• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1997.04a
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• pp.269-279
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• 1997

An investigation was peformed to study the predicting the joint strength of mechanical fasteners. Bearing failure is most important failure mode for designing joint. So in this study, the prediction method in consideration with bearing failure was chosen. In the proposed method, the characteristic length is combined with the Yamada-Sun failure criterion, Tsai-Hill failure criterion and characteristic length for Tension and Compression is determined from investigation. Especially the length of compression is determined from the "bearing failure test" that newly conceived to take bearing failure into consideration. The proposed prediction method was applied to quasi-isotropic carbon/epoxy joint showing net-tension and bearing failure experimentally. Good agreement was found between the predicted and experimental result for each joint geometry.

• Membrane Journal
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• v.14 no.1
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• pp.18-25
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• 2004

The purpose of this paper was to find out the proper equation to predict the gas transfer rate for designing intravenous artificial lung assist device. The prepared hollow fiber modules were examined under various experimental conditions through experimental modeling before inserted the artificial lung assist d $\varepsilon$ vice into as venous. As a result, we can estimate the gas transfer as a function of the packing density. The gas transfer obtained from the experiment was similar to that from the equation, confirming the usefulness equation. Therefore, we can conclude the gas transfer of the intravenous artificial lung assist device as a function of the packing density, and this functions are very useful for predicting the gas transfer of the intravenous artificial lung assist device.

• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.3
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• pp.10-17
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• 2005

A performance prediction model is developed for partially admitted axial-type turbines. Losses generated within the turbine are classified to the windage loss, expansion loss and mixing loss. The developed loss model is compared with an experimental result. The results predicted with the developed model agree well with the experimental results than those predicted with several other models because this model considers three different kinds of losses. Moreover, this model predicts well the performance even the partial admission is changed. So, this model could be applied to predict the performance of partially admitted axial turbine and it has a high accurate performance.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.596-600
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• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. They usually adopt logistic regression analysis, And, they state that the correctness of prediction is about 60${\sim}$70%, We performed a similar experiment with Eclipse 3.3 to check their prediction effectiveness, However, the result shows that correctness is about 40% which is much lower than the original results. We also found that univariate logistic regression analysis produces better results than multivariate logistic regression analysis.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Food Industry And Nutrition
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• v.10 no.1
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• pp.11-16
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• 2005

식물성 단백질의 주요 공급원이며 우리나라 전통식품 중의 하나인 두부의 유통기한을 정량적으로 예측할 수 있는 수학적 모델을 개발하고자 온도와 초기균수에 따른 두부 부패세균의 성장 실험 결과를 데이터베이스화하여 이를 바탕으로 균의 성장을 정량적으로 평가할 수 있는 수학적 모델을 개발하였다. 근의 증식 지표인 최대증식속도상수(k), 유도기(LT), 세대시간(GT)은 온도에 지배적인 영향을 받았으며, 초기균수에 따른 유의 적 인 차이 는 없었다(p<0.05). 최대증식속도상수와 온도 및 초기균수의 상관관계를 나타내는 수학적 정량평가모델인 square root model을 이 용하여 두부 부패 세균의 성장을 정량적으로 예측할 수 있는 모델$({\surd}{\kappa}=0.016861(T+6.87095))$을 개발하였으며 실험치와 예측치의 상관계수는0.969이었다. 이 예측 정량평가모델로부터 예측한 최대증식속도상수와 두부의 관능적 부패시 점을 반영 한 Gompertz 변형 모델을 이용하여 두부의 유통기한을 예측할 수 있는 모델$(Spoilage-critrion(hr)=\frac{2{\times}Ln2+Ln[(Nmax/No)-1])}{k}$을 개발하였다

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.63-69
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• 2010

Forecasting game usages hours can supply good information resolving intensive server access and ensuring stable game service. In this paper, we applied various time series analysis methods to forecast game usage hours in 2009 on famous "Ion" and "Sudden Attack" games. According to the experiment, the seasonal variation method showed better performance forecasting actual usage hours.

• Composites Research
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• v.16 no.5
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• pp.15-20
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• 2003

In this paper, the fracture strength of the surface damaged laminates was predicted by applying the fracture strengths of the unflawed and flawed laminates. For prediction, the theoretical equation about the fracture strength of laminates was simplified applying classical laminate theory and was applied to the surface damaged laminates. Lagace's and Tsai's experimental data were used for verifying the theoretical equation. Moreover, to verify the theoretical prediction, an experiment was performed. Surface unflawed laminate and flawed laminates were fabricated and the experiments were made and these results were compared with theoretical predictions. The specimens' fiber direction was same to the tensile direction and the theoretical predictions and the experimental results were showed good agreement. Therefore, by this equation, the fracture strength of structures made of composites will be able to be predicted when the surface of the structures was damaged.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.267-275
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• 2012

In this study, crack growth in a center-cracked plate is predicted under mode I variable amplitude loading, and the result is validated by experiment. Huang's model is employed to describe crack growth with acceleration and retardation due to the variable loading effect. Experiment is conducted with Al6016-T6 plate, in which the load is applied, and crack length is measured periodically. Particle Filter algorithm, which is based on the Bayesian approach, is used to estimate model parameters from the experimental data, and predict the crack growth of the future in the probabilistic way. The prediction is validated by the run-to-failure results, from which it is observed that the method predicts well the unique behavior of crack retardation and the more data are used, the closer prediction we get to the actual run-to-failure data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1779-1786
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• 2014

Artificial Intelligence-based techniques have been applied to problems where mathematical relations can not be presented due to complicatedness of the physical process. A representative example in hydraulics is the local scour around bridge piers. This study presents a GEP model for predicting the local scour around bridge piers. The model is trained by 64 laboratory data to build the regression equation, and the constructed model is verified against 33 laboratory data. Comparisons between the models with dimensional and normalized variables reveals that the GEP model with dimensional variables predicts better. The proposed model is now applied to two field datasets. It is found that the MAPE of the scour depths predicted by the GEP model increases compared with the predictions of local scours in laboratory scale. In addition, the model performance increases significantly when the model is trained by the field dataset rather than the laboratory dataset. The findings suggest that apart from the ANN model, GEP model is a sound and reliable model for predicting local scour depth.