• Journal of Broadcast Engineering
• /
• v.24 no.3
• /
• pp.401-410
• /
• 2019

In this paper, we propose a reference picture generation method for Inter-layer prediction based deep learning to improve the SHVC coding performance. A description will be given of a structure for performing filtering using a VDSR network on a DCT-IF based upsampled picture to generate a new reference picture and a training method for generating a reference picture between SHVC Inter-layer. The proposed method is implemented based on SHM 12.0. In order to evaluate the performance, we compare the method of generating Inter-layer predictor by applying dictionary learning. As a result, the coding performance of the enhancement layer showed a bitrate reduction of up to 13.14% compared to the method using dictionary learning, a bitrate reduction of up to 15.39% compared to SHM, and a bitrate reduction of 6.46% on average.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2001.06a
• /
• pp.101-104
• /
• 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 is estimated by calculating the energy of the training symbol that is 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2539-2554
• /
• 2017

Regression-based image super resolution (SR) methods have shown great advantage in time consumption while maintaining similar or improved quality performance compared to other learning-based methods. In this paper, we propose a novel single image SR method based on hierarchical regression to further improve the quality performance. As an improvement to other regression-based methods, we introduce a hierarchical scheme into the process of learning multiple regressors. First, training samples are grouped into different clusters according to their geometry similarity, which generates the structure layer. Then in each cluster, a compact dictionary can be learned by Sparse Coding (SC) method and the training samples can be further grouped by dictionary atoms to form the detail layer. Last, a series of projection matrixes, which anchored to dictionary atoms, can be learned by linear regression. Experiment results show that hierarchical scheme can lead to regression that is more precise. Our method achieves superior high quality results compared with several state-of-the-art methods.

• Computers and Concrete
• /
• v.22 no.2
• /
• pp.249-259
• /
• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• Journal of Advanced Marine Engineering and Technology
• /
• v.28 no.1
• /
• pp.90-97
• /
• 2004

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as speed detectors. but they increase cost and size of the motor and restrict the industrial drive applications. So in these days. many Papers have reported on the sensorless operation or DC motor(3)-(5). This paper Presents a new sensorless strategy using neural networks(6)-(8). Neural network structure has three layers which are input layer. hidden layer and output layer. The optimal neural network structure was tracked down by trial and error and it was found that 4-16-1 neural network has given suitable results for the instantaneous rotor speed. Also. learning method is very important in neural network. Supervised learning methods(8) are typically used to train the neural network for learning the input/output pattern presented. The back-propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• Computers and Concrete
• /
• v.25 no.6
• /
• pp.551-563
• /
• 2020

This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for the maximum contact pressures and contact areas of a contact problem. Firstly, the problem is formulated and solved theoretically by using Theory of Elasticity and Integral Transform Technique. Secondly, the contact problem has been extended based on the ANN. The multilayer perceptron (MLP) with three-layer was used to calculate the contact distances. External load, distance between the two quarter planes, layer heights and material properties were created by giving examples of different values were used at the training and test stages of ANN. Program code was rewritten in C++. Different types of network structures were used in the training process. The accuracy of the trained neural networks for the case was tested using 173 new data which were generated via theoretical solutions so as to determine the best network model. As a result, minimum deviation value (difference between theoretical and C++ ANN results) of was obtained for the network model. Theoretical results were compared with artificial neural network results and well agreements between them were achieved.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.2
• /
• pp.202-210
• /
• 2005

This paper presents a new neural network control for nonlinear bridge systems with earthquake excitation. We design multi-layer neural network controllers with a single hidden layer. The selection of an optimal number of neurons in the hidden layer is an important design step for control performance. To select an optimal number of hidden neurons, we progressively add one hidden neuron and observe the change in a performance measure given by the weighted sum of the system error and the control force. The number of hidden neurons which minimizes the performance measure is selected for implementation. A neural network was trained for mitigating vibrations of bridge systems caused by El Centro earthquake. We applied the proposed control approach to a single-degree-of-freedom (SDOF) and a two-degree-of-freedom (TDOF) bridge system. We assessed the robustness of the control system using randomly generated earthquake excitations which were not used in training the neural network. Our results show that the neural network controller drastically mitigates the effect of the disturbance.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.2
• /
• pp.319-328
• /
• 2021

In this paper, we perform prediction of amount of electric power plant for complex of wind plant using multi-layer perceptron in order to calculate exact calculation of capacity of ESS to maximize profit through generation and to minimize generation cost of wind generation. We acquire wind speed, direction of wind and air density as variables to predict the amount of generation of wind power. Then, we merge and normalize there variables. To train model, we divide merged variables into data as train and test data with ratio of 70% versus 30%. Then we train model by using training data, and we alsouate the prediction performance of model by using test data. Finally, we present the result of prediction in amount of wind power.

• Corrosion Science and Technology
• /
• v.20 no.6
• /
• pp.384-390
• /
• 2021

In this paper, durability evaluation and surface damage mechanism were investigated through solid particle erosion (SPE) test after applying hot-dip aluminizing (HDA) technology for the purpose of maintenance of marine economizer tube. Damaged surface shape was analyzed using SEM and 3D microscope. Compositional changes and microstructure of the HDA layer were analyzed through EDS and XRD. Durability was evaluated by analyzing weight loss and surface damage depth after SPE. HDA was confirmed to have a two-layer structure of Al and Al₅Fe₂. HDA+HT was made into a single alloy layer of Al₅Fe₂ by diffusion treatment. In the microstructure of HDA+HT, void and crack defect were induced during the crystal phase transformation process. The SPE damage mechanism depends on material properties. Plastic deformation occurred in the substrate and HDA due to ductility, whereas weight loss due to brittleness occurred significantly in HDA+HT. As a result, the substrate and HDA showed better SPE resistance than HDA+HT.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.5
• /
• pp.813-823
• /
• 2023

In this study, microstructure, hardening layer hardness, and case depth were evaluated after induction hardening(IH) of base metal specimen(BM) treated with annealing and quenching-tempering specimen(QT) treated with quenching and tempering. The microstructure after IH was significantly influenced by the microstructure before IH and the induction coil heating movement speed, but the effect of the induction frequency was very small. The hardness of the hardened layer at an induction coil heating movement speed of 15 mm/s or less was more influenced by the microstructure before IH than the induction coil travel speed and induction frequency. The induction coil travel speed has the significantly effect on the case depth, the induction frequency has effect and the microstructure before IH has a small effect.