• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.694-702
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• 2017

In the case of an elevated railway station, structure borne noise and vibration due to structural limitations allow the load and vibration from railway vehicles to be directly transmitted to the station structure, resulting in an increase in the number of civil complaints from customers and staff of the station. The floating slab track system, which is well known as one of the solutions for reducing the noise and vibration from elevated railway stations, usually contains rubber mounts or rubber pads under the railway slab which act as a damper. These types of device have the disadvantage that is difficult to predetermine the exact stiffness and damping ratio under the nonlinear loads resulting from train services. In this study, an isolator with a friction type of wedge is introduced, which can be applied to floating slab track systems and to be designed with precisely the required stiffness. Furthermore, a comparative analysis of the stiffness between the designed and experimental values is carried out, while the damping ratio, which is closely related to the friction wedge blocks, is deduced according to the train load condition. The performance tests of the isolator were conducted in accordance with the DIN 45673-7 standard which includes both static and dynamic load tests. The load conditions for the performance tests are designed to conform to the DIN standard related to the weight of the train and rail track, in order to perform vertical and horizontal load tests, so as to ensure the secure structural safety of the railway. Also, by checking the change aspect of the friction coefficients of the friction elements according to the loading rate, the vibration reduction performance of the friction type isolator with variable loading rate conditions is examined.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.875-888
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• 2013

The researches have recently progressed toward the use of the superelastic shape memory alloys (SMAs) to develop new smart control systems that reduce permanent deformation occurring due to severe earthquake events and that automatically recover original configuration. The superelastic SMA materials are unique metallic alloys that can return to undeformed shape without additional heat treatments only after the removal of applied loads. Once the superelastic SMA materials are thus installed at the place where large deformations are likely to intensively occur, the structural system can make the best use of recentering capabilities. Therefore, this study is intended to propose new buckling-restrained braced frames (BRBFs) with superelastic SMA bracing systems. In order to verify the performance of such bracing systems, 6-story braced frame buildings were designed in accordance with the current design specifications and then nonlinear dynamic analyses were performed at 2D frame model by using seismic hazard ground motions. Based on the analysis results, BRBFs with innovative SMA bracing systems are compared to those with conventional steel bracing systems in terms of peak and residual inter-story drifts. Finally, the analysis results show that new SMA bracing systems are very effective to reduce the residual inter-story drifts.

• Journal of the Korean Society of Propulsion Engineers
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• v.2 no.3
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• pp.99-106
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• 1998

The performance of the turbofan engine, a medium scale civil aircraft which has been developing in Rep. of Korea, was analyzed and the control scheme for optimization the performance was studied. The dynamic and real-time linear simulation was performed in the previous study The result was that the fuel scedule of the step increase overshoot the limit temperature(3105 $^{\cire}R$) of the high pressure turbine and got small surge margine of the high pressure compressor. Therefore a control scheme such as the LQR(Linear Quadratic Regulator) was applied to optimizing the performance in this studies. The linear model was expected for designing controller and the real time linear model was developed to be closed to nonlinear simulation results. The system matrices were derived from sampling operating points in the scheduled range and then the least square method was applied to the interpolation between these sampling points, where each element of matrices was a function of the rotor speed. The control variables were the fuel flow and the low pressure compressor bleed air. The controlled linear model eliminated the inlet temperature overshoot of the high pressure turbine and obtained maximum surge margins within 0.55. The SFC was stabilized in the range of 0.355 to 0.43.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.5
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• pp.333-340
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• 2019

Tests using a middle velocity propulsion impact machine (MVPIM) were performed to verify the impact resistance capability of sandwich concrete panels (SCP) in a modular liquefied natural gas (LNG) outer tank, and numerical models were constructed and analyzed. $2{\times}2m$ specimens with plain sectional characteristics and specimens including a joint section were used. A 51 kg missile was accelerated above 45 m/s and impacted to have the design code kinetic energy. Impact tests were performed twice according to the design code and once for the doubled impact speed. The numerical models for simulating impact behaviors were created by LS-DYNA. The external steel plate and filled concrete of the panel were modeled as solid elements, the studs as beam elements, and the steel plates as elasto-plastic material with fractures; the CSCM material model was used for concrete. The front plate deformations demonstrated good agreement with those of other tests. However the rear plate deformations were less. In the doubled speed test for the plain section specimen, the missile punctured both plates; however, the front plate was only fractured in the numerical analysis. The impact energy of the missile was transferred to the filled concrete in the numerical analysis.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3068-3084
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• 2021

Investigations of large commercial aircraft impact effect on nuclear power plant (NPP) buildings have been drawing extensive attentions, particularly after the 9/11 event, and this paper aims to experimentally assess the damage and vibrations of NPP buildings subjected to aircraft crash. In present Part I, two shots of reduce-scaled model test of aircraft impacting on NPP building were carried out. Firstly, the 1:15 aircraft model (weighs 135 kg) and RC NPP model (weighs about 70 t) are designed and prepared. Then, based on the large rocket sled loading test platform, the aircraft models were accelerated to impact perpendicularly on the two sides of NPP model, i.e., containment and auxiliary buildings, with a velocity of about 170 m/s. The strain-time histories of rebars within the impact area and acceleration-time histories of each floor of NPP model are derived from the pre-arranged twenty-one strain gauges and twenty tri-axial accelerometers, and the whole impact processes were recorded by three high-speed cameras. The local penetration and perforation failure modes occurred respectively in the collision scenarios of containment and auxiliary buildings, and some suggestions for the NPP design are given. The maximum acceleration in the 1:15 scaled tests is 1785.73 g, and thus the corresponding maximum resultant acceleration in a prototype impact might be about 119 g, which poses a potential threat to the nuclear equipment. Furthermore, it was found that the nonlinear decrease of vibrations along the height was well reflected by the variations of both the maximum resultant vibrations and Cumulative Absolute Velocity (CAV). The present experimental work on the damage and dynamic responses of NPP structure under aircraft impact is firstly presented, which could provide a benchmark basis for further safety assessments of prototype NPP structure as well as inner systems and components against aircraft crash.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.