• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.233-240
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• 2024

The wall friction correlations of oscillatory natural circulation loops are highly loop-specific, making it difficult to perform 1-D system simulations before obtaining specific experimental data. To better predict the friction characteristics, the nonlinear dynamics of a toroidal single-phase natural circulation loop were numerically investigated, and the transition effect was considered. The k-k_L-ω transitional turbulence and k-ω SST turbulence models were used to compute the flow characteristics of the loop under different heating powers varying from 0.48 to 1.0 W/cm², and the results of both models were compared with previous experiments. The mass flow rates and friction factors predicted by the k-k_L-ω model showed a better agreement with the experimental data than the results of the k-ω SST model. The oscillation frequencies calculated using both models agreed well with the experimental data. The k-k_L-ω transitional turbulence model provided better friction-factor predictions in oscillatory natural circulation loops because it can reproduce the temporal and spatial variation of the wall shear stress more accurately by capturing the movement of laminar, transition turbulent zones inside unstable natural circulation loops. This study shows that transition effects are a possible explanation for the highly loop-specific friction correlations observed in various oscillatory natural circulation loops.

• Nonlinear Functional Analysis and Applications
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• v.29 no.3
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• pp.649-672
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• 2024

This paper explores the significance and implications of fixed point results related to orbital contraction as a novel form of contraction in various fields. Theoretical developments and theorems provide a solid foundation for understanding and utilizing the properties of orbital contraction, showcasing its efficacy through numerous examples and establishing stability and convergence properties. The application of orbital contraction in control systems proves valuable in designing resilient and robust control strategies, ensuring reliable performance even in the presence of disturbances and uncertainties. In the realm of financial modeling, the application of fixed point results offers valuable insights into market dynamics, enabling accurate price predictions and facilitating informed investment decisions. The practical implications of fixed point results related to orbital contraction are substantiated through empirical evidence, numerical simulations, and real-world data analysis. The ability to identify and leverage fixed points grants stability, convergence, and optimal system performance across diverse applications.

• Earthquakes and Structures
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• v.22 no.5
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• Journal of the Korean Geotechnical Society
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• v.32 no.9
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• pp.51-62
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• 2016

Parametric studies for various site conditions by using 3d numerical model were carried out in order to estimate dynamic behavior of soil-pile-structure system in dry soil deposits. Proposed model was analyzed in time domain using FLAC3D which is commercial finite difference code to properly simulate nonlinear response of soil under strong earthquake. Mohr-Coulomb criterion was adopted as soil constitutive model. Soil nonlinearity was considered by adopting the hysteretic damping model, and an interface model which can simulate separation and slip between soil and pile was adopted. Simplified continuum modeling was used as boundary condition to reduce analysis time. Also, initial shear modulus and yield depth were appropriately determined for accurate simulation of system's nonlinear behavior. Parametric study was performed by varying weight of superstructure, pile length, pile head fixity, soil relative density with proposed numerical model. From the results of parametric study, it is identified that inertial force induced by superstructure is dominant on dynamic behavior of soil-pile-structure system and effect of kinematic force induced by soil movement was relatively small. Difference in dynamic behavior according to the pile length and pile head fixity was also numerically investigated.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.165-170
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• 1997

In this paper, we consider the aortic sinus baroreceptor, which is the most representative baroreceptors sensing the variance of pressure in the cardiovascular system(CVS), and propose heart activity control model to observe the effect of delay time in heart period and stroke volume under the regulation of baroreflex in arotic sinus. The proposed heart activity baroreflex regulation model contains CVS electric circuit sub-model, baroreflex regulation sub-model and time delay sub-model. In these models, applied electric circuit sub-model is researched by B.C.Choi and the baroreflex regulation sub-model transforms the input, the arotic pressure of CVS electric circuit sub-model, to outputs, heart period and stroke volume by mathematical nonlinear feedback. We constituted the time delay sub-model to observe sensitivity of heart activity baroreflex regulation model by using the variable value to represent the control signal transmission time from the output of baroreflex regulation model to efferent nerve through central nervous system. The simulation object of this model is to observe variability of the CVS by variable value in time delay sub-model. As simulation results, we observe three patterns of CVS variability by the time delay. First, if the time delay is over 2.5 sec, arotic pressure, stroke volume and heart rate is observed nonperiodically and irregularly. Second, if the time delay is from between 0.1 sec and 0.25 sec, the regular oscillation is observed. Finally, if time delay is under 0.1 sec, then heart rate and arotic pressure-heart rate trajectory is maintained in stable state.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.10
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• pp.1045-1052
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• 2015

In this paper, a stabilized control algorithm for the large rotating stand of a long-range surveillance radar (LRSR) system is introduced. The stabilized control algorithm for this large rotating stand system was designed using mathematical plant modeling. The LRSR system is located on high ground and has a wide surface, making it susceptible to the effects of wind, which increases the bearing friction and reduces the stability of the rotating stand. The disturbance caused by the wind was analyzed using computational fluid dynamics (CFD) in this study. The results of the CFD analysis were used to construct a control algorithm for the disturbance . The performance of the proposed control algorithm was demonstrated experimentally and through simulations. The plant model and the control algorithm were constructed in Matlab/Simulink.

• Journal of Korea Water Resources Association
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• v.46 no.4
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• pp.373-387
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• 2013

For the efficient operation and management of the water resources system, coordinated operation of weirs and reservoirs is required. A simulation based, and an optimization based approaches are available to deal with the operation and management problems. The simulation based approach does not guarantee an optimal solution, and the optimization based approach is not so flexible to consider, complex, nonlinear problems we will face when trying to allocate water to different uses, various demand sectors in a basin. Hence, it is important to develop a model that would compensate for the weak points in both models. We will compare and contrast intrinsic and extrinsic properties of two modeling approaches, addressing issues related to setting system operation and control rules that would lead us to more efficient use of water in the basin. As a result, we propose to use CoWMOM(Coordinated weirs and multi-reservoir operating model), a "simulation based" optimization model for a simple simulation of the past periods, and for the real-time simulation process considering uncertain inflow.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.89-98
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• 2017

The control moment gyroscope(CMG) can be used for essential balancing control of a one-wheeled mobile robot. A single-gimbal CMG has a simple structure and can supply strong restoring torque against external disturbances. However, the CMG generates unwanted directional torque also besides the restoring torque; the unwanted directional torque causes instability in the one-wheeled robot control system that has high rotational degrees of freedom. This study proposes a control system for a one-wheeled mobile robot by using a CMG scissored pair to eliminate the unwanted directional torque. The well-known LQR control algorithm is designed for robustness against modeling error in the dynamic motion equations of a one-wheeled robot. Computer simulations for 3D nonlinear dynamic equations are carried out to verify the proposed control system with the CMG scissored pair and the LQR control algorithms.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.7
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• pp.75-84
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• 2003

To reduce the effects of the uncertainties due to the modeling error and aerodynamic coefficients, a nonlinear adaptive control system based on neural networks is proposed . Neural networks parameters are adjusted by using an adaptive law. The sliding mode control scheme is used to compensate for the effect of the approximation error of neural networks. Control parameters and neural networks structures are optimized to obtain better performance by using the genetic algorithm. By introducing the concept of multi-groups of populations, the genetic algorithm is modified so that individuals and groups can be simultaneously evolved . To verify the performance of the pro posed algorithm, the optimized neural networks control system is applied to an aircraft longitudinal dynamics.