• 한국IT서비스학회지
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• 제11권1호
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.15-27
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• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

• The Journal of Asian Finance, Economics and Business
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• 제6권2호
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• pp.15-24
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• 2019

This study analyzes the effect of the capital structure of Korean manufacturing firms on default risk based on Moody's KMV option pricing model where the probability of default is obtained by measuring the distance to default as a covariant in logit model developed by Merton (1974). Based on the panel data of manufacturing firms, this study achieves its primary objective, using a fixed effect regression model and examines the effect of a firm's capital structure on default risk amongst publicly listed firms on Korea exchange during 2005-2016. Empirical results obtained suggest that the rise in short-term debt to assets leads to increase the risk of default whereas the increase in long-term debt to assets leads to decrease the default risk. The benefits of short-term debt financing over a short-term period fade out in the presence of information asymmetry. However, long-term debt financing overcomes the information asymmetry and enjoys the paybacks of tax advantage associated with long-term debt. Additionally, size, tangibility and interest coverage ratio are also the important determinants of default risk. Findings support the trade-off theory of capital structure and recommend the optimal use of long-term debt in a firm's capital structure.

• 정보보호학회논문지
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• 제29권2호
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• pp.451-463
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• 2019

본 연구의 목적은 정보보안 조직갈등의 단계와 유형이 정보보안 종사자의 직무이탈 의도에 미치는 영향을 파악하는데 있다. 폰디의 조직갈등이론을 적용하여 공기업 정보보안 종사자를 대상으로 한 설문자료를 분석하였으며 구조 방정식 모형을 사용하여 정보보안 종사자가 직무 활동에서 경험하는 갈등의 단계, 유형, 결과를 분석했다. 분석 결과, 정보보안 종사자가 조직갈등의 잠재요인을 감정적으로 받아들일수록 정보보안 종사자의 직무이탈의도가 높아지는 것으로 나타났다. 반면에, 인식메커니즘은 직무를 변경하여 조직을 이탈할 확률을 낮추는 조절 효과를 가진 것으로 나타났다. 조직 내 정보보안 종사자의 갈등에 관한 실증연구는 많지 않다. 본 연구의 분석 결과는 정보보호 조직의 관리자가 조직 내 갈등을 생산적인 방향으로 이끌어 가는데 활용할 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• 한국환경과학회지
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• 제28권6호
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• pp.535-544
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• 2019

We computed parameters that affect velocity distribution by applying Chiu's two-dimensional velocity distribution equation based on the theory of entropy probability and acoustic doppler current profiler (ADCP) of Jungmun-stream, Akgeun-stream, and Yeonoe-stream among the nine streams in Jeju Province between July 2011 and June 2015. In addition, velocity and flow were calculated using a surface image velocimeter to evaluate the parameters estimated in the velocity observation section of the streams. The mean error rate of flow based on ADCP velocity data was 16.01% with flow calculated using the conventional depth-averaged velocity conversion factor (0.85), 6.02% with flow calculated using the surface velocity and mean velocity regression factor, and 4.58% with flow calculated using Chiu's two-dimensional velocity distribution equation. If surface velocity by a non-contact velocimeter is calculated as mean velocity, the error rate increases for large streams in the inland areas of Korea. Therefore, flow can be calculated precisely by utilizing the velocity distribution equation that accounts for stream flow characteristics and velocity distribution, instead of the conventional depth-averaged conversion factor (0.85).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5898-5916
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• 2018

In this paper, the downlink performance of multi-cell distributed antenna systems (DAS) with a single user in each cell is investigated. Assuming the channel state information is available at the transmitter, four transmission modes are formulated as combinations of remote antenna units (RAUs) selection and cooperative transmission, namely, non-cooperative transmission without RAU selection (NCT), cooperative transmission without RAU selection (CT), non-cooperative transmission with RAU selection (NCT_RAUS), and cooperative transmission with RAU selection (CT_RAUS). By using probability theory, the cumulative distribution function (CDF) of a user's signal to interference plus noise ratio (SINR) and the system ergodic capacity under the above four modes are determined, and their closed-form expressions are obtained. Furthermore, the system energy efficiency (EE) is studied by introducing a realistic power consumption model of DAS. An expression for determining EE is formulated, and the closed-form tradeoff relationship between spectral efficiency (SE) and EE is derived as well. Simulation results demonstrate their consistency with the theoretical analysis and reveal the factors constraining system EE, which provide a scientific basis for future design and optimization of DAS.

