• 대한산업공학회지
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• 제34권1호
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• pp.57-65
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• 2008

In the vehicle design, the research on driving posture has stood out as one of the important issues. Recently, the research on 3D human modeling focused on more exact implementation of real driving posture. However, prediction of driving posture through the 3D human modeling fail to reflect on the model the phenomenon called sagging, which refers to the retraction or shrinking of the torso while driving. 30 male subjects participated in the experiment where total subjects were divided into four groups according to height percentile(under 50%ile, 51%ile to 75%ile, 76%ile to 95%ile, over 95%ile). The independent variables were seat back angle(4 levels) and seat pan angle(2 levels). The dependent variable was capacity or the degree of retraction of the torso. First this study measured the sagging capacity by using a paired T-test between erect and retracted posture. Secondly it was tried to find out significant anthropometric variables that were statistically correlated by the analysis of correlation. Finally, a prediction model was derived which explains the capacity of sagging.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• 한국안전학회지
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• 제18권1호
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• pp.81-88
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• 2003

Recently, pile foundations were constructed in rough or soft ground than ground of well condition thus it is important that prediction of ultimate bearing capacity and calculation of proper safety factor applied pile foundation design. This study were performed to dynamic loading tests for the thirty two piles at four different construction sites and selected pile at three site were performed to static loading tests and then compare with measured value and value of static and dynamic loading tests. The load-settlement curve form the dynamic loading tests by CAPWAP was very similar to the results obtained from the static load tests. Based on dynamic and static loading tests, the reliability of pile-driving formula were analyzed and then suggested with proper safety factor for prediction of allowable bearing capacity in this paper.

• Geomechanics and Engineering
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• 제1권3호
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• pp.259-262
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• 2009

This paper examines the potential of relevance vector machine (RVM) in prediction of pullout capacity of small ground anchors. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM showed good performance and is proven to be better than ANN model. It also estimates the prediction variance. The plausibility of RVM technique is shown by its superior performance in forecasting pullout capacity of small ground anchors providing exogenous knowledge.

• Geomechanics and Engineering
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• 제19권2호
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• 플랜트 저널
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• 제11권1호
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• pp.50-57
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• 2015

연구에서는 국내 D 석탄 화력발전소에서 비회 이송량 35,800 kg/h의 용량으로 운전 중인 비회 이송설비를 대상으로 최대 비회 이송량을 예측하였다. 수평거리 550 m, 수직거리 40 m, 엘보우 9개소, 직경 0.254 m의 이송관으로 구성된 비회 이송관로와 트립(trip) 정압 1,163 mmH2O, 풍량 5,040 m3/h인 용적식 비회 이송 송풍기로 이루어진 비회 이송 시스템에서 최대 비회 이송량은 비회 이송량의 증가에 따른 비회 이송 시스템의 압력 손실과 용적식 비회 이송 송풍기의 트립 정압이 같아질 때이며, 이 조건 하에서 가능한 최대 비회 이송량은 52,600 kg/h로 예상되었다.

• 유통과학연구
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• 제17권11호
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• pp.73-79
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• 2019

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

• 한국전자통신학회논문지
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• 제16권2호
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• pp.319-328
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• 2021

본 논문에서는 풍력 발전 수익 극대화 및 비용 최소화를 위해 설치하는 ESS에 대하여 정확한 용량 산정을 하기 위한 목적으로 풍력 단지용 전력량 예측을 다층 퍼셉트론을 이용하여 수행한다. 풍력 발전량을 예측하기 위해 풍속, 풍향, 공기밀도를 변수로 하고 그 변수를 병합하고 정규화한다. 모델을 훈련시키기 위해 병합된 변수를 70% 대 30% 비율로 훈련 및 테스트 데이터로 나눈다. 그런 다음 학습 데이터를 사용하여 모델을 학습시키고 테스트 데이터를 사용하여 모델의 예측 성능도 평가한다. 마지막으로 풍력량 예측 결과를 제시한다.

• 한국지반공학회지:지반
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• 제13권4호
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• pp.13-24
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• 1997

말뚝 해석 프로그램인 PAR에 의하여 말뚝의 극한 수직 및 수평 지지력을 예측할 수 있는 방법을 제안하였으며,현장에서 수행된 말뚝재하시험 결과들을 이용하여 PAR에 의한 사례연구를 수행하였다. PAR에 의해 해석된 말뚝의 극한지지력은 정재하시험에서 구한 지지력에 대하여 약 15%이내의 오차범위에 들었다. 또한, 강관말뚝들에 수행된 정재하시헙, 정.동재하시험 그리고 PDA 결과들을 비교하였으며, PAR에 의해 극한지지력을 예측하였다. PAR을 이용하면 말뚝의 축방향 하중의 분포를 예측할 수 있었으며, 여기서, 하중전이해석도 근사적으로 수행할 수 있었다.

• Steel and Composite Structures
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• 제39권3호
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• pp.319-335
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• 2021

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg-Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R² = 0.983, RMSE = 59.963 kN, a20 - index = 0.979) than the ANN-LM model (R² = 0.938, RMSE = 116.634 kN, a20 - index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.