• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.143-152
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• 1997

The deflection of an end mill is very important in machining process and cutting simulation because it affects directly workpiece accuracy, cutting force, and chattering. In this study, the deflection of the end mill was studied both experimentally and by using finite element analysis. And the moment of inertia of cross sections of the helical end mill is calculated for the determination of the relation between geometry of radial cross section and rigidity of the tools. Using the Bernoulli-Euler beam theory and the concept of equivalent diameter, a deflection model is established, which includes most influences from tool geomety parameters. It was found that helix angle attenuates the rigidity of the end mill by the finite element analysis. As a result, the equivalent diameter is determined by tooth number, inscribed diameter ratio, cross sectional geometry and helix angle. Because the relation betweem equivalent diameter and each factor is nonlinear, neural network is used to decide the equivalent diameter. Input patterns and desired outputs for the neural network are obtained by FEM analysis in several case of end milling operations.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.23-30
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• 2001

Among the various welding conditions, the welding current that is inversely proportional to the tip-to-work-piece distance is an essential parameter as to monitor the GMAW process and to implement the welding automation. Considering the weld pool surface geometry including weld defects, it should modify the signal processing method for automatic seam tracking in horizontal fillet welding. To meet the above necessities, a mathematical model related with the weld pool geometry was proposed as in a conjunction with the two-dimensional heat flow analysis of the horizontal fillet welding. The signal processing method based on the artificial neural network (Adaptive Resonance Theory) was proposed for discriminating the sound weld pool surface from that with the weld defects. The reliability of the numerical model and the signal processing method proposed were evaluated through the experiments of which showed that they are effective for predicting the weld bead shape with or without the weld defects in a horizontal fillet welding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5218-5233
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• 2018

The coexisting wireless body area networks (WBAN) is a very challenging issue because of strong inter-networks interference, which seriously affects energy consumption and spectrum utilization ratio. In this paper, we study a power control strategy with nearest neighbor nodes distribution for coexisting WBAN based on stochastic geometry. Using homogeneous Poisson point processes (PPP) model, the relationship between the transmission power and the networks distribution is analytically derived to reduce interference to other devices. The goal of this paper is to increase the transmission success probability and throughput through power control strategy. In addition, we evaluate the area spectral efficiency simultaneously active WBAN in the same channel. Finally, extensive simulations are conducted to evaluate the power control algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.581-582
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• 2006

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.3
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• pp.81-87
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• 2007

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. However, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis and artificial neural network were used as the research methods. And, the results of two prediction methods were compared and analyzed.

• ETRI Journal
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• v.42 no.3
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• pp.420-432
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• 2020

The reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition and computer vision. Therefore, we implement an algorithm with the help of a 3D model to perform reconstruction from the real datasets using the slope features. This approach avoids the problem of gaps created through the loss of parts of the artifacts. Therefore, the aim of this study is to assemble the object without previous knowledge about the form of the original object. We utilize the edges of the fragments as an important feature in reconstructing the objects and apply multiple procedures to extract the 3D edge points. In order to assign the positions of the unknown parts that are supposed to match, the contour must be divided into four parts. Furthermore, to classify the fragments under reconstruction, we apply a backpropagation neural network. We test the algorithm on several models of ceramic fragments. It achieves highly accurate results in reconstructing the objects into their original forms, in spite of absent pieces.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.8
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• pp.783-790
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• 2010

In this paper, we propose a turbine mass flow rate model for a variable geometry turbocharger (VGT) using an artificial neural network (ANN). The model predicts the turbine mass flow rate using the VGT vane position, engine rotational speed, exhaust manifold pressure, exhaust manifold temperature, and turbine outlet pressure. The ANN is used for the estimation of the effective flow area. In order to validate the results estimated by the proposed model, we have compared estimation results with engine experimental results. The results, in addition, represent improved estimation accuracy when compared with the performance using the turbine map.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.85-92
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• 2022

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

• Economic and Environmental Geology
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• v.53 no.3
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• pp.271-285
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• 2020

In this study, a numerical experiment related to the stress-strain analysis was performed on 3-D discrete fracture network(DFN) systems based on the distinct element method to evaluate the effect of joint geometry on deformability of jointed rock masses. Using one or two joint sets with deterministic orientation, a total of 12 3-D DFN blocks having 10m cube domain were generated with different joint density and size distribution. Directional deformation modulus of the DFN cube blocks were estimated along the axis directions of 3-D cartesian coordinate. In addition, deviatoric stress directions were chosen at every 30° of trend and plunge in 3-D for some DFN blocks to examine the variability of directional deformation modulus with respect to joint geometry. The directional deformation modulus of the DFN block were found to reduce with the increase of joint size distribution. The increase in joint density was less likely to have a significant effect on directional deformation modulus of the DFN block in case of the effect of rock bridges was relatively large because of short joint size distribution. It, however, was evaluated that the longer the joint size, the increase in the joint density had a more significant effect on the anisotropic deformation modulus of the DFN block. The variation of the anisotropic deformation modulus according to the variations in joint density and size distribution was highly dependent on the number of joint sets and their orientation in the DFN block. Finally, this study addressed a numerical procedure for stress-strain analysis of jointed rock masses considering joint geometry and discussed a methodology for practical application at the field scale.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.857-859
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• 2015

In this letter, we consider a directional routing protocol that reduces the duplicated packets for AODV-based flooding in the course of establishing the end-to-end route in the multi-hop maritime ad-hoc networks. We propose an adaptive means of reducing the routing overhead subject to the node density and the target probability of successful routing that is analyzed by the stochastic geometry.