• Journal of Digital Convergence
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• v.20 no.4
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• pp.389-396
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• 2022

Deep Learning is based on a perceptron, and is currently being used in various fields such as image recognition, voice recognition, object detection, and drug development. Accordingly, a variety of learning algorithms have been proposed, and the number of neurons constituting a neural network varies greatly among researchers. This study analyzed the learning characteristics according to the number of neurons of the currently used SGD, momentum methods, AdaGrad, RMSProp, and Adam methods. To this end, a neural network was constructed with one input layer, three hidden layers, and one output layer. ReLU was applied to the activation function, cross entropy error (CEE) was applied to the loss function, and MNIST was used for the experimental dataset. As a result, it was concluded that the number of neurons 100-300, the algorithm Adam, and the number of learning (iteraction) 200 would be the most efficient in deep learning learning. This study will provide implications for the algorithm to be developed and the reference value of the number of neurons given new learning data in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1749-1755
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• 2021

Virtual reality is actively applied in the fields of games, entertainment, communication, sports, and architecture. In particular, many virtual reality-based education systems are being developed in the field of education, creating efficient learning effects. In addition, virtual reality-based education is used in areas such as maintenance, fighter control, medical care, and firefighting as it can maximize the educational effect through the mastery process of the function itself through the curriculum as well as indirect experiences of dangerous situations that are difficult to experience. However, due to technical limitations, lack of contents, and lack of theoretical research, the level of application of military education and training is still insufficient. This paper aim to contribute to the development of a virtual reality-based education system as a military training system by developing a high-quality drop training system applicable to military group descent training, studying key technologies and implementation methods necessary for development.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.127-132
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• 2022

As the IoT technology is more developed, it is more important for the accuracy of IoT data. Since the IoT data supports a different formats and protocols, it is often happened that the IoT system is failed or the incorrect data is generated with the unreliable IoT devices(sensor, actuator). Because the abnormality of IoT device or the user situation is not detected correctly, this problem makes the user to be unsatisfied with the IoT system. This study proposes the decision methodology of IoT data consistency whether the IoT data is generated in normal range or not by using the mathematical functions('gradient descent function' and 'linear regression function'). It may be concluded that the gradient function method is suitable for the IoT data which the 'increasing velocity' is related with the next generated pattern(eg. sensor devices), the linear regression function method is suitable for the IoT data which the 'the difference from linear regression function' is related with the next generated pattern in case the data has a linear pattern(eg. water meter, electric meter).

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.281-288
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• 2022

This study quantitatively analyzed the human casualties that can occur when a multicopter-type Urban Air Mobility (UAM) with a weight of about 1 ton and a speed of about 100 km/h falls in an urban area. Based on the population density and building database in Seoul, the population exposed to collisions in the event of a UAM crash was derived. Through the ballistic descent model, the accident impact radius of the UAM fall was calculated. In addition, the change in human casualties on the ground was analyzed when the accident impact radius increased. Finally, the ground risk map was created for Seoul, and it was confirmed that about 1 to 10 people could be injured when a UAM crash.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.27-40
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• 2022

This study identifies distinguishing features and variability of intonation patterns in Korean phonological phrases. Syllable count and segmental content, which are phonological conditions, of the intonation of phonological phrases were examined. Based on the four syllables, the intonation of a phonological phrase can be set to LHLH as the basic form, and syllable count acts as a condition for making a variation. The "3 syllables or less condition" changes the intonation from a curved line to a straight line. Variation occurs in pitch bandwidth and fluctuation according to segmental content. The first segment affects the phonological phrase formation bandwidth, and the following segment affects the pitch fluctuation. If the first segment has [+aspirate], [+tense], [+continuant], the intonation is formed in the high band, otherwise, it is formed in the low band. If the second or after segment in the intonation realized in the high band has [-aspirate], [-tense], [-continuant], the pitch is lowered to the lowest level of the low bandwidth. In the intonation realized in the low band, [+aspirate], [+tense], [+continuant] is blocked by the second descent of LHLH.

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Convergence Security Journal
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• v.23 no.5
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• pp.57-64
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• 2023

In this study, we analyzed how to steal the target model without training data. Input data is generated using the generative model, and a similar model is created by defining a loss function so that the predicted values of the target model and the similar model are close to each other. At this time, the target model has a process of learning so that the similar model is similar to it by gradient descent using the logit (logic) value of each class for the input data. The tensorflow machine learning library was used as an experimental environment, and CIFAR10 and SVHN were used as datasets. A similar model was created using the ResNet model as a target model. As a result of the experiment, it was found that the model stealing method generated a similar model with an accuracy of 86.18% for CIFAR10 and 96.02% for SVHN, producing similar predicted values to the target model. In addition, considerations on the model stealing method, military use, and limitations were also analyzed.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.