
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• 2001

Recent development of a smart structures module and its successful integration with a multidisciplinary design optimization software $ASTROS^*$ and an Aeroservoelasticity module is presented. A modeled F-16 wing using piezoelectric actuators is used as an example to demonstrate the integrated software capability to design a flutter suppression system. For an active control design, neural network based controller is used for this study. A smart structures module is developed by modifying the existing thermal loads module in $ASTROS^*$ in order to include the effects of the induced strain due to piezoelectric actuation. The control surface/piezoelectric equivalence model principle is developed, which ensures the interchangeability between the control surface force input and the piezoelectric force input to the Aeroservoelasticity modules in $ASTROS^*$. The results show that the developed controller can increase the flutter speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2722-2742
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• 2014

We propose methods of linear transceiver design for two different power constraints, sum relay power constraint and per relay power constraint, which determine signal processing matrices such as base station (BS) transmitter, relay precoders and user receivers to minimize sum mean square error (SMSE) for multi-relay multi-user (MRMU) networks. However, since the formulated problem is non-convex one which is hard to be solved, we suboptimally solve the problems by defining convex subproblems with some fixed variables. We adopt iterative sequential designs of which each iteration stage corresponds to each subproblem. Karush-Kuhn-Tucker (KKT) theorem and SMSE duality are employed as specific methods to solve subproblems. The numerical results verify that the proposed methods provide comparable performance to that of a full relay cooperation bound (FRCB) method while outperforming the simple amplify-and-forward (SAF) and minimum mean square error (MMSE) relaying in terms of not only SMSE, but also the sum rate.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.201-209
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• 2014

This paper proposes a genetic algorithm to maximize the connectivity among the mobile nodes for the cooperative communication in ad-hoc networks. In general, as the movement of the mobile nodes in the networks increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time for a high-density network, we propose a genetic algorithm to obtain the optimal solution for maximizing the connectivity. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the maximum number of connections and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.254-257
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• 2012

Hedge funds can be established in Korea after the deregulation about setting up private equity funds on September, 2011. Although the variety of asset allocation strategies is the strength of hedge funds, most of Korean hedge funds uses only the equity long/short strategy. Therefore, it is need to introduce other strategies into Korea hedge funds, however all strategies can not be adopted at once because of the infrastructure of Korea financial market. In this paper, we find the optimal introductive order of strategies for Korea hedge fund in view of individual or institutional investors. For this analysis, HFRI data are used for the historical return of each hedge fund strategy and three methods (network visualization, principle component analysis and efficient frontier optimization) are used for finding the optimal order.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.17-17
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• 2009

Single-walled carbon nanotubes (SWCNTs) have attracted much attention as promising materials for transparent conducting films (TCFs), thanks to their superior electrical conductivity, high mechanical strength, and complete flexibility. The CNT-based TCFs can be used in a variety of application fields as flexible, transparent electrodes, including touch panel screens, flexible electronics, transparent heaters, etc. First of all, this study investigated the effect of a variety of surfactants on the dispersion of SWCNTs in an aqueous solution. Following the optimization of the dispersion by surfactants, flexible TCFs were fabricated by spraying the CNT suspension onto poly(ethylene terephthalate) (PET) substrates. The sheet resistances of the TCFs having different surfactants were investigated with treatment in nitric acid ($HNO_3$) whose concentration and period of treatment time were varied. It seems that the $HNO_3$ removes the surfactants from and is simultaneously doped into the SWCNT network, reducing the contact resistance between CNTs. TCFs were characterized by UV-VIS spectroscopy, thermogravimetric analyzer (TGA), scanning electron microscopy (SEM), and four-point probe.