• 아태비즈니스연구
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• 제11권4호
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• pp.37-48
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• 2020

Purpose - This study empirically investigates whether the risk property included in fat-tails of return distributions is systematic or unsystematic based on the devised statistical methods. Design/methodology/approach - This study devised empirical designs based on two traditional methods: principal component analysis (PCA) and the testing method of portfolio diversification effect. The fatness of the tails in return distributions is quantitatively measured by statistical probability. Findings - According to the results, the risk property in the fat-tails of return distributions has the economic meanings of eigenvalues having a value greater than 1 through PCA, and also systematic risk that cannot be removed through portfolio diversification. In other words, the fat-tails of return distributions have the properties of the common factors, which may explain the changes of stock returns. Meanwhile, the fatness of the tails in the portfolio return distributions shows the asymmetric relationship of common factors on the tails of return distributions. The negative tail in the portfolio return distribution has a much closer relation with the property of common factors, compared to the positive tail. Research implications or Originality - This empirical evidence may complement the existing studies related to tail risk which is utilized in pricing models as a common factor.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.346-346
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• 2017

기후변화에 따른 기상이변의 동시다발적인 발현은 농촌 지역의 홍수 발생 빈도를 증가시키고 있다. 현재의 기후시스템은 과거의 강우빈도를 기준으로 산정한 설계기준을 벗어나는 강우 사상을 빈번하게 발생시키므로 설계변수의 불확실성을 보다 합리적인 방법으로 정량화할 필요가 있다. 본 연구의 목적은 기후변화에 대응하여 확률론을 이용한 농업용 저수지의 홍수 취약성을 산정하는 데 있다. 먼저 홍수 취약성 해석에 필요한 과거와 미래 수문 자료를 수집하고 전처리 과정을 통해 해석에 적합한 자료로 구축하였다. 설계변수의 불확실성을 분석하기 위해 지속시간별 최대강우량, 유입 설계홍수량에 대해 부트스트랩 (bootstrap) 기법을 적용하여 자료를 재추출하였다. 부트스트 랩은 표본집단의 확률분포에 대해 가정을 하지 않고 표본집단의 통계적 특성을 이용하여 모집단의 통계적 추론을 할 수 있는 비모수적인 리샘플링 기법이다. 부트스트랩 추론은 표본집단의 추정치, 편의, 표준오차를 산정하고 신뢰구간을 추정한다. 부트스트랩 추론을 통해 산정하는 신뢰수준을 이용하여 농업용 저수지의 홍수 취약성을 산정하였다. 본 연구는 설계변수에 내재하는 불확실성을 부트스트랩 기법을 이용하여 정량화하고 확률적인 값을 가지는 홍수 취약성으로 산정하여 제시할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2589-2609
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• 2021

Segment routing (SR) is a highly implementable approach for traffic engineering (TE) with high flexibility, high scalability, and high stability, which can be established upon existing network infrastructure. Thus, when a network failure occurs, it can leverage the existing rerouting methods, such as rerouting based on Interior Gateway Protocol (IGP) and fast rerouting with loop-free alternates. To better exploit these features, we propose a high-performance and easy-to-deploy method SRUF (Segment Routing under Uncertain Failures). The method is inspired by the Value-at-Risk (VaR) theory in finance. Just as each investment risk is considered in financial investment, SRUF also considers each traffic distribution scheme's risk when forwarding traffic to achieve optimal traffic distribution. Specifically, SRUF takes into account that every link may fail and therefore has inherent robustness and high availability. Also, SRUF considers that a single link failure is a low-probability event; hence it can achieve high performance. We perform experiments on real topologies to validate the flexibility, high-availability, and load balancing of SRUF. The results show that when given an availability requirement, SRUF has greater load balancing performance under uncertain failures and that when given a demand requirement, SRUF can achieve higher availability.