• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1596-1603
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• 2018

This paper presents the new design and validation process of the linear transverse flux machine of the stelzer machine for hybrid vehicle application. A linear transverse flux machine is a novel electric machine that has higher force density and power than conventional electric machine. The process concentrates on 2-dimensional and 3-dimensional analysis using equivalent magnetic circuit method considering leakage elements and it is verified by finite element analysis. Besides the force characteristics of all axis of each direction are analyzed. The study is considered by dividing the transverse flux electric excited type and the transverse flux permanent magnet excited type. Additionally three-dimensional analysis in this machine is accomplished due to asymmetric structure with another three axes. Finally, it suggests the new design and validation process of linear transverse flux machine for stelzer machine.

• IE interfaces
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• v.24 no.1
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• Honam Mathematical Journal
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• v.32 no.1
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• pp.91-99
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• 2010

In this paper we introduce the concept of asymptotically almost negatively associated random variables in a Hilbert space and obtain the strong law of large numbers for a strictly stationary asymptotically almost negatively associated sequence of H-valued random variables with zero means and finite second moments. As an application we prove a strong law of large numbers for a linear process generated by asymptotically almost negatively random variables in a Hilbert space with this result.

• Honam Mathematical Journal
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• v.36 no.3
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• pp.679-688
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• 2014

The notion of asymptotically negative dependence for collection of random variables is generalized to a Hilbert space and the almost sure convergence for these H-valued random variables is obtained. The result is also applied to a linear process generated by H-valued asymptotically negatively dependent random variables.

• 연구논문집
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• s.27
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• pp.35-45
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• 1997

The dynamic design process for the articulated bogie of light rail vehicle(LRV) was studied to design a primary and secondary suspension elements. Suspension stiffness and damping is selected on the basis of the ride quality and suspension stroke trade-off. LRV was modeled as a 2 d.o.f linear system for the design of vertical suspension characteristics and a 4 d.o.f linear system for the design of lateral suspension characteristics. FRA's class-4-track irregularity was used for the exciting disturbance on track. The optimum value of primary and secondary suspension characteristics was determined using this design process.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.507-518
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• 1995

The ordinary least squares based estiamte $S^2$ of the disturbance variance is considered in the linear regression model when the disturbances follow the first-order moving-average process. It is shown that $S^2$ is weakly consistent estimate for the disturbance varaince without any restriction on the regressor matrix X. Also, simple exact bounds on the relative bias of $S^2$ are given in finite sample sizes.

• Communications of Mathematical Education
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• v.24 no.2
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• pp.445-474
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• 2010

The results of performing first survey after learning linear equation and second survey after 5 months to find out whether there is change in solving process while seventh grade students solve linear equations are as follows. First, as a result of performing McNemar Test in order to find out the correct answer ratio between first survey and second survey, it was shown as $p=.035^a$ in problem x+4=9 and $p=.012^a$ in problem $x+\frac{1}{4}=\frac{2}{3}$ of problem type A while being shown as $p=.012^a$ in problem x+3=8 and $p=.035^a$ in problem 5(x+2)=20 of problem type B. Second, while there were students not making errors in the second survey among students who made errors in the solving process of problem type A and B, students making errors in the second survey among the students who expressed the solving process correctly in the first survey were shown. Third, while there were students expressing the solving process of linear equation correctly for all problems (type A, type B and type C), there were students expressing several problems correctly and unable to do so for several problems. In conclusion, even if a student has expressed the solving process correctly on all problems, it would be difficult to foresee that the student is able to express properly in the solving process when another problem is given. According to the result of analyzing the reaction of students toward three problem types (type A, type B and type C), it is possible to determine whether a certain student is 'able' or 'unable' to express the solving process of linear equation by analyzing the problem solving process.

• Korea-Australia Rheology Journal
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• v.21 no.2
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• pp.135-141
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• 2009

Effect of activation energy and crystallization kinetics of polyethylenes (PEs) on the dynamics and stability has been investigated by changing rheological properties and crystallization rate in film casting process. The effect of changes of these properties has been shown using a typical example of short-chain branching (SCB) in linear polyethylenes. SCBs in linear polymers generally lead to the increase of the flow activation energy, and to the decrease of the crystallization rate, making polymer viscosity lower in the case of equivalent molecular weight. In general, the increment of the crystallinity of polymers under partially crystallized state helps to enhance the process stability by increasing tension, and lower fluid viscoelasticity possesses the stabilizing effect for linear polymers. It has been found that the fluid viscoelasticity plays a key role in the control of process stability than crystallization kinetics which critically depends on the cooling to stabilize the film casting process of short-chain branched polymers operated under the low aspect ratio condition.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1000-1003
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• 1996

Nonlinear unified predictive control(UPC) algorithm was applied to the temperature control of a batch polymerization reactor for polymethylmethacrylate(PMMA). Before the polymerization reaction is initiated, the parameters of the process model are determined by the recursive least squares(RLS) method. During the reaction, nonlinearities due to generation of heat of reaction and variation of heat transfer coefficients are predicted through the nonlinear model developed. These nonlinearities are added to the process output from the linear process model. And then, the predicted process output is used to calculate the control output sequence. The performance of nonlinear control algorithm was verified by simulation and compared with that of the linear unified predictive control algorithm. In the experiment of a batch PMMA polymerization, nonlinear unified predictive control was implemented to regulate the temperature of the reactor, and the validity of the nonlinear model was verified through the experimental results. The performance of the nonlinear controller turned out to be superior to that of the linear controller for tracking abrupt changes in setpoint.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.483-490
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• 2002

A weak convergence is obtained for a linear process of the form (equation omitted) where {$\varepsilon$$_{t}$ } is a strictly stationary sequence of associated random variables with E$\varepsilon$$_{t}$ = 0 and E$\varepsilon$$^{^2}$$_{t}$ < $\infty$ and {a $_{j}$ } is a sequence of real numbers with (equation omitted). We also apply this idea to the case of linearly positive quadrant dependent sequence.