• Proceedings of the Korean Geotechical Society Conference
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• 2001.10c
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• pp.103-122
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• 2001

For prediction of ground movement per the excavation step, observational results of ground movement during the construction was very different with prediction during the analysis of design. step because of the uncertainty of the numerical analysis modelling, the soil parameter, and the condition of a construction field, etc. however accuratly numerical analysis method was applied. Therefore, the management system through the construction field measurement should be achieved for grasping the situation during the excavation. Until present, the measurement system restricted by ‘Absolute Value Management system’only analyzing the stability of present step was executed. So, it was difficult situation to expect the prediction of ground movement for the next excavation step. In this situation, it was developed that ‘The Management system TOMAS-EXCAV’ consisted of ‘Absolute value management system’ analyzing the stability of present step and ‘Prediction management system’ expecting the ground movement of next excavation step and analyzing the stability of next excavation step by‘Back Analysis’. TOMAS-EXCAV could be applied to all uncertainty of earth retaining structures analysis by connecting ‘Forward analysis program’ and ‘Back analysis program’ and optimizing the main design variables using SQP-MMFD optimization method through measurement results. The application of TOMAS-EXCAV was confirmed that verifed the three earth retaing construction field by back analysis.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.329-337
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• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.03a
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• pp.58-61
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• 1998

Anisotropy is closely related to the formability of sheet metal and should be considered carefully for more realistic analysis of actual sheet metal forming operations. In order to better describe anisotropic plastic properties of aluminum alloy sheets, a planar anisotropic yield function which accounts for the anisotropy of uniaxial yield stresses and strain rate ratios simultaneously was proposed recently[1]. This yield function was used in the finite element simulations of cup drawing tests for an aluminum alloy 2008-T4. Isotropic hardening with a fixed initial back stress based on experimental tensile and compressive test results was assumed in the simulation. The computation results were in very good agreement with the experimental results. It was shown that the initial back stress as well as the yield surface shape have a large influence on the prediction of the cup height profile.

• Transactions of Materials Processing
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• v.25 no.3
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• pp.189-194
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• 2016

In the current study, spring-back of DP980 steel sheet was numerically evaluated for U-bending using a yield function with a hardening model. For spring-back prediction, two types of yield functions - Hill'48 and Yld2000-2d - were considered. Additionally, isotropic hardening and the Yoshida-Uemori model were used to investigate the spring-back behavior. The parameters for each model were obtained from uniaxial tension, uniaxial tension-compression, uniaxial tension-unloading and hydraulic bulging tests. The numerical simulations were performed using the commercial software, PAM-STAMP 2G. The results were compared with experimental data from a U-bending process.

• Tunnel and Underground Space
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• v.8 no.2
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• pp.139-145
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• 1998

The reliable estimation of the system parameters and the accurate prediction of the system behavior are important to design underground structures safely and economically. Especially, the elastic modulus and the in-situ stresses are very important parameters in predicting the behavior of the underground structure. Therefore, the back analysis using the field measurement data is developed to determine accurately the elastic modulus and the in-situ stresses of the underground structural system in this study. A back analysis using the combined finite and boundary element is developed. It can consider the far field boundary condition and is efficient in computation. In this study, a back analysis is performed to predict behaviors of underground structures for the real construction site. The comparison between the results of the back analysis with field measurement data and the obtained material properties from the field test shows good agreement for the real construction site.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.8
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• pp.600-605
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• 2014

Two mufflers for a large-size sedan are suggested aiming (1) sporty-sound and (2) quiet-sound as well as both satisfying low back-pressure and low manufacturing cost. Transmission loss prediction considering heat and flow may increase the accuracy and reduce the development cost in muffler design; thus, GT-power prediction considering heat, flow, and acoustics is utilized. By understanding the fundamentals of flow-acoustic theory in small orifice(hole), an effective muffler design concept is proposed. Vehicle tests show the consistence with predictions for sound; also a back-pressure test bench confirms the advantage in pressure drop for both suggested mufflers. Those suggested mufflers also have advantages in manufacturing cost due to simplicity of the design.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Physical Therapy Korea
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• v.18 no.3
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• pp.76-84
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• 2011

The purpose of this study was to investigate the correlations between pain intensity, physical impairments, disability, and psychological factors according to the difference in duration of low back pain. This study was a cross-sectional survey of 102 participants with low back pain, divided into two groups equal in number: The first group consisted of patients with acute and subacute low back pain, while the second group consisted of patients suffering from chronic low back pain. The results showed that gender, age, pain intensity, physical impairment, disability and Fear-Avoidance Beliefs (FABs) for work activities were not significantly different between two groups. FABs for physical activities of the first group were significantly more prevalent than in the second group. More than moderate correlations were found between pain intensity, physical impairment, and disability in the first group. Less than moderate correlations were found between pain intensity, physical impairment, disability, FABs, and depression in the second group. These findings suggest that we must consider psychological factors in the treatment of patients with chronic low back pain. Regression analyses revealed that pain intensity and FABs for work activities significantly contributed to the prediction of disability in the first group. Also, pain intensity and FABs for physical activities significantly contributed to the prediction of disability in the second group. Pain intensity was most important predictor of disability in two groups.

• Journal of Ocean Engineering and Technology
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• v.31 no.6
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• pp.425-430
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• 2017

In order to reduce the cost of corrections and time needed for the block assembly process, the reverse setting method is applied for a back-heated block to neutralize deck deformation. The proper reverse setting shape for a back-heated block to correct deformation improved the deck flatness, but an excessive amount of reverse setting could inversely affect the flatness of the block. A prediction method was developed for the proper reverse setting shape using a back-heated block, considering the complex geometry of blocks, thickness of the deck plate, and thermal loading conditions such as welding and back-heating. The prediction method was developed by combining the re-meshing technique and inherent strain-based deformation analysis using the finite element method. Because the flatness deviation was decreased until the lower critical point and thereafter it tended to increase again, the optimum value for which the flatness was the best case was selected by repeatedly calculating the predefined reverse setting values. Based on this analysis and the study of the back-heating deformation of large assembly blocks, including the reverse setting shape, the mechanism for selecting the optimum reverse setting value was identified. The developed method was applied to the actual blocks of a ship, and it was confirmed that the flatness of the block was improved. It is concluded that the developed prediction method can be used to predict the optimum reverse setting shape value of a ship's block, which will reduce the cost of corrections in the construction stage.

• Computers and Concrete
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• v.29 no.6
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.