• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.88-95
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• 2018

Maintainability indicates how easily a system can be restored to the normal state when a system failure occurs. Systems developed to have high maintainability can be competitive due to reduced maintenance time, workforce and resources. Quantification of the maintainability is possible in many ways, but only after prototype production or with historical data. As such, the graph theory and 3D model data have been used, but there are limitations in management efficiency and early use. To solve this problem, we studied the maintainability index of weapon systems using SysML-based modeling and simulation technique. A SysML structure diagram was generated to simultaneously model the system design and maintainability of system components by reflecting the maintainability attributes acquired from the system engineering tool. Then, a SysML parametric diagram was created to quantify the maintainability through simulation linked with MATLAB. As a result, an integrated model to account for system design and maintainability simultaneously has been presented. The model can be used from early design stages to identify components with low maintainability index. The design of such components can be changed to improve maintainability and thus to reduce the risks of cost overruns and time delays due to belated design changes.

• Korean Journal of Environmental Biology
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• v.36 no.4
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• pp.638-646
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• 2018

The purpose of this study was to investigate the long-term habitat morphological alteration resulting from a large dam and weirs in the Tamjin River. To achieve this, we carried out a hydrograph analysis and a substrate size distribution analysis. We also estimated the channel width, bar area and vegetation encroachment using aerial photographs taken before and after the construction of the dam and weirs. The result of the hydrological analysis showed that flooding downstream was greatly reduced with small peaks occurrence after the dam construction. Interestingly, normal hydrographs in the main channel appeared just after tributary conjunction. There was a similar pattern in the substrate size analysis. Despite coarsened substrate just downstream of the dam site, more sand appeared again after introduction of the tributary. However, there was an increase in the bar area downstream of the dam's channels with most bars covered with vegetation. The channel width increased at the upper area of weirs through impoundment, but decreased downstream because of vegetation encroachment. This study indicate that unregulated tributary plays an important role in restoring hydro-physical habitat conditions in mainstream channels below a large dam. However, numerous weirs could be a causal factor to accelerate habitat deterioration in the dam downstream channels.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.164-171
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• 2021

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.573-590
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• 2020

This study presents the current status of DECOVALEX-2023 project Task G and our research results so far. Task G, named 'Safety ImplicAtions of Fluid Flow, Shear, Thermal and Reaction Processes within Crystalline Rock Fracture NETworks (SAFENET)' aims at developing a numerical method to simulate the fracture creation and propagation, and the coupled thermohydro-mechanical processes in fracture in crystalline rocks. The first research step of Task G is a benchmark simulation, which is designed for research teams to make their modelling codes more robust and verify whether the models can represent an analytical solution for displacements of a single rock fracture. We reproduced the mechanical behavior of rock and embedded single fracture using a three-dimensional grain-based distinct element model for the simulations. In this method, the structure of the rock was represented by an assembly of rigid tetrahedral grains moving independently of each other, and the mechanical interactions at the grains and their contacts were calculated using 3DEC. The simulation results revealed that the stresses induced along the embedded fracture in the model were relatively low compared to those calculated by stress analysis due to stress redistribution and constrained fracture displacements. The fracture normal and shear displacements of the numerical model showed good agreement with the analytical solutions. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated using various experiments in a further study.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Korean Journal of Environment and Ecology
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• v.35 no.3
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• pp.265-276
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• 2021

This study investigated the characteristics of fish communities and river health in the Sincheon stream of Imgin River between April and October 2019. The survey collected 3,677 objects in 30 species belonging to 12 families from 23 survey stations. The dominant and subdominant species were Zacco platypus (28.4%) and Oryzias sinensis (13.6%), respectively, followed by Z. koreanus (11.8%), Rhynchocypris oxycephalus (11.7%), Carassius auratus (9.6%), and Pseudorasbora parva (7.9%) in that order. Among the fish species collected, 10 (33.3%) were endemic species in Korea. The exotic species were 5 (16.7%), including Micropterus salmoides, Lepomis macrochirus, Cyprinus carpio (Israeli type), Poecilia reticulata, and Xiphophorus maculatus. The land-locked species included Cottus koreanus and Rhinogobius brunneus, while C. koreanus was a climate change-sensitive species. The dominance of the fish community was low, and the diversity was high in the Sincheon mainstream, Sudongcheon and Cheongdamcheon, whereas Dongducheon and Sangpaecheon showed higher dominance and low diversity. The river health was very good and good in the uppermost and Sudongcheon areas, whereas the upper stream was normal, and the middle and lower streams were poor and very poor, respectively. The water quality was also mostly poor or very poor from the midstream to the downstream, and only Sudongcheon was very good. Therefore, the water quality had a great impact on fish habitat and eventually affected river health significantly. Although the water quality of Shincheon has improved each year, the introduction of several foreign species had a very negative effect. Improvement of river health in Shincheon requires water quality improvement and management of exotic fish species.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1339-1348
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• 2020

Phytoplankton controls marine ecosystems in terms of nutrients, photosynthetic rate, carbon cycle, etc. and the degree of its influence on the marine environment depends on their physical size. Many studies have been attempted to identify marine phytoplankton size classes using the remote sensing techniques. One of successful approach was the three-component model which estimates the chlorophyll concentrations of three phytoplankton size classes (micro-phytoplankton; >20 ㎛, nano-; 2-20 ㎛ and pico-; <2 ㎛) as a function of total chlorophyll. Here, we examined the applicability of Geostationary Ocean Colour Imager (GOCI) to the mapping of the phytoplankton size class distribution in the East Sea. A fit of the three-component model to a biomarker pigment dataset collected in the study area for some years including a large harmful algal bloom period has been carried out to derive size-fractioned chlorophyll concentration (CHL). The tuned three-component model was applied to the hourly GOCI images to identify the fractions of each phytoplankton size class for the entire CHL. Then, we investigated the distribution of phytoplankton community in terms of the size structure in the East Sea during the harmful Cochlodinium polykrikoides blooms in the summer of 2013.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.401-408
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• 2021

Proton therapy using the Bragg peak is one of the radiation therapies and can deliver its maximum energy to the tumor with giving least energy for normal tissue. A cross-sectional image of the human body taken with the computed tomography (CT) has been used for radiation therapy planning. The HU values change according to the tube voltage, which lead to the change in the boundary and thickness of the anatomical structure on the CT image. This study examined the changes in the Bragg peak of the brain region according to the thickness variation in the head phantom composed of several materials using the Geant4. In the phantom composed of a single material, the Bragg peak according to the type of media and the incident energy of the proton beams were calculated, and the reliability of Geant4 code was verified by the Bragg peak. The variation of the peak in the brain region was examined when each thickness of the head phantom was changed. When the thickness of the soft tissue was changed, there was no change in the peak position, and for the skin the change in the peak was small. The change of the peak position was mainly changed when the bone thickness. In particular, when the bone was changed only or the bone was changed together with other tissues, the amount of change in the peak position was the same. It is considered that measurement of the accurate bone thickness in CT images is one of the key factors in depth-dose distribution of the radiation therapy planning.