• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.6 s.111
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• pp.665-673
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• 2006

This paper proposes a RSM-based hybrid evolutionary Algorithm (RHEA) which combines the merits of the popular programs such as genetic algorithm (GA), tabu search method and response surface methodology (RSM). This algorithm, for improving the convergent speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation of GA offers random variety to finding the optimum solution. In this study, however, systematic variety can be secured through the use of tabu list. Efficiency of this method has been proven by applying traditional left functions and comparing the results to GA. It was also proved that the newly suggested algorithm is very effective to find the global optimum solution to minimize the weight for avoiding the resonance of fresh water tank that is placed in the after body area of ship. According to the study, GA's convergent speed in initial stages is improved by using RSM method. An optimized solution is calculated without the evaluation of additional actual objective function. In a summary, it is concluded that RHEA is a very powerful global optimization algorithm from the view point of convergent speed and global search ability.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.138-142
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• 2014

This research presents an A* based algorithm which can be applied to Unmanned Ground Vehicle self-navigation in order to make the driving path smoother. Based on the grid map, A* algorithm generated the path by using straight lines. However, in this situation, the knee points, which are the connection points when vehicle changed orientation, are created. These points make Unmanned Ground Vehicle continuous navigation unsuitable. Therefore, in this paper, B-spline curve function is applied to transform the path transfer into curve type. And because the location of the control point has influenced the B-spline curve, the optimal control selection algorithm is proposed. Also, the optimal path tracking speed can be calculated through the curvature radius of the B-spline curve. Finally, based on this algorithm, a path created program is applied to the path results of the A* algorithm and this B-spline curve algorithm. After that, the final path results are compared through the simulation.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.609-617
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• 2010

This paper is concerned with the use of a selforganizing map (SOM) to estimate the desired channel states of an unknown digital communication channel for blind equalization. The modification of SOM is accomplished by using the Bayesian likelihood fitness function and the relation between the desired channel states and channel output states. At the end of each clustering epoch, a set of estimated clusters for an unknown channel is chosen as a set of pre-defined desired channel states, and used to extract the channel output states. Next, all of the possible desired channel states are constructed by considering the combinations of extracted channel output states, and a set of the desired states characterized by the maximal value of the Bayesian fitness is subsequently selected for the next SOM clustering epoch. This modification of SOM makes it possible to search the optimal desired channel states of an unknown channel. In simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The performance of the proposed method is compared with those of the "conventional" SOM and an existing hybrid genetic algorithm. Relatively high accuracy and fast search speed have been achieved by using the proposed method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.1
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• pp.112-115
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• 2003

Robust control for servo control system in needed according to the highest precision of industrial automation. However, when a servo control system has an effect of disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem in this paper, Hybrid control method of Main controller(PIU)-Differential Supervisory controller is presented. Main controller is operated as a feedback controller. Differential Supervisory controller as a assistant controller is operated when state in unstable disturbance. The robust control function of Differential Supervisory controller is demonstrated by Speed control of Motor.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1625-1634
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• 2008

In this study, a modified Fuzzy C-Means algorithm with Gaussian weights (MFCM_GW) is presented for the problem of nonlinear blind channel equalization. The proposed algorithm searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to conventional Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in this method. In the search procedure, all possible sets of desired channel states are constructed by considering the combinations of estimated channel output states. The set of desired states characterized by the maxima] value of the Bayesian fitness is selected and updated by using the Gaussian weights. After this procedure, the Bayesian equalizer with the final desired states is implemented to reconstruct transmitted symbols. The performance of the proposed method is compared with those of a simplex genetic algorithm (GA), a hybrid genetic algorithm (GA merged with simulated annealing (SA):GASA), and a previously developed version of MFCM. In particular, a relative]y high accuracy and a fast search speed have been observed.

