• Bulletin of the Korean Mathematical Society
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• v.58 no.5
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• pp.1109-1127
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• 2021

We study the long-term dynamics for a family of incompressible three-dimensional Leray-α-like models that employ the spectral fractional Laplacian operators. This family of equations interpolates between incompressible hyperviscous Navier-Stokes equations and the Leray-α model when varying two nonnegative parameters 𝜃₁ and 𝜃₂. We prove the existence of a finite-dimensional global attractor for the continuous semigroup associated to these models. We also show that an operator which projects the weak solution of Leray-α-like models into a finite-dimensional space is determining if it annihilates the difference of two "nearby" weak solutions asymptotically, and if it satisfies an approximation inequality.

• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.341-351
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• 2022

During bolted flange assembly, the contact stiffness of some areas of the joint surface may be low due to the elastic interaction. In order to improve the contact stiffness at the lowest position of bolted flange, the correlation model between the initial bolt pre-tightening force and the contact stiffness of bolted flange is established in this paper. According to the stress distribution model of a single bolt, an assumption of uniform local contact stiffness of bolted flange is made. Moreover, the joint surface is divided into the compressive stress region and the elastic interaction region. Based on the fractal contact theory, the relationship model of contact stiffness and contact force of the joint surface is proposed. Considering the elastic interaction coefficient method, the correlation model of the initial bolt pre-tightening force and the contact stiffness of bolted flange is established. This model can be employed to reverse determine the tightening strategy of the bolt group according to working conditions. As a result, this provides a new idea for the digital design of tightening strategy of bolt group for contact stiffness of bolted flange. The tightening strategy of the bolted flange is optimized by using the correlation model of initial bolt pre-tightening force and the contact stiffness of bolted flange. After optimization, the average contact stiffness of the joint surface increased by 5%, and the minimum contact stiffness increased by 6%.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of Mechanical Science and Technology
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• v.19 no.7
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• pp.1393-1404
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• 2005

Fatigue crack growth and life have been estimated based on established empirical equations. In this paper, an alternative method using artificial neural network (ANN) -based model developed to predict fatigue damages simultaneously. To learn and generalize the ANN, fatigue crack growth rate and life data were built up using in-plane bending fatigue test results. Single fracture mechanical parameter or nondestructive parameter can't predict fatigue damage accurately but multiple fracture mechanical parameters or nondestructive parameters can. Existing fatigue damage modeling used this merit but limited real-time damage monitoring. Therefore, this study shows fatigue damage model using backpropagation neural networks on the basis of X -ray half breadth ratio B / $B_o$, fractal dimension $D_f$ and fracture mechanical parameters can estimate fatigue crack growth rate da/ dN and cycle ratio N / $N_f$ at the same time within engineering limit error ($5\%$).

• Journal of Physiology & Pathology in Korean Medicine
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• v.22 no.5
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• pp.1057-1061
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• 2008

For the purpose of investigating the reasonable logics contained in physiognomy of east and old western medicine. hypothetical researches based on hydromechanics theory were performed concerning facial types of form and pathologic features, especially 4 types of Dr. Jisan-Essence, Qi energy. Emotional Activity and Blood(EQAB). In order to infer the functional relation between facial type forming and EQAB factors, EQAB were supposed as fluid grounded on their continual flowing or periodical change and pressure effect from its congestion. and a premise that there's a linear corresponding relationship between the appearance of organ and its physical conditions of its inner vessels is formed too. Through this work, the unit fluid model(UFM) of Essence can be assumed as circle shape formed by the high viscosity and surface tension, the UFM model of Qi energy as quadrangular shape by the scattering features to outer four directions, and the UFM of emotional activity as inverted triangular shape by the flippant and uprising features, and the UFM of blood as ellipsoid triangle by the heavy and descending features in spite of circulation. The shapes made from each UFM are reproduced in the process of human development and manifest respective facial shape through the self-reproduction method like fractal theory in the last. Conclusively. it is said that the facial form analysis method like EQAB type theory can be the useful methodology to understand the human pathological and physiological features in view of hydromechanics.

• 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 Digital Convergence
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• v.11 no.8
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• pp.205-212
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• 2013

In this paper, we present a procedural modeling algorithms to create Korean traditional stone fence using the fractal subdivision. The main process of the algorithm is to get the next step mesh by subdividing each triangle in the previous step triangular mesh. This process is repeated recursively. Dividing each triangle into four sub-triangles after choosing a random point on each side of the triangle and moving each vertices in the normal direction with random perturbations make the bumpy appearance of stone fences. In each step we remove flat vertices which does not influence the shape of the stone. The discrete curvature determines the flatness of a vertex. New triangles whose vertices are the vertices around the removed vertex are added to make a triangular mesh.

• Geomechanics and Engineering
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• v.16 no.1
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• pp.85-95
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• 2018

Water affects the mechanical properties of coal and stress wave propagation. To comprehensively investigate the effect of water content on the properties of coal, laboratory tests including X-Ray Diffraction (XRD) analysis, P-wave test, S-wave test, static and dynamic compression test with different water contents were conducted. The compressive strength, elastic modulus and failure strain and their mechanism of coal specimen under coupled static-dynamic load with the increased water content were observed. Meanwhile, energy transmission and dissipation characteristics of a stress wave in coal specimens with different water contents under dynamic load and its relation with the failure features, such as fragmentation and fractal dimension, of coal was analyzed. Furthermore, the dynamic interpretation of water infusion to prevent coal burst based on water infusion model of coal seam roadway was provided.

• Environmental Engineering Research
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• v.21 no.2
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• pp.203-210
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• 2016

Hybrid materials were obtained modifying the bentonite (BC) and local clay (LC) using hexadecyltrimethylammonium bromide (HDTMA) or the clay were pillared with aluminum followed by modification with HDTMA. The materials were characterized by the SEM, FT-IR and XRD analytical tools. The batch reactor data implied that the uptake of $17{\beta}$-estradiol (E2) by the hybrid materials showed very high uptake at the neutral pH region. However, at higher and lower pH conditions, slightly less uptake of E2 was occurred. The uptake of E2 was insignificantly affected changing the sorptive concentration from 1.0 to 10.0 mg/L and the background electrolyte (NaCl) concentrations from 0.0001 to 0.1 mol/L. Moreover, the sorption of E2 by these hybrid materials was fairly efficient since within 30 mins of contact time, an apparent equilibrium between solid and solution was achieved, and the data was best fitted to the PSO (pseudo-second order) and FL-PSO (Fractal-like-pseudo second order) kinetic models compared to the PFO (pseudo-first order) model. The fixed-bed column results showed that relatively high breakthrough volume was obtained for the attenuation of E2 using these hybrid materials, and the loading capacity of E2 was estimated to be 75.984, 63.757, 58.965 and 49.746 mg/g for the solids BCH, BCAH, LCH and LCAH, respectively.