• Bulletin of the Korean Chemical Society
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• v.33 no.3
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• pp.1009-1014
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• 2012

The influence of magnetic fields on chemical processes has long been the subject of interest to researchers. For this time numerous investigations show that commonly the effect of a magnetic field on chemical reactions is insignificant with impact less than 10 percent. However, there are some papers that point to the observation of external magnetic field effect on chemical and biochemical systems actually having a significant impact on the reactions. Thus, of great interest is an active search for rather simple but realistic models, that are based on physically explicit assumptions and able to account for a strong effect of low magnetic fields. The present work theoretically deals with two models explaining how an applied weak magnetic field might influence the steady state of a non-equilibrium chemical system. It is assumed that external magnetic field can have effect on the rates of radical reactions occurring in a system. This, in turn, leads to bifurcation of the nonequilibrium stationary state and, thus, to a drastic change in the properties of chemical systems (temperature and reagent concentration).

• Computational Structural Engineering
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• v.1 no.1
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• pp.103-112
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• 1988

In this study, the earthquake response of building structures with foundation uplift was investigated. The Winkler foundation model and two-spring model are widely used to represent the interaction between foundation mat and soil. While the analysis using the Winkler foundation model results in more accurate prediction, it requires a complex procedure and longer computation time. In this study, an equivalent two-spring model(S model) is proposed. The S model can represent the Winkler foundation model more accurately and the analysis using the S model is simpler and more effective. The S model is derived by simplifying the nonlinear moment-rotation relationship of foundation mat. The dynamic responses predicted by the S model gave a good agreement to those of the Winkler foundation model.

• Journal of the Earthquake Engineering Society of Korea
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• v.18 no.1
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• pp.9-18
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• 2014

The purpose of this study is to investigate the seismic behavior of hollow reinforced concrete bridge column systems with reinforcement details for material quantity reduction and to provide the details and reference data. Five hollow reinforced concrete bridge columns were tested under a constant axial load and a cyclically reversed horizontal load. The accuracy and objectivity of the assessment process can be enhanced by using a sophisticated nonlinear finite element analysis program. The adopted numerical method gives a realistic prediction of seismic performance throughout the loading cycles for several the investigated test specimens. This study documents the testing of hollow reinforced concrete bridge column systems with reinforcement details for material quantity reduction and presents conclusions based on the experimental and analytical findings.

• Structural Engineering and Mechanics
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• v.75 no.3
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• pp.295-309
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• 2020

The construction of Reinforced Concrete (R/C) buildings with unreinforced masonry infills is part of the traditional building practice in many countries with regions of high seismicity throughout the world. When these buildings are subjected to seismic motions the presence of masonry infills and especially their configuration can highly influence the seismic damage state. The capability to avoid configurations of masonry infills prone to seismic damage at the stage of initial architectural concept would be significantly definitive in the context of Performance-Based Earthquake Engineering. Along these lines, the present paper investigates the potential of instant prediction of the damage response of R/C buildings with various configurations of masonry infills utilizing Artificial Neural Networks (ANNs). To this end, Multilayer Feedforward Perceptron networks are utilized and the problem is formulated as pattern recognition problem. The ANNs' training data-set is created by means of Nonlinear Time History Analyses of 5 R/C buildings with a large number of different masonry infills' distributions, which are subjected to 65 earthquakes. The structural damage is expressed in terms of the Maximum Interstorey Drift Ratio. The most significant conclusion which is extracted is that the ANNs can reliably estimate the influence of masonry infills' configurations on the seismic damage level of R/C buildings incorporating their optimum design.

• Structural Engineering and Mechanics
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• v.60 no.3
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• pp.471-488
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• 2016

This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.844-852
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• 2014

PBX simulant is known to exhibit highly nonlinear behaviors of deformation such as the stress softening, hysteresis under cyclic loading, residual strain after unloading, and aging. This paper proposes a new pseudo-elastic model for PBX simulant considering stress softening and residual strain. Uniaxial loading and unloading tests at quasi-static states were carried out in order to obtain the mechanical properties of the PBX simulants. And then the Dorfmann-Ogden model is modified to make it consistent with the test result of PBX simulants. Prediction with the new model shows a good correspondence to the experimental data demonstrating that the model properly describes stress softening and residual strain of PBX simulants.

• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Journal of the Korea Concrete Institute
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• v.11 no.6
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• pp.13-24
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• 1999

The external, unbonded prestressed concrete(PSC) members exhibit very different structural behavior from that of internal bonded PSC members because of eccentricity change and slip occurrence during loading process. The purpose of the present study is to propose the ultimate failure stresses of prestressing (PS) steels for those external unbonded PSC members. To this end, a comprehensive analysis has been made using the nonlinear finite element analysis program developed recently for external unbonded PSC members by authors. A series of major influencing variables have been included in the analysis. It was found that the span-depth ratio, neutral axis depth-effective depth ratio, load geometry, amount of ordinary steel, and prestressing steel ration have great influence for the ultimate failue stress of PS steel is preposed and is compared with experimental dat as well as existing formulas for internal unbonded members. The Comparison indicates that the proposed equation agrees relatively well with experimental data and that existing formulas including ACI and AASHTO equations show some discrepancies from experimental ones. The present study allows more realistic analysis and design of prestressed concrete structures with external unbonded tendons.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.4 s.247
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• pp.465-472
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• 2006

Recently, the regulations of the government and the concerns of people have rise to the interest in noise pollution levels as compared to other vehicles. In this area, many researchers have studied to reduce this noise in the field of automotive engineering. This paper proposes an optimal design scheme to reduce the noise of the intake system by adapting Kriging with two meta-heuristic techniques. For this, as a measuring tool for the performance of the intake system, the performance prediction software, was used. Then, the length and radius of each component of the current intake system are selected as input variables and the orthogonal arrays is adapted as a space-filling design. With these simulated data, we can estimate a correlation parameter in Kriging by solving the nonlinear problem with a genetic algorithm and find an optimal level for the intake system by optimizing Kriging estimated with simulated annealing. We notice that this optimal design scheme gives noticeable results and is a preferable way to analyze the intake system. Therefore, an optimal design for the intake system is proposed by reducing the noise of its system.

• Water for future
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• v.25 no.3
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• pp.105-113
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• 1992

The purpose of this paper is to introduce and to apply the artificial neural network theory to real hydrologic system for forecasting daily streamflows during flood periods. The hydrologic dynamic process of rainfall-runoff is identified by the iterated estimation of system parameters that are determined by adjusting the weights of the network according to the non-linear response characteristics which is formed the model. Back propagation algorithm of neural network model is applied for the estimation of system parameters with past daily rainfall and runoff series data, and streamflows are forecasted using the parameters. The forecasted results are analyzed by statistical methods for the comparison with the observed.