• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1077-1086
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• 2017

Malware mutants based on existing malware is widely used because it can easily avoid the existing security system even with a slight pattern change. These malware appear on average more than 1.6 million times a day, and they are gradually expanding to IoT / ICS as well as cyber space, which has a large scale of damage. In this paper, we propose an analytical method based on features of PE Section and DLL that do not give much significance, rather than pattern-based analysis, Sandbox-based analysis, and CFG, Strings-based analysis. It is expected that the proposed model will be able to cope with effective malicious code in case of combined operation of various existing analysis technologies.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.541-548
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• 2016

The use of social media is already essential as a source of information for listening user's various opinions and monitoring. We define social 'risks' that issues effect negative influences for public opinion in social media. This paper aims to discriminate various linguistic features and reveal their effects for building an automatic classification model of social risks. Expecially we adopt a word embedding technique for representation of linguistic clues in risk sentences. As a preliminary experiment to analyze characteristics of individual features, we revise errors in automatic linguistic analysis. At the result, the most important feature is NE (Named Entity) information and the best condition is when combine basic linguistic features. word embedding, and word clusters within core predicates. Experimental results under the real situation in social bigdata - including linguistic analysis errors - show 92.08% and 85.84% in precision respectively for frequent risk categories set and full test set.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.1-9
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• 2015

As attacks in cyber space become advanced and complex, monotonous defense approach of one-one matching manner between attack and defense may be limited to defend them. More efficient defense method is required. This paper proposes multi layers security scheme that can support to defend assets against diverse cyber attacks in systematical and adaptive. We model multi layers security scheme based on Defense Zone including several defense layers and also discuss essential technical elements necessary to realize multi layers security scheme such as cyber threats analysis and automated assignment of defense techniques. Also effects of multi layers security scheme and its applicability are explained. In future, for embodiment of multi layers security scheme, researches about detailed architecture design for Defense Zone, automated method to select the best defense technique against attack and modeling normal state of asset for attack detection are needed.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.549-557
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• 2019

The acoustical scattering characteristics of a target are influenced by the material properties and structural characteristics of the target, which are critical information for acoustic detection and identification of underwater target. In particular, for thin elastic target, unique scattered signals are generated around the target by the Lamb wave. In this paper, the results of scattered signal measurement of aluminum spherical shell in the water tank using the stepped frequency sweep sine signal are presented. In particular, the scattering of the water-filled aluminum spherical shell is compared with that of the air-filled one both theoretically and experimentally. The difference of the scattered signals are analyzed using the pseudo Wigner-Ville distribution in terms of average frequency, frequency distribution, and energy of the scattered signal. The result shows that all observed parameters increased when the aluminum sphere was water-filled, and it is well matched to the theoretical expectation.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.522-533
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• 2019

The compressive sensing model for range-Doppler estimation can be expressed as an under-determined linear system y = Ax. To find the solution of the linear system with the compressive sensing method, matrix A should be sufficiently incoherent and x to be sparse. In this paper, we propose a transmission waveform design method that maintains the bandwidth required by the sonar system while lowering the mutual coherence of the matrix A so that the matrix A is incoherent. The proposed method combines two methods of optimizing the sensing matrix with the alternating projection and suppressing unwanted frequency bands using the DFT (Discrete Fourier Transform) matrix. We compare range-Doppler estimation performance of existing waveform LFM(Linear Frequency Modulated) and designed waveform using the matched filter and the compressive sensing method. Simulation shows that the designed transmission waveform has better detection performance than the existing waveform LFM.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.19-24
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• 1998

This paper deals with tomographic travel time inversion of three dimensional seismic refraction survey conducted over a dipping interface. The slowness, and thus velocity as its reciprocal, distribution on the subsurface interface is to be determined applying an ART with under-relaxtion parameter. The models chosen are realistic, i.e., most likely to be met in engineering seismics, and the interface includes anomalous zones. It is found that, generally speaking, the inversion could be misleading or meaningless without the correction of the dip of the interface. This is rather surprising when we recall that usual assumption for the interpretation of refraction seismics data is the horizontal attitude of structures within the limit of $15^{\circ}$ dip or so. To make the present method tenable for a new means of routine seismics, some practical ways of identifying head wave arrivals are to be devised.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.73-77
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• 2005

To correctly invert and interpret Surface Nuclear Magnetic Resonance (SNMR) data collected in conductive terrains, an accurate estimate of subsurface conductivity structure is required. Given such an estimate, it would be useful to determine, before conducting an SNMR sounding, whether or not the conductivity structure would prevent groundwater being detected. Using SNMR forward modelling, we describe a method of determining the depth range from which most of the SNMR signal originates, given a model of subsurface conductivity structure. We use the method to estimate SNMR depth penetration in a range of halfspace models and show that for conductive halfspaces ($<10{\Omega}.m$) the depth of penetration Is less than 50 m. It is also shown that for these halfspaces, increasing coincident loop size does not significantly improve depth penetration. The results can be used with halfspace approximations of more complicated ID conductivity structures to give a reasonable estimate of the depth range over which signal is obtainable in conductive terrains.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.38-43
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• 2021

As the number of augmented reality games using augmented reality technology increases, the demands of users are also increasing. Game technologies used in augmented reality games are mainly games using MARKER, MARKERLESS, GPS, etc. Games using this technology can augment the background and other objects. To solve this problem, we want to help develop augmented reality games by analyzing objects in the background, which is an important element of augmented reality. To analyze the background in the augmented reality game, the background object was analyzed by applying a deep learning model using TensorFlow Lite in the UNITY engine. Using this result, we obtained the result that augmented objects can be placed in the game according to the types of objects analyzed in the background. By utilizing this research, it will be possible to develop advanced augmented reality games by augmenting objects that fit the background.

• Journal of IKEEE
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• v.25 no.3
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• pp.430-436
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• 2021

The non-contact body temperature measurement system is one of the key factors, which is manage febrile diseases in mass facilities using optical and thermal imaging cameras. Conventional systems can only be used for simple body temperature measurement in the face area, because it is used only a deep learning-based face detection algorithm. So, there is a limit to detecting abnormal symptoms of the infants and young children, who have difficulty expressing their opinions. This paper proposes an improved facial expression recognition algorithm for detecting abnormal symptoms in infants and young children. The proposed method uses an object detection model to detect infants and young children in an image, then It acquires the coordinates of the eyes, nose, and mouth, which are key elements of facial expression recognition. Finally, facial expression recognition is performed by applying a selective sharpening filter based on the obtained coordinates. According to the experimental results, the proposed algorithm improved by 2.52%, 1.12%, and 2.29%, respectively, for the three expressions of neutral, happy, and sad in the UTK dataset.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.