• Journal of English Language & Literature
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• v.53 no.2
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• pp.243-260
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• 2007

This article explores how Richard Powers' The Gold Bug Variations, an interdisciplinary novel through the new concepts of biocriticism and bioliterature is connected with literature/art and science/technology. Powers uses Edgar Allen Poe's "The Gold Bug" and Johann Sebastian Bach's "The Goldberg Variations" for decoding DNA in order to analogize a genomic metaphor. He imagines literature as "the book of life" genome, written by DNA code due to the complexity and multiplicity of the genome. His novel, as 'genomic narrative,' shows the articulation of the genomic reading, and expression in the life language through the discourses of the information technology and the rhetorical tropes in biology. New biological ideas are continually required to articulate these processes. In the present tendency of the Human Genome Project, such advanced devices as biocybernetics offer the potential to open up new possibilities to researching the complexity of the genome. This can only happen if the following two ideas are followed: One is to comply with advanced technologies for processing the rapidly increasing data of the genome sequence; The other is to admit the necessary paradigm shift in biology. As shown above, the complexity and multiplicity of the genomic reality is not so simple. We must go beyond determinism, even if representation of a biological reality reveals the possibility of expressing its constituent elements by the advanced biotechnology. Consequently, in the unstoppable advances of the art of decoding the genome, The Gold Bug Variations interrelates to the interdisciplinary approaches through the rhetorical tropes that unfold the complex discursive world of the genome. Powers shows that the complex mechanisms of the genome in the microworld of every cell as the plot of "the book of life" can be designed and written using DNA language. At the same time, his genomic reading and writing demonstrate the historical processes of the shifting center of new genomic development and polysemous interpretation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.7
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• pp.601-608
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• 2015

The static aeroelastic analysis and optimization of flexible wings are conducted for steady state conditions while both aerodynamic and structural parameters can be used as optimization variables. The system of multidisciplinary design optimization as a robust methodology to couple commercial codes for a static aeroelastic optimization purpose to yield a convenient adaptation to engineering applications is developed. Aspect ratio, taper ratio, sweepback angle are chosen as optimization variables and the skin thickness of the wing. The real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was used to control the optimization process. The support vector regression(SVR) is applied for optimization, in order to reduce the time of computation. For this multi-objective design optimization problem, numerical results show that several useful Pareto optimal designs exist for the flexible wing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.537-544
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• 2020

Advances in detection techniques, such as mutation and obfuscation, are being advanced with the development of malware technology. In the malware detection technology, unknown malware detection technology is important, and a method for Malware Authorship Attribution that detects an unknown malicious code by identifying the author through distributed malware is being studied. In this paper, we try to extract the compiler information affecting the binary-based author identification method and to investigate the sensitivity of feature selection, probability and non-probability models, and optimization to classification efficiency between studies. In the experiment, the feature selection method through information gain and the support vector machine, which is a non-probability model, showed high efficiency. Among the optimization studies, high classification accuracy was obtained through feature selection and model optimization through the proposed framework, and resulted in 48% feature reduction and 53 faster execution speed. Through this study, we can confirm the sensitivity of feature selection, model, and optimization methods to classification efficiency.

• Journal of Ocean Engineering and Technology
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• v.29 no.5
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• pp.366-372
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• 2015

Ocean energy is one of the most promising renewable energy resources. In particular, South Korea is one of the countries where it is economically and technically feasible to develop tidal current power plants to use tidal current energy. In this study, based on the design code for HARP_Opt (Horizontal axis rotor performance optimizer) developed by NREL (National Renewable Energy Laboratory) in the United States, and applying the BEMT (Blade element momentum theory) and GA (Genetic algorithm), the optimal shape design and performance evaluation of the horizontal axis rotor for a 200-kW-class tidal current turbine were performed using different numbers of blades (two or three) and a pitch control method (variable pitch or fixed pitch). As a result, the VSFP (Variable Speed Fixed Pitch) turbine with three blades showed the best performance. However, the performances of four different cases did not show significant differences. Hence, it is necessary when selecting the final design to consider the structural integrity related to the fatigue, along with the economic feasibility of manufacturing the blades.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.369-378
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• 2012

