• Journal of Advanced Marine Engineering and Technology
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• v.15 no.4
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• pp.46-53
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• 1991

In the present study, Navier-Stokes equation is numerically solved by use of a Finite analytic method to obtain the 2-dimensional flow field in the square cavity. The basic idea of F.A.M. is the incorporation of local analytic solutions in the numerical solution of linear or non-linear partial differential equations. In the F.A.M., the total problem is subdivided into a number of all elements. The local analytic solution is obtained for the small element in which the governing equation, if non-linear, to be linearized. The local analytic solutions are then expressed in algebraic form and are overlapped to cover the entire region of the problem. The assembly of these local analytic solutions, which still preserve the overall nonlinearity of the governing equations, results in a system of linear algebraic equations. The system of algebraic equations is then solved to provide the numerical solutions of the total problem. The computed flow field shows the same characteristics to physical concept of flow phenomena.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.193-204
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• 2012

The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2142-2153
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• 2014

Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.4
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• pp.237-246
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• 2004

The advantages of the Magnetic Fluid Linear Pump(MFLP) is that this device could Pump the non-conductive. non-magnetic liquid such as Insulin or blood because of the segregation structure of the magnetic fluid and pumping liquid. In this device. the sequential currents are needed to Produce pumping forces so that Pumping Forces and Pumping speed mainly depend on the current Patterns. The excessive forces at Pumping moment could cause the medical shock, and weak forces at intermediate moment could cause the back flow or the pumping liquid. So the ripples of the pumping forces need to be reduced for the medical application. In this research, the driving characteristics in the MFLP by operating current is analysed. The change of magnetic fluid surface according to the driving currents could be obtained be magneto-hydrodynamic analysis so that Pumping fortes could be computed by integration of the surface moving to the pumping direction at each moment. The actual MFLP with 13mm diameter was made and tested for experiments. The effects of driving current and frequency on the pumping forces and pumping speed were analyzed and compared with experimental measurements.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.168-175
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• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2299-2301
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• 1998

In this paper, a modified RBF (Radial Basis Function) neural network structure is suggested for the prediction of time series with non-linear, non-stationary characteristics. Conventional RBF neural network predicting time series by using past outputs is for sensing the trajectory of the time series and for reacting when there exists strong relation between input and hidden neuron's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden neurons are modified to react to the increments of input variable and multiplied by increments(or decrements) of out puts for prediction. When the suggested structure is applied to prediction of Lorenz equation, and Rossler equation, improved performances are obtainable.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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• pp.2012-2018
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• 2004

The vibration of a semi-circular pipe conveying fluid is studied when the pipe is clamped at both ends. To consider the geometric nonlinearity, this study adopts the Lagrange strain theory for large deformation and the extensible dynamics based on the Euler-Bernoulli beam theory for slenderness assumption. By using the Hamilton principle, the non-linear partial differential equations are derived for the in-plane motions of the pipe, considering the fluid inertia forces as a kind of non-conservative forces. The linear and non-linear terms in the governing equations are compared with those in the previous study, and some significant differences are discussed. To investigate the dynamic characteristics of the system, the discretized equations of motion are derived from the Galerkin method. The natural frequencies varying with the flow velocity are computed from the two cases, which one is the linear problem and the other is the linearized problem in the neighborhood of the equilibrium position. Finally, the time responses at various flow velocities are directly computed by using the generalized-$\alpha$ method. From these results, we should consider the geometric nonlinearity to analyze dynamics of a semi-circular pipe conveying fluid more precisely.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1996.04a
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• pp.77-92
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• 1996

When an automatic course-keeping is concerned as is quite popular in modern navigation the closed-loop steering system consists of autopilot device power unit (or telemotor unit) steering gear magnetic or gyro compass and ship dynamics. The consideration of irregular disturbances to ship dyanmics and a few non-linear mechanisms involved in the system inevitably or artificially are known to be very important in properly evaluating or analyzing the automatic steering system. In the present study the mathematical model of each element of an automatic steering system is derived which takes account of a fex non-linear mechanisms. PD(Proportional-Derivative) controller and low-pass filter with a weather adjustment are adopted to modelling the characteristics of an autopilot. The calculation method of imposing irregular disturbances to ship dynamics is proposed where irregular disturbances implying irregular wave and the fluctuating component of wind. For he evaluation of automatic steering system of ships in the open seas an important term "performance index" is introduced from the viewpoint of energy saving which derived from the concept of energy loss on ship propulsion. Finally the present methods are applied to two typical types of ship ; an ore carrier and a fishing boat. The various effects of linear and/or non-linear control constants of autopilot on propulsive energy loss are investigated to validate and clarify the present smulation technique.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.254-259
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• 2005

The heat and vibration effect is known to cause a serious deformation in linear motor system. The paper presents a heat and vibration characteristics that compare a advanced linear motor with developed linear motor through experiment and analyses. The heat in linear motor system is identified for experimental data and analytic data using the Finite Volume Method (FVM). Also, it shows that the optimum standard analyzed the acceleration patterns of the moving part cause the vibration source in linear motor. It presents the analyzed dynamics of linear motor in compliance with a deformation of the non-linear factor.

• Transactions of the Korean hydrogen and new energy society
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• v.26 no.3
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• pp.294-299
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• 2015

Free piston linear engine (FPLE) is a promising concept being explored in the mid-20th century. On the other hand, Arficial neural networks (ANNs) are non-linear computer algorithms and can model the behavior of complicated non-linear processes. Some researchers already studied this method to predict internal combustion engine characteristics. However, no investigation to predict the performance of a FPLE using ANN approach appears to have been published in the literature to date. In this study, the ability of an artificial neural network model, using a back propagation learning algorithm has been used to predict the in-cylinder pressure, frequency, maximum stroke length of a free piston linear engine. It is advised that, well-trained neural network models can provide fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.