• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.122-125
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• 2006

The Substructure Synthesis means the technology to predict the dynamic properties of an assembly from the properties of its components, or to predict the effect of a modification on a structure. The FRF Based Substructuring method is a kind of the Substructure Synthesis and very useful to predict the efficiency of the product in the early stage of development. Especially, the Hybrid FBS method is very useful to predict the vehicle NVH characteristics after modifying some components of the vehicle. Target components can be established on the basis of test models and FE models of the prototype constructed in the early stage of development. In this study, the Hybrid FBS method was applied to vehicle subframe and car-body in order to reduce vehicle interior noise induced by engine exciting force.

• Computers and Concrete
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• v.1 no.2
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• pp.211-226
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• 2004

A relatively simple multiple-vertical-line-element macro model has been incorporated into a standard computer code DRAIN-2D. It was used in blind predictions of seismic response of cantilever RC walls subjected to a series of consequent earthquakes on a shaking table. The model was able to predict predominantly flexural response with relative success. It was able to predict the stiffness and the strength of the pre-cracked specimen and time-history response of the highly nonlinear wall as well as to simulate the shift of the neutral axis and corresponding varying axial force in the cantilever wall. However, failing to identify the rupture of some brittle reinforcement in the third test, the model was not able to predict post-critical, near collapse behaviour during the subsequent response to two stronger earthquakes. The analysed macro model seems to be appropriate for global analyses of complex building structures with RC structural walls subjected to moderate/strong earthquakes. However, it cannot, by definition, be used in refined research analyses monitoring local behaviour in the post critical region.

• Steel and Composite Structures
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• v.22 no.3
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• pp.627-645
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• 2016

When an eccentrically braced frame (EBF) is subjected to severe earthquakes, the links experience inelastic deformations while beams outside of the link, braces and columns are designed to remain elastic. To perform reliable inelastic analyses of EBFs sufficient analytical model which can accurately predict the inelastic performance of the links is needed. It is said in the literature that available analytical models for shear links generally predict very well the maximum shear forces and deformations from experiments on shear links, but may underestimate the intermediary values. In this study it is shown that available analytical models do not predict very well the maximum shear forces and deformations too. In this study an analytical model which can accurately predict both maximum and intermediary values of shear force and deformation is proposed. The model parameters are established based on test results from several experiments on shear links. Comparison of available test results with the hysteresis curves obtained using the proposed analytical model established the accuracy of the model. The proposed model is recommended to be used to perform inelastic analyses of EBFs.

• Structural Engineering and Mechanics
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• v.73 no.4
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• pp.455-462
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• 2020

The hybrid method using the extended finite element method (XFEM) and the forward Euler approach is widely employed to predict the fatigue life of plate structures. Due to the accuracy of the forward Euler approach is determined by a small step size, the performance of fatigue life prediction of the hybrid method is not agreeable. Instead the forward Euler approach, a prediction method using midpoint method and support vector regression (SVR) is presented to evaluate the stress intensity factors (SIFs) and the fatigue life. Firstly, the XFEM is employed to calculate the SIFs with given crack sizes. Then use the history of SIFs as a function of either number of fatigue life cycles or crack sizes within the current cycle to build a prediction model. Finally, according to the prediction model predict the SIFs at different crack sizes or different cycles. Three numerical cases composed by a homogeneous plate with edge crack, a composite plate with edge crack and center crack are introduced to verify the performance of the proposed method. The results show that the proposed method enables large step sizes without sacrificing accuracy. The method is expected to predict the fatigue life of complex structures.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.425-432
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• 1996

Integrated performance test of Chemical and Volume Control System (CVCS) was successfully performed in 1994. However, an extensive effort to correct hardware and software problems in the letdown line was required mainly due to the lack of adequate simulation code to predict the test accurately. Although the LTC computer code was used during the YGN 3'||'&'||'4 NSSS design process, the code can not satisfactorily predict the test due to its insufficient letdown line modeling. This study developed a numerical model to simulate the letdown test by modifying the current LTC code, and then verified the model by comparing with the test data. The comparison shows that the modified LTC computer code can predict the transient behavior of letdown system tests very well. Especially, the model was verified to be able to predict the "Stiction" phenomena which caused instantaneous fluctuations in the letdown backpressure and flowrate. Therefore, it is concluded that the modified LTC computer code with the ability of calculating the "Stiction" phenomena wi11 be very useful for future plant desist and test predictions.predictions.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.573-579
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• 2021

In this study, flow analysis was performed on three types of strainers for ships with different flow rates to predict the pressure drop of the strainer due to the filter of strainer. In the case of flow analysis, the flow analysis was performed by applying the porous media method by applying the resistance value derived by Ergun's equation to the filter position. As a result of the analysis, it was found that when the dimensions of the strainer body were small, the influence of the flow rate on the pressure drop was large. In addition, the amount of pressure drop and the flow rate are almost linearly proportional, and an analysis formula that can predict the amount of pressure drop was derived. In order to predict the amount of pressure drop of the strainer when blockage exist in the strainer filter, the analysis was performed by introducing the resistance ratio to derive the prediction equation. Using this equation, it is thought that it will be possible to predict the performance of the strainer due to blockage in the future strainer design and field application.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.399-407
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• 2021

Stock movement is difficult to predict because it has dynamic characteristics and is influenced by many factors. Even so, there are some approaches to predict stock price movements, namely technical analysis, fundamental analysis, and sentiment analysis. Many researches have tried to predict stock price movement by utilizing these analysis techniques. However, the results obtained are varied and inconsistent depending on the variables and object used. This is because stock price movement is influenced by a variety of factors, and it is likely that those studies did not cover all of them. One of which is that no research considers the use of fundamental analysis in terms of currency exchange rates and the use of foreign stock price index movement related to the technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The result obtained has a prediction accuracy rate of 65,33% on an average. The inclusion of currency exchange rate and foreign stock price index movement as a predictor in this research which can increase average prediction accuracy rate by 11.78% compared to the prediction without using these two variables which only results in average prediction accuracy rate of 53.55%.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.2039-2049
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• 2020

Objective: This study was conducted to provide models to accurately predict nitrogen (N) and phosphorus (P) excretion of dairy cows on smallholder farms in Indonesia based on readily available farm data. Methods: The generic model in this study is based on the principles of the Lucas equation, describing the relation between dry matter intake (DMI) and faecal N excretion to predict the quantity of faecal N (Q_FN). Excretion of urinary N and faecal P were calculated based on National Research Council recommendations for dairy cows. A farm survey was conducted to collect input parameters for the models. The data set was used to calibrate the model to predict Q_FN for the specific case. The model was validated by comparing the predicted quantity of faecal N with the actual quantity of faecal N (Q_FNACT) based on measurements, and the calibrated model was compared to the Lucas equation. The models were used to predict N and P excretion of all 144 dairy cows in the data set. Results: Our estimate of true N digestibility equalled the standard value of 92% in the original Lucas equation, whereas our estimate of metabolic faecal N was -0.60 g/100 g DMI, with the standard value being -0.61 g/100 g DMI. Results of the model validation showed that the R² was 0.63, the MAE was 15 g/animal/d (17% from Q_FNACT), and the RMSE was 20 g/animal/d (22% from Q_FNACT). We predicted that the total N excretion of dairy cows in Indonesia was on average 197 g/animal/d, whereas P excretion was on average 56 g/animal/d. Conclusion: The proposed models can be used with reasonable accuracy to predict N and P excretion of dairy cattle on smallholder farms in Indonesia, which can contribute to improving manure management and reduce environmental issues related to nutrient losses.