A load-following operation in APR+ nuclear plants is necessary to reduce the need to adjust the boric acid concentration and to efficiently control the control rods for flexible operation. In particular, a disproportion in the axial flux distribution, which is normally caused by a load-following operation in a reactor core, causes xenon oscillation because the absorption cross-section of xenon is extremely large and its effects in a reactor are delayed by the iodine precursor. A model predictive control (MPC) method was used to design an automatic load-following controller for the integrated thermal power level and axial shape index (ASI) control for APR+ nuclear plants. Some tracking controllers employ the current tracking command only. On the other hand, the MPC can achieve better tracking performance because it considers future commands in addition to the current tracking command. The basic concept of the MPC is to solve an optimization problem for generating finite future control inputs at the current time and to implement as the current control input only the first control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The support vector regression (SVR) model that is used widely for function approximation problems is used to predict the future outputs based on previous inputs and outputs. In addition, a genetic algorithm is employed to minimize the objective function of a MPC control algorithm with multiple constraints. The power level and ASI are controlled by regulating the control banks and part-strength control banks together with an automatic adjustment of the boric acid concentration. The 3-dimensional MASTER code, which models APR+ nuclear plants, is interfaced to the proposed controller to confirm the performance of the controlling reactor power level and ASI. Numerical simulations showed that the proposed controller exhibits very fast tracking responses.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Wind and Structures
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• v.18 no.2
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• pp.195-213
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• 2014

Recently, the horizontal axis rotor performance optimizer (HARP_Opt) tool was developed in the National Renewable Energy Laboratory, USA. This innovative tool is becoming more popular in the wind turbine industry and in the field of academic research. HARP_Optwas developed on the basis of two fundamental modules, namely, WT_Perf, a performance evaluator computer code using the blade element momentum theory; and a genetic algorithm module, which is used as an optimizer. A pattern search algorithm was more recently incorporated to enhance the optimization capability, especially the calculation time and consistency of the solutions. The blade optimization is an aspect that is highly dependent on experience and requires significant consideration on rotor control strategies, wind data, and generator type. In this study, the effects of rotor control strategies including fixed speed and fixed pitch, variable speed and fixed pitch, fixed speed and variable pitch, and variable speed and variable pitch algorithms on optimal blade shapes and rotor performance are investigated using optimized blade designs. The effects of environmental wind data and the objective functions used for optimization are also quantitatively evaluated using the HARP_Opt tool. Performance indices such as annual energy production, thrust, torque, and roof-flap moment forces are compared.

• Molecules and Cells
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• v.25 no.1
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• pp.142-147
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• 2008

We recently used degenerate PCR and locus-specific PCR methods to identify the endogenous retroviruses (ERV) in the bovine genome. Using the ovine ERV classification system, the bovine ERVs (BERVs) could be classified into four families. Here, we searched the most recently released bovine genome database with the partial nucleotide sequence of the pro/pol region of the BERV ${\beta}3$ family. This allowed us to obtain and analyze the complete genome of BERV ${\beta}3$. The BERV ${\beta}3$ genome is 7666 nucleotides long and has the typical retroviral organization, namely, 5'-long terminal repeat (LTR)-gag-pro-pol-env-LTR-3'. The deduced open reading frames for gag, pro, pol and env of BERV ${\beta}3$ en- code 507, 271, 879 and 603 amino acids, respectively. BERV ${\beta}3$ showed little amino acid similarity to other betaretroviruses. Phylogenetic analysis showed that it clusters with HERV-K. This is the first report describing the genetic structure and sequence of an entire BERV.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.9
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• pp.815-824
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• 2015

In this study, we conducted the approximate multi-objective optimization of gap sizes of pressurized-water reactor (PWR) annular fuels. To determine the contacting tendency of the inner-outer gaps between the annular fuel pellets and cladding, thermoelastic-plastic-creep (TEPC)analysis of PWR annular fuels was performed, using in-house FE code. For the efficient heat transfer at certain levels of stress, we investigated the tensile, compressive hoop stress and temperature, and optimized the gap sizes using the non-dominant sorting genetic algorithm (NSGA-II). For this, response surface models of objective and constraint functions were generated, using central composite (CCD) and D-optimal design. The accuracy of approximate models was evaluated through $R^2$ value. The obtained optimal solutions by NSGA-II were verified through the TEPC analysis, and we compared the obtained optimum solutions and generated errors from the CCD and D-optimal design. We observed that optimum solutions differ, according to design of experiments (DOE) method.

• Nuclear Engineering and Technology
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• v.47 no.2
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• pp.139-147
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• 2015

Recently, severe accidents in nuclear power plants (NPPs) have become a global concern. The aim of this paper is to predict the hydrogen buildup within containment resulting from severe accidents. The prediction was based on NPPs of an optimized power reactor 1,000. The increase in the hydrogen concentration in severe accidents is one of the major factors that threaten the integrity of the containment. A method using a fuzzy neural network (FNN) was applied to predict the hydrogen concentration in the containment. The FNN model was developed and verified based on simulation data acquired by simulating MAAP4 code for optimized power reactor 1,000. The FNN model is expected to assist operators to prevent a hydrogen explosion in severe accident situations and manage the accident properly because they are able to predict the changes in the trend of hydrogen concentration at the beginning of real accidents by using the developed FNN model.