• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.1-9
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• 2021

Traditionally, most malicious codes have been analyzed using feature information extracted by domain experts. However, this feature-based analysis method depends on the analyst's capabilities and has limitations in detecting variant malicious codes that have modified existing malicious codes. In this study, we propose a ResNet-Variational AutoEncder-based variant malware classification method that can classify a family of variant malware without domain expert intervention. The Variational AutoEncoder network has the characteristics of creating new data within a normal distribution and understanding the characteristics of the data well in the learning process of training data provided as input values. In this study, important features of malicious code could be extracted by extracting latent variables in the learning process of Variational AutoEncoder. In addition, transfer learning was performed to better learn the characteristics of the training data and increase the efficiency of learning. The learning parameters of the ResNet-152 model pre-trained with the ImageNet Dataset were transferred to the learning parameters of the Encoder Network. The ResNet-Variational AutoEncoder that performed transfer learning showed higher performance than the existing Variational AutoEncoder and provided learning efficiency. Meanwhile, an ensemble model, Stacking Classifier, was used as a method for classifying variant malicious codes. As a result of learning the Stacking Classifier based on the characteristic data of the variant malware extracted by the Encoder Network of the ResNet-VAE model, an accuracy of 98.66% and an F1-Score of 98.68 were obtained.

• Bulletin of the Korean Mathematical Society
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• v.60 no.6
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• pp.1697-1704
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• 2023

We study a multidimensional nonlinear variational sine-Gordon equation, which can be used to describe long waves on a dipole chain in the continuum limit. By using the method of characteristics, we show that a solution of a nonlinear variational sine-Gordon equation with certain initial data in a multidimensional space has a singularity in finite time.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1251-1258
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• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• 한국지구물리탐사학회:학술대회논문집
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• 2004.06a
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• pp.103-113
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• 2004

We investigate the time-variational characteristics of power spectrum and transfer function of geomagnetic field by robust estimation technique. In the case of power spectrums of geomagnetic field, there are some the periodic fluctuations related with solar activity, Meanwhile, the geomagnetic transfer function shows so considerable weak time-variational fluctuation that the estimations of transfer function seem to be comparatively stable in time-variant view.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.1
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• pp.37-43
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• 2014

The extended framework of Hamilton's principle provides a new rigorous weak variational formalism for a broad range of initial boundary value problems in mathematical physics and mechanics in terms of mixed formulation. Based upon such framework, a new variational numerical method of linear elasticity is provided for the classical single-degree-of-freedom dynamical systems. For the undamped system, the algorithm is symplectic with respect to the time step. For the damped system, it is shown to be accurate with good convergence characteristics.

• Journal of Environmental Science International
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• v.9 no.5
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• pp.429-436
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• 2000

In order to study on the variational characteristics of water quality and chlorolphyll a concentration the water samples were collected daily or three times a week during the period from April 1990 to November 1991 at Kukdong port located in the northern Kamak bay of Southern Korea I made an analysis on biological factor as chlorophyll a concentration as well as physico-chemical factors such as water temperature salinity sigma-t dissolved oxygen, nutrients (ammonia, nitrite, nitrate, phosphate, and silicate) N/P ratio and chemical oxygen demand. In Northern Kamak bay seasonal variations in physical factors such as water temperature salinity and sigma-t were very marked. On the other hand chemical factors such as nutrients concentration and COD were not so. Chemical factors, in particular silicate were influenced by input of freshwater. And the roles of silicate on the seasonal succession of phytoplankton species composition was very low. Phytoplankton biomass as measured by chlorophyll a concentration was very high all the year round and it was controlled by the combination of several factors especially of N/P ratio determined by dissolved inorganic nitrogen.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.831-834
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• 2008

The spectral element model is known to provide very accurate structural dynamic characteristics, while reducing the number of degree-of-freedom to resolve the computational and cost problems. Thus, the spectral element model with variational method for an axially moving Bernoulli-Euler beam subjected to axial tension is developed in the present paper. The high accuracy of the spectral element model is the verified by comparing its solutions with the conventional finite element solutions and exact analytical solutions. The effects of the moving speed and axial tension the vibration characteristics, wave characteristics, and the static and dynamic stabilities of a moving beam are investigated.

• Journal of KIISE
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• v.45 no.2
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• pp.157-164
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• 2018

Due to the revolutionary advances in deep learning, performance of pattern recognition has increased significantly in many applications like speech recognition and image recognition, and some systems outperform human-level intelligence in specific domains. Unlike pattern recognition, in this paper, we focus on generating Korean sentences based on a few Korean sentences. We apply variational recurrent auto-encoder (VRAE) and modify the model considering some characteristics of Korean sentences. To reduce the number of words in the model, we apply a word spacing model. Also, there are many Korean sentences which have the same meaning but different word order, even without subjects or objects; therefore we change the unidirectional encoder of VRAE into a bidirectional encoder. In addition, we apply an interpolation method on the encoded vectors from the given sentences, so that we can generate new sentences which are similar to the given sentences. In experiments, we confirm that our proposed method generates better sentences which are semantically more similar to the given sentences.

• Journal of Environmental Science International
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• v.9 no.1
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• pp.1-11
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• 2000

A study on the variational characteristics of water quality and phytoplankton biomass by principal component analysis(PCA) was carried out in Kogum-sudo from February to October in 1993. We analyzed PCA on biological factors such as chlorophyll a and phytoplankton cell numbers for centric and pennate diatoms, phytoflagellates, and total phytoplankton as well as physico-chemical factors as water temperature, salinity, transparency, dissolved oxygen(DO), saturation of DO, apparent oxygen utilization (AOU), chemical oxygen demand(COD), nutrient (ammonia, nitrite, nitrate, phosphate and silicate), N/P ratio and suspended solid(SS). The source of nutrients supply depended on the mineralization of organic matters and inputs of seawater from outside rather than runoff of freshwater. The phytoplankton biomass was changed within short interval period by nutrients change. And it was controlled by the combination of several environmental factors, especially of light intensity, ammonia and phosphate. The marine environmental characteristics were determined by the mineralization of organic matters in winter, by runoff of freshwater including high nutrients concentration in spring, by ammonia uptake and high phytoplankton productivity in summer, and phosphate supplied input seawater from outside of Kogeum-sudo in autumn. And Kogum-sudo was separated with 2 regions by score distributions of PCA. That is to say, one region was middle parts of straits which was characterized by the mixing seawater and the accumulated organic matters, other one region was Pungnam Bay and the water around Kogum Island which was done by high phytoplankyon biomass and productivity year-round.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.145-156
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• 2023

Trajectory of ballistic missile is defined by inherent flight dynamics, which decided range and maneuvering characteristics. It is crucial to predict range and maneuvering characteristics of ballistic missile in KAMD (Korea Air and Missile Defense) to minimize damage due to ballistic missile attacks, Nowadays, needs for applying AI(Artificial Intelligence) technologies are increasing due to rapid developments of DNN(Deep Neural Networks) technologies. To apply these DNN technologies amount of data are required for superviesed learning, but trajectory data of ballistic missiles is limited because of security issues. Trajectory data could be considered as multivariate time series including many variables. And augmentation in time series data is a developing area of research. In this paper, we tried to augment trajectory data of ballistic missiles using recently developed methods. We used TimeVAE(Time Variational AutoEncoder) method and TimeGAN(Time Generative Adversarial Networks) to synthesize missile trajectory data. We also compare the results of two methods and analyse for future works.