• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.3
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• pp.325-330
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• 2012

This paper describes the structural analysis and optimization of stiffeners used in inner tanks for liquid natural gas (LNG) storage, so that the costs can be minimized while the critical buckling load of the inner tank still exceeds the external pressure exerted by the perlite. The original calculation of perlite pressure applied to the inner tank was based on Zick's code, which led to the overestimation of the external pressure, and consequently, an oversized stiffener. In this study, the effects of the material properties of perlite on the external pressure distribution are scrutinized, and the optimum dimensions of a single stiffener are finally obtained through a series of parametric studies. A 15% decrease in the cost of the stiffener compared with the original design is achieved.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.257-271
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• 2010

As the end of photolithographic integration era is approaching fast, numerous nanoscale devices and systems based on novel nanoscale materials and assembly techniques are recently emerging. Notably, various reconfigurable architectures with considerable promise have been proposed based on nanowire crossbar structure as the primitive building block. Unfortunately, high-density sys-tems consisting of nanometer-scale elements are likely to have numerous physical imperfections and variations. Therefore, defect-tolerance is considered as one of the most exigent challenges in nanowire crossbar systems. In this work, three different defect-avoidant logic mapping algorithms to circumvent defective crosspoints in nanowire reconfigurable crossbar systems are evaluated in terms of various performance metrics. Then, a novel method to find the most cost-effective repair solution is demonstrated by considering all major repair parameters and quantitatively estimating the performance and cost-effectiveness of each algorithm. Extensive parametric simulation results are reported to compare overall repair costs of the repair algorithms under consideration and to validate the cost-driven repair optimization technique.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.2
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• pp.139-148
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• 2013

In this study, an ejector was designed to recirculate the anodic off-gas of SOFC, and a parametric study of the system performance was conducted at various ejector entrainment ratios. Aspen Plus, a chemical engineering program, was used to calculate the operational conditions of the ejector. To minimize the calculation load of the CFD and to ensure the global optimum, a genetic algorithm and Kriging model were used for the optimization. The optimization results showed that the dominant design variables of the sonic ejector are the throat diameter and the first flow nozzle position. The designed ejector has enough flexibility for different operating conditions of a 1-kW SOFC system. When the ejector was applied to the SOFC, it reduced 56% of the steam and 8.4% of the fuel compared to the reference case.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.45-51
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• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• Journal of the Korea Society for Simulation
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• v.29 no.3
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• pp.49-55
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• 2020

A goal of improving projectile is to increasing achievable range. The shape of a projectile is generally selected on the basis of combined aerodynamics and structural considerations. The choice of body, nose and boattail shape has a large effect on aerodynamic design. One of the main design factors that affect projectile configuration is aerodynamic drag. The aerodynamic drag refers to the aerodynamic force that acts opposite to the relative motion of a projectile. An investigation was made to predict the effects of nose, boattail and body shapes on the aerodynamic characteristics of projectiles using a semi-empirical technique. A parametric study is conducted which includes different projectile geometry. Performance predictions of achievable range are conducted using a trajectory simulation model. The potential of extending the range of a projectile using optimization of projectile configuration is evaluated. The maximum range increase is achieved due to the combination of optimal body shapes.

• Korean Journal of Construction Engineering and Management
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• v.14 no.5
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• pp.35-43
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• 2013

Nowadays the widely used media in architecture include visualizations, animations and three-dimensional models. 3D digital methods using active CAM(Computer Aided Manufacturing) and CNC(Computerized Numerical Control) imaging have been developed for accurate shape and 3D measurements in freeform buildings. In contrast to a conventional building using auto CAD system and others, the proposed digital optimization method is based on a combination of 3D numerical data and parametric 3D model for design and construction. The objective of this paper is therefore to present digital optimization process for constructability of freeform building. The method can be useful in the effective implementation of an error-proofing process of freeform building during design and construction phase. 3D digital coordinate data can be used effectively to identify correct size of structural and finish members and installation location of each members in construction field. In addition, architects, engineers and contractors can evaluate design, materials, constructability and identify error-proofing opportunities. Other project participants can also include representatives from all levels of management, departments as well as workers and key subcontractors' personnel, if necessary. The 3D digital optimization process is therefore appropriate to serious variations in freeform shape. For future study, the developed digital optimization method is necessary to be carried out to verify the robustness and accuracy for constructability in construction field.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.524-532
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• 2005

This paper concerns delay-dependent guaranteed cost control (GCC) problem for a class of linear state-delayed systems with norm-bounded time-varying parametric uncertainties. By incorporating the free weighing matrix approach developed recently, new delay-dependent conditions for the existence of the guaranteed cost controller are presented in terms of matrix inequalities for both nominal state-delayed systems and uncertain state-delayed systems. An algorithm involving convex optimization is proposed to design a controller achieving a suboptimal guaranteed cost such that the system can be stabilized for all admissible uncertainties. Through numerical examples, it is shown that the proposed method can yield less guaranteed cost than the existing delay-dependent methods.