• Journal of the korean Society of Automotive Engineers
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• v.13 no.2
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• pp.67-87
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• 1991

The computer program which predicts the gas exchange process of multi-cylinder 4-Stroke cycle spark-ignition engine, can be great assistance for the design and development of new engine. In this study, the computer program was developed to predict the gas exchange process of multi-cylinder four stroke cycle spark ignition engine including intake and exhaust systems. When gas exchange process is to be calculated, the evaluation of the variation of the thermo-dynamic properties with time and position in the intake and exhaust systems is required. For the purpose, the application of the generalized method of characteristics to the gas exchange process is known as one of the method. The simulation model developed was investigated to the analysis of the branch system of multi-cylinder. The models used were the 2-zone expansion model and single zone model for in cylinder calculation and the generalized method of characteristic including area change, friction, heat transfer and entropy gradients for pipe flow calculation. The empirical constants reduced to least number as possible were determined through the comparison with the experimented indicator diagram of one particular operation condition and these constants were applied to other operating condition. The predicted pressures in cylinder were compared with the experimental results over the wide range of equivalence ratio and ignition timing. The predicted values have shown good agreement with the experimental results. The thermodynamic properties in the intake and exhaust system were predicted over the wide range of equivalence ratio and ignition timing. The obtained results can be summarized as follows. 1. Pressures in the exhaust manifold have a little influence on the equivalence ratio, a great influence on the ignition timing. 2. Pressures in the inlet manifold are nearly unchanged by the equivalence ratio and the ignition timing. 3. In this study, the behaviors of the exhaust temperature, gas in the exhaust manifold were ascertained.

• Journal of Ocean Engineering and Technology
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• v.20 no.6 s.73
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• pp.101-107
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• 2006

Recently, with the increase in requirements for marine development, a marine urbanism is being visualized, with more and more huge-scale structures at the scope of the ocean space utilization. In particular, a pontoon-type structure has attracted attention, since The Floating Structures Association of Japan proposed a new concept as the most suitable one of floating airports. The Very Lage Floating Structure (VLFS) is considered a flexible structure, for a quite large length-to-breadth ratio and its geometrical flexibility. The main objective of this study is to makean exact and convenient prediction about the hydro-elastic response on very large offshore structures in waves. The numerical approach for the hydro-elastic responses is based on the combination of the three dimensional source distribution method and the dynamic response analysis method, which assumed a dividing pontoon type structure, as many rigid bodies connected elastic beam elements. The established hydo-elastic theory was applied to the radiation forces caused by motions of a whole structure, formulated using the global coordinate system, which has the origin at the center of the structure. However, in this paper, we took radiation forces, occurred by individual motions of floating bodies, into consideration. The calculated results show good agreement with the experimental and calculated results by Yago.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1879-1885
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• 2017

This paper discusses the trajectory tracking system of unmanned ground vehicles based on predictive control. Because the unmanned ground vehicles can not satisfactorily complete the path tracking task, highly efficient and stable trajectory control system is necessary for unmanned ground vehicle to be realized intelligent and practical. According to the characteristics of unmanned vehicle, this paper built the kinematics tracking models firstly. Then studied algorithm solution with the tools of the optimal stability analysis method and proposed a tracking control method based on the model predictive control. The controller used a kinematics-based prediction model to calculate the predictive error. This controller helps the unmanned vehicle drive along the target trajectory quickly and accurately. The designed control strategy has the true robustness, simplicity as well as generality for kinematics model of the unmanned vehicle. Furthermore, the computer Simulink/Carsim results verified the validity of the proposed control method.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.49-58
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• 2012

In many countries including Korea, the design concept of pavement structure has been converted from empirical method to mechanisticempirical method since the advent of compaction control based on resilient modulus proposed by AASHTO in 1986. Studies of last decades indicates that the classical compaction control method based on relative compaction and plate bearing test(PBT) will necessarily move to the methods taking advantage of light falling weight deflectometer(LFWD) and dynamic cone penetrometer(DCP) in addition to PBT. In this study, the validity of resilient modulus prediction equation proposed by Korean Pavement Design Guide is verified by comparison with physical properties of subgrade soil and the results of structural analysis. In addition, correlational equations between elastic modulus measured by various field tests and resilient modulus estimated by empirical model are proposed. Finally, a field test-based compaction control procedure for subgrade is suggested by using proposed correlational equations.

