• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.167-173
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• 2002

In this paper, we propose an interactive genetic algorithm (IGA) to implement an automated 2D avatar synthesis. The IGA technique is capable of expressing user's personality in the avatar synthesis by using the user's response as a candidate for the fitness value. Our suggested IGA method is applied to creating avatars automatically. Unlike the previous works, we introduce the concepts of 'hidden population', as well as 'primitive avatar' and 'simplified genotype', which are used to overcome the shortcomings of IGA such as human fatigue or reliability, and reasonable rates of convergence with a less number of iterations. The procedure of designing avatar models consists of two steps. The first step is to detect the facial feature points and the second step is to create the subjectively optimal avatars with diversity by embedding user's preference, intuition, emotion, psychological aspects, or a more general term, KANSEI. Finally, the combined processes result in human-friendly avatars in terms of both genetic optimality and interactive GUI with reliability.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.241-248
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• 2000

Until now, all of the port goods are transported manually by container transporter in the port. Recently there are a lot of studies about unmanned vehicle driven automatically. In terms of the vehicle automation, the control of steering and velocity on vehicle systems is very important part in container transporter. In common sense, vehicle systems have lots of nonlinear parameters so we have many difficulties in designing the optimal controller of them. In this paper, we present a design of the TDOF PID controller using a hybrid schematic algorithm to control the steering system optimally. We used the single-track model to pre-test the designed controller before appling to AGV. We also used the ES(evolutionary strategy) and SA(simulated annealing) algorithms to construct the hybrid tuning algorithm for parameters of controller. Finally, we had the computer simulation to verify that our designed controller has better performance than the other one.

• Structural Engineering and Mechanics
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• v.72 no.2
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• pp.181-190
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• 2019

Topology optimization of structures seeking the best distribution of mass in a design space to improve the structural performance and reduce the weight of a structure is one of the most comprehensive issues in the field of structural optimization. In addition to structures stiffness as the most common objective function, frequency optimization is of great importance in variety of applications too. In this paper, an efficient multi-objective Bi-directional Evolutionary Structural Optimization (BESO) method is developed for topology optimization of frequency and stiffness in continuum structures simultaneously. A software package including a Matlab code and Abaqus FE solver has been created for the numerical implementation of multi-objective BESO utilizing the weighted function method. At the same time, by considering the weaknesses of the optimized structure in single-objective optimizations for stiffness or frequency problems, slight modifications have been done on the numerical algorithm of developed multi-objective BESO in order to overcome challenges due to artificial localized modes, checker boarding and geometrical symmetry constraint during the progressive iterations of optimization. Numerical results show that the proposed Multiobjective BESO method is efficient and optimal solutions can be obtained for continuum structures based on an existent finite element model of the structures.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.206-215
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• 2005

In this paper, we propose a new design method of Genetic Algorithm Processor(GAP) and Evolvable Hardware(EHW). All sorts of creature evolve its structure or shape in order to adapt itself to environments. Evolutionary Computation based on the process of natural selection not only searches the quasi-optimal solution through the evolution process, but also changes the structure to get best results. On the other hand, Genetic Algorithm(GA) is good fur finding solutions of complex optimization problems. However, it has a major drawback, which is its slow execution speed when is implemented in software of a conventional computer. Parallel processing has been one approach to overcome the speed problem of GA. In a point of view of GA, long bit string length caused the system of GA to spend much time that clear up the problem. Evolvable Hardware refers to the automation of electronic circuit design through artificial evolution, and is currently increased with the interested topic in a research domain and an engineering methodology. The studies of EHW generally use the XC6200 of Xilinx. The structure of XC6200 can configure with gate unit. Each unit has connected up, down, right and left cell. But the products can't use because had sterilized. So this paper uses Vertex-E (XCV2000E). The cell of FPGA is made up of Configuration Logic Block (CLB) and can't reconfigure with gate unit. This paper uses Vertex-E is composed of the component as cell of XC6200 cell in VertexE

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.67-74
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• 2009

Incorrect deployment of RFID readers occurs reader-to-reader interferences in many applications using RFID technologies. Reader-to-reader interference occurs when a reader transmits a signal that interferes with the operation of another reader, thus preventing the second reader from communicating with tags in its interrogation zone. Interference detected by one reader and caused by another reader is referred to as a reader collision. In RFID systems, the reader collision problem is considered to be the bottleneck for the system throughput and reading efficiency. In this paper, we propose a novel RFID reader anti-collision algorithm based on evolutionary algorithm(EA). First, we analyze characteristics of RFID antennas and build database. Also, we propose EA encoding algorithm, fitness algorithm and genetic operators to deploy antennas efficiently. To show superiority of our proposed algorithm, we simulated our proposed algorithm. In the result, our proposed algorithm obtains 95.45% coverage rate and 10.29% interference rate after about 100 generations.

• Journal of Life Science
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• v.22 no.10
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• pp.1295-1306
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• 2012

A microorganism producing carboxymethylcellulase (CMCase) was isolated from seawater and identified as Bacillus atrophaeus. This species was designated as B. atrophaeus LBH-18 based on its evolutionary distance and the phylogenetic tree resulting from 16S rDNA sequencing and the neighbor-joining method. The optimal conditions for rice bran (68.1 g/l), peptone (9.1 g/l), and initial pH (7.0) of the medium for cell growth was determined by Design Expert Software based on the response surface method; conditions for production of CMCase were 55.2 g/l, 6.6 g/l, and 7.1, respectively. The optimal temperature for cell growth and the production of CMCase by B. atrophaeus LBH-18 was $30^{\circ}C$. The optimal conditions of agitation speed and aeration rate for cell growth in a 7-l bioreactor were 324 rpm and 0.9 vvm, respectively, whereas those for production of CMCase were 343 rpm and 0.6 vvm, respectively. The optimal inner pressure for cell growth and production of CMCase in a 100-l bioreactor was 0.06 MPa. Maximal production of CMCase under optimal conditions in a 100-l bioreactor was 127.5 U/ml, which was 1.32 times higher than that without an inner pressure. In this study, rice bran was developed as a carbon source for industrial scale production of CMCase by B. atrophaeus LBH-18. Reduced time for the production of CMCase from 7 to 10 days to 3 days by using a bacterial strain with submerged fermentation also resulted in increased productivity of CMCase and a decrease in its production cost.