• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.117-124
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• 2018

This paper describes the localization development of an axial fan for KM-SAM multi-function radar. The multi-function radar, which is constantly affected by the external environment, is a key instrument for detecting and tracking low and medium altitude threat targets. Operating this equipment smoothly requires a fan for controlling the internal temperature and humidity. Presently, all such fans are imported. To solve these problems, localization development research was proposed. The development of localization includes analysis of requirements through review of related technical reports such as original equipment and system equipment specification, prototype design, and verification of design requirement through performance test and environmental test. The study results are described. The blower consisted of an axial fan with guide vanes and the motor was designed to generate a maximum airflow of 970 CFM and a wind pressure of 4.8 IWG. Six prototypes were manufactured for performance evaluation. In addition, for reliable data acquisition, AC power supply, fan performance tester and data acquisition equipment were designed and tested. All prototypes were verified as having design requirements equal to or better than those of imports.

• The Journal of the Acoustical Society of Korea
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• v.24 no.6
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• pp.317-324
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• 2005

Detection and localization of a quiet target in shallow water environments is a challenging problem because of the complicated acoustic Propagation and the Prevalence of loud surface ship interference. Matched Field Processing (MFP) can help address the concern by using a Propagation model to determine the steering vectors, thus Providing optimal away gain and localization accuracy. However, Performance of MFP have yet realized in practice, for several reasons. The most important limitation is that precise information on the underwater environments is generally not available. To examine the Performance of MFP in the East Sea of Korea, we have accomplished a series of matched acoustic Properties and localization experiment (MAPLE). We analyzed the array data measured from MAPLE which is accomplished using a vertical line array and a towed acoustic source off the east cost of Korea in Oct. 2003. We localized the acoustic source using MFP. It is well known that the temperature structure in the experimental site is affected by the short period fluctuation such as internal wave. In this paper, it is found that the sidelobe level on the MFP ambiguity surface is increased being affected by the short period fluctuation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.56-64
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• 2021

Leakage due to deterioration and damage is one of the major causes of volume change by freezing and thawing, and it leads micro-cracking and surface scaling in concrete structures. The deterioration of damaged concrete accelerates with the chloride attack. Thus, in the detailed guidelines for facility performance evaluation (2020), the quality of cover concrete and the freeze-thaw (FT) repetition cycle were newly suggested for concrete durability assessment. The quality of cover concrete should be evaluated by the rebound hammer test and the FT repetition cycle should be also considered in the deterioration environmental assessment. This study suggested the application of fast dynamic based nonlinear ultrasound method to monitor initial micro-scale damage under freezing and thawing environment. Concrete specimens were fabricated with different water-cement ratios (40%, 60%) and air contents (1.5% and 3.0%). The compressive strength, rebound number, relative dynamic modulus, and nonlinear ultrasound were measured with different FT cycles. The scanning electron microscopy was also performed to investigate the micro-scale FT damage. As a result, both the rebound number and the relative dynamic modulus had difficulty to detect early damage but the proposed method showed a potential to detect initial micro-scale damage and predict the FT resistance performance of concrete.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.509-523
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• 2023

The high-level nuclear waste (HLW) repository is exposed to complex environmental conditions consisting of high temperature, high humidity, and radiation, resulting in structural deterioration. Therefore, structural health monitoring is essential, and piezo sensors are used to detect cracks and estimate strength. However, since the monitoring sensors installed in the disposal tunnel and disposal container cannot be replaced or removed, the quantitative life of the monitoring sensor and its suitability must be assessed. In this study, the life of a piezo sensor for monitoring was assessed using an accelerated life test (ALT). The failure mode and mechanism of the piezo sensor under high temperature conditions were determined, and temperature stress's influence on the piezo sensor's life was analyzed. ALT was conducted on temperature stress and the relationship between temperature stress and piezo sensor life was suggested. The life of the piezo sensor was assessed using the Weibull probability distribution and the Arrhenius acceleration model. The suggested relationship can be used in multiple stress ALT designs for more precise life assessment.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.181-190
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• 2011

