• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.4 s.97
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• pp.429-436
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• 2005

In turbomachinery rotor, there are small differences in the structural and/or geometrical properties of individual blades, which are referred to as blade mistuning. Mistuning effect of the forced response of bladed disks can be extremely large as often reported in many studies. In this paper, the pattern optimization of intentional mistuning for bladed disks considering with damping and coupling effect is the focus of the present investigation. More specifically, the class of intentionally mistuned disks considered here is limited, for cost reasons, to arrangements of two types of blades (A and B, say) and Genetic Algorithm and steepest descent method are used to optimize the arrangement of these blades around the disk to reduce the forced response of blade with different damping and coupling stiffness. Examples of application involving both simple bladed disk models and a 17-blade industrial rotor clearly demonstrate the significant benefits of using this class of intentionally mistuned disks.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.82-96
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• 2019

A semi-submersible structure has been widely used for offshore drilling and production of oil and gas. The small water plane area makes the structure very sensitive to weight increase in terms of payload and stability. Therefore, it is necessary to lighten the substructure from the early design stage. This study aims at an optimization of hull structure based on a sophisticated yield and buckling strength in accordance with classification rules. An in-house strength assessment system is developed to automate the procedure such as a generation of buckling panels, a collection of required panel information, automatic buckling and yield check and so on. The developed system enables an automatic yield and buckling strength check of all panels composing the hull structure at each iteration of the optimization. Design variables are plate thickness and stiffener section profiles. In order to overcome the difficulty of large number of design variables and the computational burden of FE analysis, various methods are proposed. The steepest descent method is selected as the optimization algorithm for an efficient search. For a reduction of the number of design variables and a direct application to practical design, the stiffener section variable is determined by selecting one from a pre-defined standard library. Plate thickness is also discretized at 0.5t interval. The number of FE analysis is reduced by using equations to analytically estimating the stress changes in gradient calculation and line search steps. As an endeavor to robust optimization, the number of design variables to be simultaneously optimized is divided by grouping the scantling variables by the plane. A sequential optimization is performed group by group. As a verification example, a central column of a semi-submersible structure is optimized and compared with a conventional optimization of all design variables at once.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.29-37
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.82-89
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the GradientDescentOptimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.121-126
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• 1996

The hydrodynamic coefficients estimation (HCE) is important to design the autopilot and to predict the maneuverability of an underwater vehicle. In this paper, a system identification is proposed for an HCE of an underwater vehicle. First, we attempt to design the HCE algorithm which is insensitive to initial conditions and has good convergence, and which enables the estimation of the coefficents by using measured displacements only. Second, the sensor and measurement system which gauges the data from the full scale trials is constructed and the data smoothing algorithm is also designed to filter the noise due to irregular fluid flow without changing the data characteristics itself. Lastly the hydrodynamic coefficients are estimated by applying the measured data of full scale trials to the developed algorithm, and the estimated coefficients are verified by full scale trials.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.03a
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• pp.101-108
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• 2002

This paper presents an input and system identification technique for a free-field system using forced vibration data. Identification is carried out on geotechnical experiment site at Yong-jong Island where Inchon International Airport being constructed. The identified quantities are the input load as well as the shear moduli of the free-field soil regions. The dynamic response analysis on the free-field system is carried out using the finite element method incorporating the infinite element formulation fur the unbounded layered soil medium. The criterion function for the parameter estimation is constructed using the frequency response amplitude ratios of the dynamic responses measured at several points of the free-field, so that the information on the input loading may be excluded. The constrained steepest descent method is employed to obtain the revised parameters. The simulated dynamic responses using the identified parameters and input load show excellent agreements with the measured responses.

• Transactions of Materials Processing
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• v.7 no.3
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• pp.207-214
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• 1998

Influence of final thickness distribution in superplastic forming processes on mechanical properties of the product becomes very crucial. We should improve the thickness distribution of products by combining process parameters adequately In this paper we adopt a non-linear optimization technique for optimal process design of superplastic forming. And optimum design variable which makes the most adequate thickness distribution in combined stretc/blow forming and blow forming is predicted by this optimization scheme and rigid-viscoplastic finite element method.

• Proceedings of the Optical Society of Korea Conference
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• 2009.02a
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• pp.297-298
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• 2009

파장 분할 방식의 광섬유 격자 센서의 멀티플렉싱 능력을 향상시키기 위하여 이중 격자의 파장 간격 변조로 구분되는 다중 광섬유 격자 센서 시스템을 제안하였으며, 서로 다른 파장 간격을 가지는 센서의 상태와 위치를 정확히 구별할 수 있는 방법을 연구하고 씨뮬레이션을 통하여 확인하였다. 또한 변형력 하에서 광섬유격자 센서들간의 스펙트럼 중첩으로 불명확한 브래그 피크를 최급 강하법(steepest descent method) 알고리즘을 사용하여 약 3 pm이내의 표준편차로 결정할 수 있었다.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2005.11a
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• pp.153-156
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• 2005

This paper concerns an efficient iterative approach for eliminating coherent interference signals in linearly constrained adaptive arrays. The Alternate Mainbeam Nulling Algorithm[1] is implemented iteratively to find an optimum weight vector. The convergence parameters in the unit gain and null constraints are calculated using steepest descent method with gradient estimation. The nulling performance of the proposed method is compared with that of conventional ones. It is shown that the proposed method performs better than conventional ones when the power of the coherent signals is large compared with a desired signal. Also, it performs consistently well for more number of interferences.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.112-118
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• 2000

This paper deals with the application of sensitivity method to the parameter estimation for the dynamic analysis of gener-al mechanical system. In this procedure we take the derivatives of the given system with respect to a certain parameter and use this information to implement the steepest descent method. This paper will give two examples of this technique applied to simple vehicle models. This paper will give two examples of this technique applied to simple vehicle models. Simulation results show excellent convergence and accuracy of parameter estimates.