• Journal of Microbiology and Biotechnology
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• v.25 no.10
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• pp.1714-1727
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• 2015

This work describes a new strategy for optimal design of Multiplex-PCR primer sequences. The process is based on the Particle Swarm Optimization-Simplex algorithm (Mult-PSOS). Diverging from previous solutions centered on heuristic tools, the Mult-PSOS is selfconfigured because it does not require the definition of the algorithm's initial search parameters. The successful performance of this method was validated in vitro using Multiplex-PCR assays. For this validation, seven gene sequences of the most prevalent bacteria implicated in urinary tract infections were taken as DNA targets. The in vitro tests confirmed the good performance of the Mult-PSOS, with respect to infectious disease diagnosis, in the rapid and efficient selection of the optimal oligonucleotide sequences for Multiplex-PCRs. The predicted sequences allowed the adequate amplification of all amplicons in a single step (with the correct amount of DNA template and primers), reducing significantly the need for trial and error experiments. In addition, owing to its independence from the initial selection of the heuristic constants, the Mult-PSOS can be employed by non-expert users in computational techniques or in primer design problems.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.121-130
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• 2014

Essential ideas, successes, and difficulties of Areal Density Analysis (ADA) for color-magnitude diagrams (CMD's) of resolved stellar populations are examined, with explanation of various algorithms and strategies for optimal performance. A CMD-generation program computes theoretical datasets with simulated observational error and a solution program inverts the problem by the method of Differential Corrections (DC) so as to compute parameter values from observed magnitudes and colors, with standard error estimates and correlation coefficients. ADA promises not only impersonal results, but also significant saving of labor, especially where a given dataset is analyzed with several evolution models. Observational errors and multiple star systems, along with various single star characteristics and phenomena, are modeled directly via the Functional Statistics Algorithm (FSA). Unlike Monte Carlo, FSA is not dependent on a random number generator. Discussions include difficulties and overall requirements, such as need for fast evolutionary computation and realization of goals within machine memory limits. Degradation of results due to influence of pixelization on derivatives, Initial Mass Function (IMF) quantization, IMF steepness, low Areal Densities ($\mathcal{A}$), and large variation in $\mathcal{A}$ are reduced or eliminated through a variety of schemes that are explained sufficiently for general application. The Levenberg-Marquardt and MMS algorithms for improvement of solution convergence are contained within the DC program. An example of convergence, which typically is very good, is shown in tabular form. A number of theoretical and practical solution issues are discussed, as are prospects for further development.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.535-540
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• 2009

Recently, autonomous robots which can achieve the complex tasks have been required with the advance of robotics. Advanced robot vision for recognition is necessary for the realization of such robots. In this paper, we propose a method to recognize an object in the actual environment. We assume that a 3D-object model used in our proposal method is the voxel data. Its inside is full up and its surface has color information. We also define the word "recognition" as the estimation of a target object's condition. This condition means the posture and the position of a target object in the actual environment. The proposal method consists of three steps. In Step 1, we extract features from the 3D-object model. In Step 2, we estimate the position of the target object. At last, we estimate the posture of the target object in Step 3. And we experiment in the actual environment. We also confirm the performance of our proposal method from results.

• Journal of Korea Game Society
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• v.11 no.2
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• pp.19-28
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• 2011

Level design is one of the important factors in tower defense game development. The difficulty of tower defense game depends on its wave design. In general, it requires a lot of manual labor to generate well-balanced waves with fun. In this paper, we propose a new automated wave generation system by using a genetic algorithm. With our system, a game designer can easily generate an optimized wave by designating the difficulty level in the initial stage of game design. Our system can be useful in reducing the trial-errors in the initial level design process of tower defense game development.