• Advances in Computational Design
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• v.6 no.1
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• pp.31-42
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• 2021

This research aims to demonstrate the optimal geometrical design of splayed multiple cross-sectional pin fin heat sinks (SMCSPFHS), which are a type of side-inlet-side-outlet heat sink (SISOHS). The optimiser strength Pareto evolutionary algorithm2 (SPEA2)is employed to explore a set of Pareto optimalsolutions. Objective functions are the fan pumping power and junction temperature. Function evaluations can be accomplished using computational fluid dynamics(CFD) analysis. Design variablesinclude pin cross-sectional areas, the number of fins, fin pitch, thickness of heatsink base, inlet air speed, fin heights, and fin orientations with respect to the base. Design constraints are defined in such a way as to make a heat sink usable and easy to manufacture. The optimum results obtained from SPEA2 are compared with the straight pin fin design results obtained from hybrid population-based incremental learning and differential evolution (PBIL-DE), SPEA2, and an unrestricted population size evolutionary multiobjective optimisation algorithm (UPSEMOA). The results indicate that the splayed pin-fin design using SPEA2 issuperiorto those reported in the literature.

• Ocean Systems Engineering
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• v.7 no.2
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• pp.143-155
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• 2017

For complex flexible structures such as nets, the determination of drag forces and its deformation is a challenging task. The accurate prediction of loads on cages is one of the key steps in designing fish farm facilities. The basic physics with a simple cage, can be addressed by the use of experimental studies. However, to design more complex cage system for various environmental conditions, a reliable numerical simulation tool is essential. In this work, the current load on a cage is calculated using a Morison-force model applied at instantaneous positions of equivalent-net modeling. Variations of solidity ratio ($S_n$) of the net and current speed are considered. An equivalent array of cylinders is built to represent the physical netting. Based on the systematic comparisons between the published experimental data for Raschel nets and the current numerical simulations, carried out using the commercial software OrcaFlex, a new formulation for $C_d$ values, used in the equivalent-net model, is presented. The similar approach can also be applied to other netting materials following the same procedure. In case of high solidity ratio and current speed, the hybrid model defines $C_d$ as a function of Re (Reynolds number) and $S_n$ to better represent the corresponding weak diffraction effects. Otherwise, the conventional $C_d$ values depending only on Re can be used with including shielding effects for downstream elements. This new methodology significantly improves the agreement between numerical and experimental data.

• Wind and Structures
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• v.35 no.4
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• pp.269-285
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• 2022

The pressure-mitigating effects of a high-speed train passing through a tunnel with a partially reduced cross-section are investigated via the numerical approach. A compressible, three-dimensional RNG k-ε turbulence model and a hybrid mesh strategy are adopted to reproduce that event, which is validated by the moving model test. Three step-like tunnel forms and two additional transitions at the tunnel junction are proposed and their aerodynamic performance is compared and scrutinized with a constant cross-sectional tunnel as the benchmark. The results show that the tunnel step is unrelated to the pressure mitigation effects since the case of a double-step tunnel has no advantage in comparison to a single-step tunnel, but the excavated volume is an essential matter. The pressure peaks are reduced at different levels along with the increase of the excavated earth volume and the peaks are either fitted with power or logarithmic function relationships. In addition, the Arc and Oblique-transitions have very limited gaps, and their pressure curves are identical to each other, whereas the Rec-transition leads to relatively lower pressure peaks in C_Pmax, C_Pmin, and ΔC_P, with 5.2%, 4.0%, and 4.1% relieved compared with Oblique-transition. This study could provide guidance for the design of the novel railway tunnel.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.117-125
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• 2015

The information about protein structure gives the clues for the function of protein. It is needed for the improvement for the efficacy and fast development of protein drugs. So, the studies visualizing the structure of protein effectively increase. Most studies of visualization focus on the structural prediction for protein or the improvement on the rendering speed. However, studies of information delivery depending on the form of protein visualization are very limited. The major objective of this study is to analyze the information representation goodness-of-fit for the patterns of the hybrid visualization with primary and secondary structures of protein. Those hybrid visualizations included the patterns which updated current representative visualization services, Chimera, PDB and Cn3D. Information factor to analyze information representation goodness-of-fit is assorted by protein primary structure, secondary protein structure, the location of amino acid and ratio information about protein secondary structure, based on the result of subject-analysis. Subject is the group of experts who are involved in protein drug development over 5 years. The result of this study shows the meaningful difference in the information representation goodness-of-fit by the patterns of hybrid visualization and proves the difference in the information by the pattern of visualization.