• Steel and Composite Structures
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• v.44 no.6
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• pp.867-882
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• 2022

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

• Computers and Concrete
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• v.15 no.6
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• pp.951-972
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• 2015

The focus of the present study is to investigate both local and global behaviour of a precast concrete sandwich panel. The selected prototype consists of two reinforced concrete layers coupled by a system of cold-drawn steel profiles and one intermediate layer of insulating material. High-definition nonlinear finite element (FE) models, based on 3D brick and 2D interface elements, are used to assess the capacity of this technology under shear, tension and compression. Geometrical nonlinearities are accounted via large displacement-large strain formulation, whilst material nonlinearities are included, in the series of simulations, by means of Von Mises yielding criterion for steel elements and a classical total strain crack model for concrete; a bond-slip constitutive law is additionally adopted to reproduce steel profile-concrete layer interaction. First, constitutive models are calibrated on the basis of preliminary pull and pull-out tests for steel and concrete, respectively. Geometrically and materially nonlinear FE simulations are performed, in compliance with experimental tests, to validate the proposed modeling approach and characterize shear, compressive and tensile response of this system, in terms of global capacity curves and local stress/strain distributions. Based on these experimental and numerical data, the structural performance is then quantified under various loading conditions, aimed to reproduce the behaviour of this solution during production, transport, construction and service conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.97-104
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• 2015

An 'associative cube', a class of auto-associative memories, is revisited here, in which training data and hidden orthogonal basis functions such as wavelet packets or Fourier kernels, are combined in the weight cube. This weight cube has hidden units in its depth, represented by a three dimensional cubic structure. We develop an unsupervised incremental learning mechanism based upon the adaptive least squares method. Training data are mapped into orthogonal basis vectors in a least-squares sense by updating the weights which minimize an energy function. Therefore, a prescribed orthogonal kernel is incrementally assigned to an incoming data. Next, we show how a decoding procedure finds the closest one with a competitive network in the hidden layer. As noisy test data are applied to an associative cube, the nearest one among the original training data are restored in an optimal sense. The simulation results confirm robustness of associative cubes even if test data are heavily distorted by various types of noise.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.71-76
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• 2001

In this paper we propose the Digital Automatic Gain Controller for IEEE 802.11a-High-speed Physical Layer in the 5 GHz Band. The input gain it estimated by calculating the energy of the training symbol that it a synchronizing signal. The renewal gain is calculated by comparing the estimated gain with the ideal gain. The renewal gain is converted into the controlled voltage for GCA to reduce or amplify the input signals. We used a piecewise-linear approximation to reduce the hardware size. The gain control is performed seven times to provide more accurate gain control. The proposed automatic gain controller is designed with VHDL and verified by using the Xilinx FPGA.

• Structural Engineering and Mechanics
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• v.18 no.3
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• pp.303-314
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• 2004

An artificial neural network (ANN) application is presented for flexural and axial vibration analysis of elastic beams with various support conditions. The first three natural frequencies of beams are obtained using multi layer neural network based back-propagation error learning algorithm. The natural frequencies of beams are calculated for six different boundary conditions via direct solution of governing differential equations of beams and Rayleigh's approximate method. The training of the network has been made using these data only flexural vibration case. The trained neural network, however, had been tested for cantilever beam (C-F), and both end free (F-F) in case the axial vibration, and clamped-clamped (C-C), and Guided-Pinned (G-P) support condition in case the flexural vibrations which were not included in the training set. The results found by using artificial neural network are sufficiently close to the theoretical results. It has been demonstrated that the artificial neural network approach applied in this study is highly successful for the purposes of free vibration analysis of elastic beams.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.87-93
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• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.63-73
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• 2007

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.

• PNF and Movement
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• v.4 no.1
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• pp.51-62
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• 2006

Purpose : The purposes of this study were to test the effect of proprioceptive and vestibular sensory input on expression of BDNF after traumatic brain injury in the rat. Subject : The control group was sacrificed at 24 hours after traumatic brain injury. The experimental group I was housed in standard cage for 7 days. The experimental group II was housed in standard cage after intervention to proprioceptive and vestibular sensory(balance training) for 7 days. Method : Traumatic brain injury was induced by weight drop model and after operation they were housed in individual standard cages for 24 hours. After 7th day, rats were sacrificed and cryostat coronal sections were processed individual1y in goat polyclonal anti-BDNF antibody. The morphologic characteristics and the BDNF expression were investigated in injured hemisphere section and contralateral brain section from immunohistochemistry using light microscope. Result : The results of this experiment were as follows: 1. In control group, cell bodies in lateral nucleus of cerebellum, superior vestibular nucleus, purkinje cell layer of cerebellum and pontine nucleus changed morphologically. 2. The expression of BDNF in contralateral hemisphere of group II were revealed. 3. On 7th day after operation, immunohistochemical response of BDNF in lateral nucleus, superior vestibular nucleus, purkinje cell layer and pontine nucleus appeared in group II. Conclusion : The present results revealed that intervention to proprioceptive and vestibular sensory input is enhance expression of BDNF and it is useful in neuronal reorganization improvement after traumatic brain injury.

• Advances in concrete construction
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• v.3 no.2
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• pp.91-102
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• 2015

In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Recurrent Neural Network (RNN) is investigated. Six reinforced concrete slabs having dimension $1800{\times}400{\times}120mm$ with similar steel bar of 2T10 and strengthened using different length and width of CFRP were tested and compared with similar samples without CFRP. The experimental load-deflection results were normalized and then uploaded in MATLAB software. Loading, CFRP length and width were as neurons in input layer and mid-span deflection was as neuron in output layer. The network was generated using feed-forward network and a internal nonlinear condition space model to memorize the input data while training process. From 122 load-deflection data, 111 data utilized for network generation and 11 data for the network testing. The results of model on the testing stage showed that the generated RNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.99. The ratio between predicted deflection by RNN and experimental output was in the range of 0.99 to 1.11.