Since the radar satellite missions such as TerraSAR-X and COSMO-SkyMed were launched in 2007, the spatial resolution of spaceborne SAR(Synthetic Aperture Radar) images reaches about 1 meter at spotlight mode. In 2011, the first Korean SAR satellite, KOMPSAT-5, will be launched, operating at X-band with the highest spatial resolution of 1 m as well. The improved spatial resolution of state-of-the-art SAR sensor suggests expanding InSAR(Interferometric SAR) analysis in urban monitoring. By the way, the shadow and layover phenomena are more prominent in urban areas due to building structure because of inherent side-looking geometry of SAR system. Up to date the most conventional algorithms do not consider the return signals at the frontage of building during InSAR phase and SAR intensity simulation. In this study the new algorithm introducing multi-scattering in layover region is proposed for phase and intensity simulation, which is utilized a precise LIDAR DSM(Digital Surface Model) in urban areas. The InSAR phases simulated by the proposed method are compared with TerraSAR-X spotlight data. As a result, both InSAR phases are well matched, even in layover areas. This study will be applied to urban monitoring using high resolution SAR data, in terms of change detection and displacement monitoring at the scale of building unit.

• Journal of dental hygiene science
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• v.13 no.4
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• pp.403-409
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• 2013

The purpose of this study was to evaluate the correlation between caries experience, Quantitative Light-Induced Fluorescence-Digital (QLF-D) redings and new caries activity test (Cariview) results in preschool children. Fifty-seven healthy kindergarten children (male 28, female 29) were participated this study. The calibrated dentist investigated the caries experience of children and new caries activity test. Cariview samples were incubated in the activated medium at $37^{\circ}C$ for 48 hours. All QLF-D taking and readings were performed by one experienced and trained operator under identical conditions in a dental unit chair located in a darkened room. Analysis range was limited to the maxillary and mandibular anterior teeth. QLF-D redings (white spot and dental plaque) were analysed using QLF system. The dft index had a relatively high correlation with the QLF-D redings (white spot: r=0.617, simple plaque score: r=0.500) (p<0.01). Also, there was significant correlation between dft index and the Cariview score (r=0.286) (p<0.05). However, the Cariview score had no significant correlation between dt index and ft index (p>0.05). QLF-D can be evaluated objectively the initial caries lesions and dental plaque correlated with caries experience. Therefore, QLF-D will be useful to the study of caries prediction.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.15 no.3
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• pp.191-197
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• 2017

The Korea Atomic Energy Research Institute (KAERI) has developed a safeguards technology for pyroprocessing based on the Safeguards-By-Design (SBD) concept. KAERI took part in a Member-State Support Program (MSSP) to establish a pyroprocessing safeguards approach. A Reference Engineering-scale Pyroprocessing Facility (REPF) concept was designed on which KAERI developed its safeguards system. Recently the REPF is being upgraded to the REPF+, a scaled-up facility. For assessment of the nuclear-material accountancy (NMA) system, KAERI has developed a simulation program named Pyroprocessing Material Flow and MUF Uncertainty Simulation (PYMUS). The PYMUS is currently being upgraded to include a Near-Real-Time Accountancy (NRTA) statistical analysis function. The Advanced Spent Fuel Conditioning Process Safeguards Neutron Counter (ASNC) has been updated as Non-Destructive Assay (NDA) equipment for input-material accountancy, and a Hybrid Induced-fission-based Pu-Accounting Instrument (HIPAI) has been developed for the NMA of uranium/transuranic (U/TRU) ingots. Currently, performance testing of Compton-suppressed Gamma-ray measurement, Laser-Induced Breakdown Spectroscopy (LIBS), and homogenization sampling are underway. These efforts will provide an essential basis for the realization of an advanced nuclear-fuel cycle in the ROK.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.257-274
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• 2021

Although image segmentation is a critical part of object-based analysis of high resolution imagery, there has been lack of studies to evaluate the quality of image segmentation. In this study, we aimed to find practical and effective methods to obtain optimal parameters for image segmentation. Evaluations of image segmentation are divided into unsupervised, supervised, and qualitative visual interpretation methods. Using the multispectral UAV images, sampled from urban and forest over the Incheon Metropolitan City Park, three evaluation methods were compared. In overall, three methods showed very similar results regardless of the computational costs and applicability, although the optimal parameters determined by the evaluations were different between the urban and forest images. There is no single measure that outperforms in the unsupervised evaluation. Any combinations of intra-segment measures (V, COV, WV) and inter-segment measures (MI, BSH, DTNP) provided almost the same results. Although supervised method may be biased by subjective selection of reference data, it can be easily applied to detect object of interest. The qualitative visual interpretation on the segmentation results corresponded with the unsupervised and supervised evaluations.