• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.4
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• pp.321-331
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• 2011

The present work is aimed to analyze the creep characteristics of a turbojet engine turbine blade using the theta projection method. The theta projection method has been widely used due to its advantages and flexibility. For the creep characteristic analysis of the turbine blade, tests are performed considering the operating conditions and the non-linear material properties. Results from the creep test are fitted using the four theta model. The predicted proprieties using the four theta model are compared with the prediction model and creep test results. To obtain an optimum value of the four theta parameters in non-linear square method, a number of computing processes in the non-linear least square method were carried out to obtain full creep curves. Results using the theta model has more than 0.95 value of $R^2$. The results between the experimental values and predicted four theta model has about 90.0% accuracy. The theta projection method can be utilized for a design purpose to predict the creep behavior.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.263-268
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• 2017

The determination algorithm of the location of a line source with strike and dip using the gravity gradient tensor on a single profile is proposed. We already proposed the determination of strike and dip in the previous paper and then, now we improved the algorithm to locate a line source after determining strike and dip. The strike and dip of the line source can be determined by rotating the gravity gradient tensor matrix as reducing 2 independent components. Using the ratio of remaining 2 components, the location can be determined by the least square manner of the pointing vectors on each observation point. A synthetic model is tested for proving the usefulness of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.261-263
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• 2009

본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 각 노드의 후반부 파라미터들은 최소자승법을 이용하여 최적화 하였다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

• Environmental and Resource Economics Review
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• v.24 no.4
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• pp.607-627
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• 2015

As observed and experienced in EU ETS, allowance price volatility is one of major concerns in decision making process for $CO_2$ abatement investment. The problem of linearly non-separable profits functions could emerge when one power company holds several power plants with different technology specifications. Under this circumstance, conventional analytical solution for investment option is no longer available, thereby calling for the development of numerical analysis. This paper attempts to develop a Monte-Carlo least squares model to analyze investment options for power companies under emission trading scheme regulations. Stochastic allowance price is considered, and simulation is performed to verify model performance.

• KCID journal
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• v.3 no.2
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• pp.44-52
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• 1996

To make a real hydraulic simulation model, Hazen-Williams' coefficient must be calibrated using the measured discharges and hydraulic head loss. In this paper, calibrating method of Hazen-Williams' coefficient was proposed for pipeline system. The calibra

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.5
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• pp.1104-1111
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• 1990

A method to identify nonlinear elements position of a nonlinear system is presented. Nonlinear elements position can be identified by an equivalent error damping and stiffness matrices which are based on the equivalent linearization technique. The procedures of this technique are: (1) Obtain input force and system response. (2) Define error between the actual and linearized restoring forces. (3) Calculate linearized damping and stiffness coefficients to minimize the square error sum. Several examples are tested and found that these methods are very effective not only to locate the nonlinear elements position but also to identify the degree of nonlinearity qualitatively. Nonlinear type can be qualitatively identified by examining the plots of restoring force vs equivalent state values.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2278-2280
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• 2004

이 논문은 이동식 수레형 도립진자 제어에 있어서 비선형적인 특성을 발생시키는 원인중의 하나인 마찰력을 보상하는 방법에 대하여 고찰한다. 여기에 사용된 방법은 모터에 전압을 인가하였을 때의 이동식 수레형 도립진자의 위치와 속도에 대한 데이터를 가지고, 가우시안 반복을 적용한 비선형 최소 자승법을 이용하여 마찰력을 추정하고, 모델링을 하여, 모델링 된 파라미터를 이용하여 비선형적인 특성을 최소화하는 것이다. 이 논문의 주된 결과는 수학적으로 모델링 한 마찰력이 실제의 마찰력과 유사하며, 이 마찰력을 보상함으로서, 이동식 수레형 도립진자의 제어 성능이 향상되었음을 보여준다. 따라서 이 결과는 기존의 마찰력을 무시하고, 이동식 수레형 도립진자를 제어하였을 때보다 마찰력을 보상하였을 때가 더욱 안정된 시스템이 됨을 실험적으로 확인한다.

• Korean Journal of Food Science and Technology
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• v.25 no.2
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• pp.178-184
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• 1993

The procedure using equivalent time at reference temperature has been assessed for the estimation of kinetic parameters with experimental data. Kinetic studies of nonenzymatic browning reaction in model and food system were carried out with linearly increasing temperature method. These kinetic parameters, n, $k_{ref}$ and $E_a$ of the systems were evaluated from original data in one step by nonlinear least square regression. The one step procedure yielded efficiently accurate parameter estimation. Computer simulated data with the kinetic models were well consistent with experimental data (average correlation coefficient=0.96).