• 방사선산업학회지
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• 제18권3호
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• pp.177-182
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• 2024

The purpose of this study is to develop a CNN based deep learning model that can effectively detect pneumonia by analyzing chest X-ray images of adults over the age of 20 and compare it with VUNO, LUNIT a commercialized AI model. The data of chest X-ray image was evaluate based on accuracy, precision, recall, F1 score, and AUC score. The CNN model recored an accuracy of 82%, precision 76%, recall 99%, F1 score 86%, and AUC score 0.7937. The VUNO model recordded an accuracy of 84%, precision 81%, recall 94%, F1 score 87%, and AUC score 0.8233. The LUNIT model recorded an accuracy of 77%, precision 72%, recall 96%, F1 score 83%, and AUC score 0.7436. As a result of the Confusion Matrix analysis, the CNN model showe FN (3), showing the highest recall rate (99%) in the diagnosis of pneumonia. The VUNO model showed excellent overall perfomance with high accuracy (84%) and AUC score (0.8233), and the LUNIT model showed high recall rate (96%) but the accuracy and precision showed relatively low results. This study will be able to provide basic data useful for the development of a pneumonia diagnosis system by comprehensively considers the perfomance of the medel is necessary to effectively discriminate between penumonia and normal groups.

• 경영과학
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• 제28권1호
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• pp.141-158
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• 2011

This study concerns with an applicability of the management science approach to the score adjustment among the College Scholastic Aptitude Test(CSAT) optional subjects. A linear programming model is developed to minimize the sum of score distortions between optional subjects. Based on the analysis of the 377,089 CSAT(2010) applicants' performances in social science test section, this study proposes a new approach for the score equating or linking method of the educational measurement theory. This study makes up for the weak points in the previous linear programming model. First, the model utilize the standard score which we can get. Second, the model includes a goal programming concept which minimizes the gap between the adjusting goal and the result of the adjustment. Third, the objective function of the linear programing is the weighted sum of the score distortion and the number of applicants. Fourth, the model is applied to the score adjustment problem for the whole 11 optional subjects of the social science test section. The suggested linear programming model is a generalization of the multi-tests linking problem. So, the approach is consistent with the measurement theory for the two tests and can be applied to the optional three or more tests which do not have a common anchor test or a common anchor group. The college admission decision with CSAT score can be improved by using the suggested linear programming model.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.201-208
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• 1993

Given the specific mean shift outlier model, the score test for multiple outliers in nonlinear regression is discussed as an alternative to the likelihood ratio test. The geometric interpretation of the score statistic is also presented.

• 품질경영학회지
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• 제32권2호
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권4호
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• pp.323-330
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• 2016

Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

• 한국융합학회논문지
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• 제13권2호
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• pp.13-20
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• 2022

본 연구는 딥러닝 기법을 활용하여 범죄 수사 도메인에 특화된 개체명 인식 모델을 개발하는 연구이다. 본 연구를 통해 비정형의 형사 판결문·수사 문서와 같은 텍스트 기반의 데이터에서 자동으로 범죄 수법과 범죄 관련 정보를 추출하고 유형화하여, 향후 데이터 분석기법을 활용한 범죄 예방 분석과 수사에 기여할 수 있는 시스템을 제안한다. 본 연구에서는 범죄 수사 도메인 텍스트를 수집하고 범죄 분석의 관점에서 필요한 개체명 분류를 새로 정의하였다. 또한 최근 자연어 처리에서 높은 성능을 보이고 있는 사전학습 언어모델인 KoELECTRA를 적용한 제안 모델은 본 연구에서 정의한 범죄 도메인 개체명 실험 데이터의 9종의 메인 카테고리 분류에서 micro average(이하 micro avg) F1-score 99%, macro average(이하 macro avg) F1-score 96%의 성능을 보이고, 56종의 서브 카테고리 분류에서 micro avg F1-score 98%, macro avg F1-score 62%의 성능을 보인다. 제안한 모델을 통해 향후 개선 가능성과 활용 가능성의 관점에서 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권12호
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1007-1015
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• 2003

In this paper we introduced a new score generating function for the rank dispersion function in a general linear model. Based on the new score function, we derived the null asymptotic theory of the rank-based hypothesis testing in a linear model. In essence we showed that several rank test statistics, which are primarily focused on our new score generating function and new dispersion function, are mainly distribution free and asymptotically converges to a chi-square distribution.

• Journal of the Korean Data and Information Science Society
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• 제15권1호
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• pp.129-139
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• 2004

We consider the problem of testing for outliers in generalized linear model. We proceed by first specifying a mean shift outlier model, assuming the suspect set of ourliers is known. Given this model, we discuss standard approaches to obtaining score test for outliers as an alternative to the likelihood ratio test.

• Journal of the Korean Data and Information Science Society
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• 제23권3호
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• pp.495-504
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• 2012

이 연구에서는 면접 점수가 발생되는 통계적 모형을 사영에 근거하여 제시하였다. 이 모형은 피면접자의 개인별 참값과 이와 관련된 변수값이 2차원 평면의 X와 Y축의 값으로 주어졌을 때, 심사위원의 시각을 X축과의 각도로 생각하여 이 축에 사영된 값으로서 심사위원의 면접 점수의 평균으로 잡는 방법이다. 이 값에 개인적 편향과 관측 오차를 더해져 심사위원의 관측 면접 점수가 얻어지게 된다. 이 통계적 모형을 사용하여 흔히 사용되고 있는 면접 점수 표준화 방법인 절사평균법, 순위평균법, z-점수평균법을 비교하였다. 이 모의실험에서는 두 가지 면접 형태, 두 가지 면접자 수, 두 가지 면접자의 전문성 정도, 실제 점수와 관련된 변수 간의 분포 두 가지와 세 가지 상관계수가 고려되었다.