• International conference on construction engineering and project management
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• 2013.01a
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• pp.294-299
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• 2013

In many urban cities, super-tall buildings have been being constructed around New York and Chicago as the center since 1930 to improve the efficiency of land use and respond to new residential type. In terms of energy consumption, super-tall buildings are classified as a top energy consumption building. Also, as time passed, the degradation of energy performance occurs in super-tall buildings like general things so that these cannot show the initial performance planned in the design phase. Accordingly, building owners need to make a plan to apply energy saving measures to existing building during the operation phase. In order to select energy saving measures, calculus-based methods and enumerative schemes have been typically used. However, these methods are time-consuming and previous studies which used these methods have problems with not considering the initial construction cost. Consequently, this study proposes a model for selecting an optimal combination of energy saving measures which derives maximum energy saving within allowable cost using genetic algorithms. As a contribution of this research, it would be expected that a model is utilized as one of the decision-making tools during the planning stage for energy saving.

• Journal of Advanced Research in Ocean Engineering
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• v.1 no.2
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• pp.94-105
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• 2015

In this study, a thruster allocation method of a deep-sea working vessel was proposed with the aims of producing the demanded generalized forces and moment for dynamic positioning while at the same time minimizing total power. For this, an optimization problem for thrust allocation was mathematically formulated with design variables, objective function, and constraints. The genetic algorithms (GA) was used to solve the formulated problem. The proposed method was applied to an example of finding optimal thrust allocation of the deep-sea working vessel having 5 thrusters. The result showed that the method could be used to determine better strategy for thruster allocation of the vessel as compared to existing study.

• Journal of Welding and Joining
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• v.27 no.2
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• pp.57-62
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• 2009

This research was carried out to improve the productivity in the subassembly process of shipbuilding through optimal work planning for the shortest work time. The work time consist of welding time, moving time of gantry, teaching time of robot and robot motion time. The shortest work time is accomplished by even distribution of work and the shortest welding sequence. Even distribution of work was done by appling the simple algorithm. The shortest work sequence was determined by using GA. The optimal work planning decreased the total work time of the subassembly process by 4.1%. The result showed the effectiveness of the suggested simple algorithm for even distribution of work and GA for the shortest welding sequence.

• Management Science and Financial Engineering
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• v.17 no.1
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• pp.19-43
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• 2011

Tuning the architecture of SVM (support vector machine) is to build an SVM model of better performance. Two different tuning methods of the grid search and the GA (genetic algorithm) have been addressed in the literature, each of which has its own methodological pros and cons. This paper suggests a combined method for tuning the architecture of SVM models, which employs the GAM (generalized additive models), the grid search, and the GA in sequence. The GAM is used for selecting input variables, and the grid search and the GA are employed for finding optimal parameter values of the SVM models. Applying the method to a bankruptcy prediction problem, we show that SVM model tuned by the proposed method outperforms other SVM models.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.295-304
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• 2003

In this paper, we proposed a hardware-oriented Genetic Algorithm Processor(GAP) based on subpopulation architecture for high-performance convergence and reducing computation time. The proposed architecture was applied to enhancing population diversity for correspondence to premature convergence. In addition, the crossover operator selection and linear ranking subpop selection were newly employed for efficient exploration. As stochastic search space selection through linear ranking and suitable genetic operator selection with respect to the convergence state of each subpopulation was used, the elapsed time of searching optimal solution was shortened. In the experiments, the computation speed was increased by over $10\%$ compared to survival-based GA and Modified-tournament GA. Especially, increased by over $20\%$ in the multi-modal function. The proposed Subpop GA processor was implemented on FPGA device APEX EP20K600EBC652-3 of AGENT 2000 design kit.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.38-46
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• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.152-154
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• 2005

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 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 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.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.505-514
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• 2021

Among non-timber forest products, Tricholoma matsutake is a high value added item. Many countries, including Korea, China, and Japan, are doing research and technology development to increase artificial cultivation and productivity. However, the production of T. matsutake is on the decline due to global warming, abnormal temperatures and pine tree pest problems. Therefore, it is necessary to identify trends in domestic and foreign research on T. matsutake, respond to preemptive research and development to preserve the genetic resources of T. matsutake and increase its productivity. Based on the correlation between keywords in the high frequency keywords, it was observed that microbial clusters of T. matsutake are mainly found in Korea. The main focus in China has been the pharmacology studies on the ingredients of T. matsutake. The main focus in Japan has been on preserving the genetic diversity and species of T. matsutake. Thus, future domestic studies of T. matsutake will require pharmacological studies on the ingredients of T. matsutake and on its genetic diversity and species conservation. In addition, unlike China and Japan, genetic keywords did not appear in Korea at high frequency. Therefore, Korea will have to proceed with research using modern molecular biology techniques.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.612-618
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• 1998

The success of a fuzzy logic control system solving any given problem critically depends on the architecture of th network. Various attempts have been made in optimizing its structure its structure using genetic algorithm automated designs. In a regular genetic algorithm , a difficulty exists which lies in the encoding of the problem by highly fit gene combinations of a fixed-length. This paper presents a new approach to structurally optimized designs of a fuzzy model. We use a messy genetic algorithm, whose main characteristics is the variable length of chromosomes. A messy genetic algorithms used to obtain structurally optimized fuzzy models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the exampled of a cart-pole balancing.

• Journal of Ocean Engineering and Technology
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• v.28 no.5
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• pp.404-410
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• 2014

The purpose of this study was to find the optimal arrangement of FPSO equipment in a module while considering the economic value and fire risk. We estimated the economic value using the pipe connections and pump installation cost in an HP (high pressure) gas compression module. The equipment risks were also analyzed using fire scenarios based on historical data. To consider the wind effect during a fire accident, fuzzy modeling was applied to improve the accuracy of the analysis. The objective functions consisted of the economic value and fire risk, and the constraints were the equipment maintenance and weight balance of the module. We generated a Pareto-optimal front group using a multi-objective GA (genetic algorithm) and suggested an equipment arrangement method that included the opinions of the designer.