• 전자공학회논문지 IE
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• v.46 no.2
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• pp.7-10
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• 2009

This paper presents the implementation of libraries of hardware modules for genetic algorithm using VHDL. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. So, it is especially suited to applications where no hardware specifications can be given in advance. Evolvable hardware is based on the idea of combining reconfigurable hardware device with evolutionary computation, such as genetic algorithm. Because of parallel, no function call overhead and pipelining, a hardware genetic algorithm give speedup over a software genetic algorithm. This paper suggests the hardware genetic algorithm for evolvable embedded system chip. That includes simulation results and analysis for several fitness functions. It can be seen that our design works well for the three examples.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.27-32
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• 2008

This paper presents the implementation of simple genetic algorithm using hardware description language for evolvable hardware embedded system. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. So, it is especially suited to applications where no hardware specifications can be given in advance. Evolvable hardware is based on the idea of combining reconfigurable hardware device with evolutionary computation, such as genetic algorithm. Because of parallel, no function call overhead and pipelining, a hardware genetic algorithm give speedup over a software genetic algorithm. This paper suggests the hardware genetic algorithm for evolvable embedded system chip. That includes simulation results for several fitness functions.

• Journal of the Korea Computer Graphics Society
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• v.15 no.4
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• pp.13-21
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• 2009

Game level design is one of the important parts in the commercial game development. Because of its complexity in combining game components, game design work could be classified into a non-linear problem. In this paper, we propose a new automated game level design system by using genetic algorithms. With our system, a game designer easily generates an optimized game level by designating the key parameters m the initial stage of game design. Our system can be useful in reducing the trial-errors in the initial game level design process.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1864-1873
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• 2018

Energy shifting is an innovative method used to obtain the highest profit from the operation of energy storage systems (ESS) by controlling the charge and discharge schedules according to the electricity prices in a given period. Therefore, in this study, we propose an optimal charge and discharge scheduling method that performs energy shift operations derived from an ESS efficiency model. The efficiency model reflects the construction of power conversion systems (PCSs) and lithium battery systems (LBSs) according to the rated discharge time of a MWh-scale ESS. The PCS model was based on measurement data from a real system, whereas for the LBS, we used a circuit model that is appropriate for the MWh scale. In addition, this paper presents the application of a genetic algorithm to obtain the optimal charge and discharge schedules. This development represents a novel evolutionary computation method and aims to find an optimal solution that does not modify the total energy volume for the scheduling process. This optimal charge and discharge scheduling method was verified by various case studies, while the model was used to realize a higher profit than that realized using other scheduling methods.

• Korean Institute of Interior Design Journal
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• v.17 no.3
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• pp.156-164
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• 2008

Through staggering advancements of technology and network as we know them as digital revolution, we have established a foundation of space in which we can express reality by eliminating the boundaries between expression, space, and movement. There are many ongoing approaches that aim to overcome the physically-fixed property of space where the mathematical-geometric notion of Voronoi Diagram is one of them. Although the repetitive increment based on self-organization during the process in which space is generated by the Voronoi Diagram forms a pattern and focuses on the formation, its pattern is not restricted to a single method of expression but evolves over self-control. The result of having analyzed spaces generated by the Voronoi Diagram in this study can be summarized as follows. First, the Voronoi computation method with self-organization property creates multiple levels, increments, and evolves through feedbacks among changes with the slightest order and in the absence of control. Secondly, after forming a pattern through such feedbacks comes the differentiation phase due to the presence of different properties. Thirdly, a space that has gone through the generation process retransforms through active interaction between changes and it obtains ambiguous boundaries and a repetitive pattern. This leads to an evolution of space through repetitive increments based on self-organization. Such flexible space creation is supported by various digital technologies where we believe a converging application of these studies, sciences, engineering concepts, and space design is and effective and new method in terms of space creation.

• Structural Engineering and Mechanics
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• v.76 no.4
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• pp.529-540
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• 2020

Concurrent topology optimization of macrostructure and microstructure has attracted significant interest due to its high structural performance. However, most of the existing works are carried out under deterministic conditions, the obtained design may be vulnerable or even cause catastrophic failure when the load position exists uncertainty. Therefore, it is necessary to take load position uncertainty into consideration in structural design. This paper presents a computational method for robust concurrent topology optimization with consideration of load position uncertainty. The weighted sum of the mean and standard deviation of the structural compliance is defined as the objective function with constraints are imposed to both macro- and micro-scale structure volume fractions. The Bivariate Dimension Reduction method and Gauss-type quadrature (BDRGQ) are used to quantify and propagate load uncertainty to calculate the objective function. The effective properties of microstructure are evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The bi-directional evolutionary structural optimization (BESO) method is used to obtain the black-and-white designs. Several 2D and 3D examples are presented to validate the effectiveness of the proposed robust concurrent topology optimization method.