• Convergence Security Journal
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• v.19 no.1
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• pp.11-17
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• 2019

Recently, the abuse of Internet technology has caused economic and mental harm to society as a whole. Especially, malicious code that is newly created or modified is used as a basic means of various application hacking and cyber security threats by bypassing the existing information protection system. However, research on small-capacity executable files that occupy a large portion of actual malicious code is rather limited. In this paper, we propose a model that can analyze the characteristics of known small capacity executable files by using data mining techniques and to use them for detecting unknown malicious codes. Data mining analysis techniques were performed in various ways such as Naive Bayesian, SVM, decision tree, random forest, artificial neural network, and the accuracy was compared according to the detection level of virustotal. As a result, more than 80% classification accuracy was verified for 34,646 analysis files.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.39-59
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• 2019

The Korean Demilitarized Zone (DMZ) has the great ecosystem as all the artificial activities in DMZ have been prohibited over half a century. The ecosystem should be conserved even after the reunification of Korea and hence the conservation plan should be established not after the reunification but before it. It requires a considerable budget to conserve DMZ, considering management of ecology resource, recovery, and research. The objective of this paper is to analyze a fund-raising measure for DMZ conservation, using economic incentives mechanism when multiple developers participate in the auction to get the right to develop North Korean regions, have private information about their sunk costs and pay a part of their profits for the fund. First, we analyze the real option model to decide the optimal investment time. Second, we construct the auction for bidders not to misrepresent their private information, based on Bayesian Nash equilibrium.

• Journal of Korean Society on Water Environment
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• v.37 no.3
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• pp.204-216
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• 2021

It is important to understand the factors influencing the temporal and spatial variability of water quality in order to establish an effective customized management strategy for contaminated aquatic ecosystems. In this study, the spatial diversity of the 5-year (2015 - 2019) average total phosphorus (TP) concentration observed in 40 Total Maximum Daily Loads unit-basins in the Nakdong River watershed was analyzed using 50 predictive variables of watershed characteristics, climate characteristics, land use characteristics, and soil characteristics. Cross-correlation analysis, a two-stage exhaustive search approach, and Bayesian inference were applied to identify predictors that best matched the time-averaged TP. The predictors that were finally identified included watershed altitude, precipitation in fall, precipitation in winter, residential area, public facilities area, paddy field, soil available phosphate, soil magnesium, soil available silicic acid, and soil potassium. Among them, it was found that the most influential factors for the spatial difference of TP were watershed altitude in watershed characteristics, public facilities area in land use characteristics, and soil available silicic acid in soil characteristics. This means that artificial factors have a great influence on the spatial variability of TP. It is expected that the proposed statistical modeling approach can be applied to the identification of major factors affecting the spatial variability of the temporal average state of various water quality parameters.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of Preventive Medicine and Public Health
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• v.55 no.2
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• pp.164-172
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• 2022

Objectives: We aimed to estimate the space-time distribution of the risk of suicide mortality in Iran from 2006 to 2016. Methods: In this repeated cross-sectional study, the age-standardized risk of suicide mortality from 2006 to 2016 was determined. To estimate the cumulative and temporal risk, the Besag, York, and Mollié and Bernardinelli models were used. Results: The relative risk of suicide mortality was greater than 1 in 43.0% of Iran's provinces (posterior probability >0.8; range, 0.46 to 3.93). The spatio-temporal model indicated a high risk of suicide in 36.7% of Iran's provinces. In addition, significant upward temporal trends in suicide risk were observed in the provinces of Tehran, Fars, Kermanshah, and Gilan. A significantly decreasing pattern of risk was observed for men (β, -0.013; 95% credible interval [CrI], -0.010 to -0.007), and a stable pattern of risk was observed for women (β, -0.001; 95% CrI, -0.010 to 0.007). A decreasing pattern of suicide risk was observed for those aged 15-29 years (β, -0.006; 95% CrI, -0.010 to -0.0001) and 30-49 years (β, -0.001; 95% CrI, -0.018 to -0.002). The risk was stable for those aged >50 years. Conclusions: The highest risk of suicide mortality was observed in Iran's northwestern provinces and among Kurdish women. Although a low risk of suicide mortality was observed in the provinces of Tehran, Fars, and Gilan, the risk in these provinces is increasing rapidly compared to other regions.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.70.1-70.1
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• 2021

