• Journal of Power System Engineering
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• v.18 no.5
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• pp.28-34
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• 2014

This paper deals with optimum PI controller design using genetic algorithm to improve control performance and robustness for an oil cooler system. The optimum PI gain was found to minimize an object function, integrated absolute error, and to satisfy control design specifications such as overshoot and settling time based on practical transfer function of the oil cooler system. The control performance and robustness were investigated by comparing indicial responses and Bode diagram analysis with respect to three kinds of PI gains obtained from different gain decision manners. Moreover, the robustness against to input disturbances, sinusoidal wave form and abrupt single pulse, was evaluated. The computer simulation results showed that the suggested optimum gain can establish desirable control performance and strong robustness with easy design process.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.291-293
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• 1999

This paper proposed the optimal operational scheduling of cogeneration system for paper mill connected with several auxiliary devices. Auxiliary devices that include auxiliary boilers, waste heat boilers and sludge incinerators operate with multi-cogeneration systems. Especially environment element was considered in objective function to solve the environment problem. And GAs(Genetic Algorithms) was applied to optimize and to analyse nonlinear operational property of cogeneration system of paper mill connected with several auxiliary devices. C-language was used to GAs computation. Electricity can be purchased through power system from utility. The proposed operational strategy on cogeneration system for paper mill to increase energy efficiency can be applied to the similar cogeneration system of industrial field.

• Journal of Korean Association for Spatial Structures
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• v.15 no.4
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• pp.81-89
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• 2015

Damped outrigger systems have been proposed as a novel energy dissipation system to protect tall buildings from severe earthquakes and strong wind loads. In this study, semi-active damping devices such as magnetorheological (MR) dampers instead of passive dampers are installed vertically between the outrigger and perimeter columns to achieve large and adaptable energy dissipation. Control performance of semi-active outrigger damper system mainly depends on the control algorithm. Fuzzy logic control algorithm was used to generate command voltage sent to MR damper. Genetic algorithm was used to optimize the fuzzy logic controller. An artificial earthquake load was generated for numerical simulation. A simplified numerical model of damped outrigger system was developed. Based on numerical analyses, it has been shown that the semi-active damped outrigger system can effectively reduce both displacement and acceleration responses of the tall building in comparison with a passive outrigger damper system.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.9-11
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• 2003

In power system planing and operation, voltage and reactive power control are very important. The voltage deviation and system losses can be reduced through control of reactive power sources. In general, there are several different reactive power sources, we used UPFC and switched shunt capacitor to improve the voltage profile and to reduce system losses in this study. Since there are many switched shunt capacitors in power system, so it is necessary to coordinate these switched shunt capacitors. In this study, Genetic Algorithm(GA) is used to find optimal coordination of UPFC and switched shunt capacitors in a local area of power system. In case study, the effectiveness of the proposed method is demonstrated in KEPCO's power system. The simulation is performed by PSS/E.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.75-80
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• 1999

This paper presents a new method to find a optimal solution for stochastic system. This method uses Genetic Algorithm(GA) and simulation. GA is used to search for new alternative and simulation is used to evaluate alternative. The stochastic system has one or more random variables as inputs. Random inputs lead to random outputs. Since the outputs are random, they can be considered only as estimates of the true characteristics of they system. These estimates could greatly differ from the corresponding real characteristics for the system. We need multiple replications to get reliable information on the system. And we have to analyze output data to get a optimal solution. It requires too much computation to be practical. We address the problem of reducing computation. The procedure on this paper use GA character, an iterative process, to reduce the number of replications. The same chromosomes could exit in post and present generation. Computation can be reduced by using the information of the same chromosomes which exist in post and present current generation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.26-35
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• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.38-45
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• 2006

We present a client-server system architecture for inferring genetic interaction networks based on Bayesian networks. It is typical to take tens of hours when genome-wide large-scale genetic interaction networks are inferred in the form of Bayesian networks. To deal with this situation, batch-style distributed system architectures are preferable to interactive standalone architectures. Thus, we have implemented a loosely coupled client-server system for network inference and user interface. The network inference consists of two stages. Firstly, the proposed method divides a whole gene set into overlapped modules, based on biological annotations and expression data together. Secondly, it infers Bayesian networks for each module, and integrates the learned subnetworks to a global network through common genes across the modules.

• Korean Journal of Plant Resources
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• v.25 no.1
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• pp.63-71
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• 2012

To assess parameters of mating system in seed orchard, such as outcrossing rates, number of potential pollen contributors, and degree of pollen contamination, seeds, produced in '77 plot of the Japanese red pine (Pinus densiflora S et Z) seed orchard at Anmyeon island, were collected in 2007 and analysed by nSSR and cpSSR markers. Estimates of outcrossing rates ranged from 91.2 to 100% (mean 97.7%) on the basis of the analysis of cpSSR haplotypes and from 81.6 to 100% (mean 95.3%) on the basis of the analysis of nSSR genotypes. By cross checking of both DNA markers, seeds, presumed to be products of self pollination on the basis of single marker, were confirmed as outcrossed seeds, which resulted in cumulative outcrossing rates of 98.9%. On the basis of pooled cpSSR haplotype of each seed, the number of pollen contributors and paternal contribution rates were estimated as 14.8 and 0.512, respectively. In conclusion, considering pretty high level of outcrossing rates observed in a seed orchard, good genetic potential of the seeds, produced in '77 plot of the seed orchard of Japanese red pines at Anmyeon island, may be guaranteed. Investigated results from the analysis of mating system of Japanese red pines in a '77 plot of the seed orchard may also be expected to provide useful information for the management and establishment of the seed orchard of the progressive generation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.185-192
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• 2014

Planetary gear design is complex because it involves a combination of discrete variables such as module, integer variables such as the number of teeth, and continuous variables such as face width and aspect ratio. Thus, an optimum design technique is needed. In this study, we applied a genetic algorithm to the design optimization of a planetary gear. In this algorithm, tooth root strength and surface durability are assessed with fundamental variables such as the number of teeth, module, pressure angle, and face width. With the help of this technique, gear designers could reduce trial and error in the initial design stages, thus cutting the time required for planetary gear design.