• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.327-336
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• 2016

Prediction of customer failure rates plays an important role for establishing appropriate management policies and improving the profitability for industries. For these reasons, many LCD (Liquid crystal display) manufacturing industries have attempted to construct prediction models for customer failure rates. However, most traditional models are based on the parametric approaches requiring the assumption that the data follow a certain probability distribution. To address the limitation posed by the distributional assumption underpinning traditional models, we propose using parameter-free data mining models for predicting customer failure rates. In addition, we use various information associated with product attributes and field return for more comprehensive analysis. The effectiveness and applicability of the proposed method were demonstrated with a real dataset from one of the leading LCD companies in South Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.55-61
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• 2011

Battery cases have been made of polypropylene and its warpage is relatively large due to the crystalline characteristic of polypropylene. In this study, the battery case when the injection mold used to improve the Gate by simplifying the process of production cost savings and focus on improving the quality of molding CAE analysis was carried out. The result could be produced in plastic and products of the imbalance in the flow and deformation and to predict reliability of the product will contribute to reduced scrap.

• Annual Conference of KIPS
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• 2015.10a
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• pp.903-905
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• 2015

제조공정은 작업자의 능력에 의해 셋업 설정이 좌우되기 때문에 불량률의 산출 및생산 예측이 어려우며, 제조공정 상의 다양한 데이터들을 수집할 수 있으나, 각 프로세스 사이에서 서로 연관성 있게 통합되지 못하고 있다. 따라서 논문에서는 제조공정 개선을 위한 실시간 통합 모니터링 시스템을 제안하고자 한다. 이를 통해서 제조업체의 작업자 및 경영자에게 보다 효율적으로 실시간 데이터 분석 정보를 시각화하여 제공함으로써, 제조 공정 개선에 활용할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.255-255
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• 2021

내수 침수는 강물과 같은 외수보다는 제내지에서 하수 또는 우수의 배제가 불량하여 발생되는 범람이다. 내수 침수 상황에서 침수심의 정확한 관측과 예보된 강수로부터 침수심을 예측할 수 있는 시스템 즉, 스마트 목자판이 개발되고 있다. 시스템 운용에 사용될 소프트웨어 가운데 하나로서 침수 해석을 위한 수치 모형이 필요하다. 내수에 의한 침수와 그것의 배제를 무리 없이 모의하려면, 물이 차고 빠지는 물리 현상을 타당하게 해석하는 것이 관건일 것이다. 그에 따라 2차원 천수 방정식을 유한 체적법으로 해석할 때 흐름률(flux) 계산에 근사 Riemann 해법을 적용하는 모형을 도입하였다. 단순하면서도 내수 침수의 재현에서 드러날 수 있는 취약점들을 포괄할 수 있도록 경사면과 계단으로 가상 지형을 구성하였으며, 강수로 인한 지형의 침수에 대해 개발된 모형을 시험하였다. 근사 Riemann 해법은 흐름률의 정확한 평가로 잠김과 드러남 모의가 자연스런 장점이 있으나, 해석 방법이 복잡하여 계산 시간이 비교적 오래 걸리므로 그에 대한 대책이 요구된다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.3
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• pp.341-349
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• 2014

Field emergence of Azuki bean is poor due to hard seed coat as compared to other legumes. In this study, an attempt was made to develop prediction method with regression analysis based on various seed vigor tests in laboratory for field emergence of azuki bean. Azuki bean seeds artificially aged to provide various levels of seed quality were evaluated by the standard germination test (SGT), cold germination test (CT), cool germination test (CGT), complex stressing vigor test (CSVT), tetrazolium(TZ) vigor test and electroconductivity test. The SGT was suitable for predicting the field emergence in the unaged high vigor seeds. The abnormal seedling percentage and shoot length in the CGT were highly correlated with field emergence of moderate vigor seeds artificially aged for 2 days. Electroconductivity, seed viability in the CSVT, and vigor and predicted germinability in the tetrazolium vigor test were also useful for predicting field emergence. Percent of ungerminated seed in the CSVT was correlated with field emergence in the low vigor seeds artificially aged for 4 days. In a stepwise multiple regression analysis, seed viability in the SGT, normal seedling percentage and dry matter weight in the CGT accounted for 86.9% of the predicted value of field emergence in azuki bean.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• Journal of agricultural medicine and community health
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• v.28 no.1
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• pp.79-92
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• 2003

Objectives: To investigate the reinfection rate of Helicobacter pylori and the factors related to reinfection of H. pylori, 86 persons were examined in April 2000 after 1 year follow-up period and 77 persons were examined in October 2001 after two and a half-year follow-up period in Gyeongju-si, Gyeongsangbuk-do, Korea. Methods: The subjects were confirmed as H. pylori negative by urea breath test(UBT), and asked to answer the questionnaire regarding demographic characteristics, dyspepsia symptoms, health-related behaviors and family history. Results: The reinfection rate on the first year of the eradication of H. pylori was 15.6%, when the 77 subjects have finished follow-up observation for one year. In the urea breath test performed after two and a half year, 13 out of 77 were positive, with the reinfection rate of 16.9%. Age, sex, socio-economical status, educational level and family history were not associated with the reinfection, while there was significant association between the reinfection and postprandial fullness and epigastric bloating in subjective dyspepsia that the subjects who were determined to be negative in the urea breath test for the following year. The treatment compliance and drinking were significant variables in univariate analysis. Meanwhile, the cases in which the dyspepsia symptom scores for the recent year were 2 to 3 points served as the only statistically significant variable in multiple logistic regression analysis, with the odds ratio of 4.5. The cases in which salt intake during meals was exceeded were 8.7 in the odds ratio, but statistically insignificant. Conclusions: Conclusively, the first-year reinfection rate was 15.6%, and the second-year reinfection rate was 16.9%. Thecomplaints of subjective dyspeptic symptoms and the treatment compliance, as the basis for predicting the H. pylori reinfection in communities, can be used as the basis to screen the subjects for follow-up examination to find out H. pylori infection.

• Tuberculosis and Respiratory Diseases
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• v.64 no.4
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• pp.266-271
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• 2008

Background: Estimation of the probability of a patient having an acute pulmonary embolism (PE) for patients with a suspected PE are well established in North America and Europe. However, an assessment of the prediction rules for a PE has not been clearly defined in Korea. The aim of this study is to assess the prediction rules for patients with a suspected PE in Korea. Methods: We performed a retrospective study of 210 inpatients or patients that visited the emergency ward with a suspected PE where computed tomography pulmonary angiography was performed at a single institution between January 2005 and March 2007. Simplified Wells rules and revised Geneva rules were used to estimate the clinical probability of a PE based on information from medical records. Results: Of the 210 patients with a suspected PE, 49 (19.5%) patients had an actual diagnosis of a PE. The proportion of patients classified by Wells rules and the Geneva rules had a low probability of 1% and 21%, an intermediate probability of 62.5% and 76.2%, and a high probability of 33.8% and 2.8%, respectively. The prevalence of PE patients with a low, intermediate and high probability categorized by the Wells rules and Geneva rules was 100% and 4.5% in the low range, 18.2% and 22.5% in the intermediate range, and 19.7% and 50% in the high range, respectively. Receiver operating characteristic curve analysis showed that the revised Geneva rules had a higher accuracy than the Wells rules in terms of detecting PE. Concordance between the two prediction rules was poor ($\kappa$ coefficient=0.06). Conclusion: In the present study, the two prediction rules had a different predictive accuracy for pulmonary embolisms. Applying the revised Geneva rules to inpatients and emergency ward patients suspected of having PE may allow a more effective diagnostic process than the use of the Wells rules.