• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.76-82
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• 2017

Malware, including ransomware to quickly detect, in this study, to provide an analysis method of malicious code through the image analysis that has been learned in the deep learning of artificial intelligence. First, to analyze the 2,400 malware data, and learning in artificial neural network Convolutional neural network and to image data. Extracts subgraphs to convert the graph of abstracted image, summarizes the set represent malware. The experimentally analyzed the malware is not how similar. Using deep learning of artificial intelligence by classifying malware and It shows the possibility of accurate malware detection.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.43-51
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• 2008

Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). For rational management of tunnels from planning to construction and maintenance stages, prediction, control and monitoring of displacements of and around the tunnel have to be performed with high accuracy. Computational method tools, such as finite element method, have been and are indispensable tool for tunnel engineers for many years. It is, however, a commonly acknowledged fact that determination of input parameters, especially material properties exhibiting nonlinear stress-strain relationship, is not an easy task even for an experienced engineer. Use and application of the acquired tunnel information is important for prediction accuracy and improvement of tunnel behavior on construction. Artificial Neural Network (ANN) model is a form of artificial intelligence that attempts to mimic behavior of human brain and nervous system. The main objective of this paper is to perform the deformation analysis in NATM tunnel by means of numerical simulation and artificial neural network (ANN) with field database. Developed ANN model can achieve a high level of prediction accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.445-451
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• 2016

This paper introduces a postprocessing method, an iteration method for melody, and an average neural network method for learning a large number of songs in order to improve musically insufficient parts in automatic composition using existing artificial neural network. The melody of songs composed by artificial neural networks is produced according to the melodies of trained songs, so it can not be a specific tonality and it is difficult to have a repetitive composition. In order to solve these problems, we propose a postprocessing method that converts the melody composed by artificial neural networks into a melody having a specific tonality according to music theory and an iteration method for melody by iteratively composing measure divisions of artificial neural networks. In addition, the existing training method of many songs has some disadvantages. To solve this problem, we adopt an average neural network that is made by averaging the weights of artificial neural networks trained each song. From some experiments, it was confirmed that the proposed method solves the existing problems.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.294-297
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• 2004

The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and then to apply these to the selected study area of Janghung in Korea. We aimed to verify the effect of data selection on training sites. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use was constructed. Thirteen landslide-related factors were extracted from the spatial database. Using these factors, landslide susceptibility was analyzed using an artificial neural network. The weights of each factor were determined by the back-propagation training method. Five different training datasets were applied to analyze and verify the effect of training. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. The results of the landslide susceptibility maps were verified and compared using landslide location data. GIS data were used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool to analyze landslide susceptibility.

• Journal of Platform Technology
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• v.6 no.4
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• pp.34-40
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• 2018

Currently, artificial neural networks perform well for a single task, but NN have the problem of forgetting previous learning by learning other kinds of tasks. This is called catastrophic forgetting. To use of artificial neural networks in general purpose this should be solved. There are many efforts to overcome catastrophic forgetting. However, even though there was a lot of effort, it did not completely overcome the catastrophic forgetting. In this paper, we propose sequential iterative learning using core concepts used in elastic weight consolidation (EWC). The experiment was performed to reproduce catastrophic forgetting phenomenon using EMNIST data set which extended MNIST, which is widely used for artificial neural network learning, and overcome it through sequential iterative learning.

• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.64-71
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• 2016

Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas-liquid two-phase flows by using ${\gamma}-ray$ attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam ${\gamma}-ray$ attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.17-24
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• 2017

Recently Deep-learning, Technologies using Large or Deep Artificial Neural Networks, have Shown Remarkable Performance, and the Increasing Size of the Network Contributes to its Performance Improvement. However, the Increase in the Size of the Neural Network Leads to an Increase in the Calculation Amount, which Causes Problems Such as Circuit Complexity, Price, Heat Generation, and Real-time Restriction. In This Paper, We Propose and Test a Method to Reduce the Number of Network Connections by Effectively Pruning the Redundancy in the Connection and Showing the Difference between the Performance and the Desired Range of the Original Neural Network. In Particular, we Proposed a Simple Method to Improve the Performance by Re-learning and to Guarantee the Desired Performance by Allocating the Error Rate per Layer in Order to Consider the Difference of each Layer. Experiments have been Performed on a Typical Neural Network Structure such as FCN (full connection network) and CNN (convolution neural network) Structure and Confirmed that the Performance Similar to that of the Original Neural Network can be Obtained by Only about 1/10 Connection.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1769-1776
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• 2000

In this paper a new method is proposed to identify a modulation method in the case of unknown digitally modulated input signals. The proposed identification method is implemented with an artificial neural network which is based on characteristic feature extracted from the instantaneous amplitude the instantaneous phase and the instantaneous frequency of the input signals. The proposed method was simulated with 9 type signals (ASK2, FSK2, FSK4, PSK2, PSK4, PSK8, QAM8, QAM16) in a noisy communication environment. The results show that the artificial neural network can accurately recognize all kinds of patterns.