• 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.

• 한국간담췌외과학회지
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• 제27권2호
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• pp.195-200
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• 2023

Backgrounds/Aims: We aimed to build a machine learning tool to help predict low-grade intraductal papillary mucinous neoplasms (IPMNs) in order to avoid unnecessary surgical resection. IPMNs are precursors to pancreatic cancer. Surgical resection remains the only recognized treatment for IPMNs yet carries some risks of morbidity and potential mortality. Existing clinical guidelines are imperfect in distinguishing low-risk cysts from high-risk cysts that warrant resection. Methods: We built a linear support vector machine (SVM) learning model using a prospectively maintained surgical database of patients with resected IPMNs. Input variables included 18 demographic, clinical, and imaging characteristics. The outcome variable was the presence of low-grade or high-grade IPMN based on post-operative pathology results. Data were divided into a training/validation set and a testing set at a ratio of 4:1. Receiver operating characteristics analysis was used to assess classification performance. Results: A total of 575 patients with resected IPMNs were identified. Of them, 53.4% had low-grade disease on final pathology. After classifier training and testing, a linear SVM-based model (IPMN-LEARN) was applied on the validation set. It achieved an accuracy of 77.4%, with a positive predictive value of 83%, a specificity of 72%, and a sensitivity of 83% in predicting low-grade disease in patients with IPMN. The model predicted low-grade lesions with an area under the curve of 0.82. Conclusions: A linear SVM learning model can identify low-grade IPMNs with good sensitivity and specificity. It may be used as a complement to existing guidelines to identify patients who could avoid unnecessary surgical resection.

• Journal of the Korean Wood Science and Technology
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• 제26권2호
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• pp.16-23
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• 1998

This paper examines the effects of specimen length and grade on the strength and stiffness properties of structural timber of radiata pine. The tensile strength and modulus of elasticity of 1,902 machine-graded boards with 3.15- and 1.62-m clear span lengths, were determined using a horizontal tension test machine. The mean failure and characteristic stress values for tensile strength show an extremely high dependency on test specimen length. The mean and characteristic values of both modulus of elasticity and tensile strength show significant dependency on machine stress grades.

• 한국초등과학교육학회지:초등과학교육
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• 제40권1호
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• pp.66-80
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• 2021

This study is a case study that applies plant classification learning using machine learning to fourth graders in elementary school in online learning situations. In this study, a plant classification learning education program associated with 2015 revision science curriculum was developed by applying the Artificial Intelligence biological classification teaching Learning model. The study participants were 31 fourth graders who agreed to participate voluntarily. Plant classification learning using machine learning was applied six hours for three weeks. The results of this study are as follows. First, as a result of image analysis on artificial intelligence, participants were mainly aware of artificial intelligence as mechanical (27%), human (23%) and household goods (23%). Second, an artificial intelligence recognition survey by semantic discrimination found that artificial intelligence was recognized as smart, good, accurate, new, interesting, necessary, and diverse. Third, there was a difference between men and women in perception and emotion of artificial intelligence, and there was no difference in perception of the ability of artificial intelligence. Fourth, plant classification learning using machine learning in this study influenced changes in artificial intelligence perception. Fifth, plant classification learning using machine learning in this study had a positive effect on reasoning ability.

• 대한가정학회지
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• 제11권4호
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• pp.363-373
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• 1973

The Sewability was tested with the seam strength and Puckering Grade by a general sewing machine according to some properties of simulated Leather, yarn tensile strength needle and stitches. The main results tested are as follows ; 1. The thick and uncomfortable leather is unable to be sewed by a general sewing machine, but the thin and soft one is able to. 2. The interval between stitches depends on type of leather used, and the variance in accordance with type of leather varies much more in the case of narrower interval. 3. When the sewability of leather-surface is not so good, is desirable to pour oil on the surface for the purpose of better efficiency. 4. The seam strength is directly proportional to interval of stitch and tensile strength of yarn and leather used, and needle No. 14 is more effective than No.1l. 5. The more the soft and thin leather is, the lower the Puckering Grade becomes. Type of yarn and interval of stitches do not seem to effect the Puckering Grade.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.513-516
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• 2013

Earthmoving equipment's haul-route has a great influence on the productivity of the earth work operation. Haul-route grade is a critical factor in selecting the haul-route. The route that has low grade resistance contributes to increase machine travel speed and production. This study presents a mathematical model called "Hauling-Unit Optimal Routes Selecting system" (HUORS). The system identifies optimal path that maximize the earth-work productivity. It consists of 3 modules, i.e., (1) Module 1 which inputs site characteristic data and computes site location and elevation using GIS(Geographical Information System); (2) Module 2 which calculates haul time; (3) Module 3 which displays an optimum haul-route by considering the haul-route's gradient resistances (i.e., from the departure to the destination) and hauling time. This paper presents the system prototype in detail. A case study is presented to demonstrate the system and verifies the validity of the model.

• ETRI Journal
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• 제43권4호
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• pp.694-701
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• 2021

Catastrophic events cause casualties, damage property, and lead to huge social impacts. To build common standards and facilitate international communications regarding disasters, the relevant authorities in social management rank them in subjectively imposed terms such as direct economic losses and loss of life. Terrorist attacks involving uncertain human factors, which are roughly graded based on the rule of property damage, are even more difficult to interpret and assess. In this paper, we collected 114 183 open-source records of terrorist attacks and used a machine learning method to grade them synthetically in an automatic and objective way. No subjective claims or personal preferences were involved in the grading, and each derived common factor contains the comprehensive and rich information of many variables. Our work presents a new automatic ranking approach and is suitable for a broad range of gradation problems. Furthermore, we can use this model to grade all such attacks globally and visualize them to provide new insights.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1995년도 춘계학술대회 논문집
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• pp.174-180
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• 1995

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권7호
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• pp.327-332
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• 2016

본 연구는 비 정형 농산물 중 6년근 수삼의 자동 등급 분류하기 위한 선행연구로, 이를 위해 4방향에서 이미지 취득이 가능한 수삼 영상 측정기를 제작 하였으며 총 245 수삼 개체에 대해서 영상을 취득하였다. 취득된 영상의 각 수삼 개체마다 12개의 파라미터를 추출하였으며, KGC 인삼공사의 수삼등급 분류 기준과 각 등급별 평균 파라미터의 분포를 조사하여 최종 4개 파라미터를 선정하였다. 패턴인식 분류기는 Support Vector Machine을 사용하였으며 공용 소프트웨어인 OpenCV Library를 사용하여 k-Class 분류기를 설계하였다. 각 등급별 학습 데이터 수를 10, 15, 20으로 조정하여 등급별 인식률, 본인 거부율, 타인 인식율을 조사하였으며, 학습데이터 수가 10개일 때 1등급 인식률 94%, 2등급 인식률 98%, 3등급 인식률 90%로 가장 높은 인식 성능을 보였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 춘계학술대회 논문집
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• pp.85-86
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• 2008