• Wind and Structures
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• 제2권1호
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• pp.25-40
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• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

• 한국태양에너지학회 논문집
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• 제32권6호
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• pp.37-43
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• 2012

Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

• Journal of Mechanical Science and Technology
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• 제16권9호
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• pp.1054-1064
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• 2002

Numerical prediction of welding-induced residual stresses using the finite element method has been a common practice in the development or refinement of welded product designs. Various researchers have studied several thermal models associated with the welding process. Among these thermal models, ramp heat input and double-ellipsoid moving source have been investigated. These heat-source models predict the temperature fields and history with or without accuracy. However, these models can predict the thermal characteristics of the welding process that influence the formation of the inherent plastic strains, which ultimately determines the final state of residual stresses in the weldment. The magnitude and distribution of residual stresses are compared. Although the two models predict similar magnitude of the longitudinal stress, the double-ellipsoid moving source model predicts wider tensile stress zones than the other one. And, both the ramp heating and moving source models predict the stress results in reasonable agreement with the experimental data.

• 한국의류학회지
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• 제44권3호
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• pp.556-571
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• 2020

This study predicts consumer preference for social clothing at work, excluding uniforms using the self-product congruence theory that also establishes a model to predict the preference for recommended products that match the consumer's own image. A total of 490 Korean male office workers participated in this study. Participants' self-image and the product images of 20 apparel items were measured using nine adjective semantic scales (namely elegant, stable, sincere, refined, intense, luxury, bold, conspicuous, and polite). A model was then constructed to predict the consumer preferences using a neural network with Python and TensorFlow. The resulting Predict Preference Model using Product Image (PPMPI) was trained using product image and the preference of each product. Current research confirms that product preference can be predicted by the self-image instead of by entering the product image. The prediction accuracy rate of the PPMPI was over 80%. We used 490 items of test data consisting of self-images to predict the consumer preferences for using the PPMPI. The test of the PPMPI showed that the prediction rate differed depending on product attributes. The prediction rate of work apparel with normative images was over 70% and higher than for other forms of apparel.

• Structural Engineering and Mechanics
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• 제68권2호
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• pp.237-245
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• 2018

This study proposes an empirical formulation to predict the maximum deformation of offshore blast wall structure that is subjected to impact loading caused by hydrocarbon explosion. The blast wall model is assumed to be supported by a simply-supported boundary condition and corrugated panel is modelled. In total, 1,620 cases of LS-DYNA simulations were conducted to predict the maximum deformation of blast wall, and they were then used as input data for the development of the empirical formulation by regression analysis. Stainless steel was employed as materials and the strain rate effect was also taken into account. For the development of empirical formulation, a wide range of parametric studies were conducted by considering the main design parameters for corrugated panel, such as geometric properties (corrugation angle, breadth, height and thickness) and load profiles (peak pressure and time). In the case of the blast profile, idealised triangular shape is assumed. It is expected that the obtained empirical formulation will be useful for structural designers to predict maximum deformation of blast wall installed in offshore topside structures in the early design stage.

• Journal of Ecology and Environment
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• 제34권3호
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• pp.287-293
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• 2011

Analysing companion species from unrelated taxa concentrated so far mainly on identifying biosurrogacy in terms of conservation biology. No study has investigated companion bird and plant species to predict breeding and migrant habitats of a bird genus. In this study we recorded and analysed companion bird and plant species of the breeding bird Cyprus Wheatear Oenanthe cypriaca and four migranting Oenanthe species on Cyprus. We found characteristic companion species in Cyprus Wheatear's, Wheatear migrant's and in control habitats where no Wheatears were present. We show that plant and bird companion species can be used as discriminating factors to predict breeding and migrant habitats of the genus Oenanthe on Cyprus. Furthermore, habitat preferences of Cyprus Wheatear's companion species indicate bushy and vegetation rich habitats avoiding woodland on the one hand and managed farmland on the other hand. In comparison, migrant Wheatear and control habitats were characterised by companion species pointing to a high openness. These results support former habitat descriptions of Cyprus Wheatear and migrant Wheatears. In more general, this study shows that companion species from unrelated taxa can be used to predict breeding and migrant habitats of a bird genus.

