• 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.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.537-542
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• 2004

Multi-Layer Perceptron network has been mainly applied to many practical problems because of its nonlinear mapping ability. However the generalization ability of MLP networks may be affected by the number of hidden nodes, the initial values of weights and the training errors. These factors, if improperly chosen, may result in poor generalization ability of MLP networks. It is important to identify these factors and their interaction in order to control effectively the generalization ability of MLP networks. In this paper, we have empirically identified the factors that affect the generalization ability of MLP networks, and compared their relative effects on the generalization performance for the conventional and visualized weight selecting methods using the controller box.

• Corrosion Science and Technology
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• v.22 no.2
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• pp.108-114
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• 2023

In this study, anti-corrosion effect was investigated through various electrochemical experiments after applying Al thermal spraying technology to AA5083-H321. Open circuit potential and anodic polarization curves were analyzed through electrochemical experiments in natural seawater. The shape of the surface was observed using a scanning electron microscope (SEM) and a 3D microscope before and after the experiment. Component and crystal structure were analyzed through EDS and XRD. As a result, the surface roughness of AA5083-H321 and the Al thermal sprayed coating layer increased due to surface damage caused by anodic dissolution reaction during the anodic polarization experiment. The corrosion rate of AA5083-H321 was relatively low because the Al thermal spray coating layer contained structural defects such as pores and crevices. Nevertheless, the open circuit potential of the Al thermal spray coating layer in natural seawater was measured about 0.2 V lower than that of AA5083-H321. Thus, a sacrificial anode protection effect can be expected.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.621-631
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• 2023

Adversarial training improves the robustness of deep neural networks for adversarial examples. However, the previous adversarial training method focuses only on the adversarial loss function, ignoring that even a small perturbation of the input layer causes a significant change in the hidden layer features. Consequently, the accuracy of a defended model is reduced for various untrained situations such as clean samples or other attack techniques. Therefore, an architectural perspective is necessary to improve feature representation power to solve this problem. In this paper, we apply an attention module that generates an attention map of an input image to a general model and performs PGD adversarial training upon the augmented model. In our experiments on the CIFAR-10 dataset, the attention augmented model showed higher accuracy than the general model regardless of the network structure. In particular, the robust accuracy of our approach was consistently higher for various attacks such as PGD, FGSM, and BIM and more powerful adversaries. By visualizing the attention map, we further confirmed that the attention module extracts features of the correct class even for adversarial examples.

• ETRI Journal
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• v.38 no.1
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• pp.81-89
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• 2016

In this paper, a beam ID preamble (BIDP) technique, where a beam ID is transmitted in the physical layer, is proposed for efficient beam training in millimeter-wave cellular communication systems. To facilitate beam ID detection in a multicell environment with multiple beams, a BIDP is designed such that a beam ID is mapped onto a Zadoff-Chu sequence in association with its cell ID. By analyzing the correlation property of the BIDP, it is shown that multiple beams can be transmitted simultaneously with the proposed technique with minimal interbeam interference in a multicell environment, where beams have different time delays due to propagation delay or multipath channel delay. Through simulation with a spatial channel model, it is shown that the best beam pairs can be found with a significantly reduced processing time of beam training in the proposed technique.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.485-492
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• 2018

In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. The proposed structure has general classification performance for any environment or subject. For this purpose, we collect a variety of databases and organize the database into six expression classes such as 'expressionless', 'happy', 'sad', 'angry', 'surprised' and 'disgusted'. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. In the existing CNN structure, the optimal structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of nodes of fully-connected layer. The experimental results show good classification performance compared to the state-of-the-arts in experiments of the cross validation and the cross database. Also, compared to other conventional models, it is confirmed that the proposed structure is superior in classification performance with less execution time.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1052-1070
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• 2017

For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.

• ETRI Journal
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• v.46 no.3
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• pp.526-537
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• 2024

Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer-vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U-Net fetal head measurement tool that leverages a hybrid Dice and binary cross-entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse-fitted two-dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse field-layer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersection-over-union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U-Net models.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.585-597
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• 2021

This paper presents a comparative study of analytical method, finite element method (FEM) and Multilayer Perceptron (MLP) for analysis of a contact problem. The problem consists of a functionally graded (FG) layer resting on a half plane and pressed with distributed load from the top. Firstly, analytical solution of the problem is obtained by using theory of elasticity and integral transform techniques. The problem is reduced a system of integral equation in which the contact pressure are unknown functions. The numerical solution of the integral equation was carried out with Gauss-Jacobi integration formulation. Secondly, finite element model of the problem is constituted using ANSYS software and the two-dimensional analysis of the problem is carried out. The results show that contact areas and the contact stresses obtained from FEM provide boundary conditions of the problem as well as analytical results. Thirdly, the contact problem has been extended based on the MLP. The MLP with three-layer was used to calculate the contact distances. Material properties and loading states were created by giving examples of different values were used at the training and test stages of MLP. Program code was rewritten in C++. As a result, average deviation values such as 0.375 and 1.465 was obtained for FEM and MLP respectively. The contact areas and contact stresses obtained from FEM and MLP are very close to results obtained from analytical method. Finally, this study provides evidence that there is a good agreement between three methods and the stiffness parameters has an important effect on the contact stresses and contact areas.