• 지능정보연구
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• 제21권2호
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• pp.173-190
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• 2015

부도는 막대한 사회적, 경제적 손실을 야기할 수 있으므로, 미리 부도여부를 정확하게 예측하여 선제 대응하는 것은 경영분야에서 대단히 중요한 의사결정문제 중 하나이다. 이에 지능정보시스템 분야에서도 그간 기업의 재무 데이터에 기반해 부도예측을 개선하기 위한 노력을 기울여왔는데, 안타깝게도 기존의 연구들은 대부분 분류모형의 성능 개선을 통해 예측 정확도를 개선하는 것에만 주로 초점을 맞추어 다른 요소들을 충분히 고려하지 못했다는 한계가 있다. 이러한 배경에서 본 연구는 부도예측 모형의 정확도를 개선하기 위한 방편으로 새로운 데이터 전처리 방법, 그 중에서도 효과적인 표본추출 방법을 제안하고자 한다. 일반적으로 부도예측을 위해 사용되는 데이터들은 극심한 데이터 불균형 문제에 노출되어 있는데, 본 연구에서는 k-reverse nearest neighbor(k-RNN)와 one-class support vector machine(OCSVM) 방법을 결합한 하이브리드 언더샘플링(hybrid under-sampling) 접근법을 통해 이같은 데이터 불균형 문제를 해결하고자 하였다. 본 연구에서 제안한 접근법에서 k-RNN은 이상치를 효과적으로 제거할 수 있으며, OCSVM은 다수를 구성하는 등급의 데이터로부터 정보량이 풍부한 표본만 효과적으로 선택할 수 있는 수단으로 활용될 수 있다. 제안된 기법의 성능을 검증하기 위해, 본 연구에서는 국내 한 은행의 비외감기업 부도예측모형 구축에 제안 기법을 적용해 본 뒤, 일반적으로 많이 사용되는 랜덤샘플링(random sampling)과 제안 기법의 성능을 비교해 보았다. 그 결과, 로지스틱 회귀분석, 판별분석, 의사결정나무, SVM 등 대다수의 분류모형에 있어 분류 정확도가 개선됨을 확인할 수 있었으며, 모든 분류모형에 있어 부정 오류, 즉 부실기업을 정상으로 예측하는 오류율이 크게 감소함을 확인할 수 있었다.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.719-729
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• 2003

We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.619-629
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• 2004

Binomial group testing or composite sampling is often used to estimate the proportion, p, of positive(infects, defectives) in a population when that proportion is known to be small; the potential benefits of group testing over one-at-a-time testing are well documented. The literature has focused on maximum likelihood estimation. We provide two Bayes estimators and compare them with the MLE. The first of our Bayes estimators uses an uninformative Uniform (0, 1) prior on p; the properties of this estimator are poor. Our second Bayes estimator uses a much more informative prior that recognizes and takes into account key aspects of the group testing context. This estimator compares very favorably with the MSE, having substantially lower mean squared errors in all of the wide range of cases we considered. The priors uses a Beta distribution, Beta ($\alpha$, $\beta$), and some advice is provided for choosing the parameter a and $\beta$ for that distribution.

• 한국학교ㆍ지역보건교육학회지
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• 제7권
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• pp.59-74
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• 2006

Objectives: The purpose of this research is to find out the relations among children‘s perceived competences, expressive disposition of interpersonal relations, and parents’ social support, which influence their self image. Methods: The samples to achieve the purpose of this research are composed of 294 students in G elementary school, OO city, Kyounggido, the number of male students in the fifth year being 71, female 77, and male students in the sixth year being 73, female 73. After having excluded the data of 9 students among them due to their insincere reply, this research analyzed the data of 285 students by using SPSS WIN Ver. 13.0. Results: First, after having observed the average difference stemming from each method, this research found that there is no sexual difference in parents‘ social support. (p<.05). In children's perceived competences, female students is higher only in active aspect than male students, but there is no meaningful difference in other aspects. In expressive disposition of interpersonal relations, the showing-off and narcissistic expressive tendency of female students is higher than that of male students, but there is no meaningful difference in other aspects. Second, this research found out the meaningful static correlation among parents' social support, children's perceived competences, and expressive disposition of interpersonal relations. Parents' emotional, appreciative, and informative support has the static correlation with all the other aspects of children's perceived competences, and material support has the static correlation with children's academic, active, and general self-esteeming sense (p<.05, p<.01). Third, this research observed, of all the aspects of parents' social support and expressive disposition of interpersonal relations, what variable is affecting children's perceived competences, and found that parents' appreciative support, showing-off and narcissistic expressive tendency, material support, and informative support, each in order, has meaningful influence (p<.05, p<.01). This fact shows that the further the children recognize these supports of their parents', the more affirmatively they recognize their competences, and the further the children of showing-off and narcissistic expressive disposion recognize their competences. Conclusions: First, sampling was conducted of the fifth and sixth male and female students of OO elementary school, and so the result of this research has the applicative limitation in the generalization for all the elementary school students. Therefore this research suggests further researches by broad sampling for the more advanced generalization. Second, this research only observed the differnce of perception deriving from gender and school year, but the parents' social support levels perceived by children reveal many differnces according to children's characters or their families' variables. Therefore this research suggests subsequent researches on various variables.

