• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.27 no.12
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• pp.767-775
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• 2014

New material design has obtained tremendous attention in material science community as the performance of new materials, especially in nano length scale, could be greatly improved to applied in modern industry. In certain conditions limiting experimental synthesis of these new materials, new approach by computer simulation has been proposed to be applied, being able to save time and cost. Recent development of computer systems with high speed, large memory, and parallel algorithms enables to analyze individual atoms using first principle calculation to predict quantum phenomena. Beyond the quantum level calculations, mesoscopic scale and continuum limit can be addressed either individually or together as a multi-scale approach. In this article, we introduced current endeavors on material design using analytical theory and computer simulations in multi-length scales and on multi-physical properties. Some of the physical phenomena was shown to be interconnected via a cross-link rule called 'cross-property relation'. It is suggested that the computer simulation approach by multi-physics analysis can be efficiently applied to design new materials for multi-functional characteristics.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.3
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• pp.175-182
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• 2022

In many engineering problems, the dynamic substructuring can be useful to analyze complex structures which made with many substructures, such as aircrafts and automotive vehicles. It was originally intended as a method to simplify the engineering problem. The powerful advantage to this is that computational efficiency dramatically increases with eliminating unnecessary degrees-of-freedom of the system and the system targets are concurrently satisfied. Craig-Bampton method has been widely used for the linear system reduction. Recently, multi-level optimization (such as target cascading), which propagates the system-level targets to the subsystem-level targets, has been widely utilized. To this concept, the genetic algorithm which one of the global optimization technique has been utilized to the substructure optimization. The number of internal modes for each substructure can be obtained by the genetic algorithm. Simultaneously, the reduced system meets the top-level targets. In this paper, various numerical examples are tested to verify this concept.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.9 no.3
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• pp.410-425
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• 1997

Buildings are mostly under part load conditions causing an inefficient system operation in terms of energy consumption. It is critical to operate building air-conditioning system with a scientific or optimal manner which minimizes energy consumption and maintains thermal comfort by matching building sensible and latent loads. Little research has been performed in developing general methodologies for the optimal operation of air-conditioning system. Based on this research motivation, system simulation program was developed by adopting various equipment operating strategies which are energy efficient especially for humidity control in summer. A numerical optimization technique was utilized to search optimal solution for multi-independent variables and then linked to the developed system simulation model within a mam program. The main goal of the study is to provide a systematic framework and guideline for the optimal operation of air-conditioning system focusing on air-side. For given cooling loads and ambient outdoor conditions the optimal operating strategies of a commercial building are determined by minimizing a constrained objective function by a nonlinear programming technique. Desired space setpoint conditions were found through evaluating the trade-offs between comfort and system power consumption. The results show that supply airflow rate and compressor fraction play main roles in the optimization process. It was found that variable setpoint optimization technique could produce lower indoor humidity level demanding less power consumption which will be benefits for building applications of humidity problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1106-1127
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• 2012

Service oriented architecture (SOA) lends itself to model the application components to coarse-grained services in such a way that the composition of different services could be feasible. Service composition fulfills numerous service requirements by constructing composite applications with various services. As it is the case in many real-world applications, different users have diverse QoS demands issuing for composite applications. In this paper, we present a service composition framework for a typical service overlay network (SON) considering both multiple QoS constraints and load balancing factors. Moreover, a service selection algorithm based on niching technique and particle swarm optimization (PSO) is proposed for the service composition problem. It supports optimization problems with multiple constraints and objective functions, whether linear or nonlinear. Simulation results show that the proposed algorithm results in an acceptable level of efficiency regarding the service composition objective under different circumstances.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) 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 genetic algorithms (GAs). The proposed SOFPNN 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. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.339-350
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• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.187-198
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• 2022

This paper addresses the maintenance optimization problem in multi-component systems in which parts are connected in series, carrying out several missions interspersed with scheduled finite breaks. Due to limited time or resources, maintenance actions can be only carried out on a limited set of components. The decision maker then has to decide which components to maintain to ensure a pre-specified performance level during next mission. Most of the existing models in the literature usually assume only one system and enough spare parts. However, there are situations in which maintenance is required for multiple systems of the same type. To overcome this restrictive assumption, this study optimizes the maintenance problem considering the lack of repair parts and cannibalism for many identical systems. This study presents two optimization models with different objectives to solve the problem and analyzes the results so that the decision maker can decide. The results of this study are expected to be used for the maintenance of multiple systems of the same type, such as swarm drones.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1471-1480
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• 2020

This research presents a case study on the remediation of a radioactive waste (uranium: U) utilizing a multi-objective fuzzy optimization in an electrocoagulation process for the iron-stainless steel and aluminum-stainless steel anode/cathode systems. The incorporation of the cumulative uncertainty of result, operational cost and energy consumption are essential key elements in determining the feasibility of the developed model equations in satisfying specific maximum contaminant level (MCL) required by stringent environmental regulations worldwide. Pareto-optimal solutions showed that the iron system (0 ㎍/L U: 492 USD/g-U) outperformed the aluminum system (96 ㎍/L U: 747 USD/g-U) in terms of the retained uranium concentration and energy consumption. Thus, the iron system was further carried out in a multi-objective analysis due to its feasibility in satisfying various uranium standard regulatory limits. Based on the 30 ㎍/L MCL, the decision-making process via fuzzy logic showed an overall satisfaction of 6.1% at a treatment time and current density of 101.6 min and 59.9 mA/㎠, respectively. The fuzzy optimal solution reveals the following: uranium concentration - 5 ㎍/L, cumulative uncertainty - 25 ㎍/L, energy consumption - 461.7 kWh/g-U and operational cost based on electricity cost in the United States - 60.0 USD/g-U, South Korea - 55.4 USD/g-U and Finland - 78.5 USD/g-U.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.9
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• pp.146-155
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• 1994

In this paper, we desribe a new system, called BEST, that is used to develop a macromodel or behavioral model easily. It automatically calculates the component values of macromodel represented by equations to satisfy the given specification. Also, it gives the way to analyze both the behavioral model and transistor level circuit, and then compare the analysis results of them to check the correspondence under specific temperature and bias condition, and BEST optimizes the component values of macromodel. Other feature is to characterize MOSFET as switch model which consists of PWL-RC network. Finally, it is possible to generage multi-level netlist which consists of macro/switch/transistor level circuits, and user can determine the trade-off between simulation speed and accuracy. With the graphic user interface form of macromodel development system described above. BEST enable designers to make macromodel by themselves and to uas it. We applied BEST to develop the macromodel for the test circuit and got the 18.6 times simulation speed up with preserving the accuracy within 10% compared to the conventional transistor level circuit simulation. Also, applicability of optimization capability was verified.