• Nuclear Engineering and Technology
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• 제50권1호
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• pp.88-96
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• 2018

Concerns over reliability assessments of the main components in nuclear power plants (NPPs) related to aging and continuous operation have increased. The conventional reliability assessment for main components uses experimental correlations under general conditions. Most NPPs have been operating in Korea for a long time, and it is predictable that NPPs operating for the same number of years would show varying extent of aging and degradation. The conventional reliability assessment does not adequately reflect the characteristics of an individual plant. Therefore, the reliability of individual components and an individual plant was estimated according to operating data and conditions. It is essential to reflect aging as a characteristic of individual NPPs, and this is performed through prognostics. To handle this difficulty, in this paper, the general path model/Bayes, a data-based prognostic method, was used to update the reliability estimated from the generic database. As a case study, the authors consider the aging for steam generator tubes in NPPs and demonstrate the suggested methodology with data obtained from the probabilistic algorithm for the steam generator tube assessment program.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.860-880
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• 2024

Breast cancer is a major health concern affecting women and men globally. Early detection and accurate classification of breast cancer are vital for effective treatment and survival of patients. This study addresses the challenge of accurately classifying breast tumors using machine learning classifiers such as MLP, AdaBoostM1, logit Boost, Bayes Net, and the J48 decision tree. The research uses a dataset available publicly on GitHub to assess the classifiers' performance and differentiate between the occurrence and non-occurrence of breast cancer. The study compares the 10-fold and 5-fold cross-validation effectiveness, showing that 10-fold cross-validation provides superior results. Also, it examines the impact of varying split percentages, with a 66% split yielding the best performance. This shows the importance of selecting appropriate validation techniques for machine learning-based breast tumor classification. The results also indicate that the J48 decision tree method is the most accurate classifier, providing valuable insights for developing predictive models for cancer diagnosis and advancing computational medical research.

• 인터넷정보학회논문지
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• 제21권1호
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• pp.79-85
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• 2020

오늘날 스마트폰에서 스팸 문자를 차단하기 위해 문자 내용과 스팸 키워드의 단순 문자열 비교 또는 스팸 전화번호를 차단하는 방식을 사용하고 있다. 이에 따라 스팸 문자가 자동으로 차단되는 것을 방지하기 위해 점차 변화된 방식으로 스팸 문자를 전송한다. 특히 스팸 키워드에 포함되는 단어의 경우 단순 문자열 비교로 검색되지 않도록 특수문자, 한자, 띄어쓰기 등을 이용하여 비정상적인 단어로 스팸 문자를 발송한다. 기존 스팸 필터링 방식의 경우 이러한 스팸 문자를 차단할 수 없다는 한계가 있다. 따라서 변화하는 스팸 문자에 대응할 수 있는 새로운 기술이 필요한 시점이다. 본 논문에서는 스팸 문자에서 자주 사용되는 신조어를 검출하여 변화하는 스팸 문자에 대응할 수 있는 지식 그래프 기반의 신조어 감지 매커니즘을 제안한다. 또한 기본 Naive Bayes에 감지한 신조어를 적용하여 제안한 방법의 성능 실험 결과를 보여준다.