• 복식문화연구
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• 제19권6호
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• pp.1247-1258
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• 2011

This study aimed to clarify the relationships among the characteristics of consumers and their influence on fashion leadership. Two kinds of variables were investigated in this study: centrality of visual product aesthetics and consumer innovativeness as personal characteristics, and role-relaxed consumption and consumer susceptibility to interpersonal influence as interpersonal characteristics. Data were gathered by surveying university students in the Seoul metropolitan area, using convenience sampling, and 322 questionnaires were used in the statistical analysis. In analyzing data, correlation analysis, factor analysis, and regression analysis were conducted. Factor analysis on the centrality of visual product aesthetics revealed three sub-factors: value, acumen, and response intensity. Meanwhile, factor analysis for consumer susceptibility to interpersonal influence revealed two sub-factors: informative and normative conformities. However, consumer innovativeness, role-relaxed consumption, and fashion leadership revealed only one factor. Regression analysis showed that visual product aesthetics, especially acumen and response intensity, were the most influential factors; furthermore, consumer innovativeness and normative conformity had positive influence on fashion leadership. However, role-relaxed consumption had negative influence on fashion leadership.

• 한국공간정보시스템학회 논문지
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• 제11권2호
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• 한국임상수의학회지
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• 제32권4호
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• pp.319-323
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• 2015

The performance of diagnostic test accuracy is usually summarized by a variety of statistics such as sensitivity, specificity, predictive value, likelihood ratio, and kappa. These indices are most commonly presented when evaluations of competing diagnostic tests are reported, and it is of utmost importance to compare the accuracies of diagnostic tests to decide on the best available test for certain medical disorder. However, it is important to emphasize that specific point values of these indices are merely estimates. If parameter estimates are reported without a measure of uncertainty (precision), knowledgeable readers cannot know the range within which the true values of the indices are likely to lie. Therefore, when evaluations of diagnostic accuracy are reported the precision of estimates should be stated in parallel. To reflect the precision of any estimate of a diagnostic performance characteristic or of the difference between performance characteristics, the computation of confidential interval (CI), an indicator of precision, is widely used in medical literatures in that CIs are more informative to interpret test results than the simple point estimates. The majority of peer-reviewed journals usually require CIs to be specified for descriptive estimates, whereas domestic veterinary journals seem less vigilant on this issues. This paper describes how to calculate the indices and associated CIs using practical examples when assessing diagnostic test performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• 여성건강간호학회지
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• 제16권1호
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• pp.29-36
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• 2010

Purpose: This study aimed to identify the scores of postpartum depression(PPD) on the first day, 1st week, and 6th week after the delivery and to explore their related factors before and after delivery in postpartum women. Methods: With a survey design, 293 postpartum women were recruited from a postpartum unit, Ilsin Christian hospital in Pusan via convenience sampling and were followed at 1st week and 6th week in the outpatient clinic. Results: Results showed that the scores of PPD(EPDS score) were low at postpartum 1st day, 1st week and 6th week but prevalence of PPD(EPDS ${\geq}13$)was 3.1%at 1st day, 8.2%at 1st week and 7.5%at 6th week, respectively. The pre-delivery factors were experience of depression, and the post-delivery factors were baby's sex(1st day), no caregiver for baby(1st week), and no help and concern for taking care of baby from husband and family(1st day and 6th week). The greater satisfaction with becoming a mother and her life, and greater maternal attachment were related to lower level of PPD at the three time points. Conclusion: Regular screening for postpartum depression and supportive and informative education is needed for postpartum women visiting the outpatient clinic for follow-up.