• Design & Manufacturing
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• v.15 no.2
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• pp.63-69
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• 2021

Springback is an essential task to be solved in order to make high-precision products in sheet metal forming. In this study, materials with four different elastic regions were used. For the forming analysis, the change of springback caused by the frictional force generated in the flange part during hat shape forming was considered by using the AutoForm analysis program. Factors affecting frictional force were blank holder force, friction coefficient, bead R and bead height. As a result of the forming analysis, the springback increases as the material with a larger elastic region increases. In addition, as the frictional force of the flange part increased, the tensile force in the forming direction increased and the springback decreased. In particular, the blank holder force and friction coefficient had a great effect on springback in mild materials (DC04, Al6016), and the bead effectively affects all materials. Through this study, it was considered that the springback decreased as the material with a smaller elastic region and the tensile force in the forming direction increased.

• Journal of the Optical Society of Korea
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• v.20 no.1
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• pp.174-179
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• 2016

In this paper, a novel full-aperture reflective null measuring method is proposed to detect the transmission wavefront of a conformal dome surface. An aspheric compensator is designed and placed behind the dome to reflect the aspheric testing wave back to the same path. To ensure the feasibility of this method, tolerance analysis is conducted, and guidance to assembly is given accordingly. The accuracy of this method is verified to be λ/30 (λ =3.39 μm) by Monte Carlo algorithm. In addition, the influence of different error factors, including the thickness error and decenter error of the dome, on the testing wavefront is analyzed. Simulation and experiment indicate that this method is practical and simple, and can measure the conformal domes precisely and comprehensively.

• Physical Therapy Korea
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• v.23 no.1
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• pp.38-45
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• 2016

Background: Chronic back pain shows a high correlation with lumbar disability, physical disability for daily activities, and psychosocial factors, such as depression. Object: The purpose of this study was to examine the correlation of the level of pain and disability with psychosocial factors, which are potential disturbance variables, in patients with chronic lumbar pain. Method: The sample included 258 patients, who had complained of chronic lumbar pain for more than three months. The Quadruple Visual Analogue Scale was used to measure the level of pain, and a Korean version of Oswestry Disability Index was used to measure the level of disability. Psychosocial factors were measured using the Tampa scale for Kinesiophobia-11, Fear Avoidance Beliefs Questionnaire, and Pain Self-Efficacy Questionnaire. The collected data was analyzed using PASW 18.0, and an independent samples t-test was used to examine frequency, percentage, mean, and standard deviation of sociodemographic characteristics and major variables. Pearson's correlation coefficient was used to investigate the correlation between the level of pain and disability and psychosocial factors. Stepwise multiple regression analysis was done to determine the level of pain and psychosocial factors of functional disorder. The significance level was set at ${\alpha}=.05$. Result: There is a strong correlation between the level of pain and functional disorder and psychosocial factors in patients with chronic lumbar pain. The study also revealed that as the levels of pain and fear avoidance increase, pain self-efficacy decreases. Conclusion: The results suggest that negative perceptions towards pain, limitations of physical movement, and severe fear avoidance directly affect the decrease in pain self-efficacy. Therefore, it is recommended to test pain self-efficacy when measuring the level of pain and disability in patients with chronic low back pain.

• Journal of Aerospace System Engineering
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• v.18 no.5
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• pp.73-80
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• 2024

A deep neural network (DNN) was employed to predict the spring-back of a CFRP composite spar induced by the curing process. A total of 816 spring-back data points, derived from varying stacking angles, layer counts, and flange radii, were generated through finite element method (FEM)-based curing analysis to train the DNN model. The trained model demonstrated an R-squared value of 0.99 and a mean squared error of 0.00093, indicating excellent performance. For untrained flange radii, the spring-back predicted by the DNN exhibited a mean relative error of 2.18% when compared to FEM results. Additionally, while FEM analysis required approximately 20 minutes, the DNN-based prediction required only about 14 milliseconds. These results highlight the potential of using DNNs for the rapid prediction of process-induced deformation in CFRP composites.

• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.827-837
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• 1998

We consider the M/G/1 queue with instantaneous feed-back. The probabilities of feedback are determined by the state of the underlaying Markov chain. by using the supplementary variable method we derive the generating function of the number of customers in the system. In the analysis it is required to calculate the matrix equations. To solve the matrix equations we use the notion of Ex-tended Laplace Transform.

• Advances in aircraft and spacecraft science
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• v.2 no.2
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• pp.199-215
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• 2015

This study focuses on the limit cycle oscillations (LCOs) of cantilever swept-back wings containing a cubic nonlinearity in an incompressible flow. The governing aeroelastic equations of two degrees-of-freedom swept wings are derived through applying the strip theory and unsteady aerodynamics. In order to apply strip theory, mode shapes of the cantilever beam are used. The harmonic balance method is used to calculate the frequencies of LCOs. Linear flutter analysis is conducted for several values of sweep angles to obtain the flutter boundaries.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.49-52
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• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1069-1070
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• 2011

This paper deals with the characteristic analysis of permanent magnet linear oscillatory motor (LOM). The back EMF and airgap flux density was calculated by Equivalent Magnetic Circuit (EMC) method, it can be computing times less than the finite element method (FEM). In addition, analysis results obtained by proposed EMC are validated by comparison with those by Finite Element Method.

• Journal of the Korean Geosynthetics Society
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• v.17 no.1
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• pp.139-152
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• 2018

This study is an analysis of slope failure examples of cut sedimentary hills during construction road in Kyoungsang Basin, especially Yangsan Fault system (Ilkwang-Dongrae fault). This area involved a lot of hillslope failures compared to other areas during road construction. The exposed failure slopes were first face-mapped, and then back analyzed based on the limit equilibrium method to assess the shear strength parameters of discontinuity (bedding). The results of this analysis indicate that the shear strength parameters of discontinuity (bedding) are significantly smaller than those used in the design stage and presented in the existing works. The filling in the bedding and emerging groundwater may be decreasing strength parameters. Especially, the clay in the bedding plays a key role in the effect of the shear strength. The study also suggests that the bedding angle and the internal friction angle are proportional to each other. Using this relationship and knowing the bedding angle, the friction can easily be estimated.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.1
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• pp.37-53
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• 2012

There is considerable uncertainty in ground properties used in tunnel designs. In this study, a back analysis was performed to find optimal ground properties based on the artificial neural network facility of MATLAB program of using tunnel monitoring data. Total 81 data were constructed by changing elastic modulus and coefficient of lateral pressure which have great influence on tunnel convergence. A sensitivity analysis was conducted to establish an optimal training model by varying the number of hidden layers, the number of nodes, learning rate, and momentum. Meanwhile, the optimal training model was selected by comparing MSE (Mean Squared Error) and coefficient of determination ($R^2$) and was used to find the correct elastic moduli of layers and the coefficient of lateral pressure. In future, it is expected that the suggested method of this study can be applied to determine the optimum tunnel support pattern under given ground conditions.