• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.34-40
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• 2004

In this paper, peliminary design procedure of magnetorheological (MR) dampers is developed for controlling the building response induced by seismic excitation. Hysteretic biviscous model which is simple and can describe the hysteretic characteristics of MR damper is used for parametric studies. The capacity of MR damper is determined as a portion of not the building weight but the lateral restoring force. A method is proposed for the optimal placement and number of MR dampers, and its effectiveness is verified by comparing it with the simplified search algorithm. Numerical results indicate that the capacity, number and the placement can be reasonably determined using the proposed design procedure.

• Transactions of the Society of Information Storage Systems
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• v.1 no.2
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• pp.137-142
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• 2005

In this paper, we propose a new servo system design strategy to reduce the position error signal(PES) and track mis-registration(TMR) in magnetic disk drive systems. The proposed method provides a systematic design procedure based on the plant model and an optimal solution via an optimization with a 'Robust Random Neighborhood Search(RRNS)' algorithm. In addition, it guarantees the minimum PES level as well as stability to parametric uncertainties. Furthermore, the proposed method can be used to estimate the performance at the design stage and thus can reduce the cost and time for the design of the next generation product. The reduction of PES as well as robust stability is demonstrated by simulation and experiments.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.72-79
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• 2006

Derailment is likely to have a direct connection with human life and must be eliminated. A traveling safety evaluation method based mainly on derailment coefficient has already established. But this method is very difficult because Derailment is caused by multiple factors. To evaluate the derailment factor of running train that runs on the curved track, we make use of mechanism that wheel loads and lateral forces were affected by track and rolling stock parameter. In this paper, deal with a search on the parameter and derailment factor. According to results of computer simulation value of Q/P, running safety is connected with operation velocity, curve radius, cant, track irregularity, suspension stiffness and static wheel load ratio, etc.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2002.10a
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• pp.40-44
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• 2002

선박 설계시 최적화에 있어 종래에는 Random search Parametric study, Hook&Jeeves Method등이 사용되어져 왔으나 1960년대 Genetic algorithm이 소개되고 꾸준히 발전함과 함께 선박 설계에서도 Genetic algorithm이 사용되기 시작하였다. 본 논문에서는 이러한 Genetic algorithm 중 Simple Genetic algorithm(SGA), Micro Genetic algorithm(MGA), Threshold Genetic algorithm(TGA), Hybrid Genetic algorithm(HGA)을 선박 설계에 적용하여 그 성능을 비교 검토해 보았다. MGA는 계산 부담을 줄이기 위해 작은 개체로 효율적인 탐색을 하며, TGA는 local optimum에서 쉽게 벗어나게 할 수 있는 특징이 있다. HGA는 Hook&Jeeves Method를 Genetic algorithm과 병합되어 있다. 이를 바탕으로 본 논문에서 망간단괴 수송선의 경제성을 평가한다. 평가 방법은 연간 300만톤을 생산한다고 가정하여 연간 운송 용적을 동호제약으로 해서 최적화를 한 뒤, 이를 이용하여 몇가지 Case로 나누어서 초기 자본, 연간 비용, 20년간 총 비용을 계산하여 가장 경제적인 선박을 선택한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.10a
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• pp.313-318
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• 1996

In this paper, a parametric study of the unbalance response and the stability is carried out to show the influence of seal parameters on the response of rotor. The seal parameters optimized are the seal clearance and the seal length. The minimum quantity of a Q factor in the critical speed and the maximum quantity of a logarithmic decreement in the operating speed, avoiding the reign of resonance, are the objective function. This paper describes a new approach to find a seal parameter of rotor system. The optimization method is used genetic algorithms, which are search algorithms based on the mechanics of natural selection and natural genetics. The results show the capability of this method and indicate that an optimal design of seals can improve the unbalance and the stability of rotor.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.113-124
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• 2009

The line shape functions for the Doppler-broadened gamma ray spectrum are considered in the $^{10}B(n,{\alpha}{\gamma})^7Li$ reaction occurring in a surrounding medium where the excited $^7Li$ nucleus is slowed down and stopped before decay. The phenomenological form of the stopping power was used for the broadening effect. Convolution with the detailed response of a germanium detector is taken into consideration for the simplest case of solely electronic stopping. A numerical study for the analysis of $^{10}B$ by thermal neutron capture is conducted by performing a parametric search and fitting the measured spectrum in a least-squares approach. In comparison with the previous numerical approach using the same analysis, the computational speed is increased and reliable information concerning the stopping power of the medium is obtained while estimating the uncertainty. Implementation of the routine analysis of $^{10}B$ is facilitated on a recent version of the gamma ray spectrum analysis package HyperGam.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.149-162
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• 2013

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.