• Journal of the Korean Wood Science and Technology
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• v.28 no.1
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• pp.18-27
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• 2000

Even the same materials are assembled in flush door skin panel, warping is not simply prevented under the changes of environmental conditions since wood and wood-based material have large variations in their physical and mechanical properties. The parameters such as linear movement coefficient(LMC), modulus of elasticity (MOE), required to predict warping could be estimated by oven drying method and dynamic method instead of American Society for Testing Materials(ASTM) procedure. The relationship between warping and LMC was curvilinear, while it between warping and MOE was linear. LMC had a larger effect on warping than MOE. Material propensity of skin panel such as hardboard and plywood showed normal distributions. The variation of material properties, however, was much larger in plywood than in hardboard. Monte Carlo simulation also indicated that rejection ratio of flush door due to the occurrence of warping could be predicted with consideration of the relationship of warping and parameters of probability distribution of MOE, LMC, and moisture content.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.4 s.109
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• pp.319-328
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• 2006

This paper presents a method for analysis of the mechanical behavior of a crankshaft in a four-cylinder internal combustion engine. The purpose of the analysis was to study the characteristics of the shaft in which the pin and arm parts were assumed to have a uniform section in order to simplify the modal analysis. The results of natural frequency transfer function and mode shape were compared with those obtained by experimental work. The results obtained from the comparison showed a good agreement with each other and consequently verified the analysis model. Furthermore, a prediction of crankshaft characteristics under the firing condition, by using the model, was performed. This study describes a new method for analyzing the dynamic behavior of crankshaft vibrations in the frequency domain based on the initial firing stages. The new method used RMS values to calculate the energy at each bearing journal and counter weight shape modification under the operating conditions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.2
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• pp.141-153
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• 1987

The prediction of the exact behavior of multistory building is one of the most complicated problem encountered in structural engineering practice. An efficient computer method for the three dimensional analysis of building structures is presented in this paper. A multistory building is idealized as an assemblage of a series of rectangular plane frames interconnected by rigid floor diaphragms. The matrix condensation technique is employed for the reduction of degrees of freedom, which results in a significant saving in computational efforts and the required memory size. Kinematical approach was used to assemble condensed stiffness matrices of plane frames into a three dimensional stick model stiffness matrix. The static analysis follows the modified tridiagonal approach. Since this procedure utilizes the condensed stiffness matrix of the structure, the dynamic equations of motion for the story displacement are developed by assigning proper mass for each story. Analysis results of several example structures are compared to those obtained by using the well-known SAP IV for verification of the accuracy and efficiency of the computer program PFS which was developed utilizing the method proposed in this study. The analysis method proposed in this study can be used as an efficient and economical means for the analysis of multistory buildings.

• Structural Engineering and Mechanics
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• v.60 no.6
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• pp.1021-1038
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• 2016

Considerable part of reinforced concrete building has suffered from destructive earthquakes in Turkey. This situation makes necessary to determine nonlinear behavior and seismic performance of existing RC buildings. Inelastic response of buildings to static and dynamic actions should be determined by considering both flexural plastic hinges and brittle shear hinges. However, shear capacities of members are generally neglected due to time saving issues and convergence problems and only flexural response of buildings are considered in performance assessment studies. On the other hand, recent earthquakes showed that the performance of older buildings is mostly controlled by shear capacities of members rather than flexure. Demand estimation is as important as capacity estimation for the reliable performance prediction in existing RC buildings. Demand estimation methods based on strength reduction factor (R), ductility (${\mu}$), and period (T) parameters ($R-{\mu}-T$) and damping dependent demand formulations are widely discussed and studied by various researchers. Adopted form of $R-{\mu}-T$ based demand estimation method presented in Eurocode 8 and Turkish Earthquake Code-2007 and damping based Capacity Spectrum Method presented in ATC-40 document are the typical examples of these two different approaches. In this study, eight different existing RC buildings, constructed before and after Turkish Earthquake Code-1998, are selected. Capacity curves of selected buildings are obtained with and without considering the brittle shear capacities of members. Seismic drift demands occurred in buildings are determined by using both $R-{\mu}-T$ and damping based estimation methods. Results have shown that not only capacity estimation methods but also demand estimation approaches affect the performance of buildings notably. It is concluded that including or excluding the shear capacity of members in nonlinear modeling of existing buildings significantly affects the strength and deformation capacities and hence the performance of buildings.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.277-284
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• 2024

This study proposes a method to predict noise reduction based on noise-reduction measures, using harmonic response analysis, for transformer design. The dynamic elastic coefficients of the components comprising the actual transformer were determined by manufacturing the materials of the transformer components into simple-shaped specimens, followed by a comparison of the modes between the experiments and the analyses. A finite element model of the transformer was implemented, and harmonic response analysis was performed by deriving the exciting force of the transformer. Subsequently, the theoretical sound power level of the transformer was derived from the results of the harmonic response analysis. Finally, noise reduction measures were established, and the noise reduction amounts were compared between the experiments and the analyses, before and after applying the measures. Through the comparison and analyses of the noise reduction measures, it was confirmed that the trends in the experiments and analyses matched.

• 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.