• 콘크리트학회논문집
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• 제12권1호
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• pp.3-12
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• 2000

A fracture energy concept proposed by Ouyang and Shah's fracture mechanics approach was used to predict cracking of reinforced concrete members subjected to tension. In this approach, fracture properties in plain concrete which incorporate the presence of the fracture process zone are first determined from the generalized size effect method, then fracture energy required for crack propagation with the same dimension and material properties are evaluated using an R-curve. Subsequently taking into account the material properties in Ouyang and Shah's approach, a theoretical analysis to predict the mechanical behavior of reinforced concrete members subjected to tension was performed and compared to observed experimental results. It is seen that the predicted average crack spacing curves agree well with the experimental results, whereas the analytical method seems to predict lower values for this study. The analytical approach predicts well responses of stress-strain curves before and after the first crack is formed. It is concluded from this study that a fracture energy concept based on the R-curve and the generalized size effect method is a rational approach to predict cracking of reinforced concrete members subjected to tension.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2008년도 춘계학술대회논문집
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• pp.652-655
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• 2008

Despite of the laminar-turbulent transition region co-exist with fully turbulence region around the leading edge of an airfoil, still lots of researchers apply to fully turbulence models to predict aerodynamic characteristics. It is well known that fully turbulent model such as standard k-${\varepsilon}$ model couldn't predict the complex stall and the separation behavior on an airfoil accurately, it usually leads to over prediction of the aerodynamic characteristics such as lift and drag forces. So, we apply correlation based transition model to predict aerodynamic performance of the NREL (National Renewable Energy Laboratory) Phase IV wind turbine. And also, compare the computed results from transition model with experimental measurement and fully turbulence results. Results are presented for a range of wind speed, for a NREL Phase IV wind turbine rotor. Low speed shaft torque, power, root bending moment, aerodynamic coefficients of 2D airfoil and several flow field figures results included in this study. As a result, the low speed shaft torque predicted by transitional turbulence model is very good agree with the experimental measurement in whole operating conditions but fully turbulent model(k-${\varepsilon}$) over predict the shaft torque after 7m/s. Root bending moment is also good agreement between the prediction and experiments for most of the operating conditions, especially with the transition model.

• Korea-Australia Rheology Journal
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• 제13권1호
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• pp.47-54
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• 2001

Prediction of the maximum packing volume fraction with non-spherical particles has been one of the important problems in powder technology. The sphericity of fly ash particles depending on the particle diameter was measured by means of a CCD image processing instrument. An algorithm to predict the maximum packing volume fraction with non-spherical particles is proposed. The maximum packing volume fraction is used to predict the slurry viscosity under well dispersed conditions. For this purpose, Simha's cell model is applied for concentrated slurry with wide particle size distribution. Also, Usui's model developed for aggregative slurries is applied to predict the non-Newtonian viscosity of dense fly ash - water slurry. It is certified that the maximum packing volume fraction for non-spherical particles can be successfully used to predict slurry viscosity. The pressure drop in a pipe flow is predicted by using the non-Newtonian viscosity of dense fly ash-water slurry obtained by the present model. The predicted relationship between pressure drop and flow rate results in a good agreement with the experimented data obtained for a test rig with 50 mm inner diameter tube. Base on the design procedure proposed in this study, a feasibility study of fly ash hydraulic transportation system from a coal-fired power station to a controlled deposit site is carried out to give a future prospect of inexpensive fly ash transportation technology.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 춘계학술대회논문집
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• pp.782-785
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• 2006

The Power Flow Analysis(PFA) can be effectively used to predict structural vibration in medium-to-high frequency range. In this paper, Power Flow Finite Element Method (PFFEM) based on PFA has been used to predict the vibration of an automobile door. The predicted results for the frequency response function of the door have been compared with corresponding experimental results. In the experiment, the automobile door has been divided into several subsystems and the loss factor of each subsystem has been measured. The input mobility at a source point has been also measured. The data for the loss factors and the input mobility have been used as the input data to predict the vibration of the automobile door with PFFEM. The frequency response functions have been measured over the surface of the door. The comparison between the experimental results and the predicted results for the frequency response functions showed that PFFEM could be an effective tool to predict the structural vibration.