• Smart Structures and Systems
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• v.28 no.6
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• pp.827-838
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• 2021

The dynamic displacement is considered to be an important indicator of structural safety, and becomes an indispensable part of Structural Health Monitoring (SHM) system for high-speed railway bridges. This paper proposes an indirect strain based dynamic displacement reconstruction methodology for high-speed railway box girders. For the typical box girders under eccentric train load, the plane section assumption and elementary beam theory is no longer applicable due to the bend-torsion coupling effects. The monitored strain was decoupled into bend and torsion induced strain, pre-trained multi-output support vector regression (M-SVR) model was employed for such decoupling process considering the sensor layout cost and reconstruction accuracy. The decoupled strained based displacement could be reconstructed respectively using box girder plate element analysis and mode superposition principle. For the transformation modal matrix has a significant impact on the reconstructed displacement accuracy, the modal order would be optimized using particle swarm algorithm (PSO), aiming to minimize the ill conditioned degree of transformation modal matrix and the displacement reconstruction error. Numerical simulation and dynamic load testing results show that the reconstructed displacement was in good agreement with the simulated or measured results, which verifies the validity and accuracy of the algorithm proposed in this paper.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.120-127
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• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.139-146
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• 2010

In this study, a new equivalent modeling technique of rollingstock for 1-D collision dynamics was proposed using crash analysis of 3-D finite element model in some detail. To obtain good simulation results of 1-D dynamic model, the force-deformation curves of crushable structures should be well modelled with crash analysis of 3-D finite element model. Up to now, the force-deformation curves of the crushable structures have been extracted from crash analyses of sectionally partitioned parts of the carbody, and integrated into 1-D dynamic model. However, the results of the 1-D model were not satisfactory in terms of crash accelerations. To improve this problem, the force-deformation curves of the crushable structures were extracted from collision analysis of a simplified train consist in this study. A comparative study applying the suggested technique shows in good agreements in simulation results between two models for KHST.

• KNOM Review
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• v.23 no.1
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• pp.34-42
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• 2020

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point because it takes for the process to be applied and the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain training data for machine learning model from an Integer Linear Programming (ILP) solution. We use the simulation data to train the machine learning model which predicts the optimal VNF deployment in a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution in a 5-minute future time point.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.30-38
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• 2006

In this paper, we improved to drops recognition rate according to distance using distance and depth information with 3D from stereo face images. A monocular face image has problem to drops recognition rate by uncertainty information such as distance of an object, size, moving, rotation, and depth. Also, if image information was not acquired such as rotation, illumination, and pose change for recognition, it has a very many fault. So, we wish to solve such problem. Proposed method consists of an eyes detection algorithm, analysis a pose of face, md principal component analysis (PCA). We also convert the YCbCr space from the RGB for detect with fast face in a limited region. We create multi-layered relative intensity map in face candidate region and decide whether it is face from facial geometry. It can acquire the depth information of distance, eyes, and mouth in stereo face images. Proposed method detects face according to scale, moving, and rotation by using distance and depth. We train by using PCA the detected left face and estimated direction difference. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.617-628
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• 2016

This paper proposes the structure of a smart transformer to improve the performance of the 60Hz main power transformer for rolling stock. The proposed smart transformer is a kind of solid state transformer that consists of semiconductor switching devices and high frequency transformers. This smart transformer would have smaller size than the conventional 60Hz main transformer for rolling stock, making it possible to operate AC electrified track efficiently by power factor control. The proposed structure employs a cascade H-Bridge converter to interface with the high voltage AC single phase grid as the rectifier part. Each H-Bridge converter in the rectifier part is connected by a Dual-Active-Bridge (DAB) converter to generate an isolated low voltage DC output source of the system. Because the AC voltage in the train system is a kind of medium voltage, the number of the modules would be several tens. To control the entire smart transformer, the inner DC voltage of the modules, the AC input current, and the output DC voltage must be controlled instantaneously. In this paper, a control algorithm to operate the proposed structure is suggested and confirmed through computer simulation.