• Journal of the Korean Society for Precision Engineering
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• v.18 no.6
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• pp.43-49
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• 2001

A reduced machining time and increased accuracy for the sculptured surface are very important when producing complicated parts. The step-size and tool-path interval are essential components in high speed and high resolution machining. If they are small, the machining time will increase, whereas if they are large, rough surfaces will be caused. In particular, the machining time, which is key in high speed machining, is affected by the tool-path interval more than the step-size. The conventional method for calculating the tool=path interval is to select a small parametric increment of a small increment based on the curvature of the surface. However, this approach also has limitations. The first is that the tool-path interval can not be calculated precisely. The second is that a separate tool-path interval needs to be calculated in each of the three cases. The third is that the conversion from Cartesian domain to parametric domain or vice versa must be necessary. Accordingly, the current study proposes a new tool-path interval algorithm that do not involve a curvature and that is not necessary for any conversion and a variable step-size algorithm for NURBS.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.851-860
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• 2009

Recently, among the computational methods of protein-protein interaction prediction, vast amounts of domain based methods originated from domain-domain relation consideration have been developed. However, it is true that multi domains collaboration is avowedly ignored because of computational complexity. In this paper, we implemented a protein interaction prediction system based the Interaction Significance matrix, which quantified an influence of domain combination pair on a protein interaction. Unlike conventional domain combination methods, IS matrix contains weighted domain combinations and domain combination pair power, which mean possibilities of domain collaboration and being the main body on a protein interaction. About 63% of sensitivity and 94% of specificity were measured when we use interaction data from DIP, IntAct and Pfam-A as a domain database. In addition, prediction accuracy gradually increased by growth of learning set size, The prediction software and learning data are currently available on the web site.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.103-111
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• 2009

In SCA, the core framework must include the domain parser to parse the domain profile and thus reconstructs the platform on the time including the starting of the platform, the initialization of the new radio, and etc. The domain profile is described in XML and it includes the characteristics about the software component or the hardware device in a platform. Elementarily, the core framework has to have within the domain profile parser in order to parse the domain profile. In this paper, in order to apply to the limited environment like the mobile terminal, we propose the method for reducing the size of the domain profile parser and for strengthening the independency of the XML parser vendor to have with the domain profile parser. Therefore, domain profile parser can be solve the problem like the overhead about the DOM tree creation due to the repetitive parsing of the domain profile, the compatibility degradation by the specific XML parser vender, the dependency about the domain profile technique, and etc.

• Journal of the Korean Ceramic Society
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• v.54 no.6
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• pp.506-510
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• 2017

One of the characteristics of polycrystalline graphene that determines its material properties is grain size. Mechanical properties such as Young's modulus, yield strain and tensile strength depend on the grain size and show a reverse Hall-Petch effect at small grain size limit for some properties under certain conditions. While there is agreement on the grain size effect for Young's modulus and yield strain, certain MD simulations have led to disagreement for tensile strength. Song et al. showed a decreasing behavior for tensile strength, that is, a pseudo Hall-Petch effect for the small grain size domain up to 5 nm. On the other hand, Sha et al. showed an increasing behavior, a reverse Hall-Petch effect, for grain size domain up to 10 nm. Mortazavi et al. also showed results similar to those of Sha et al. We suspect that the main difference of these two inconsistent results is due to the different modeling. The modeling of polycrystalline graphene with regular size and (hexagonal) shape shows the pseudo Hall-Petch effect, while the modeling with random size and shape shows the reverse Hall-Petch effect. Therefore, this study is conducted to confirm that different modeling is the main reason for the different behavior of tensile strength of the polycrystalline structures. We conducted MD simulations with models derived from the Voronoi tessellation for two types of grain size distributions. One type is grains of relatively similar sizes; the other is grains of random sizes. We found that the pseudo Hall-Petch effect and the reverse Hall-Petch effect of tensile strength were consistently shown for the two different models. We suspect that this result comes from the different crack paths, which are related to the grain patterns in the models.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.119-127
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• 2006

A non-destructive time domain approach to examine structural damage using parameterized partial differential equations and Galerkin approximation techniques is presented. The time domain analysis for damage detection is independent of modal parameters and analytical models unlike frequency domain methods which generally rely on analytical models. The time history of the vibration response of the structure was used to identify the presence of damage. Damage in a structure causes changes in the physical coefficients of mass density, elastic modulus and damping coefficients. This is a part of our ongoing effort on the general problem of modeling and parameter estimation for internal damping mechanisms in a composite beam. Namely, in detecting damage through time-domain or frequency-domain data from smart sensors, the common damages are changed in modal properties such as natural frequencies, mode shapes, and mode shape curvature. This paper examines the use of beam-like structures with piezoceramic sensors and actuators to perform identification of those physical parameters, and detect the damage. Experimental results are presented from tests on cantilevered composite beams damaged at different locations and different dimensions. It is demonstrated that the method can sense the presence of damage and obtain the position of a damage.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1121-1130
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• 2004

The Genetic Algorithm (GA), an optimization technique based on the theory of natural selection, has proven to be a relatively robust means of searching for global optimum. It converges to the global optimum point without auxiliary information such as differentiation of function. In the case of a complex problem, the GA involves a large population number and requires a lot of computing time. To improve the process, this research used parallel processing with several personal computers. Parallel process technique is classified into two methods according to subpopulation's size and number. One is the fine-grained method (FGM), and the other is the coarse-grained method (CGM). This study selected the CGM as a parallel process technique because the load is equally divided among several computers. The given design domain should be reduced according to the degree of feasibility, because mechanical system problems have constraints. The reduced domain is used as an initial design domain. It is consistent with the feasible domain and the infeasible domain around feasible domain boundary. This parallel process used the Message Passing Interface library.

• Proceedings of the Korean Magnestics Society Conference
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• 1991.05a
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• pp.65-66
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• 1991

• Proceedings of the Korean Magnestics Society Conference
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• 1991.11a
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• pp.30-31
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• 1991

• Proceedings of the Korean Magnestics Society Conference
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• 2012.05a
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• pp.101-101
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• 2012