• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.290-299
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• 2009

Evolutionary Artificial Neural Networks (EANNs) has been highly effective in Artificial Intelligence (AI) and in training NPCs in video games. When EANNs is applied to design game NPCs' smart AI which can make the game more interesting, there always comes two important problems: the more complex situation NPCs are in, the more complex structure of neural networks needed which leads to large operation cost. In this paper, the Dynamic State Evolutionary Neural Networks (DSENNs) is proposed based on EANNs which deletes or fixes the connection of the neurons to reduce the operation cost in evolution and evaluation process. Darwin Platform is chosen as our test bed to show its efficiency: Darwin offers the competitive team game playing behaviors by teams of virtual football game players.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.4
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• pp.199-202
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• 2007

In this paper, we examined the operating principle of a passive tag antenna for RFID system in UHF band. Based on the study, we proposed a novel RFID tag antenna which adopts the inductively coupled feeding structure to match antenna impedance to a capacitively loaded commercial tag chip. The proposed tag antenna consists of microstrip lines on a thin PET substrate for low-cost fabrication. The detail structure of the tag antenna were optimized using a full electromagnetic wave simulator of IE3D in conjunction with a Pareto genetic algorithm, and the size of the tag antenna can be reduced up to kr=0.27(2 cm2). We built some sample antennas and measured the antenna characteristics such as a return loss, an efficiency, and radiation patterns. The readable range of the tag antenna with a commercial RFID system showed about 1 to 3 m.

• Journal of Satellite, Information and Communications
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• v.5 no.2
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• pp.57-63
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• 2010

Many efficient frequency sharing methods are issued in present because of increasing users with various wireless communication terminals. In the satellite communications, the service coverage is generally very wide so frequency sharing with terrestrial system is essentially needed, and the research is progressing dynamically related on this frequency sharing method. But if we adopt the terrestrial system which is commonly used, it can't avoid the interference from terrestrial service to satellite service. Therefore, this paper will introduce methods for reducing the interference from terrestrial station to earth station using cognitive radio system Satellite system is guaranteed with decreasing interference from terrestrial stations using Genetic Algorithm based power control method. Furthermore, terrestrial systems can have increased QoS because the frequency reuse factor in proposed method is higher than existing methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.108-118
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• 2014

On the optimization design problem using surrogate model, it requires considerable number of sampling points to construct a surrogate model which retains the accuracy. As an alternative to reduce construction cost of the surrogate model, Variable-Fidelity Modeling(VFM) technique, where correct high fidelity model based on the low fidelity surrogate model is introduced. In this study, hierarchical kriging model for variable-fidelity surrogate modeling is used and an optimization framework with multi-objective genetic algorithm(MOGA) is presented. To prove the feasibility of this framework, airfoil design optimization process is performed for the transonic region. The parameters of PARSEC are used to design variables and the optimization process is performed in case of varying number of grid and varying fidelity. The results showed that pareto front of all variable-fidelity models are similar with its single-level of fidelity model and calculation time is considerably reduced. Based on computational results, it is shown that VFM is a more efficient way and has an accuracy as high as that single-level of fidelity model optimization.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.9 s.100
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• pp.872-882
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• 2005

In this paper, we examined the operating principle of a passive tag antenna for RFID system in UHF band. Based on the study, we proposed a novel RFID tag antenna which adopts the inductively coupled feeding structure to match antenna impedance to a capacitively loaded commercial tag chip. The proposed tag antenna consists of microstrip lines on a thin PET substrate for low-cost fabrication. The detail structure of the tag antenna were optimized using a full electromagnetic wave simulator of IE3D in conjunction with a Pareto genetic algorithm and the size of the tag antenna can be reduced up to kr=0.27($2 cm^2$). We built some sample antennas and measured the antenna characteristics such as a return loss, an efficiency, and radiation patterns. The readable range of the tag antenna with a commercial RFID system showed about 1 to 3 m.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.