We examine the HI gas kinematics and distributions of galaxy pairs in group or cluster environments from high-resolution Australian Square Kilometer Array Pathfinder (ASKAP) WALLABY pilot observations. We use 32 well-resolved close pair galaxies from the Hydra, Norma, and NGC 4636, two clusters and a group of which are identified by their spectroscopy information and additional visual inspection. We perform profile decomposition of HI velocity profiles of the galaxies using a new tool, BAYGAUD which allows us to separate a line-of-sight velocity profile into an optimal number of Gaussian components based on Bayesian MCMC techniques. Then, we construct super profiles via stacking of individual HI velocity profiles after aligning their central velocities. We fit a model which consists of double Gaussian components to the super profiles, and classify them as kinematically cold and warm HI gas components with respect to their velocity dispersions, narrower or wider 𝜎, respectively. The kinematically cold HI gas reservoir (M_cold/M_HI) of the paired galaxies is found to be relatively higher than that of unpaired control samples in the clusters and the group, showing a positive correlation with the HI mass in general. Additionally, we quantify the gravitational instability of the HI gas disk of the sample galaxies using their Toomre Q parameters and HI morphological disturbances. While no significant difference is found for the Q parameter values between the paired and unpaired galaxies, the paired galaxies tend to have larger HI asymmetry values which are derived using their moment0 map compared to those of the non-paired control sample galaxies in the distribution.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.79-79
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• 2021

기상학적 측면에서 강수 부족으로 인한 수생태환경(하천), 호소환경(저수지) 및 유역환경(중권역)으로 미치는 환경학적 가뭄의 영향을 평가하기 위한 시도는 매우 중요하다. 만약 동일한 규모의 강수부족 현상이 발생할지라도, 환경적 측면에서의 수질 및 수생태에 미치는 영향이 매우 큰 유역이 있고, 반면 어느 정도의 복원력을 유지할 수 있는 유역이 있을 것이다. 즉, 서로 다른 유역환경에 따라 가뭄으로 인한 환경적 영향은 달라질 가능성이 크며, 이처럼 환경적 가뭄에 취약한 지역을 위해서는 지속적인 환경가뭄 모니터링이 중요하다. 환경적 측면에서 가뭄의 영향을 평가하기 위해서는 다양한 수질 관련 항목을 연계한 환경가뭄 감시가 중요하며, 이와 더불어 가뭄과 관련한 다양한 이해관계자 간의 효율적인 의사결정 도구가 필요하다. 따라서 본 연구에서는 다양한 시나리오 정보를 제공할 수 있는 베이지안 네트워크 모형을 적용하여 환경가뭄 민감도 평가 방안을 제시하고자 한다. 본 모형에서는 수질 문제가 가장 심하게 대두되고 있는 낙동강 유역을 대상으로, 기상학적 가뭄에 의한 수생태 및 환경 관련 변수들(BOD, T-P, TOC)의 복잡한 상호의존성을 파악할 수 있는 베이지안 네트워크 모형을 활용하였다. 또한, 기상학적 가뭄에 의한 상류와 하류 간의 환경적 영향을 연계하여 해석하기 위한 모형을 구축하였다. 그 결과, 기상학적 가뭄으로 인한 환경적 민감도가 크게 나타나는 중권역(예: 임하댐유역)과 이와 반대인 중권역(예: 병성천유역)의 구분이 가능하였다. 또한, 상류에서 발생한 심한 기상학적 가뭄이 하류 지역 내 환경적인 영향을 지속할 가능성이 있음을 확인되었다. 따라서 본 연구에서 제안한 방법은 환경적 가뭄의 취약지역을 우선 선정하고, 나아가 상-하류 간의 환경적 가뭄을 감시하는 데 있어 활용도가 있을 것으로 기대된다.

• Asia Marketing Journal
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• v.15 no.1
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• pp.155-168
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• 2013

This study describes consumers' movie choices in conjunction with other group members and attempts to reassess the effect of the online word of mouth (WOM) source in a joint decision context. The tendency of many people to go to movies in groups has been mentioned in previous literature but there is no modeling research that studies movie choice from the group decision perspective. We found that ignoring the group movie-going perspective can result in a misunderstanding, especially underestimation of genre preference and the impact of the WOM variables. Most of the studies to measure online WOM effects were done at the aggregate level, and the role of online WOM variables(volume vs valence) is mixed in the literature. We postulate that group-level analysis might offer insight to resolve these mixed understanding of WOM effects in the literature. We implemented the study via a random effect model with group-level heterogeneity. Romance, drama, and action were selected as genre variables; valence and volume were selected as online WOM variables. A choice-based conjoint survey was used for data collection and the models was estimated via Bayesian MCMC method. The empirical results show that (i) both genre and online WOM are important variables when consumers choose movies, especially as group, and (ii) the WOM valence effect are amplified more than the volume effect does as individuals are engaged in group decision. This research contributes to the literature in several ways. First, we investigate movie choice from a group movie-going perspective that is more realistic and consistent with the market behavior. Secondly, the study sheds new light on the WOM effect. At group-level, both valence and volume significantly affect movie choices, which adds to the understanding of the role of online WOM in consumers' movie choice.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.