• Steel and Composite Structures
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• v.18 no.3
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.1
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• pp.35-45
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• 1991

Multi layer neural network using backpropagation learning algorithm is used to achieve identification and orientation estimation of different classes of aircraft in the variety of 3-D orientations. In-plane distortion invarient$(L,\;{\Phi})$ feature was extracted from each aircraft image to be used for training neural network aircraft classifier. For aircraft identification the optimum structure of the neural network classifier is studied to obtain high classification performance. Effective reductioin of learning time was achieved by using modified backpropagation learning algorithm and varying, learning parameters.

• Geomechanics and Engineering
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• v.18 no.6
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• pp.595-603
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• 2019

A modified model combining Kriging and Monte Carlo method (MC) is proposed for probabilistic estimation of tunnel face stability in this paper. In the model, a novel uniform design is adopted to train the Kriging, instead of the existing active learning function. It has advantage of avoiding addition of new training points iteratively, and greatly saves the computational time in model training. The kinematic approach of limit analysis is employed to define the deterministic computational model of face failure, in which the Hoek-Brown failure criterion is introduced to account for the nonlinear behaviors of rock mass. The trained Kriging is used as a surrogate model to perform MC with dramatic reduction of calls to actual limit state function. The parameters in Hoek-Brown failure criterion are considered as random variables in the analysis. The failure probability is estimated by direct MC to test the accuracy and efficiency of the proposed probabilistic model. The influences of uncertainty level, correlation relationship and distribution type of random variables are further discussed using the proposed approach. In summary, the probabilistic model is an accurate and economical alternative to perform probabilistic stability analysis of tunnel face excavated in spatially random Hoek- Brown media.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.475-482
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• 2018

In this paper, we present a study on data augmentation methods for DNN (Deep Neural Network)-based audio tagging. In this system, an audio signal is converted into a mel-spectrogram and used as an input to the DNN for audio tagging. To cope with the problem associated with a small number of training data, we augment the training samples using time stretching, pitch shifting, dynamic range compression, and block mixing. In this paper, we derive optimal parameters and combinations for the augmentation methods through audio tagging simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2321-2338
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• 2021

Compared with vehicle trajectories, pedestrian trajectories have stronger degrees of freedom and complexity, which poses a higher challenge to trajectory prediction tasks. This paper designs a mode to divide the trajectory of pedestrians at a traffic intersection, which converts the trajectory regression problem into a trajectory classification problem. This paper builds a deep model for pedestrian trajectory prediction at intersections for the task of pedestrian short-term trajectory prediction. The model calculates the spatial correlation and temporal dependence of the trajectory. More importantly, it captures the interactive features among pedestrians through the Attention mechanism. In order to improve the training speed, the model is composed of pure convolutional networks. This design overcomes the single-step calculation mode of the traditional recurrent neural network. The experiment uses Vulnerable Road Users trajectory dataset for related modeling and evaluation work. Compared with the existing models of pedestrian trajectory prediction, the model proposed in this paper has advantages in terms of evaluation indicators, training speed and the number of model parameters.

• Journal of Science Education
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• v.45 no.1
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• pp.42-54
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• 2021

In this study, we examine how scientific background is developed in order to raise the systematic strategy of scientific literate person's training, and provide the necessary foundation for the structure of future scientific literate person's training programs. For this purpose, we analyzed newspaper articles at the expert workshop of science education, discussed the true nature of scientific literate person, and derived the mechanism of manifesting scientific background. The derived mechanism was ranked in the order of understanding, empathy, execution, and sympathy. Using the presented stages and the parameters of stage transition, it is possible to construct an educational program and train the actual scientific literate person through science education when it is applied to school sites.

• Korean Journal of Exercise Nutrition
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• v.24 no.3
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• pp.32-38
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• 2020

[Purpose] Sarcopenia is considered one of the major causes of disability in the elderly population and is highly associated with aging. Exercise is an essential strategy for improving muscle health while aging and involves multiple metabolic and transcriptional adaptations. Although the beneficial effects of exercise modalities on skeletal muscle structure and function in aging are well recognized, the exact cellular and molecular mechanisms underlying the influence of exercise have not been fully elucidated. [Methods] We summarize the biochemical pathways involved in the progression and pathogenesis of sarcopenia and describe the beneficial effects of exercise training on the relevant signaling pathways associated with sarcopenia. [Results] This study briefly introduces current knowledge on the signaling pathways involved in the development of sarcopenia, effects of aerobic exercise on mitochondria-related parameters and mitochondrial function, and role of resistance exercise in the regulation of muscle protein synthesis against sarcopenia. [Conclusion] This review suggested that the beneficial effects of exercise are still under-explored, and accelerated research will help develop better modalities for the prevention, management, and treatment of sarcopenia.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Smart Structures and Systems
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• v.30 no.2
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• pp.183-193
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• 2022

Structural health monitoring and structural vibration control are multidisciplinary and frontier research directions of civil engineering. As intelligent materials that integrate sensing and actuation capabilities, shape memory alloys (SMAs) exhibit multiple excellent characteristics, such as shape memory effect, superelasticity, corrosion resistance, fatigue resistance, and high energy density. Moreover, SMAs possess excellent resistance sensing properties and large deformation ability. Superfine NiTi SMA wires have potential applications in structural health monitoring and micro-drive system. In this study, the mechanical properties and electrical resistance sensing characteristics of superfine NiTi SMA wires were experimentally investigated. The mechanical parameters such as residual strain, hysteretic energy, secant stiffness, and equivalent damping ratio were analyzed at different training strain amplitudes and numbers of loading-unloading cycles. The results demonstrate that the detwinning process shortened with increasing training amplitude, while austenitic mechanical properties were not affected. In addition, superfine SMA wires showed good strain-resistance linear correlation, and the loading rate had little effect on their mechanical properties and electrical resistance sensing characteristics. This study aims to provide an experimental basis for the application of superfine SMA wires in engineering.